Since its release on August 30 2012, data from the ABIDE repository has been used by researchers spanning a broad range of scientists, disciplines and countries to inform our understanding of the neural bases of autism, as well as to promote biomarker discovery and innovation of imaging analyses methodologies. Below we provide a list of the peer-reviewed manuscripts that have made use of all or parts of the ABIDE repository.

Additionally, to keep up with the spirit of open science that has inspired the ABIDE initiative, in order to facilitate replications and interpretation of results, we asked the authors of these empirical studies to share the data ID list used for their primary analyses. Whenever available we attach the list along with the publication name. We encourage any new user of the ABIDE repository to inform us on their new peer-reviewed publication and share the dataset ID list by contacting either Adriana Di Martino or Rowen Gesue.

Empirical Studies

Last updated on March 2025.

ABIDE I and ABIDE II Announcing Manuscripts

Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., Deen, B., Delmonte, S., Dinstein, I., Ertl-Wagner, B., Fair, D. A., Gallagher, L., Kennedy, D. P., Keown, C. L., Keysers, C., Lainhart, J. E., Lord, C., Luna, B., Menon, V., Minshew, N. J., Monk, C. S., Mueller, S., Müller, R. A., Nebel, M. B., Nigg, J. T., O'Hearn, K., Pelphrey, K. A., Peltier, S. J., Rudie, J. D., Sunaert, S., Thioux, M., Tyszka, J. M., Uddin, L. Q., Verhoeven, J. S., Wenderoth, N., Wiggins, J. L., Mostofsky, S. H., & Milham, M. P.
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.
Mol Psychiatry. 2014 Jun;19(6):659-67. doi: 10.1038/mp.2013.78. Epub 2013 Jun 18.

Di Martino, A., O'Connor, D., Chen, B., Alaerts, K, Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L., Beggiato, A., Bernaerts, S., Blanken, L. M., Bookheimer, S. Y., Braden, B. B., Byrge, L., Castellanos, F. X., Dapretto, M., Delorme, R., Fair, D. A., Fishman, I., Fitzgerald, J., Gallagher, L., Keehn, R. J., Kennedy, D. P., Lainhart, J. E., Luna, B., Mostofsky, S. H., Müller, R. A., Nebel, M. B., Nigg, J. T., O'Hearn, K., Solomon, M., Toro, R., Vaidya, C. J., Wenderoth, N., White, T., Craddock, R. C., Lord, C., Leventhal, B., & Milham, M. P.
Enhancing studies of the connectome in autism using the autism brain imaging data exchange II.
Sci Data. 2017 March 14;4:170010. doi: 10.1038/sdata.2017.10.

Other Empirical Articles

Anderson JS, Nielsen JA, Ferguson MA, Burback MC, Cox ET, Dai L, Gerig G, Edgin JO, Korenberg JR.
Abnormal brain synchrony in Down Syndrome.
Neuroimage Clin. 2013; 2:703-15. doi: 10.1016/j.nicl.2013.05.006.

Chen CP, Keown CL, Muller RA.
Towards Understanding Autism Risk Factors: A Classification of Brain Images With Support Vector Machines.
Int. J. Semantic Computing. 2013; 07:205 doi: 10.1142/S1793351X13400102.

Nielsen JA, Zielinski BA, Fletcher PT, Alexander AL, Lange N, Bigler ED, Lainhart JE, Anderson JS.
Multisite functional connectivity MRI classification of autism: ABIDE results.
Front Hum Neurosci. 2013; 7:599. doi: 10.3389/fnhum.2013.00599.

Vigneshwaran S, Mahanand BS, Suresh S, Savitha R.
Autism spectrum disorder detection using projection based learning meta-cognitive RBF network.
Front Hum Neurosci. 2013; 7:599. doi: 10.3389/fnhum.2013.00599.

Abrams DA, Lynch CJ, Cheng KM, Phillips J, Supekar K, Ryali S, Uddin LQ, Menon V.
Underconnectivity between voice-selective cortex and reward circuitry in children with autism.
Proc Natl Acad Sci U S A. 2013; 110:12060-5. doi: 10.1073/pnas.1302982110.

Mueller S, Keeser D, Samson AC, Kirsch V, Blautzik J, Grothe M, Erat O, Hegenloh M, Coates U, Reiser MF, Hennig-Fast K, Meindl T.
Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study.
PLoS One. 2013; 8:e67329. doi: 10.1371/journal.pone.0067329.

Di Martino A, Zuo XN, Kelly C, Grzadzinski R, Mennes M, Schvarcz A, Rodman J, Lord C, Castellanos FX, Milham MP.
Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder.
Biol Psychiatry. 2013; 74:623-32. doi: 10.1016/j.biopsych.2013.02.011.

Alaerts K, Woolley DG, Steyaert J, Di Martino A, Swinnen SP, Wenderoth N.
Underconnectivity of the Superior Temporal Sulcus Predicts Emotion Recognition Deficits in Autism.
Soc Cogn Affect Neurosci. 2014; 9:1589-600. doi: 10.1093/scan/nst156.

Haar S, Berman S, Behrmann M, Dinstein I.
Anatomical Abnormalities in Autism?
Cereb Cortex. 2016; 26:1440-52. doi: 10.1093/cercor/bhu242.

Jiang L, Hou XH, Yang N, Yang Z, Zuo XN.
Examination of Local Functional Homogeneity in Autism.
Biomed Res Int. 2015; 2015:174371. doi: 10.1155/2015/174371.

Nielsen JA, Zielinski BA, Fletcher PT, Alexander AL, Lange N, Bigler ED, Lainhart JE, Anderson JS.
Abnormal Lateralization of Functional Connectivity Between Language and Default Mode Regions in Autism.
Mol Autism. 2014; 5:8. doi: 10.1186/2040-2392-5-8.

Ray S, Miller M, Karalunas S, Robertson C, Grayson DS, Cary RP, Hawkey E, Painter JG, Kriz D, Fombonne E, Nigg JT, Fair DA.
Structural and Functional Connectivity of the Human Brain in Autism Spectrum Disorders and Attention-Deficit/Hyperactivity Disorder: A Rich Club-Organization Study.
Hum Brain Mapp. 2014; 35:6032-48. doi: 10.1002/hbm.22603.

Spisák T, Jakab A, Kis SA, Opposits G, Aranyi C, Berényi E, Emri M.
Voxel-wise Motion Artifacts in Population-Level Whole-Brain Connectivity Analysis of resting-state FMRI.
PLoS One. 2014; 9:e104947. doi: 10.1371/journal.pone.0104947.

Nebel MB, Eloyan A, Barber AD, Mostofsky SH.
Precentral Gyrus Functional Connectivity Signatures of Autism.
Front Syst Neurosci. 2014; 8:80. doi: 10.3389/fnsys.2014.00080.

Fredo AJ, Kavitha G, Ramakrishnan S.
Analysis of Sub-cortical Regions in Cognitive Processing Using Fuzzy C-Means Clustering and Geometrical Measure in Autistic MR Images.
Biomed Sci Instrum. 2014;50:140-9.

Zhou Y, Yu F, Duong T.
Multiparametric MRI Characterization and Prediction in Autism Spectrum Disorder Using Graph Theory and Machine Learning.
PLoS One. 2014; 9:e90405. doi: 10.1371/journal.pone.0090405.

Maximo JO, Cadena EJ, Kana RK.
The implications of brain connectivity in the neuropsychology of autism.
Neuropsychol Rev. 2014; 24:16-31. doi: 10.1007/s11065-014-9250-0.

Price T, Wee CY, Gao W, Shen D.
Multiple-network classification of childhood autism using functional connectivity dynamics.
Med Image Comput Comput Assist Interv. 2014; 17:177-84. doi: 10.1007/978-3-319-10443-0_23.

Fredo ARJ, Kavitha G, Ramakrishnan S.
Analysis of Sub-cortical Regions in Cognitive Processing Using Fuzzy C-Means Clustering and Geometrical Measure in Autistic MR Images.
2014 40th Annual Northeast Bioengineering Conference (NEBEC), Boston, MA, USA, 2014, pp. 1-2, doi: 10.1109/NEBEC.2014.6972791.

Bos DJ, Merchán-Naranjo J, Martínez K, Pina-Camacho L, Balsa I, Boada L, Schnack H, Oranje B, Desco M, Arango C, Parellada M, Durston S, Janssen J.
Reduced Gyrification Is Related to Reduced Interhemispheric Connectivity in Autism Spectrum Disorders.
J Am Acad Child Adolesc Psychiatry. 2015; 54:668-76. doi: 10.1016/j.jaac.2015.05.011.

Alaerts K, Nayar K, Kelly C, Raithel J, Milham MP, Di Martino A.
Age-Related Changes in Intrinsic Function of the Superior Temporal Sulcus in Autism Spectrum Disorders.
Soc Cogn Affect Neurosci. 2015; 10:1413-23. doi: 10.1093/scan/nsv029.

Bos DJ, Merchán-Naranjo J, Martínez K, Pina-Camacho L, Balsa I, Boada L, Schnack H, Oranje B, Desco M, Arango C, Parellada M, Durston S, Janssen J.
Reduced Gyrification Is Related to Reduced Interhemispheric Connectivity in Autism Spectrum Disorders.
J Am Acad Child Adolesc Psychiatry. 2015; 54:668-76. doi: 10.1016/j.jaac.2015.05.011.

Cerliani L, Mennes M, Thomas RM, Di Martino A, Thioux M, Keysers C.
Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder.
JAMA Psychiatry. 2015; 72:767-77. doi: 10.1001/jamapsychiatry.2015.0101.

Chen CP, Keown CL, Jahedi A, Nair A, Pflieger ME, Bailey BA, Müller RA.
Diagnostic Classification of Intrinsic Functional Connectivity Highlights Somatosensory, Default Mode, and Visual Regions in Autism.
Neuroimage Clin. 2015; 8:238-45. doi: 10.1016/j.nicl.2015.04.002.

Chen H, Kelly C, Castellanos FX, He Y, Zuo XN, Reiss PT.
Quantile Rank Maps: A New Tool for Understanding Individual Brain Development.
Neuroimage. 2015; 111:454-63. doi: 10.1016/j.neuroimage.2014.12.082.

Cheng W, Rolls ET, Gu H, Zhang J, Feng J.
Autism: Reduced Connectivity Between Cortical Areas Involved in Face Expression, Theory of Mind, and the Sense of Self.
Brain. 2015; 138:1382-93. doi: 10.1093/brain/awv051.

Dajani DR, Uddin LQ.
Local Brain Connectivity Across Development in Autism Spectrum Disorder: A Cross-Sectional Investigation.
Autism Res. 2016; 9:43-54. doi: 10.1002/aur.1494.

Hahamy A, Behrmann M, Malach R.
The Idiosyncratic Brain: Distortion of Spontaneous Connectivity Patterns in Autism Spectrum Disorder.
Nat Neurosci. 2015; 18:302-9. doi: 10.1038/nn.3919.

Iidaka T.
Resting State Functional Magnetic Resonance Imaging and Neural Network Classified Autism and Control.
Cortex. 2015; 63:55-67. doi: 10.1016/j.cortex.2014.08.011.

Lefebvre A, Beggiato A, Bourgeron T, Toro R.
Neuroanatomical Diversity of Corpus Callosum and Brain Volume in Autism: Meta-analysis, Analysis of the Autism Brain Imaging Data Exchange Project, and Simulation.
Biol Psychiatry. 2015; 78:126-34. doi: 10.1016/j.biopsych.2015.02.010.

Plitt M, Barnes KA, Martin A.
Functional Connectivity Classification of Autism Identifies Highly Predictive Brain Features but Falls Short of Biomarker Standards.
Neuroimage Clin. 2015; 7:359-66. doi: 10.1016/j.nicl.2014.12.013.

Schaer M, Kochalka J, Padmanabhan A, Supekar K, Menon V.
Sex Differences in Cortical Volume and Gyrification in Autism.
Mol Autism. 2015; 6:42. doi: 10.1186/s13229-015-0035-y.

Valk SL, Di Martino A, Milham MP, Bernhardt BC.
Multicenter Mapping of Structural Network Alteration in Autism.
Hum Brain Mapp. 2015 Jun;36(6):2364-73. doi: 10.1002/hbm.22776. Epub 2015 Feb 25.

Venkataraman A, Duncan JS, Yang DY, Pelphrey KA.
An Unbiased Bayesian Approach to Functional Connectomics Implicates Social-Communication Networks in Autism.
Neuroimage Clin. 2015; 8:356-66. doi: 10.1016/j.nicl.2015.04.021.

Vinette SA, Bray S.
Variation in Functional Connectivity Along Anterior-to-Posterior Intraparietal Sulcus, and Relationship with Age Across Late Childhood and Adolescence.
Dev Cogn Neurosci. 2015; 13:32-42. doi: 10.1016/j.dcn.2015.04.004.

Chen S, Kang J, Wang G.
An Empirical Bayes Normalization Method for Connectivity Metrics in resting state fMRI.
Front Neurosci. 2015; 9:316. doi: 10.3389/fnins.2015.00316.

Nebel MB, Eloyan A, Nettles CA, Sweeney KL, Ament K, Ward RE, Choe AS, Barber AD, Pekar JJ, Mostofsky SH.
Intrinsic Visual-Motor Synchrony Correlates With Social Deficits in Autism.
Biol Psychiatry. 2016; 79:633-41. doi: 10.1016/j.biopsych.2015.08.029.

Nomi JS, Uddin LQ.
Developmental Changes in Large-Scale Network Connectivity in Autism.
Neuroimage Clin. 2015; 7:732-41. doi: 10.1016/j.nicl.2015.02.024.

Supekar K, Menon V.
Sex Differences in Structural Organization of Motor Systems and their Dissociable Links with Repetitive/Restricted Behaviors in Children with Autism.
Mol Autism. 2015; 6:50. doi: 10.1186/s13229-015-0042-z.

Katuwal GJ, Cahill ND, Baum SA, Michael AM.
The Predictive Power of Structural MRI in Autism Diagnosis.
Annu Int Conf IEEE Eng Med Biol Soc. 2015; 2015:4270-3. doi: 10.1109/EMBC.2015.7319338.

Baldwin PR, Curtis KN, Patriquin MA, Wolf V, Viswanath H, Shaw C, Sakai Y, Salas R.
Identifying Diagnostically-Relevant resting state Brain Functional Connectivity in the Ventral Posterior Complex via Genetic Data Mining in Autism Spectrum Disorder.
Autism Res. 2016; 9:553-62. doi: 10.1002/aur.1559.

Vigneshwaran S, Mahanand BS, Suresh S, Sundararajan N.
Using Regional Homogeneity from Functional MRI for Diagnosis of ASD Among Males.
Proc Int Jt Conf Neural Netw. 2015 Jul;1-8. doi: 10.1109/ijcnn.2015.7280562.

Vigneshwaran S, Suresh S, Mahanand BS, Sundararajan N.
ASD detection in males using MRI- an age-group based study.
Proc Int Jt Conf Neural Netw. 2015 Jul;1-8. doi: 10.1109/IJCNN.2015.7280537.

Subbaraju V, Sundaram S, Narasimhan S, Suresh BM.
Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network.
Expert Systems with Applications. 2015 Dec 1;42(22):8775-8790. doi: 10.1016/j.eswa.2015.07.031.

Fredo A R Jac, Kavitha G, Ramakrishnan S.
Segmentation and analysis of corpus callosum in autistic MR brain images using reaction diffusion level sets.
Journal of Medical Imaging and Health Informatics, Volume 5, Number 4, August 2015, pp. 737-741(5). doi:doi.org/10.1166/jmihi.2015.1442.

Nair A, Carper RA, Abbott AE, Chen CP, Solders S, Nakutin S, Datko MC, Fishman I, Müller RA.
Regional specificity of aberrant thalamocortical connectivity in autism.
Hum Brain Mapp. 2015; 36:4497-511. doi: 10.1002/hbm.22938.

Alaerts K, Geerlings F, Herremans L, Swinnen SP, Verhoeven J, Sunaert S, Wenderoth N.
Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach.
PLoS One. 2015; 10:e0137020. doi: 10.1371/journal.pone.0137020.

Lange N, Travers BG, Bigler ED, Prigge MB, Froehlich AL, Nielsen JA, Cariello AN, Zielinski BA, Anderson JS, Fletcher PT, Alexander AA, Lainhart JE.
Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years.
Autism Res. 2015; 8:82-93. doi: 10.1002/aur.1427.

Sabuncu MR, Konukoglu E, Alzheimer's Disease Neuroimaging Initiative.
Clinical prediction from structural brain MRI scans: a large-scale empirical study.
Neuroinformatics. 2015; 13:31-46. doi: 10.1007/s12021-014-9238-1.

Jac Fredo AR, Kavitha G, Ramakrishnan S.
Automated segmentation and analysis of corpus callosum in autistic MR brain images using fuzzy-c-means-based level set method.
J. Med. Biol. Eng. 35, 331-337 (2015). doi: 10.1007/s40846-015-0047-2.

Osipowicz K, Bosenbark DD, Patrick KE.
Cortical Changes Across the Autism Lifespan.
Autism Res. 2015; 8:379-85. doi: 10.1002/aur.1453.

Watanabe T, Rees G.
Age-associated changes in rich-club organisation in autistic and neurotypical human brains.
Sci Rep. 2015; 5:16152. doi: 10.1038/srep16152.

Chen H, Duan X, Liu F, Lu F, Ma X, Zhang Y, Uddin LQ, Chen H.
Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity-A multi-center study.
Prog Neuropsychopharmacol Biol Psychiatry. 2016; 64:1-9. doi: 10.1016/j.pnpbp.2015.06.014.

Kucharsky Hiess R, Alter R, Sojoudi S, Ardekani BA, Kuzniecky R, Pardoe HR.
Corpus callosum area and brain volume in autism spectrum disorder: quantitative analysis of structural MRI from the ABIDE database.
J Autism Dev Disord. 2015; 45:3107-14. doi: 10.1007/s10803-015-2468-8.

Sato JR, Balardin J, Vidal MC, Fujita A.
Identification of segregated regions in the functional brain connectome of autistic patients by a combination of fuzzy spectral clustering and entropy analysis.
J Psychiatry Neurosci. 2016; 41:124-32. doi: 10.1503/jpn.140364.

Blackmon K, Ben-Avi E, Wang X, Pardoe HR, Di Martino A, Halgren E, Devinsky O, Thesen T, Kuzniecky R.
Periventricular white matter abnormalities and restricted repetitive behavior in autism spectrum disorder.
Neuroimage Clin. 2016; 10:36-45. doi: 10.1016/j.nicl.2015.10.017.

Elton A, Di Martino A, Hazlett HC, Gao W.
Neural Connectivity Evidence for a Categorical-Dimensional Hybrid Model of Autism Spectrum Disorder.
Biol Psychiatry. 2016; 80:120-128. doi: 10.1016/j.biopsych.2015.10.020.

Alaerts K, Swinnen SP, Wenderoth N.
Sex differences in autism: a resting-state fMRI investigation of functional brain connectivity in males and females.
Soc Cogn Affect Neurosci. 2016; 11:1002-16. doi: 10.1093/scan/nsw027.

Glerean E, Pan RK, Salmi J, Kujala R, Lahnakoski JM, Roine U, Nummenmaa L, Leppämäki S, Nieminen-von Wendt T, Tani P, Saramäki J, Sams M, Jääskeläinen IP.
Reorganization of functionally connected brain subnetworks in high-functioning autism.
Hum Brain Mapp. 2016; 37:1066-79. doi: 10.1002/hbm.23084.

Katuwal GJ, Baum SA, Cahill ND, Michael AM.
Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry.
PLoS One. 2016; 11:e0153331. doi: 10.1371/journal.pone.0153331.

Narayan M, Allen GI.
Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity.
Front Neurosci. 2016; 10:108. doi: 10.3389/fnins.2016.00108.

Pardoe HR, Kucharsky Hiess R, Kuzniecky R.
Motion and morphometry in clinical and nonclinical populations.
Neuroimage. 2016; 135:177-85. doi: 10.1016/j.neuroimage.2016.05.005.

Turner AH, Greenspan KS, van Erp TGM.
Pallidum and lateral ventricle volume enlargement in autism spectrum disorder.
Psychiatry Res Neuroimaging. 2016; 252:40-45. doi: 10.1016/j.pscychresns.2016.04.003.

Lee JM, Kyeong S, Kim E, Cheon KA.
Abnormalities of Inter- and Intra-Hemispheric Functional Connectivity in Autism Spectrum Disorders: A Study Using the Autism Brain Imaging Data Exchange Database.
Front Neurosci. 2016; 10:191. doi: 10.3389/fnins.2016.00191.

Eilam-Stock T, Wu T, Spagna A, Egan LJ, Fan J.
Neuroanatomical Alterations in High-Functioning Adults with Autism Spectrum Disorder.
Front Neurosci. 2016; 10:237. doi: 10.3389/fnins.2016.00237.

Dougherty CC, Evans DW, Katuwal GJ, Michael AM.
Asymmetry of fusiform structure in autism spectrum disorder: trajectory and association with symptom severity.
Mol Autism. 2016; 7:28. doi: 10.1186/s13229-016-0089-5.

Ypma RJ, Moseley RL, Holt RJ, Rughooputh N, Floris DL, Chura LR, Spencer MD, Baron-Cohen S, Suckling J, Bullmore ET, Rubinov M.
Default Mode Hypoconnectivity Underlies a Sex-Related Autism Spectrum.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1:364-371. doi: 10.1016/j.bpsc.2016.04.006.

Falahpour M, Thompson WK, Abbott AE, Jahedi A, Mulvey ME, Datko M, Liu TT, Müller RA.
Underconnected, But Not Broken? Dynamic Functional Connectivity MRI Shows Underconnectivity in Autism Is Linked to Increased Intra-Individual Variability Across Time.
Brain Connect. 2016 June;6(5):403-14. doi:10.1089/brain.2015.0389. Epub 2016 April 22.

Torres EB, Denisova K.
Motor noise is rich signal in autism research and pharmacological treatments.
Sci Rep. 2016; 6:37422. doi: 10.1038/srep37422.

Katuwal GJ, Baum SA, Cahill ND, Dougherty CC, Evans E, Evans DW, Moore GJ, Michael AM.
Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism.
Front Neurosci. 2016; 10:439. doi: 10.3389/fnins.2016.00439.

Guo X, Duan X, Long Z, Chen H, Wang Y, Zheng J, Zhang Y, Li R, Chen H.
Decreased amygdala functional connectivity in adolescents with autism: A resting-state fMRI study.
Psychiatry Res Neuroimaging. 2016; 257:47-56. doi: 10.1016/j.pscychresns.2016.10.005.

Chen H, Uddin LQ, Zhang Y, Duan X, Chen H.
Atypical effective connectivity of thalamo-cortical circuits in autism spectrum disorder.
Autism Res. 2016; 9:1183-1190. doi: 10.1002/aur.1614.

Balsters JH, Mantini D, Apps MAJ, Eickhoff SB, Wenderoth N.
Connectivity-based parcellation increases network detection sensitivity in resting state fMRI: An investigation into the cingulate cortex in autism.
Neuroimage Clin. 2016; 11:494-507. doi: 10.1016/j.nicl.2016.03.016.

Zhou Y, Shi L, Cui X, Wang S, Luo X.
Functional Connectivity of the Caudal Anterior Cingulate Cortex Is Decreased in Autism.
PLoS One. 2016; 11:e0151879. doi: 10.1371/journal.pone.0151879.

Wee CY, Yap PT, Shen D.
Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting-State Functional Connectivity Networks.
CNS Neurosci Ther. 2016; 22:212-9. doi: 10.1111/cns.12499.

Yao Z, Hu B, Xie Y, Zheng F, Liu G, Chen X, Zheng W.
Resting-State Time-Varying Analysis Reveals Aberrant Variations of Functional Connectivity in Autism.
Front Hum Neurosci. 2016; 10:463. doi: 10.3389/fnhum.2016.00463.

Ghiassian S, Greiner R, Jin P, Brown MR.
Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism.
PLoS One. 2016 Dec 28;11(12):e0166934. doi: 10.1371/journal.pone.0166934. eCollection 2016.

Kassraian-Fard P, Matthis C, Balsters JH, Maathuis MH, Wenderoth N.
Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example.
Front Psychiatry. 2016; 7:177. doi: 10.3389/fpsyt.2016.00177.

Zhao Y, Chen H, Li Y, Lv J, Jiang X, Ge F, Zhang T, Zhang S, Ge B, Lyu C, Zhao S, Han J, Guo L, Liu T.
Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder.
Neuroimage Clin. 2016; 12:23-33. doi: 10.1016/j.nicl.2016.06.004.

Long Z, Duan X, Mantini D, Chen H.
Alteration of functional connectivity in autism spectrum disorder: effect of age and anatomical distance.
Sci Rep. 2016; 6:26527. doi: 10.1038/srep26527.

Farrant K, Uddin LQ.
Atypical developmental of dorsal and ventral attention networks in autism.
Dev Sci. 2016; 19:550-63. doi: 10.1111/desc.12359.

Hoffmann F, Koehne S, Steinbeis N, Dziobek I, Singer T.
Preserved Self-other Distinction During Empathy in Autism is Linked to Network Integrity of Right Supramarginal Gyrus.
J Autism Dev Disord. 2016; 46:637-48. doi: 10.1007/s10803-015-2609-0.

Zhu Y, Zhu X, Zhang H, Gao W, Shen D, Wu G.
Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification.
Med Image Comput Comput Assist Interv. 2016; 9900:106-114. doi: 10.1007/978-3-319-46720-7_13.

Di X, Biswal BB.
Similarly Expanded Bilateral Temporal Lobe Volumes in Female and Male Children With Autism Spectrum Disorder.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1:178-185. doi: 10.1016/j.bpsc.2015.11.006.

Pappaianni E, Siugzdaite R, Grecucci A.
An Abnormal Cerebellar Network in Children with Autistic Spectrum Disorder: A Morphometric Study.
Autism Open Access 2016, 6:3. doi: 10.4172/2165-7890.1000178.

Schuetze M, Park MT, Cho IY, MacMaster FP, Chakravarty MM, Bray SL.
Morphological Alterations in the Thalamus, Striatum, and Pallidum in Autism Spectrum Disorder.
Neuropsychopharmacology. 2016; 41:2627-37. doi: 10.1038/npp.2016.64.

Chen H, Zhao B, Porges EC, Cohen RA, Ebner NC.
Edgewise and subgraph-level tests for brain networks.
Stat Med. 2016; 35:4994-5008. doi: 10.1002/sim.7039.

Auzias G, Takerkart S, Deruelle C.
On the Influence of Confounding Factors in Multisite Brain Morphometry Studies of Developmental Pathologies: Application to Autism Spectrum Disorder.
IEEE J Biomed Health Inform. 2016; 20:810-817. doi: 10.1109/JBHI.2015.2460012.

Grecucci A, Rubicondo D, Siugzdaite R, Surian L, Job R.
Uncovering the social deficits in the autistic brain A source-based morphometric study.
Front Neurosci. 2016 Aug 31;10:388. doi: 10.3389/fnins.2016.00388.

Zhang J, Cheng W, Liu Z, Zhang K, Lei X, Yao Y, Becker B, Liu Y, Kendrick KM, Lu G, Feng J.
Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.
Brain. 2016; 139:2307-21. doi: 10.1093/brain/aww143.

Wang L, Wee CY, Tang X, Yap PT, Shen D.
Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder.
Brain Imaging Behav. 2016; 10:33-40. doi: 10.1007/s11682-015-9360-1.

.
Influence of group on individual subject maps in SPM voxel based morphometry.
Front Neurosci. 2016 Dec 2;10:522. doi: 10.3389/fnins.2016.00522.

Chen R, Nixon E, Herskovits E.
Advanced Connectivity Analysis (ACA): a Large Scale Functional Connectivity Data Mining Environment.
Neuroinformatics. 2016; 14:191-9. doi: 10.1007/s12021-015-9290-5.

Watanabe T, Rees G.
Anatomical imbalance between cortical networks in autism.
Sci Rep. 2016; 6:31114. doi: 10.1038/srep31114.

Zu C, Gao Y, Munsell B, Kim M, Peng Z, Zhu Y, Gao W, Zhang D, Shen D, Wu G.
Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph.
Mach Learn Med Imaging. 2016; 10019:1-9. doi: 10.1007/978-3-319-47157-0_1.

Burrows CA, Laird AR, Uddin LQ.
Functional connectivity of brain regions for self- and other-evaluation in children, adolescents and adults with autism.
Dev Sci. 2016; 19:564-80. doi: 10.1111/desc.12400.

Eilam-Stock T, Wu T, Spagna A, Egan LJ, Fan J.
Neuroanatomical Alterations in High-Functioning Adults with Autism Spectrum Disorder.
Front Neurosci. 2016; 10:237. doi: 10.3389/fnins.2016.00237.

Chen H, Uddin LQ, Duan X, Zheng J, Long Z, Zhang Y, Guo X, Zhang Y, Zhao J, Chen H.
Shared atypical default mode and salience network functional connectivity between autism and schizophrenia.
Autism Res. 2017; 10:1776-1786. doi: 10.1002/aur.1834.

Sabuncu MR, Ge T, Holmes AJ, Smoller JW, Buckner RL, Fischl B, Alzheimer's Disease Neuroimaging Initiative.
Morphometricity as a measure of the neuroanatomical signature of a trait.
Proc Natl Acad Sci U S A. 2016; 113:E5749-56. doi: 10.1073/pnas.1604378113.

Wong E, Palande S, Wang B, Zielinski B, Anderson J, Fletcher PT.
KERNEL PARTIAL LEAST SQUARES REGRESSION FOR RELATING FUNCTIONAL BRAIN NETWORK TOPOLOGY TO CLINICAL MEASURES OF BEHAVIOR.
Proc IEEE Int Symp Biomed Imaging. 2016; 2016:1303-1306. doi: 10.1109/isbi.2016.7493506.

Riddle K, Cascio CJ, Woodward ND.
Brain structure in autism: a voxel-based morphometry analysis of the Autism Brain Imaging Database Exchange (ABIDE).
Brain Imaging Behav. 2017; 11:541-551. doi: 10.1007/s11682-016-9534-5.

Moradi E, Khundrakpam B, Lewis JD, Evans AC, Tohka J.
Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data.
Neuroimage. 2017; 144:128-141. doi: 10.1016/j.neuroimage.2016.09.049.

Zhao F, Qiao L, Shi F, Yap PT, Shen D.
Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder.
Brain Imaging Behav. 2017; 11:1050-1060. doi: 10.1007/s11682-016-9587-5.

Abraham A, Milham MP, Di Martino A, Craddock RC, Samaras D, Thirion B, Varoquaux G.
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example.
Neuroimage. 2017; 147:736-745. doi: 10.1016/j.neuroimage.2016.10.045.

Igelström KM, Webb TW, Graziano MSA.
Functional Connectivity Between the Temporoparietal Cortex and Cerebellum in Autism Spectrum Disorder.
Cereb Cortex. 2017; 27:2617-2627. doi: 10.1093/cercor/bhw079.

Cheng W, Rolls ET, Zhang J, Sheng W, Ma L, Wan L, Luo Q, Feng J.
Functional connectivity decreases in autism in emotion, self, and face circuits identified by Knowledge-based Enrichment Analysis.
Neuroimage. 2017; 148:169-178. doi: 10.1016/j.neuroimage.2016.12.068.

Tomasi D, Volkow ND.
Reduced Local and Increased Long-Range Functional Connectivity of the Thalamus in Autism Spectrum Disorder.
Cereb Cortex. 2019; 29:573-585. doi: 10.1093/cercor/bhx340.

Li W, Wang Z, Zhang L, Qiao L, Shen D.
Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification.
Front Neuroinform. 2017; 11:55. doi: 10.3389/fninf.2017.00055.

Syed MA, Yang Z, Hu XP, Deshpande G.
Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data.
Front Neurosci. 2017; 11:459. doi: 10.3389/fnins.2017.00459.

Guo X, Dominick KC, Minai AA, Li H, Erickson CA, Lu LJ.
Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.
Front Neurosci. 2017; 11:460. doi: 10.3389/fnins.2017.00460.

Vidal MC, Sato JR, Balardin JB, Takahashi DY, Fujita A.
ANOCVA in R: A Software to Compare Clusters between Groups and Its Application to the Study of Autism Spectrum Disorder.
Front Neurosci. 2017 Jan 24;11:16. doi: 10.3389/fnins.2017.00016.

Wang J, Wang Q, Peng J, Nie D, Zhao F, Kim M, Zhang H, Wee CY, Wang S, Shen D.
Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study.
Hum Brain Mapp. 2017; 38:3081-3097. doi: 10.1002/hbm.23575.

Chaddad A, Desrosiers C, Toews M.
Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age.
Sci Rep. 2017; 7:45639. doi: 10.1038/srep45639.

Chen S, Xing Y, Kang J.
Latent and Abnormal Functional Connectivity Circuits in Autism Spectrum Disorder.
Front Neurosci. 2017; 11:125. doi: 10.3389/fnins.2017.00125.

Saygin ZM, Kliemann D, Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M, Stevens A, Van Leemput K, McKee A, Frosch MP, Fischl B, Augustinack JC, Alzheimer's Disease Neuroimaging Initiative.
High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas.
Neuroimage. 2017; 155:370-382. doi: 10.1016/j.neuroimage.2017.04.046.

Woodward ND, Giraldo-Chica M, Rogers B, Cascio CJ.
Thalamocortical dysconnectivity in autism spectrum disorder: An analysis of the Autism Brain Imaging Data Exchange.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2017; 2:76-84. doi: 10.1016/j.bpsc.2016.09.002.

Bethlehem RAI, Romero-Garcia R, Mak E, Bullmore ET, Baron-Cohen S.
Structural Covariance Networks in Children with Autism or ADHD.
Cereb Cortex. 2017 Aug 1;27(8):4267-4276. doi: 10.1093/cercor/bhx135.

Torres EB, Mistry S, Caballero C, Whyatt CP.
Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders.
Front Integr Neurosci. 2017; 11:10. doi: 10.3389/fnint.2017.00010.

Duan X, Chen H, He C, Long Z, Guo X, Zhou Y, Uddin LQ, Chen H.
Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism.
Prog Neuropsychopharmacol Biol Psychiatry. 2017; 79:434-441. doi: 10.1016/j.pnpbp.2017.07.027.

Chen H, Nomi JS, Uddin LQ, Duan X, Chen H.
Intrinsic functional connectivity variance and state-specific under-connectivity in autism.
Hum Brain Mapp. 2017; 38:5740-5755. doi: 10.1002/hbm.23764.

Keown CL, Datko MC, Chen CP, Maximo JO, Jahedi A, Müller RA.
Network organization is globally atypical in autism: A graph theory study of intrinsic functional connectivity.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2017; 2:66-75. doi: 10.1016/j.bpsc.2016.07.008.

Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F.
Identification of autism spectrum disorder using deep learning and the ABIDE dataset.
Neuroimage Clin. 2018; 17:16-23. doi: 10.1016/j.nicl.2017.08.017.

Dvornek NC, Ventola P, Pelphrey KA, Duncan JS.
Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks.
Mach Learn Med Imaging. 2017; 10541:362-370. doi: 10.1007/978-3-319-67389-9_42.

Dona O, Hall GB, Noseworthy MD.
Temporal fractal analysis of the rs-BOLD signal identifies brain abnormalities in autism spectrum disorder.
PLoS One. 2017; 12:e0190081. doi: 10.1371/journal.pone.0190081.

Watanabe T, Rees G.
Brain network dynamics in high-functioning individuals with autism.
Nat Commun. 2017; 8:16048. doi: 10.1038/ncomms16048.

Wang J, Wang Q, Wang S, Shen D.
Sparse Multi-view Task-Centralized Learning for ASD Diagnosis.
Mach Learn Med Imaging. 2017; 10541:159-167. doi: 10.1007/978-3-319-67389-9_19.

Jung M, Tu Y, Lang CA, Ortiz A, Park J, Jorgenson K, Kong XJ, Kong J.
Decreased structural connectivity and resting-state brain activity in the lateral occipital cortex is associated with social communication deficits in boys with autism spectrum disorder.
Neuroimage. 2019; 190:205-212. doi: 10.1016/j.neuroimage.2017.09.031.

Jahedi A, Nasamran CA, Faires B, Fan J, Müller RA.
Distributed Intrinsic Functional Connectivity Patterns Predict Diagnostic Status in Large Autism Cohort.
Brain Connect. 2017; 7:515-525. doi: 10.1089/brain.2017.0496.

Rane S, Jolly E, Park A, Jang H, Craddock C.
Developing predictive imaging biomarkers using whole-brain classifiers: Application to the ABIDE I dataset.
Research Ideas and Outcomes 3: e12733. doi: 10.3897/rio.3.e12733.

Traut N, Beggiato A, Bourgeron T, Delorme R, Rondi-Reig L, Paradis AL, Toro R.
Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort.
Biol Psychiatry. 2018; 83:579-588. doi: 10.1016/j.biopsych.2017.09.029.

Lee Y, Park BY, James O, Kim SG, Park H.
Autism Spectrum Disorder Related Functional Connectivity Changes in the Language Network in Children, Adolescents and Adults.
Front Hum Neurosci. 2017; 11:418. doi: 10.3389/fnhum.2017.00418.

Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ.
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites.
PLoS One. 2017; 12:e0184661. doi: 10.1371/journal.pone.0184661.

Guo X, Chen H, Long Z, Duan X, Zhang Y, Chen H.
Atypical developmental trajectory of local spontaneous brain activity in autism spectrum disorder.
Sci Rep. 2017; 7:39822. doi: 10.1038/srep39822.

Khundrakpam BS, Lewis JD, Kostopoulos P, Carbonell F, Evans AC.
Cortical Thickness Abnormalities in Autism Spectrum Disorders Through Late Childhood, Adolescence, and Adulthood: A Large-Scale MRI Study.
Cereb Cortex. 2017; 27:1721-1731. doi: 10.1093/cercor/bhx038.

Subbaraju V, Suresh MB, Sundaram S, Narasimhan S.
Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging : A spatial filtering approach.
Med Image Anal. 2017 Jan;35:375-389. doi: 10.1016/j.media.2016.08.003. Epub 2016 Aug 23.

Chaddad A, Desrosiers C, Hassan L, Tanougast C.
Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder.
BMC Neurosci 18, 52 (2017). doi: 10.1186/s12868-017-0373-0.

Alexander LM, Escalera J, Ai L, Andreotti C, Febre K, Mangone A, Vega-Potler N, Langer N, Alexander A, Kovacs M, Litke S, O'Hagan B, Andersen J, Bronstein B, Bui A, Bushey M, Butler H, Castagna V, Camacho N, Chan E, Citera D, Clucas J, Cohen S, Dufek S, Eaves M, Fradera B, Gardner J, Grant-Villegas N, Green G, Gregory C, Hart E, Harris S, Horton M, Kahn D, Kabotyanski K, Karmel B, Kelly SP, Kleinman K, Koo B, Kramer E, Lennon E, Lord C, Mantello G, Margolis A, Merikangas KR, Milham J, Minniti G, Neuhaus R, Levine A, Osman Y, Parra LC, Pugh KR, Racanello A, Restrepo A, Saltzman T, Septimus B, Tobe R, Waltz R, Williams A, Yeo A, Castellanos FX, Klein A, Paus T, Leventhal BL, Craddock RC, Koplewicz HS, Milham MP.
An open resource for transdiagnostic research in pediatric mental health and learning disorders.
Sci Data. 2017; 4:170181. doi: 10.1038/sdata.2017.181.

Liu W, Wei D, Chen Q, Yang W, Meng J, Wu G, Bi T, Zhang Q, Zuo XN, Qiu J.
Longitudinal test-retest neuroimaging data from healthy young adults in southwest China.
Sci Data. 2017; 4:170017. doi: 10.1038/sdata.2017.17.

Laidi C, Boisgontier J, Chakravarty MM, Hotier S, d'Albis MA, Mangin JF, Devenyi GA, Delorme R, Bolognani F, Czech C, Bouquet C, Toledano E, Bouvard M, Gras D, Petit J, Mishchenko M, Gaman A, Scheid I, Leboyer M, Zalla T, Houenou J.
Cerebellar anatomical alterations and attention to eyes in autism.
Sci Rep. 2017; 7:12008. doi: 10.1038/s41598-017-11883-w.

Mejia AF, Nebel MB, Eloyan A, Caffo B, Lindquist MA.
PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data.
Biostatistics. 2017; 18:521-536. doi: 10.1093/biostatistics/kxw050.

Chen H, Li Y, Ge F, Li G, Shen D, Liu T.
Gyral net: A new representation of cortical folding organization.
Med Image Anal. 2017 Dec;42:14-25. doi: 10.1016/j.media.2017.07.001.

de Lacy N, Doherty D, King BH, Rachakonda S, Calhoun VD.
Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.
Neuroimage Clin. 2017; 15:513-524. doi: 10.1016/j.nicl.2017.05.024.

Joshi G, Arnold Anteraper S, Patil KR, Semwal M, Goldin RL, Furtak SL, Chai XJ, Saygin ZM, Gabrieli JDE, Biederman J, Whitfield-Gabrieli S.
Integration and Segregation of Default Mode Network Resting-State Functional Connectivity in Transition-Age Males with High-Functioning Autism Spectrum Disorder: A Proof-of-Concept Study.
Brain Connect. 2017; 7:558-573. doi: 10.1089/brain.2016.0483.

Kam TE, Suk HI, Lee SW.
Multiple functional networks modeling for autism spectrum disorder diagnosis.
Hum Brain Mapp. 2017; 38:5804-5821. doi: 10.1002/hbm.23769.

Linke AC, Olson L, Gao Y, Fishman I, Müller RA.
Psychotropic medication use in autism spectrum disorders may affect functional brain connectivity.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2017; 2:518-527. doi: 10.1016/j.bpsc.2017.06.008.

Sadeghi M, Khosrowabadi R, Bakouie F, Mahdavi H, Eslahchi C, Pouretemad H.
Screening of autism based on task-free fMRI using graph theoretical approach.
Psychiatry Res Neuroimaging. 2017; 263:48-56. doi: 10.1016/j.pscychresns.2017.02.004.

Fujita A, Vidal MC, Takahashi DY.
A Statistical Method to Distinguish Functional Brain Networks.
Front Neurosci. 2017; 11:66. doi: 10.3389/fnins.2017.00066.

Power JD, Plitt M, Laumann TO, Martin A.
Sources and implications of whole-brain fMRI signals in humans.
Neuroimage. 2017; 146:609-625. doi: 10.1016/j.neuroimage.2016.09.038.

Xie J, Kang J.
High-dimensional tests for functional networks of brain anatomic regions.
J Multivar Anal. 2017; 156:70-88. doi: 10.1016/j.jmva.2017.01.011.

Riedel BC, Jahanshad N, Thompson PM.
Graph theoretical approaches towards understanding differences in frontoparietal and default mode networks in Autism.
Proc IEEE Int Symp Biomed Imaging. 2017; 2017:460-463. doi: 10.1109/ISBI.2017.7950560.

Zhu Y, Zhu X, Kim M, Yan J, Wu G.
A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity.
Inf Process Med Imaging. 2017; 10265:398-410. doi: 10.1007/978-3-319-59050-9_32.

Wei L, Zhong S, Nie S, Gong G.
Aberrant development of the asymmetry between hemispheric brain white matter networks in autism spectrum disorder.
Eur Neuropsychopharmacol. 2018; 28:48-62. doi: 10.1016/j.euroneuro.2017.11.018.

Chen S, Huang L, Qiu H, Nebel MB, Mostofsky SH, Pekar JJ, Lindquist MA, Eloyan A, Caffo BS.
Parallel group independent component analysis for massive fMRI data sets.
PLoS One. 2017; 12:e0173496. doi: 10.1371/journal.pone.0173496.

Calhoun VD, Wager TD, Krishnan A, Rosch KS, Seymour KE, Nebel MB, Mostofsky SH, Nyalakanai P, Kiehl K.
The impact of T1 versus EPI spatial normalization templates for fMRI data analyses.
Hum Brain Mapp. 2017; 38:5331-5342. doi: 10.1002/hbm.23737.

Dolz J, Desrosiers C, Ben Ayed I.
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.
Neuroimage. 2018; 170:456-470. doi: 10.1016/j.neuroimage.2017.04.039.

Subbaraju V, Sundaram S, Narasimhan S.
Identification of lateralized compensatory neural activities within the social brain due to autism spectrum disorder in adolescent males.
Eur J Neurosci. 2018; 47:631-642. doi: 10.1111/ejn.13634.

Zhang W, Groen W, Mennes M, Greven C, Buitelaar J, Rommelse N.
Revisiting subcortical brain volume correlates of autism in the ABIDE dataset: effects of age and sex.
Psychol Med. 2018; 48:654-668. doi: 10.1017/S003329171700201X.

Chatham CH, Taylor KI, Charman T, Liogier D'ardhuy X, Eule E, Fedele A, Hardan AY, Loth E, Murtagh L, Del Valle Rubido M, San Jose Caceres A, Sevigny J, Sikich L, Snyder L, Tillmann JE, Ventola PE, Walton-Bowen KL, Wang PP, Willgoss T, Bolognani F.
Adaptive behavior in autism: Minimal clinically important differences on the Vineland-II.
Autism Res. 2018; 11:270-283. doi: 10.1002/aur.1874.

Traut N, Beggiato A, Bourgeron T, Delorme R, Rondi-Reig L, Paradis AL, Toro R.
Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort.
Biol Psychiatry. 2018; 83:579-588. doi: 10.1016/j.biopsych.2017.09.029.

Ktena SI, Parisot S, Ferrante E, Rajchl M, Lee M, Glocker B, Rueckert D.
Metric learning with spectral graph convolutions on brain connectivity networks.
Neuroimage. 2018; 169:431-442. doi: 10.1016/j.neuroimage.2017.12.052.

Bhaumik R, Pradhan A, Das S, Bhaumik DK.
Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation.
Neuroinformatics. 2018; 16:197-205. doi: 10.1007/s12021-018-9366-0.

Bi XA, Wang Y, Shu Q, Sun Q, Xu Q.
Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster.
Front Genet. 2018; 9:18. doi: 10.3389/fgene.2018.00018.

Zhao Y, Kang J, Long Q.
Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.
IEEE/ACM Trans Comput Biol Bioinform. 2018; 15:537-550. doi: 10.1109/TCBB.2015.2440244.

Besseling R, Lamerichs R, Michels B, Heunis S, de Louw A, Tijhuis A, Bergmans J, Aldenkamp B.
Functional network abnormalities consistent with behavioral profile in Autism Spectrum Disorder.
Psychiatry Res Neuroimaging. 2018; 275:43-48. doi: 10.1016/j.pscychresns.2018.02.006.

Sen B, Borle NC, Greiner R, Brown MRG.
A general prediction model for the detection of ADHD and Autism using structural and functional MRI.
PLoS One. 2018; 13:e0194856. doi: 10.1371/journal.pone.0194856.

Zhao Y, Ge F, Liu T.
Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.
Med Image Anal. 2018; 47:111-126. doi: 10.1016/j.media.2018.04.002.

Henry TR, Dichter GS, Gates K.
Age and Gender Effects on Intrinsic Connectivity in Autism Using Functional Integration and Segregation.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2018; 3:414-422. doi: 10.1016/j.bpsc.2017.10.006.

Akhavan Aghdam M, Sharifi A, Pedram MM.
Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.
J Digit Imaging. 2018; 31:895-903. doi: 10.1007/s10278-018-0093-8.

Parisot S, Ktena SI, Ferrante E, Lee M, Guerrero R, Glocker B, Rueckert D.
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.
Med Image Anal. 2018; 48:117-130. doi: 10.1016/j.media.2018.06.001.

Bi XA, Liu Y, Jiang Q, Shu Q, Sun Q, Dai J.
The Diagnosis of Autism Spectrum Disorder Based on the Random Neural Network Cluster.
Front Hum Neurosci. 2018 Jun 26;12:257. doi: 10.3389/fnhum.2018.00257.

Harlalka V, Bapi RS, Vinod PK, Roy D.
Age, Disease, and Their Interaction Effects on Intrinsic Connectivity of Children and Adolescents in Autism Spectrum Disorder Using Functional Connectomics.
Brain Connect. 2018; 8:407-419. doi: 10.1089/brain.2018.0616.

Yan W, Rangaprakash D, Deshpande G.
Aberrant hemodynamic responses in autism: Implications for resting state fMRI functional connectivity studies.
Neuroimage Clin. 2018; 19:320-330. doi: 10.1016/j.nicl.2018.04.013.

Li H, Parikh NA, He L.
A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes.
Front Neurosci. 2018; 12:491. doi: 10.3389/fnins.2018.00491.

Zhao X, Rangaprakash D, Yuan B, Denney TS, Katz JS, Dretsch MN, Deshpande G.
Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning.
Front Appl Math Stat. 2018; 4:None. doi: 10.3389/fams.2018.00025.

Cechmanek B, Johnston H, Vazhappilly S, Lebel C, Bray S.
Somatosensory Regions Show Limited Functional Connectivity Differences in Youth with Autism Spectrum Disorder.
Brain Connect. 2018; 8:558-566. doi: 10.1089/brain.2018.0614.

Agastinose Ronicko JF, Thomas J, Thangavel P, Koneru V, Langs G, Dauwels J.
Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation.
J Neurosci Methods. 2020; 345:108884. doi: 10.1016/j.jneumeth.2020.108884.

Bezgin G, Lewis JD, Evans AC.
Developmental changes of cortical white-gray contrast as predictors of autism diagnosis and severity.
Transl Psychiatry. 2018; 8:249. doi: 10.1038/s41398-018-0296-2.

Hanaie R, Mohri I, Kagitani-Shimono K, Tachibana M, Matsuzaki J, Hirata I, Nagatani F, Watanabe Y, Katayama T, Taniike M.
Aberrant Cerebellar-Cerebral Functional Connectivity in Children and Adolescents With Autism Spectrum Disorder.
Front Hum Neurosci. 2018; 12:454. doi: 10.3389/fnhum.2018.00454.

Bi XA, Chen J, Sun Q, Liu Y, Wang Y, Luo X.
Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster.
Front Physiol. 2018; 9:1646. doi: 10.3389/fphys.2018.01646.

King JB, Prigge MBD, King CK, Morgan J, Dean DC, Freeman A, Villaruz JAM, Kane KL, Bigler ED, Alexander AL, Lange N, Zielinski BA, Lainhart JE, Anderson JS.
Evaluation of Differences in Temporal Synchrony Between Brain Regions in Individuals With Autism and Typical Development.
JAMA Netw Open. 2018; 1:e184777. doi: 10.1001/jamanetworkopen.2018.4777.

Arnold Anteraper S, Guell X, D'Mello A, Joshi N, Whitfield-Gabrieli S, Joshi G.
Disrupted Cerebrocerebellar Intrinsic Functional Connectivity in Young Adults with High-Functioning Autism Spectrum Disorder: A Data-Driven, Whole-Brain, High-Temporal Resolution Functional Magnetic Resonance Imaging Study.
Brain Connect. 2019; 9:48-59. doi: 10.1089/brain.2018.0581.

Balsters JH, Mantini D, Wenderoth N.
Connectivity-based parcellation reveals distinct cortico-striatal connectivity fingerprints in Autism Spectrum Disorder.
Neuroimage. 2018; 170:412-423. doi: 10.1016/j.neuroimage.2017.02.019.

Bhaumik D, Jie F, Nordgren R, Bhaumik R, Sinha BK.
A Mixed-Effects Model for Detecting Disrupted Connectivities in Heterogeneous Data.
IEEE Trans Med Imaging. 2018; 37:2381-2389. doi: 10.1109/TMI.2018.2821655.

Bi XA, Zhao J, Xu Q, Sun Q, Wang Z.
Abnormal Functional Connectivity of Resting State Network Detection Based on Linear ICA Analysis in Autism Spectrum Disorder.
Front Physiol. 2018; 9:475. doi: 10.3389/fphys.2018.00475.

Caballero C, Mistry S, Vero J, Torres EB.
Characterization of Noise Signatures of Involuntary Head Motion in the Autism Brain Imaging Data Exchange Repository.
Front Integr Neurosci. 2018; 12:7. doi: 10.3389/fnint.2018.00007.

Wang M, Zhang D, Huang J, Shen D, Liu M.
Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification.
Med Image Comput Comput Assist Interv. 2018; 11070:647-654. doi: 10.1007/978-3-030-00928-1_73.

Dickie EW, Ameis SH, Shahab S, Calarco N, Smith DE, Miranda D, Viviano JD, Voineskos AN.
Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder.
Biol Psychiatry. 2018; 84:278-286. doi: 10.1016/j.biopsych.2018.02.1174.

Fu Z, Tu Y, Di X, Du Y, Sui J, Biswal BB, Zhang Z, de Lacy N, Calhoun VD.
Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism.
Neuroimage. 2019; 190:191-204. doi: 10.1016/j.neuroimage.2018.06.003.

Guzman GEC, Sato JR, Vidal MC, Fujita A.
Identification of alterations associated with age in the clustering structure of functional brain networks.
PLoS One. 2018; 13:e0195906. doi: 10.1371/journal.pone.0195906.

Kernbach JM, Satterthwaite TD, Bassett DS, Smallwood J, Margulies D, Krall S, Shaw P, Varoquaux G, Thirion B, Konrad K, Bzdok D.
Shared endo-phenotypes of default mode dsfunction in attention deficit/hyperactivity disorder and autism spectrum disorder.
Transl Psychiatry. 2018; 8:133. doi: 10.1038/s41398-018-0179-6.

Kohli JS, Kinnear MK, Fong CH, Fishman I, Carper RA, Müller RA.
Local Cortical Gyrification is Increased in Children With Autism Spectrum Disorders, but Decreases Rapidly in Adolescents.
Cereb Cortex. 2019; 29:2412-2423. doi: 10.1093/cercor/bhy111.

Mastrovito D, Hanson C, Hanson SJ.
Differences in atypical resting-state effective connectivity distinguish autism from schizophrenia.
Neuroimage Clin. 2018; 18:367-376. doi: 10.1016/j.nicl.2018.01.014.

Nair S, Jao Keehn RJ, Berkebile MM, Maximo JO, Witkowska N, Müller RA.
Local resting state functional connectivity in autism: site and cohort variability and the effect of eye status.
Brain Imaging Behav. 2018; 12:168-179. doi: 10.1007/s11682-017-9678-y.

Pua EPK, Malpas CB, Bowden SC, Seal ML.
Different brain networks underlying intelligence in autism spectrum disorders.
Hum Brain Mapp. 2018; 39:3253-3262. doi: 10.1002/hbm.24074.

Yang J, Lee J.
Different aberrant mentalizing networks in males and females with autism spectrum disorders: Evidence from resting-state functional magnetic resonance imaging.
Autism. 2018; 22:134-148. doi: 10.1177/1362361316667056.

Zhao F, Zhang H, Rekik I, An Z, Shen D.
Diagnosis of Autism Spectrum Disorders Using Multi-Level High-Order Functional Networks Derived From Resting-State Functional MRI.
Front Hum Neurosci. 2018; 12:184. doi: 10.3389/fnhum.2018.00184.

Zu C, Gao Y, Munsell B, Kim M, Peng Z, Cohen JR, Zhang D, Wu G.
Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning.
Brain Imaging Behav. 2019; 13:879-892. doi: 10.1007/s11682-018-9899-8.

Hong SJ, Valk SL, Di Martino A, Milham MP, Bernhardt BC.
Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder.
Cereb Cortex. 2018; 28:3578-3588. doi: 10.1093/cercor/bhx229.

Demirhan A.
The effect of feature selection on multivariate pattern analysis of structural brain MR images.
Phys Med. 2018; 47:103-111. doi: 10.1016/j.ejmp.2018.03.002.

Di X, Azeez A, Li X, Haque E, Biswal BB.
Disrupted focal white matter integrity in autism spectrum disorder: A voxel-based meta-analysis of diffusion tensor imaging studies.
Prog Neuropsychopharmacol Biol Psychiatry. 2018; 82:242-248. doi: 10.1016/j.pnpbp.2017.11.007.

Pappaianni E, Siugzdaite R, Vettori S, Venuti P, Job R, Grecucci A.
Three shades of grey: detecting brain abnormalities in children with autism using source-, voxel- and surface-based morphometry.
Eur J Neurosci. 2018; 47:690-700. doi: 10.1111/ejn.13704.

Levman J, Vasung L, MacDonald P, Rowley S, Stewart N, Lim A, Ewenson B, Galaburda A, Takahashi E.
Regional volumetric abnormalities in pediatric autism revealed by structural magnetic resonance imaging.
Int J Dev Neurosci. 2018; 71:34-45. doi: 10.1016/j.ijdevneu.2018.08.001.

Bernas A, Aldenkamp AP, Zinger S.
Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism.
Comp Methods & Prog in Biomed. 2018; 154:143-151. doi: 10.1016/j.cmpb.2017.11.017.

Floris DL, Lai MC, Nath T, Milham MP, Di Martino A.
Network-specific sex differentiation of intrinsic brain function in males with autism.
Mol Autism. 2018; 9:17. doi: 10.1186/s13229-018-0192-x.

Keshavan A, Datta E, M McDonough I, Madan CR, Jordan K, Henry RG.
Mindcontrol: A web application for brain segmentation quality control.
Neuroimage. 2018; 170:365-372. doi: 10.1016/j.neuroimage.2017.03.055.

Kernbach JM, Satterthwaite TD, Bassett DS, Smallwood J, Margulies D, Krall S, Shaw P, Varoquaux G, Thirion B, Konrad K, Bzdok D.
Shared endo-phenotypes of default mode dsfunction in attention deficit/hyperactivity disorder and autism spectrum disorder.
Transl Psychiatry. 2018; 8:133. doi: 10.1038/s41398-018-0179-6.

Brown CJ, Kawahara J, Hamarneh G.
Connectome priors in deep neural networks to predict autism.
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 110-113, doi: 10.1109/ISBI.2018.8363534.

Xiao Z, Wang C, Jia N, Wu J.
SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging.
Multimed Tools Appl 77, 22809–22820 (2018). doi: 10.1007/s11042-018-5625-1.

Gagliano A, Puligheddu M, Ronzano N, Congiu P, Tanca MG, Cursio I, Carucci S, Sotgiu S, Grossi E, Zuddas A.
Artificial Neural Networks Analysis of polysomnographic and clinical features in Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS): from sleep alteration to "Brain Fog".
Nat Sci Sleep. 2021; 13:1209-1224. doi: 10.2147/NSS.S300818.

Wang J, Wang Q, Zhang H, Chen J, Wang S, Shen D.
Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns.
IEEE Trans Cybern. 2019; 49:3141-3154. doi: 10.1109/TCYB.2018.2839693.

Wong E, Anderson JS, Zielinski BA, Fletcher PT.
Riemannian Regression and Classification Models of Brain Networks Applied to Autism.
Connect Neuroimaging (2018). 2018; 11083:78-87. doi: 10.1007/978-3-030-00755-3_9.

Bertero A, Liska A, Pagani M, Parolisi R, Masferrer ME, Gritti M, Pedrazzoli M, Galbusera A, Sarica A, Cerasa A, Buffelli M, Tonini R, Buffo A, Gross C, Pasqualetti M, Gozzi A.
Autism-associated 16p11.2 microdeletion impairs prefrontal functional connectivity in mouse and human.
Brain. 2018; 141:2055-2065. doi: 10.1093/brain/awy111.

Traut N, Beggiato A, Bourgeron T, Delorme R, Rondi-Reig L, Paradis AL, Toro R.
Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort.
Biol Psychiatry. 2018; 83:579-588. doi: 10.1016/j.biopsych.2017.09.029.

Monté-Rubio GC, Falcón C, Pomarol-Clotet E, Ashburner J.
A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods.
Neuroimage. 2018; 178:753-768. doi: 10.1016/j.neuroimage.2018.05.065.

Pardoe HR, Kuzniecky R.
NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.
Neuroinformatics. 2018; 16:43-49. doi: 10.1007/s12021-017-9346-9.

Soussia M, Rekik I.
Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis.
Front Neuroinform. 2018; 12:70. doi: 10.3389/fninf.2018.00070.

Parisot S, Ktena SI, Ferrante E, Lee M, Guerrero R, Glocker B, Rueckert D.
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.
Med Image Anal. 2018; 48:117-130. doi: 10.1016/j.media.2018.06.001.

Dickie EW, Ameis SH, Shahab S, Calarco N, Smith DE, Miranda D, Viviano JD, Voineskos AN.
Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder Supplemental Information.
Biol Psychiatry. 2018 Aug 15;84(4):278-286. doi: 10.1016/j.biopsych.2018.02.1174. Epub 2018 Mar 17.

Bezgin G, Lewis JD, Evans AC.
Developmental changes of cortical white-gray contrast as predictors of autism diagnosis and severity.
Transl Psychiatry. 2018; 8:249. doi: 10.1038/s41398-018-0296-2.

He L, Li H, Holland SK, Yuan W, Altaye M, Parikh NA.
Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.
NeuroImage: Clinical. Volume 18, 2018; Pages 290-297, ISSN 2213-1582, doi: doi.org/10.1016/j.nicl.2018.01.032.

Voorhies W, Dajani DR, Vij SG, Shankar S, Turan TO, Uddin LQ.
Aberrant functional connectivity of inhibitory control networks in children with autism spectrum disorder.
Autism Res. 2018; 11:1468-1478. doi: 10.1002/aur.2014.

Pascual-Belda A, Díaz-Parra A, Moratal D.
Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics.
Diagnostics (Basel). 2018; 8:None. doi: 10.3390/diagnostics8030051.

van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto GF, Calderoni S, Daly E, Deruelle C, Di Martino A, Dinstein I, Duran FLS, Durston S, Ecker C, Fair D, Fedor J, Fitzgerald J, Freitag CM, Gallagher L, Gori I, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, Lerch J, Luna B, Martinho MM, McGrath J, Muratori F, Murphy CM, Murphy DGM, O'Hearn K, Oranje B, Parellada M, Retico A, Rosa P, Rubia K, Shook D, Taylor M, Thompson PM, Tosetti M, Wallace GL, Zhou F, Buitelaar JK.
Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group.
Am J Psychiatry. 2018 Apr 1;175(4):359-369. doi: 10.1176/appi.ajp.2017.17010100.

Xu J, Wang H, Zhang L, Xu Z, Li T, Zhou Z, Zhou Z, Gan Y, Hu Q.
Both Hypo-Connectivity and Hyper-Connectivity of the Insular Subregions Associated With Severity in Children With Autism Spectrum Disorders.
Front Neurosci. 2018; 12:234. doi: 10.3389/fnins.2018.00234.

Sato JR, Calebe Vidal M, de Siqueira Santos S, Brauer Massirer K, Fujita A.
Complex Network Measures in Autism Spectrum Disorders.
IEEE/ACM Trans Comput Biol Bioinform. 2018; 15:581-587. doi: 10.1109/TCBB.2015.2476787.

Zhou Y, Zhang L, Teng S, Qiao L, Shen D.
Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification.
Front Neurosci. 2018; 12:959. doi: 10.3389/fnins.2018.00959.

Zhao G, Walsh K, Long J, Gui W, Denisova K.
Reduced structural complexity of the right cerebellar cortex in male children with autism spectrum disorder.
PLoS One. 2018; 13:e0196964. doi: 10.1371/journal.pone.0196964.

Yan W, Rangaprakash D, Deshpande G.
Estimated hemodynamic response function parameters obtained from resting state BOLD fMRI signals in subjects with autism spectrum disorder and matched healthy subjects.
Data Brief. 2018; 19:1305-1309. doi: 10.1016/j.dib.2018.04.126.

Holland M, Budday S, Goriely A, Kuhl E.
Symmetry Breaking in Wrinkling Patterns: Gyri Are Universally Thicker than Sulci.
Phys Rev Lett. 2018; 121:228002. doi: 10.1103/PhysRevLett.121.228002.

Abraham A, Milham MP, Di Martino A, Craddock RC, Samaras D, Thirion B, Varoquaux G.
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example.
Neuroimage. 2017; 147:736-745. doi: 10.1016/j.neuroimage.2016.10.045.

Nunes AS, Peatfield N, Vakorin V, Doesburg SM.
Idiosyncratic organization of cortical networks in autism spectrum disorder.
Neuroimage. 2019; 190:182-190. doi: 10.1016/j.neuroimage.2018.01.022.

Wang J, Wang Q, Zhang H, Chen J, Wang S, Shen D.
Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns.
IEEE Trans Cybern. 2019; 49:3141-3154. doi: 10.1109/TCYB.2018.2839693.

Jun E, Kang E, Choi J, Suk HI.
Modeling regional dynamics in low-frequency fluctuation and its application to Autism spectrum disorder diagnosis.
Neuroimage. 2019; 184:669-686. doi: 10.1016/j.neuroimage.2018.09.043.

Delbruck E, Yang M, Yassine A, Grossman ED.
Functional connectivity in ASD: Atypical pathways in brain networks supporting action observation and joint attention.
Brain Res. 2019; 1706:157-165. doi: 10.1016/j.brainres.2018.10.029.

Margolis AE, Pagliaccio D, Thomas L, Banker S, Marsh R.
Salience network connectivity and social processing in children with nonverbal learning disability or autism spectrum disorder.
Neuropsychology. 2019 Jan;33(1):135-143. doi: 10.1037/neu0000494. Epub 2018 Nov 8.

Brown CJ, Miller SP, Booth BG, Zwicker JG, Grunau RE, Synnes AR, Chau V, Hamarneh G.
Predictive connectome subnetwork extraction with anatomical and connectivity priors.
Comput Med Imaging Graph. 2019; 71:67-78. doi: 10.1016/j.compmedimag.2018.08.009.

Smith RX, Jann K, Dapretto M, Wang DJJ.
Imbalance of Functional Connectivity and Temporal Entropy in Resting-State Networks in Autism Spectrum Disorder: A Machine Learning Approach.
Front Neurosci. 2018; 12:869. doi: 10.3389/fnins.2018.00869.

Díaz-Caneja CM, Schnack H, Martínez K, Santonja J, Alemán-Gomez Y, Pina-Camacho L, Moreno C, Fraguas D, Arango C, Parellada M, Janssen J.
Neuroanatomical deficits shared by youth with autism spectrum disorders and psychotic disorders.
Hum Brain Mapp. 2019; 40:1643-1653. doi: 10.1002/hbm.24475.

Maximo JO, Kana RK.
Aberrant "deep connectivity" in autism: A cortico-subcortical functional connectivity magnetic resonance imaging study.
Autism Res. 2019; 12:384-400. doi: 10.1002/aur.2058.

Kazeminejad A, Sotero RC.
Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification.
Front Neurosci. 2018; 12:1018. doi: 10.3389/fnins.2018.01018.

Ramos TC, Balardin JB, Sato JR, Fujita A.
Abnormal Cortico-Cerebellar Functional Connectivity in Autism Spectrum Disorder.
Front Syst Neurosci. 2018; 12:74. doi: 10.3389/fnsys.2018.00074.

Harlalka V, Bapi RS, Vinod PK, Roy D.
Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder.
Front Hum Neurosci. 2019; 13:6. doi: 10.3389/fnhum.2019.00006.

Sujit SJ, Coronado I, Kamali A, Narayana PA, Gabr RE.
Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks.
J Magn Reson Imaging. 2019; 50:1260-1267. doi: 10.1002/jmri.26693.

Parikh MN, Li H, He L.
Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data.
Front Comput Neurosci. 2019; 13:9. doi: 10.3389/fncom.2019.00009.

Borràs-Ferrís L, Pérez-Ramírez Ú, Moratal D.
Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study.
Diagnostics (Basel). 2019; 9:None. doi: 10.3390/diagnostics9010032.

Aghdam MA, Sharifi A, Pedram MM.
Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks.
J Digit Imaging. 2019; 32:899-918. doi: 10.1007/s10278-019-00196-1.

Noriega G.
Restricted, Repetitive, and Stereotypical Patterns of Behavior in Autism-an fMRI Perspective.
IEEE Trans Neural Syst Rehabil Eng. 2019; 27:1139-1148. doi: 10.1109/TNSRE.2019.2912416.

Khosla M, Jamison K, Kuceyeski A, Sabuncu MR.
Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction.
Neuroimage. 2019; 199:651-662. doi: 10.1016/j.neuroimage.2019.06.012.

Chen CM, Yang P, Wu MT, Chuang TC, Huang TY.
Deriving and validating biomarkers associated with autism spectrum disorders from a large-scale resting-state database.
Sci Rep. 2019; 9:9043. doi: 10.1038/s41598-019-45465-9.

Kahathuduwa CN, West B, Mastergeorge A.
Effects of Overweight or Obesity on Brain Resting State Functional Connectivity of Children with Autism Spectrum Disorder.
J Autism Dev Disord. 2019 Dec;49(12):4751-4760. doi: 10.1007/s10803-019-04187-7.

Takeda Y, Itahashi T, Sato MA, Yamashita O.
Estimating repetitive spatiotemporal patterns from many subjects' resting-state fMRIs.
Neuroimage. 2019; 203:116182. doi: 10.1016/j.neuroimage.2019.116182.

King JB, Prigge MBD, King CK, Morgan J, Weathersby F, Fox JC, Dean DC, Freeman A, Villaruz JAM, Kane KL, Bigler ED, Alexander AL, Lange N, Zielinski B, Lainhart JE, Anderson JS.
Generalizability and reproducibility of functional connectivity in autism.
Mol Autism. 2019; 10:27. doi: 10.1186/s13229-019-0273-5.

Dvornek NC, Ventola P, Duncan JS.
Combining phenotypic and resting-state fMRI data for autism classification with recurrent neural networks.
Proc IEEE Int Symp Biomed Imaging. 2018; 2018:725-728. doi: 10.1109/ISBI.2018.8363676.

Rathore A, Palande S, Anderson JS, Zielinski BA, Fletcher PT, Wang B.
Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale.
Med Image Comput Comput Assist Interv. 2019; 11766:736-744. doi: 10.1007/978-3-030-32248-9_82.

Braden BB, Riecken C.
Thinning Faster? Age-Related Cortical Thickness Differences in Adults with Autism Spectrum Disorder.
Res Autism Spectr Disord. 2019; 64:31-38. doi: 10.1016/j.rasd.2019.03.005.

Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F.
ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.
Front Neuroinform. 2019; 13:70. doi: 10.3389/fninf.2019.00070.

Spera G, Retico A, Bosco P, Ferrari E, Palumbo L, Oliva P, Muratori F, Calderoni S.
Evaluation of Altered Functional Connections in Male Children With Autism Spectrum Disorders on Multiple-Site Data Optimized With Machine Learning.
Front Psychiatry. 2019; 10:620. doi: 10.3389/fpsyt.2019.00620.

Wang C, Xiao Z, Wang B, Wu J.
Identification of autism based on SVM-RFE and stacked sparse auto-encoder.
IEEE Access, vol. 7, pp. 118030-118036, 2019, doi: 10.1109/ACCESS.2019.2936639.

Mostafa S, Tang L, Wu F.
Diagnosis of autism spectrum disorder based on eigenvalues of brain networks.
IEEE Access, vol. 7, pp. 128474-128486, 2019, doi: 10.1109/ACCESS.2019.2940198.

Yang X, Islam MS, Khaled AMA.
Functional connectivity magnetic resonance imaging classification of autism spectrum disorder using the multisite ABIDE dataset.
2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019, pp. 1-4, doi: 10.1109/BHI.2019.8834653.

Sairam K, Naren J, Vithya G, Srivathsan S.
Computer Aided System for Autism Spectrum Disorder Using Deep Learning Methods.
International Journal of Psychosocial Rehabilitation. 2019. 23:418-425. doi: 10.37200/IJPR/V23I1/PR190254.

Dammu PS, Bapi RS.
Employing Temporal Properties of Brain Activity for Classifying Autism Using Machine Learning.
Pattern Recognition and Machine Intelligence: 8th Inter Conf, PReMI 2019, Tezpur, India, December 17-20, 2019, Proceedings, Part II. Springer-Verlag, Berlin, Heidelberg, 193–200. doi: 10.1007/978-3-030-34872-4_22

Kong Y, Gao J, Xu Y, Pan Y, Wang J, Liu J.
Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier.
Neurocomputing. 2019. 324:63-68. doi: 10.1016/j.neucom.2018.04.080.

Eslami T, Saeed F.
Auto-ASD-network: a technique based on deep learning and support vector machines for diagnosing autism spectrum disorder using fMRI data.
BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2019. 646-651. doi: 10.1145/3307339.3343482.

Zhao Y, Dai H, Zhang W, Ge F, Liu T.
Two-stage spatial temporal deep learning framework for functional brain network modeling.
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). 2019. 1576-1580. doi: 10.1109/ISBI.2019.8759503.

Pua EPK, Barton S, Williams K, Craig JM, Seal ML.
Individualised MRI training for paediatric neuroimaging: A child-focused approach.
Dev Cogn Neurosci. 2020; 41:100750. doi: 10.1016/j.dcn.2019.100750.

Martínez K, Martínez-García M, Marcos-Vidal L, Janssen J, Castellanos FX, Pretus C, Villarroya Ó, Pina-Camacho L, Díaz-Caneja CM, Parellada M, Arango C, Desco M, Sepulcre J, Carmona S.
Sensory-to-Cognitive Systems Integration Is Associated With Clinical Severity in Autism Spectrum Disorder.
J Am Acad Child Adolesc Psychiatry. 2020; 59:422-433. doi: 10.1016/j.jaac.2019.05.033.

Ashburner J, Brudfors M, Bronik K, Balbastre Y.
An algorithm for learning shape and appearance models without annotations.
Med Image Anal. 2019; 55:197-215. doi: 10.1016/j.media.2019.04.008.

Li Q, Becker B, Jiang X, Zhao Z, Zhang Q, Yao S, Kendrick KM.
Decreased Interhemispheric Functional Connectivity Rather Than Corpus Callosum Volume as a Potential Biomarker for Autism Spectrum Disorder.
Cortex. 2019; 119:258-266. doi: 10.1016/j.cortex.2019.05.003.

Wang C, Xiao Z, Wu J.
Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data.
Phys Med. 2019; 65:99-105. doi: 10.1016/j.ejmp.2019.08.010.

Hong SJ, Hyung B, Paquola C, Bernhardt BC.
The Superficial White Matter in Autism and Its Role in Connectivity Anomalies and Symptom Severity.
Cereb Cortex. 2019; 29:4415-4425. doi: 10.1093/cercor/bhy321.

Kozhemiako N, Vakorin V, Nunes AS, Iarocci G, Ribary U, Doesburg SM.
Extreme male developmental trajectories of homotopic brain connectivity in autism.
Hum Brain Mapp. 2019; 40:987-1000. doi: 10.1002/hbm.24427.

Jung M, Tu Y, Park J, Jorgenson K, Lang C, Song W, Kong J.
Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder.
Br J Psychiatry. 2019; 214:339-344. doi: 10.1192/bjp.2018.248.

Reiter MA, Mash LE, Linke AC, Fong CH, Fishman I, Müller RA.
Distinct Patterns of Atypical Functional Connectivity in Lower-Functioning Autism.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2019; 4:251-259. doi: 10.1016/j.bpsc.2018.08.009.

Iidaka T, Kogata T, Mano Y, Komeda H.
Thalamocortical Hyperconnectivity and Amygdala-Cortical Hypoconnectivity in Male Patients With Autism Spectrum Disorder.
Front Psychiatry. 2019; 10:252. doi: 10.3389/fpsyt.2019.00252.

Hong SJ, Vos de Wael R, Bethlehem RAI, Lariviere S, Paquola C, Valk SL, Milham MP, Di Martino A, Margulies DS, Smallwood J, Bernhardt BC.
Atypical functional connectome hierarchy in autism.
Nat Commun. 2019; 10:1022. doi: 10.1038/s41467-019-08944-1.

Lake EMR, Finn ES, Noble SM, Vanderwal T, Shen X, Rosenberg MD, Spann MN, Chun MM, Scheinost D, Constable RT.
The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder.
Biol Psychiatry. 2019; 86:315-326. doi: 10.1016/j.biopsych.2019.02.019.

Romero-Garcia R, Warrier V, Bullmore ET, Baron-Cohen S, Bethlehem RAI.
Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism.
Mol Psychiatry. 2019; 24:1053-1064. doi: 10.1038/s41380-018-0023-7.

Farooq H, Chen Y, Georgiou TT, Tannenbaum A, Lenglet C.
Network curvature as a hallmark of brain structural connectivity.
Nat Commun. 2019; 10:4937. doi: 10.1038/s41467-019-12915-x.

Pua EPK, Ball G, Adamson C, Bowden S, Seal ML.
Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders.
Sci Rep. 2019; 9:9898. doi: 10.1038/s41598-019-45774-z.

de Lange SC, Scholtens LH, Alzheimer's Disease Neuroimaging Initiative, van den Berg LH, Boks MP, Bozzali M, Cahn W, Dannlowski U, Durston S, Geuze E, van Haren NEM, Hillegers MHJ, Koch K, Jurado MÁ, Mancini M, Marqués-Iturria I, Meinert S, Ophoff RA, Reess TJ, Repple J, Kahn RS, van den Heuvel MP.
Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders.
Nat Hum Behav. 2019; 3:988-998. doi: 10.1038/s41562-019-0659-6.

Simon-Martinez C, Jaspers E, Alaerts K, Ortibus E, Balsters J, Mailleux L, Blommaert J, Sleurs C, Klingels K, Amant F, Uyttebroeck A, Wenderoth N, Feys H.
Influence of the corticospinal tract wiring pattern on sensorimotor functional connectivity and clinical correlates of upper limb function in unilateral cerebral palsy.
Sci Rep. 2019; 9:8230. doi: 10.1038/s41598-019-44728-9.

He Y, Byrge L, Kennedy DP.
Nonreplication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies.
Hum Brain Mapp. 2020; 41:1334-1350. doi: 10.1002/hbm.24879.

Wang K, Xu M, Ji Y, Zhang L, Du X, Li J, Luo Q, Li F.
Altered social cognition and connectivity of default mode networks in the co-occurrence of autistic spectrum disorder and attention deficit hyperactivity disorder.
Aust N Z J Psychiatry. 2019; 53:760-771. doi: 10.1177/0004867419836031.

Mohajer B, Masoudi M, Ashrafi A, Mohammadi E, Bayani Ershadi AS, Aarabi MH, Uban KA.
Structural white matter alterations in male adults with high functioning autism spectrum disorder and concurrent depressive symptoms; a diffusion tensor imaging study.
J Affect Disord. 2019; 259:40-46. doi: 10.1016/j.jad.2019.08.010.

Beer JC, Aizenstein HJ, Anderson SJ, Krafty RT.
Incorporating prior information with fused sparse group lasso: Application to prediction of clinical measures from neuroimages.
Biometrics. 2019; 75:1299-1309. doi: 10.1111/biom.13075.

Xiao Z, Wu J, Wang C, Jia N, Yang X.
Computer-aided diagnosis of school-aged children with ASD using full frequency bands and enhanced SAE: A multi-institution study.
Exp Ther Med. 2019 May;17(5):4055-4063. doi: 10.3892/etm.2019.7448.

Chen H, Uddin LQ, Guo X, Wang J, Wang R, Wang X, Duan X, Chen H.
Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes.
Hum Brain Mapp. 2019; 40:628-637. doi: 10.1002/hbm.24400.

Chouinard B, Gallagher L, Kelly C.
He said, she said: Autism spectrum diagnosis and gender differentially affect relationships between executive functions and social communication.
Autism. 2019; 23:1793-1804. doi: 10.1177/1362361318815639.

He K, Xu H, Kang J.
A selective overview of feature screening methods with applications to neuroimaging data.
Wiley Interdiscip Rev Comput Stat. 2019; 11:None. doi: 10.1002/wics.1454.

Chen H, Uddin LQ, Guo X, Wang J, Wang R, Wang X, Duan X, Chen H.
Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes.
Hum Brain Mapp. 2019; 40:628-637. doi: 10.1002/hbm.24400.

Zuo C, Wang D, Tao F, Wang Y.
Changes in the development of subcortical structures in autism spectrum disorder.
Neuroreport. 2019; 30:1062-1067. doi: 10.1097/WNR.0000000000001300.

Shofty B, Bergmann E, Zur G, Asleh J, Bosak N, Kavushansky A, Castellanos FX, Ben-Sira L, Packer RJ, Vezina GL, Constantini S, Acosta MT, Kahn I.
Autism-associated Nf1 deficiency disrupts corticocortical and corticostriatal functional connectivity in human and mouse.
Neurobiol Dis. 2019; 130:104479. doi: 10.1016/j.nbd.2019.104479.

Zhang F, Wu Y, Norton I, Rathi Y, Golby AJ, O'Donnell LJ.
Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering.
Hum Brain Mapp. 2019; 40:3041-3057. doi: 10.1002/hbm.24579.

Laidi C, Boisgontier J, de Pierrefeu A, Duchesnay E, Hotier S, d'Albis MA, Delorme R, Bolognani F, Czech C, Bouquet C, Amestoy A, Petit J, Holiga Š, Dukart J, Gaman A, Toledano E, Ly-Le Moal M, Scheid I, Leboyer M, Houenou J.
Decreased Cortical Thickness in the Anterior Cingulate Cortex in Adults with Autism.
J Autism Dev Disord. 2019; 49:1402-1409. doi: 10.1007/s10803-018-3807-3.

Pinaya WHL, Mechelli A, Sato JR.
Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.
Hum Brain Mapp. 2019; 40:944-954. doi: 10.1002/hbm.24423.

Levman J, MacDonald P, Rowley S, Stewart N, Lim A, Ewenson B, Galaburda A, Takahashi E.
Structural Magnetic Resonance Imaging Demonstrates Abnormal Regionally-Differential Cortical Thickness Variability in Autism: From Newborns to Adults.
Front Hum Neurosci. 2019; 13:75. doi: 10.3389/fnhum.2019.00075.

Dhifallah S, Rekik I, Alzheimer's Disease Neuroimaging Initiative.
Clustering-based multi-view network fusion for estimating brain network atlases of healthy and disordered populations.
J Neurosci Methods. 2019; 311:426-435. doi: 10.1016/j.jneumeth.2018.09.028.

Bednarz HM, Kana RK.
Patterns of Cerebellar Connectivity with Intrinsic Connectivity Networks in Autism Spectrum Disorders.
J Autism Dev Disord. 2019; 49:4498-4514. doi: 10.1007/s10803-019-04168-w.

Anteraper SA, Guell X, Taylor HP, D'Mello A, Whitfield-Gabrieli S, Joshi G.
Intrinsic Functional Connectivity of Dentate Nuclei in Autism Spectrum Disorder.
Brain Connect. 2019; 9:692-702. doi: 10.1089/brain.2019.0692.

Aggarwal P, Gupta A.
Multivariate graph learning for detecting aberrant connectivity of dynamic brain networks in autism.
Med Image Anal. 2019; 56:11-25. doi: 10.1016/j.media.2019.05.007.

Wan B, Wang Z, Jung M, Lu Y, He H, Chen Q, Jin Y.
Effects of the Co-occurrence of Anxiety and Attention-Deficit/Hyperactivity Disorder on Intrinsic Functional Network Centrality among Children with Autism Spectrum Disorder.
Autism Res. 2019; 12:1057-1068. doi: 10.1002/aur.2120.

Odriozola P, Dajani DR, Burrows CA, Gabard-Durnam LJ, Goodman E, Baez AC, Tottenham N, Uddin LQ, Gee DG.
Atypical frontoamygdala functional connectivity in youth with autism.
Dev Cogn Neurosci. 2019; 37:100603. doi: 10.1016/j.dcn.2018.12.001.

Dadi K, Rahim M, Abraham A, Chyzhyk D, Milham M, Thirion B, Varoquaux G, Alzheimer's Disease Neuroimaging Initiative.
Benchmarking functional connectome-based predictive models for resting-state fMRI.
Neuroimage. 2019; 192:115-134. doi: 10.1016/j.neuroimage.2019.02.062.

Guo X, Duan X, Suckling J, Chen H, Liao W, Cui Q, Chen H.
Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder.
Hum Brain Mapp. 2019; 40:1264-1275. doi: 10.1002/hbm.24447.

Huang H, Liu X, Jin Y, Lee SW, Wee CY, Shen D.
Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis.
Hum Brain Mapp. 2019; 40:833-854. doi: 10.1002/hbm.24415.

Watanabe T, Rees G, Masuda N.
Atypical intrinsic neural timescale in autism.
Elife. 2019; 8:None. doi: 10.7554/eLife.42256.

Song Y, Epalle TM, Lu H.
Characterizing and Predicting Autism Spectrum Disorder by Performing Resting-State Functional Network Community Pattern Analysis.
Front Hum Neurosci. 2019; 13:203. doi: 10.3389/fnhum.2019.00203.

Damiani S, Scalabrini A, Gomez-Pilar J, Brondino N, Northoff G.
Increased scale-free dynamics in salience network in adult high-functioning autism.
Neuroimage Clin. 2019; 21:101634. doi: 10.1016/j.nicl.2018.101634.

Easson AK, Fatima Z, McIntosh AR.
Functional connectivity-based subtypes of individuals with and without autism spectrum disorder.
Netw Neurosci. 2019; 3:344-362. doi: 10.1162/netn_a_00067.

Liu C, Xue J, Cheng X, Zhan W, Xiong X, Wang B.
Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI.
Comput Intell Neurosci. 2019; 2019:9027803. doi: 10.1155/2019/9027803.

Easson AK, McIntosh AR.
BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder.
Dev Cogn Neurosci. 2019; 36:100630. doi: 10.1016/j.dcn.2019.100630.

Graa O, Rekik I.
Multi-view learning-based data proliferator for boosting classification using highly imbalanced classes.
J Neurosci Methods. 2019; 327:108344. doi: 10.1016/j.jneumeth.2019.108344.

Tang L, Mostafa S, Liao B, Wu FX.
A network clustering based feature selection strategy for classifying autism spectrum disorder.
BMC Med Genomics. 2019; 12:153. doi: 10.1186/s12920-019-0598-0.

Watanabe T, Lawson RP, Walldén YSE, Rees G.
A Neuroanatomical Substrate Linking Perceptual Stability to Cognitive Rigidity in Autism.
J Neurosci. 2019; 39:6540-6554. doi: 10.1523/JNEUROSCI.2831-18.2019.

Olson LA, Mash LE, Linke A, Fong CH, Müller RA, Fishman I.
Sex-related patterns of intrinsic functional connectivity in children and adolescents with autism spectrum disorders.
Autism. 2020; 24:2190-2201. doi: 10.1177/1362361320938194.

Corps J, Rekik I.
Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants.
Sci Rep. 2019; 9:9676. doi: 10.1038/s41598-019-46145-4.

van den Heuvel MP, Scholtens LH, de Lange SC, Pijnenburg R, Cahn W, van Haren NEM, Sommer IE, Bozzali M, Koch K, Boks MP, Repple J, Pievani M, Li L, Preuss TM, Rilling JK.
Evolutionary modifications in human brain connectivity associated with schizophrenia.
Brain. 2019; 142:3991-4002. doi: 10.1093/brain/awz330.

Klapwijk ET, van de Kamp F, van der Meulen M, Peters S, Wierenga LM.
Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data.
Neuroimage. 2019; 189:116-129. doi: 10.1016/j.neuroimage.2019.01.014.

Sidhu G.
Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification.
IEEE J Transl Eng Health Med. 2019; 7:2200211. doi: 10.1109/JTEHM.2019.2936348.

Tomasiello S.
A granular functional network classifier for brain diseases analysis.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8:4, 382-388, doi: 10.1080/21681163.2019.1627910.

Guell X, D'Mello AM, Hubbard NA, Romeo RR, Gabrieli JDE, Whitfield-Gabrieli S, Schmahmann JD, Anteraper SA.
Functional Territories of Human Dentate Nucleus.
Cereb Cortex. 2020; 30:2401-2417. doi: 10.1093/cercor/bhz247.

Rolls ET, Zhou Y, Cheng W, Gilson M, Deco G, Feng J.
Effective connectivity in autism.
Autism Res. 2020; 13:32-44. doi: 10.1002/aur.2235.

Caballero C, Mistry S, Torres EB.
Age-Dependent Statistical Changes of Involuntary Head Motion Signatures Across Autism and Controls of the ABIDE Repository.
Front Integr Neurosci. 2020; 14:23. doi: 10.3389/fnint.2020.00023.

Floris DL, Wolfers T, Zabihi M, Holz NE, Zwiers MP, Charman T, Tillmann J, Ecker C, Dell'Acqua F, Banaschewski T, Moessnang C, Baron-Cohen S, Holt R, Durston S, Loth E, Murphy DGM, Marquand A, Buitelaar JK, Beckmann CF, EU-AIMS Longitudinal European Autism Project Group.
Atypical Brain Asymmetry in Autism-A Candidate for Clinically Meaningful Stratification.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2021; 6:802-812. doi: 10.1016/j.bpsc.2020.08.008.

Williams CM, Peyre H, Toro R, Beggiato A, Ramus F.
Adjusting for allometric scaling in ABIDE I challenges subcortical volume differences in autism spectrum disorder.
Hum Brain Mapp. 2020; 41:4610-4629. doi: 10.1002/hbm.25145.

Alvarez-Jimenez C, Múnera-Garzón N, Zuluaga MA, Velasco NF, Romero E.
Autism spectrum disorder characterization in children by capturing local-regional brain changes in MRI.
Med Phys. 2020; 47:119-131. doi: 10.1002/mp.13901.

Sewani H, Kashef R.
An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism.
Children (Basel). 2020; 7:None. doi: 10.3390/children7100182.

Wang Y, Wang J, Wu FX, Hayrat R, Liu J.
AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning.
J Neurosci Methods. 2020; 343:108840. doi: 10.1016/j.jneumeth.2020.108840.

Dekhil O, Ali M, Haweel R, Elnakib Y, Ghazal M, Hajjdiab H, Fraiwan L, Shalaby A, Soliman A, Mahmoud A, Keynton R, Casanova MF, Barnes G, El-Baz A.
A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI.
Semin Pediatr Neurol. 2020; 34:100805. doi: 10.1016/j.spen.2020.100805.

Sherkatghanad Z, Akhondzadeh M, Salari S, Zomorodi-Moghadam M, Abdar M, Acharya UR, Khosrowabadi R, Salari V.
Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network.
Front Neurosci. 2019; 13:1325. doi: 10.3389/fnins.2019.01325.

Wylie KP, Tregellas JR, Bear JJ, Legget KT.
Autism Spectrum Disorder Symptoms are Associated with Connectivity Between Large-Scale Neural Networks and Brain Regions Involved in Social Processing.
J Autism Dev Disord. 2020; 50:2765-2778. doi: 10.1007/s10803-020-04383-w.

Liu Y, Xu L, Li J, Yu J, Yu X.
Attentional Connectivity-based Prediction of Autism Using Heterogeneous rs-fMRI Data from CC200 Atlas.
Exp Neurobiol. 2020; 29:27-37. doi: 10.5607/en.2020.29.1.27.

Nunes AS, Vakorin VA, Kozhemiako N, Peatfield N, Ribary U, Doesburg SM.
Atypical age-related changes in cortical thickness in autism spectrum disorder.
Sci Rep. 2020; 10:11067. doi: 10.1038/s41598-020-67507-3.

Rakić M, Cabezas M, Kushibar K, Oliver A, Lladó X.
Improving the detection of autism spectrum disorder by combining structural and functional MRI information.
Neuroimage Clin. 2020; 25:102181. doi: 10.1016/j.nicl.2020.102181.

Thomas RM, Gallo S, Cerliani L, Zhutovsky P, El-Gazzar A, van Wingen G.
Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks.
Front Psychiatry. 2020; 11:440. doi: 10.3389/fpsyt.2020.00440.

Qi S, Morris R, Turner JA, Fu Z, Jiang R, Deramus TP, Zhi D, Calhoun VD, Sui J.
Common and unique multimodal covarying patterns in autism spectrum disorder subtypes.
Mol Autism. 2020; 11:90. doi: 10.1186/s13229-020-00397-4.

Bartolotti J, Sweeney JA, Mosconi MW.
Functional brain abnormalities associated with comorbid anxiety in autism spectrum disorder.
Dev Psychopathol. 2020; 32:1273-1286. doi: 10.1017/S0954579420000772.

Xu Q, Zuo C, Liao S, Long Y, Wang Y.
Abnormal development pattern of the amygdala and hippocampus from childhood to adulthood with autism.
J Clin Neurosci. 2020; 78:327-332. doi: 10.1016/j.jocn.2020.03.049.

Rakhimberdina Z, Liu X, Murata AT.
Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder.
Sensors (Basel). 2020; 20:None. doi: 10.3390/s20216001.

Srinivasan V, Udayakumar N, Anandan K.
Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study.
Curr Med Imaging. 2020; 16:1059-1073. doi: 10.2174/1573405615666191111142039.

Richards R, Greimel E, Kliemann D, Koerte IK, Schulte-Körne G, Reuter M, Wachinger C.
Increased hippocampal shape asymmetry and volumetric ventricular asymmetry in autism spectrum disorder.
Neuroimage Clin. 2020; 26:102207. doi: 10.1016/j.nicl.2020.102207.

Rohr CS, Kamal S, Bray S.
Building functional connectivity neuromarkers of behavioral self-regulation across children with and without Autism Spectrum Disorder.
Dev Cogn Neurosci. 2020; 41:100747. doi: 10.1016/j.dcn.2019.100747.

Kozhemiako N, Nunes AS, Vakorin V, Iarocci G, Ribary U, Doesburg SM.
Alterations in Local Connectivity and Their Developmental Trajectories in Autism Spectrum Disorder: Does Being Female Matter?
Cereb Cortex. 2020; 30:5166-5179. doi: 10.1093/cercor/bhaa109.

Khan NA, Waheeb SA, Riaz A, Shang X.
A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder.
Brain Sci. 2020; 10:None. doi: 10.3390/brainsci10100754.

Hu J, Cao L, Li T, Liao B, Dong S, Li P.
Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.
Comput Math Methods Med. 2020; 2020:1394830. doi: 10.1155/2020/1394830.

Shahamat H, Saniee Abadeh M.
Brain MRI analysis using a deep learning based evolutionary approach.
Neural Netw. 2020; 126:218-234. doi: 10.1016/j.neunet.2020.03.017.

Lombardi A, Amoroso N, Diacono D, Monaco A, Tangaro S, Bellotti R.
Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction.
Brain Sci. 2020; 10:None. doi: 10.3390/brainsci10060364.

Ahmed MR, Zhang Y, Liu Y, Liao H.
Single Volume Image Generator and Deep Learning-Based ASD Classification.
IEEE J Biomed Health Inform. 2020; 24:3044-3054. doi: 10.1109/JBHI.2020.2998603.

Jiang H, Cao P, Xu M, Yang J, Zaiane O.
Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction.
Comput Biol Med. 2020; 127:104096. doi: 10.1016/j.compbiomed.2020.104096.

Brahim A, Farrugia N.
Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging.
Artificial Intelligence in Medicine. 2020. 106:101870. doi: 10.1016/j.artmed.2020.101870

Lanka P, Rangaprakash D, Gotoor SSR, Dretsch MN, Katz JS, Denney TS, Deshpande G.
MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data.
Data Brief. 2020; 29:105213. doi: 10.1016/j.dib.2020.105213.

Raamana PR, Strother SC, Australian Imaging Biomarkers, Lifestyle flagship study of ageing, for The Alzheimer’s Disease Neuroimaging Initiative.
Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight.
Brain Struct Funct. 2020; 225:2475-2493. doi: 10.1007/s00429-020-02136-0.

Wang J, Zhang L, Wang Q, Chen L, Shi J, Chen X, Li Z, Shen D.
Multi-Class ASD Classification Based on Functional Connectivity and Functional Correlation Tensor via Multi-Source Domain Adaptation and Multi-View Sparse Representation.
IEEE Trans Med Imaging. 2020; 39:3137-3147. doi: 10.1109/TMI.2020.2987817.

Huang F, Tan EL, Yang P, Huang S, Ou-Yang L, Cao J, Wang T, Lei B.
Self-weighted adaptive structure learning for ASD diagnosis via multi-template multi-center representation.
Med Image Anal. 2020; 63:101662. doi: 10.1016/j.media.2020.101662.

Hong SJ, Vogelstein JT, Gozzi A, Bernhardt BC, Yeo BTT, Milham MP, Di Martino A.
Toward Neurosubtypes in Autism.
Biol Psychiatry. 2020; 88:111-128. doi: 10.1016/j.biopsych.2020.03.022.

Tang M, Kuman P, Chen H, Shrivastava A.
Deep multimodal learning for the diagnosis of autism spectrum disorder.
J. Imaging 2020. 6(6), 47; doi: 10.3390/jimaging6060047

Khundrakpam B, Vainik U, Gong J, Al-Sharif N, Bhutani N, Kiar G, Zeighami Y, Kirschner M, Luo C, Dagher A, Evans A.
Neural correlates of polygenic risk score for autism spectrum disorders in general population.
Brain Commun. 2020; 2:fcaa092. doi: 10.1093/braincomms/fcaa092.

Xie Y, Zhang X, Liu F, Qin W, Fu J, Xue K, Yu C.
Brain mRNA Expression Associated with Cortical Volume Alterations in Autism Spectrum Disorder.
Cell Rep. 2020; 32:108137. doi: 10.1016/j.celrep.2020.108137.

Yang X, Schrader PT, Zhang N.
A deep neural network study of the ABIDE repository on autism spectrum classification.
International Journal of Advanced Computer Science and Applications(IJACSA), 11(4), 2020. doi: 10.14569/IJACSA.2020.0110401.

Li X, Gu Y, Dvornek N, Staib LH, Ventola P, Duncan JS.
Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results.
Med Image Anal. 2020; 65:101765. doi: 10.1016/j.media.2020.101765.

Tang S, Sun N, Floris DL, Zhang X, Di Martino A, Yeo BTT.
Reconciling Dimensional and Categorical Models of Autism Heterogeneity: A Brain Connectomics and Behavioral Study.
Biol Psychiatry. 2020; 87:1071-1082. doi: 10.1016/j.biopsych.2019.11.009.

Niu K, Guo J, Pan Y, Gao X, Peng X, Li N, Li H.
Multichannel deep attention neural networks for the classification of autism spectrum disorder using neuroimaging and personal characteristic data.
Complexity. 2020; 1076-2787. doi: 10.1155/2020/1357853.

Liu J, Sheng Y, Lan W, Guo R, Wang Y, Wang J.
Improved ASD classification using dynamic functional connectivity and multi-task feature selection.
Pattern Recognition Letters. 2020. 138:82-87. doi: 10.1016/j.patrec.2020.07.005.

Ahmed MR, Ahammed MS, Niu S, Zhang Y.
Deep learning approached features for asd classification using SVM.
2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), 2020, pp. 287-290, doi: 10.1109/ICAIIS49377.2020.9194791.

Benabdallah FZ, El Maliani AD, Lotfi D, El Hassouni M.
Analysis of the Over-Connectivity in Autistic Brains Using the Maximum Spanning Tree: Application on the Multi-Site and Heterogeneous ABIDE Dataset.
2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM), 2020, pp. 1-7, doi: 10.1109/WINCOM50532.2020.9272441.

Pervaiz U, Vidaurre D, Woolrich MW, Smith SM.
Optimising network modelling methods for fMRI.
Neuroimage. 2020; 211:116604. doi: 10.1016/j.neuroimage.2020.116604.

King DJ, Seri S, Beare R, Catroppa C, Anderson VA, Wood AG.
Developmental divergence of structural brain networks as an indicator of future cognitive impairments in childhood brain injury: Executive functions.
Dev Cogn Neurosci. 2020; 42:100762. doi: 10.1016/j.dcn.2020.100762.

Gupta A, Sadri AR, Viswanath SE, Tiwari P.
Quality assessment of brain MRI scans using a dense neural network model and image metrics.
Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 113120W (2020) doi: doi.org/10.1117/12.2551348.

Bethlehem RAI, Seidlitz J, Romero-Garcia R, Trakoshis S, Dumas G, Lombardo MV.
A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder
Commun Biol 3, 486 (2020). doi: 10.1038/s42003-020-01212-9.

Chaitra N, Vijaya PA, Deshpande G.
Diagnostic Prediction of Autism Spectrum Disorder Using Complex Network Measures in a Machine Learning Framework.
Biomedical Signal Processing and Control. 2020. 62:102099. doi: 10.1016/j.bspc.2020.102099.

Ferrari E, Bosco P, Calderoni S, Oliva P, Palumbo L, Spera G, Fantacci ME, Retico A.
Dealing with confounders and outliers in classification medical studies: The Autism Spectrum Disorders case study.
Artif Intell Med. 2020; 108:101926. doi: 10.1016/j.artmed.2020.101926.

Ke F, Choi S, Kang YH, Cheon K-A, Lee SW.
Exploring the Structural and Strategic Bases of Autism Spectrum Disorders With Deep Learning.
IEEE Access, vol. 8, pp. 153341-153352, 2020, doi: 10.1109/ACCESS.2020.3016734.

Kazeminejad A, Sotero RC.
The Importance of Anti-correlations in Graph Theory Based Classification of Autism Spectrum Disorder.
Front Neurosci. 2020; 14:676. doi: 10.3389/fnins.2020.00676.

Cai DC, Wang Z, Bo T, Yan S, Liu Y, Liu Z, Zeljic K, Chen X, Zhan Y, Xu X, Du Y, Wang Y, Cang J, Wang GZ, Zhang J, Sun Q, Qiu Z, Ge S, Ye Z, Wang Z.
MECP2 Duplication Causes Aberrant GABA Pathways, Circuits and Behaviors in Transgenic Monkeys: Neural Mappings to Patients with Autism.
J Neurosci. 2020; 40:3799-3814. doi: 10.1523/JNEUROSCI.2727-19.2020.

Bedford SA, Park MTM, Devenyi GA, Tullo S, Germann J, Patel R, Anagnostou E, Baron-Cohen S, Bullmore ET, Chura LR, Craig MC, Ecker C, Floris DL, Holt RJ, Lenroot R, Lerch JP, Lombardo MV, Murphy DGM, Raznahan A, Ruigrok ANV, Smith E, Spencer MD, Suckling J, Taylor MJ, Thurm A, MRC AIMS Consortium, Lai MC, Chakravarty MM.
Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder.
Mol Psychiatry. 2020; 25:614-628. doi: 10.1038/s41380-019-0420-6.

Moreau CA, Urchs SGW, Kuldeep K, Orban P, Schramm C, Dumas G, Labbe A, Huguet G, Douard E, Quirion PO, Lin A, Kushan L, Grot S, Luck D, Mendrek A, Potvin S, Stip E, Bourgeron T, Evans AC, Bearden CE, Bellec P, Jacquemont S.
Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia.
Nat Commun. 2020; 11:5272. doi: 10.1038/s41467-020-18997-2.

He C, Chen H, Uddin LQ, Erramuzpe A, Bonifazi P, Guo X, Xiao J, Chen H, Huang X, Li L, Sheng W, Liao W, Cortes JM, Duan X.
Structure-Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain.
Cereb Cortex. 2020 Jul 30;30(9):5028-5037. doi: 10.1093/cercor/bhaa098.

Liu X, Huang H.
Alterations of functional connectivities associated with autism spectrum disorder symptom severity: a multi-site study using multivariate pattern analysis.
Sci Rep. 2020; 10:4330. doi: 10.1038/s41598-020-60702-2.

He L, Li H, Wang J, Chen M, Gozdas E, Dillman JR, Parikh NA.
A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants.
Sci Rep. 2020; 10:15072. doi: 10.1038/s41598-020-71914-x.

Qi S, Bustillo J, Turner JA, Jiang R, Zhi D, Fu Z, Deramus TP, Vergara V, Ma X, Yang X, Stevens M, Zhou C, Xu Y, Calhoun VD, Sui J.
The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study.
Transl Psychiatry 10, 149 (2020). doi: 10.1038/s41398-020-0834-6.

Chen T, Chen Y, Yuan M, Gerstein M, Li T, Liang H, Froehlich T, Lu L.
The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study.
JMIR Med Inform. 2020; 8:e15767. doi: 10.2196/15767.

Guo X, Duan X, Chen H, He C, Xiao J, Han S, Fan YS, Guo J, Chen H.
Altered inter- and intrahemispheric functional connectivity dynamics in autistic children.
Hum Brain Mapp. 2020; 41:419-428. doi: 10.1002/hbm.24812.

Bednarz HM, Trapani JA, Kana RK.
Metacognition and behavioral regulation predict distinct aspects of social functioning in autism spectrum disorder.
Child Neuropsychol. 2020; 26:953-981. doi: 10.1080/09297049.2020.1745166.

Xu J, Wang C, Xu Z, Li T, Chen F, Chen K, Gao J, Wang J, Hu Q.
Specific Functional Connectivity Patterns of Middle Temporal Gyrus Subregions in Children and Adults with Autism Spectrum Disorder.
Autism Res. 2020; 13:410-422. doi: 10.1002/aur.2239.

Li Y, Zhu Y, Nguchu BA, Wang Y, Wang H, Qiu B, Wang X.
Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper-connected Pattern in Autism Spectrum Disorder.
Autism Res. 2020; 13:230-243. doi: 10.1002/aur.2212.

Dvornek NC, Li X, Zhuang J, Ventola P, Duncan JS.
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.
Mach Learn Med Imaging. 2020; 12436:363-372. doi: 10.1007/978-3-030-59861-7_37.

Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G.
Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets.
Brain Imaging Behav. 2020; 14:2378-2416. doi: 10.1007/s11682-019-00191-8.

Bilgen I, Guvercin G, Rekik I.
Machine learning methods for brain network classification: Application to autism diagnosis using cortical morphological networks.
J Neurosci Methods. 2020; 343:108799. doi: 10.1016/j.jneumeth.2020.108799.

Leming M, Górriz JM, Suckling J.
Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks.
Int J Neural Syst. 2020; 30:2050012. doi: 10.1142/S0129065720500124.

Zhao F, Chen Z, Rekik I, Lee SW, Shen D.
Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks.
Front Neurosci. 2020; 14:258. doi: 10.3389/fnins.2020.00258.

Wang M, Zhang D, Huang J, Yap PT, Shen D, Liu M.
Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation.
IEEE Trans Med Imaging. 2020; 39:644-655. doi: 10.1109/TMI.2019.2933160.

Mhiri I, Rekik I.
Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism.
Med Image Anal. 2020; 60:101596. doi: 10.1016/j.media.2019.101596.

D'Souza NS, Nebel MB, Wymbs N, Mostofsky SH, Venkataraman A.
A joint network optimization framework to predict clinical severity from resting state functional MRI data.
Neuroimage. 2020; 206:116314. doi: 10.1016/j.neuroimage.2019.116314.

Gupta S, Rajapakse JC, Welsch RE, Alzheimer’s Disease Neuroimaging Initiative.
Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.
Neuroimage Clin. 2020; 25:102186. doi: 10.1016/j.nicl.2020.102186.

Agastinose Ronicko JF, Thomas J, Thangavel P, Koneru V, Langs G, Dauwels J.
Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation.
J Neurosci Methods. 2020; 345:108884. doi: 10.1016/j.jneumeth.2020.108884.

Dryburgh E, McKenna S, Rekik I.
Predicting full-scale and verbal intelligence scores from functional Connectomic data in individuals with autism Spectrum disorder.
Brain Imaging Behav. 2020; 14:1769-1778. doi: 10.1007/s11682-019-00111-w.

Zhang L, Wang XH, Li L.
Diagnosing autism spectrum disorder using brain entropy: A fast entropy method.
Comput Methods Programs Biomed. 2020; 190:105240. doi: 10.1016/j.cmpb.2019.105240.

Huang Y, Zhang B, Cao J, Yu S, Wilson G, Park J, Kong J.
Potential Locations for Noninvasive Brain Stimulation in Treating Autism Spectrum Disorders - A Functional Connectivity Study.
Front Psychiatry. 2020; 11:388. doi: 10.3389/fpsyt.2020.00388.

Du Y, Fu Z, Sui J, Gao S, Xing Y, Lin D, Salman M, Abrol A, Rahaman MA, Chen J, Hong LE, Kochunov P, Osuch EA, Calhoun VD, Alzheimer's Disease Neuroimaging Initiative.
NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders.
Neuroimage Clin. 2020; 28:102375. doi: 10.1016/j.nicl.2020.102375.

Triana AM, Glerean E, Saramäki J, Korhonen O.
Effects of spatial smoothing on group-level differences in functional brain networks.
Netw Neurosci. 2020; 4:556-574. doi: 10.1162/netn_a_00132.

Jiao Z, Li H, Fan Y.
Improving Diagnosis of Autism Spectrum Disorder and Disentangling its Heterogeneous Functional Connectivity Patterns Using Capsule Networks.
Proc IEEE Int Symp Biomed Imaging. 2020; 2020:1331-1334. doi: 10.1109/isbi45749.2020.9098524.

Holland MA, Budday S, Li G, Shen D, Goriely A, Kuhl E.
Folding drives cortical thickness variations.
Eur. Phys. J. Spec. Top. 229, 2757-2778 (2020). doi: doi.org/10.1140/epjst/e2020-000001-6.

van Eijk L, Zietsch BP.
Testing the extreme male brain hypothesis: Is autism spectrum disorder associated with a more male-typical brain?.
Autism Res. 2021 Aug;14(8):1597-1608. doi: 10.1002/aur.2537.

Zhang F, Cetin Karayumak S, Hoffmann N, Rathi Y, Golby AJ, O'Donnell LJ.
Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation.
Med Image Anal. 2020; 65:101761. doi: 10.1016/j.media.2020.101761.

Mhiri I, Khalifa AB, Mahjoub MA, Rekik I.
Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning.
Med Image Anal. 2020; 65:101768. doi: 10.1016/j.media.2020.101768.

Zhou Z, Chen X, Zhang Y, Hu D, Qiao L, Yu R, Yap PT, Pan G, Zhang H, Shen D.
A toolbox for brain network construction and classification (BrainNetClass).
Hum Brain Mapp. 2020; 41:2808-2826. doi: 10.1002/hbm.24979.

Thyreau B, Taki Y.
Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks.
Med Image Anal. 2020 Apr;61:101639. doi: 10.1016/j.media.2020.101639.

Du Y, Li B, Hou Y, Calhoun VD.
A deep learning fusion model for brain disorder classification: Application to distinguishing schizophrenia and autism spectrum disorder.
ACM BCB. 2020; 2020:None. doi: 10.1145/3388440.3412478.

Marshall E, Nomi JS, Dirks B, Romero C, Kupis L, Chang C, Uddin LQ.
Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder.
Netw Neurosci. 2020; 4:1219-1234. doi: 10.1162/netn_a_00163.

Zhan Y, Wei J, Liang J, Xu X, He R, Robbins TW, Wang Z.
Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model.
Am J Psychiatry. 2021; 178:65-76. doi: 10.1176/appi.ajp.2020.19101091.

Huang ZA, Zhu Z, Yau CH, Tan KC.
Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network.
IEEE Trans Neural Netw Learn Syst. 2020; 32:2847-2861. doi: 10.1109/TNNLS.2020.3007943.

Yin W, Mostafa S, Wu FX.
Diagnosis of Autism Spectrum Disorder Based on Functional Brain Networks with Deep Learning.
J Comput Biol. 2021; 28:146-165. doi: 10.1089/cmb.2020.0252.

Ma X, Wang XH, Li L.
Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony.
Neurosci Lett. 2021; 742:135519. doi: 10.1016/j.neulet.2020.135519.

Chen H, Long J, Yang S, He B.
Atypical Functional Covariance Connectivity Between Gray and White Matter in Children With Autism Spectrum Disorder.
Autism Res. 2021; 14:464-472. doi: 10.1002/aur.2435.

Fu Y, Zhang J, Li Y, Shi J, Zou Y, Guo H, Li Y, Yao Z, Wang Y, Hu B.
A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder.
Prog Neuropsychopharmacol Biol Psychiatry. 2021; 104:109989. doi: 10.1016/j.pnpbp.2020.109989.

Bellantuono L, Marzano L, La Rocca M, Duncan D, Lombardi A, Maggipinto T, Monaco A, Tangaro S, Amoroso N, Bellotti R.
Predicting brain age with complex networks: From adolescence to adulthood.
Neuroimage. 2021; 225:117458. doi: 10.1016/j.neuroimage.2020.117458.

Verrall CE, Yang JYM, Chen J, Schembri A, d'Udekem Y, Zannino D, Kasparian NA, du Plessis K, Grieve SM, Welton T, Barton B, Gentles TL, Celermajer DS, Attard C, Rice K, Ayer J, Mandelstam S, Winlaw DS, Mackay MT, Cordina R.
Neurocognitive Dysfunction and Smaller Brain Volumes in Adolescents and Adults With a Fontan Circulation.
Circulation. 2021; 143:878-891. doi: 10.1161/CIRCULATIONAHA.120.048202.

Floris DL, Filho JOA, Lai MC, Giavasis S, Oldehinkel M, Mennes M, Charman T, Tillmann J, Dumas G, Ecker C, Dell'Acqua F, Banaschewski T, Moessnang C, Baron-Cohen S, Durston S, Loth E, Murphy DGM, Buitelaar JK, Beckmann CF, Milham MP, Di Martino A.
Towards robust and replicable sex differences in the intrinsic brain function of autism.
Mol Autism. 2021; 12:19. doi: 10.1186/s13229-021-00415-z.

Pua EPK, Thomson P, Yang JY, Craig JM, Ball G, Seal M.
Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders.
Cereb Cortex. 2021; 31:681-693. doi: 10.1093/cercor/bhaa252.

Cai S, Wang X, Yang F, Chen D, Huang L.
Differences in Brain Structural Covariance Network Characteristics in Children and Adults With Autism Spectrum Disorder.
Autism Res. 2021; 14:265-275. doi: 10.1002/aur.2464.

Ayub R, Sun KL, Flores RE, Lam VT, Jo B, Saggar M, Fung LK.
Thalamocortical connectivity is associated with autism symptoms in high-functioning adults with autism and typically developing adults.
Transl Psychiatry. 2021; 11:93. doi: 10.1038/s41398-021-01221-0.

Maximo JO, Nelson CM, Kana RK.
"Unrest while Resting"? Brain entropy in autism spectrum disorder.
Brain Res. 2021 Jul 1;1762:147435. doi: 10.1016/j.brainres.2021.147435. Epub 2021 Mar 19.

Itani S, Thanou D.
Combining anatomical and functional networks for neuropathology identification: A case study on autism spectrum disorder.
Med Image Anal. 2021; 69:101986. doi: 10.1016/j.media.2021.101986.

Gao J, Chen M, Li Y, Gao Y, Li Y, Cai S, Wang J.
Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks.
Front Neurosci. 2020; 14:629630. doi: 10.3389/fnins.2020.629630.

Vandekar SN, Stephens J.
Improving the replicability of neuroimaging findings by thresholding effect sizes instead of p-values.
Hum Brain Mapp. 2021; 42:2393-2398. doi: 10.1002/hbm.25374.

Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G.
Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset.
IEEE Trans Biomed Eng. 2021; 68:3628-3637. doi: 10.1109/TBME.2021.3080259.

Johnson CN, Ramphal B, Koe E, Raudales A, Goldsmith J, Margolis AE.
Cognitive correlates of autism spectrum disorder symptoms.
Autism Res. 2021; 14:2405-2411. doi: 10.1002/aur.2577.

Lorenzini L, van Wingen G, Cerliani L.
Atypically high influence of subcortical activity on primary sensory regions in autism.
Neuroimage Clin. 2021; 32:102839. doi: 10.1016/j.nicl.2021.102839.

Yang C, Wang P, Tan J, Liu Q, Li X.
Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks.
Comput Biol Med. 2021; 139:104963. doi: 10.1016/j.compbiomed.2021.104963.

Ning M, Li C, Gao L, Fan J.
Core-Symptom-Defined Cortical Gyrification Differences in Autism Spectrum Disorder.
Front Psychiatry. 2021; 12:619367. doi: 10.3389/fpsyt.2021.619367.

Loomba N, Beckerson ME, Ammons CJ, Maximo JO, Kana RK.
Corpus callosum size and homotopic connectivity in Autism spectrum disorder.
Psychiatry Res Neuroimaging. 2021; 313:111301. doi: 10.1016/j.pscychresns.2021.111301.

Zanghieri M, Menichetti G, Retico A, Calderoni S, Castellani G, Remondini D.
Node Centrality Measures Identify Relevant Structural MRI Features of Subjects with Autism.
Brain Sci. 2021; 11:None. doi: 10.3390/brainsci11040498.

Li J, Wang F, Pan J, Wen Z.
Identification of Autism Spectrum Disorder With Functional Graph Discriminative Network.
Front Neurosci. 2021; 15:729937. doi: 10.3389/fnins.2021.729937.

Liang D, Xia S, Zhang X, Zhang W.
Analysis of Brain Functional Connectivity Neural Circuits in Children With Autism Based on Persistent Homology.
Front Hum Neurosci. 2021; 15:745671. doi: 10.3389/fnhum.2021.745671.

Li L, Jiang H, Wen G, Cao P, Xu M, Liu X, Yang J, Zaiane O.
TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis.
Neuroinformatics. 2021; None:None. doi: 10.1007/s12021-021-09548-1.

Zhao F, Zhang X, Thung KH, Mao N, Lee SW, Shen D.
Constructing Multi-View High-Order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder.
IEEE Trans Biomed Eng. 2022; 69:1237-1250. doi: 10.1109/TBME.2021.3122813.

Li L, Zuo Y, Chen Y.
Relationship between local gyrification index and age, intelligence quotient, symptom severity with Autism Spectrum Disorder: A large-scale MRI study.
J Clin Neurosci. 2021; 91:193-199. doi: 10.1016/j.jocn.2021.07.003.

Yang M, Cao M, Chen Y, Chen Y, Fan G, Li C, Wang J, Liu T.
Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model.
Front Hum Neurosci. 2021; 15:687288. doi: 10.3389/fnhum.2021.687288.

Wang N, Yao D, Ma L, Liu M.
Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI.
Med Image Anal. 2022; 75:102279. doi: 10.1016/j.media.2021.102279.

Almuqhim F, Saeed F.
ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data.
Front Comput Neurosci. 2021; 15:654315. doi: 10.3389/fncom.2021.654315.

Ahammed MS, Niu S, Ahmed MR, Dong J, Gao X, Chen Y.
DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network.
Front Neuroinform. 2021; 15:635657. doi: 10.3389/fninf.2021.635657.

Tummala S, Thadikemalla VSG, Kreilkamp BAK, Dam EB, Focke NK.
Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning.
Comput Biol Med. 2021; 139:104997. doi: 10.1016/j.compbiomed.2021.104997.

Wang Q, Hu K, Wang M, Zhao Y, Liu Y, Fan L, Liu B.
Predicting brain age during typical and atypical development based on structural and functional neuroimaging.
Hum Brain Mapp. 2021; 42:5943-5955. doi: 10.1002/hbm.25660.

Wang Z, Peng D, Shang Y, Gao J.
Autistic Spectrum Disorder Detection and Structural Biomarker Identification Using Self-Attention Model and Individual-Level Morphological Covariance Brain Networks.
Front Neurosci. 2021; 15:756868. doi: 10.3389/fnins.2021.756868.

Mendes SL, Pinaya WHL, Pan P, Sato JR.
Estimating Gender and Age from Brain Structural MRI of Children and Adolescents: A 3D Convolutional Neural Network Multitask Learning Model.
Comput Intell Neurosci. 2021; 2021:5550914. doi: 10.1155/2021/5550914.

Manic KS, Biju R, Patel W, Khan MA, Raja NSM, Uma S.
Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm.
Comput Math Methods Med. 2021; 2021:5524637. doi: 10.1155/2021/5524637.

Hu J, Cao L, Li T, Dong S, Li P.
GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification.
BMC Bioinformatics. 2021; 22:379. doi: 10.1186/s12859-021-04295-1.

Epalle TM, Song Y, Liu Z, Lu H.
Multi-atlas classification of autism spectrum disorder with hinge loss trained deep architectures: ABIDE I results
Applied Soft Computing. 2021. 107:107375. doi: 10.1016/j.asoc.2021.107375.

Liu Y, Xu L, Yu J, Li J, Yu X.
Identification of autism spectrum disorder using multi-regional resting-state data through an attention learning approach.
Biomedical Signal Processing and Control. 2021. 69:102833. doi: 10.1016/j.bspc.2021.102833.

Kashef R.
ECNN: Enhanced Convolutional Neural Network for Efficient Diagnosis of The Autism Spectrum Disorder.
Cognitive Systems Research. 2022. 71:41-49. doi: 10.1016/j.cogsys.2021.10.002.

Reiter MA, Jahedi A, Jac Fredo AR, Fishman I, Bailey B, Müller RA.
Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.
Neural Comput Appl. 2021; 33:3299-3310. doi: 10.1007/s00521-020-05193-y.

Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, Colliot O.
Deep learning for brain disorders: from data processing to disease treatment.
Brief Bioinform. 2021; 22:1560-1576. doi: 10.1093/bib/bbaa310.

Shao L, Fu C, You Y, Fu D.
Classification of ASD based on fMRI data with deep learning.
Cogn Neurodyn. 2021; 15:961-974. doi: 10.1007/s11571-021-09683-0.

Yarger HA, Nordahl CW, Redcay E.
Examining Associations Between Amygdala Volumes and Anxiety Symptoms in Autism Spectrum Disorder.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2021; None:None. doi: 10.1016/j.bpsc.2021.10.010.

Bhagwat N, Barry A, Dickie EW, Brown ST, Devenyi GA, Hatano K, DuPre E, Dagher A, Chakravarty M, Greenwood CMT, Misic B, Kennedy DN, Poline JB.
Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses.
Gigascience. 2021; 10:None. doi: 10.1093/gigascience/giaa155.

Lombardi A, Diacono D, Amoroso N, Monaco A, Tavares JMRS, Bellotti R, Tangaro S.
Explainable Deep Learning for Personalized Age Prediction With Brain Morphology.
Front Neurosci. 2021; 15:674055. doi: 10.3389/fnins.2021.674055.

Sharif H, Khan RA.
A Novel Machine Learning Based Framework for Detection of Autism Spectrum Disorder (ASD).
Applied Artificial Intelligence. 2021. doi: 10.1080/08839514.2021.2004655.

Leming MJ, Baron-Cohen S, Suckling J.
Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.
Mol Autism. 2021; 12:34. doi: 10.1186/s13229-021-00439-5.

Cao M, Yang M, Qin C, Zhu X, Chen Y, Wang J, Liu T.
Using DeepGCN to identify the autism spectrum disorder from multi-site resting-state data.
Biomedical Signal Processing and Control. 2021. 70:103015. doi: 10.1016/j.bspc.2021.103015.

Dominic N Daniel, Cenggoro TW, Budiarto A, Pardamean B.
Transfer learning using inception-ResNet-v2 model to the augmented neuroimages data for autism spectrum disorder classification.
Commun. Math. Biol. Neurosci. 2021:39. doi: 10.28919/cmbn/5565.

Husna RNS, Syafeeza AR, Hamid NA, Wong YC, Raihan RA.
FUNCTIONAL MAGNETIC RESONANCE IMAGING FOR AUTISM SPECTRUM DISORDER DETECTION USING DEEP LEARNING.
Jurnal Teknologi. 2021. 83:45-52. doi: 10.11113/jurnalteknologi.v83.16389.

Park BY, Hong SJ, Valk SL, Paquola C, Benkarim O, Bethlehem RAI, Di Martino A, Milham MP, Gozzi A, Yeo BTT, Smallwood J, Bernhardt BC.
Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism.
Nat Commun. 2021; 12:2225. doi: 10.1038/s41467-021-21732-0.

Olafson E, Bedford SA, Devenyi GA, Patel R, Tullo S, Park MTM, Parent O, Anagnostou E, Baron-Cohen S, Bullmore ET, Chura LR, Craig MC, Ecker C, Floris DL, Holt RJ, Lenroot R, Lerch JP, Lombardo MV, Murphy DGM, Raznahan A, Ruigrok ANV, Spencer MD, Suckling J, Taylor MJ, MRC AIMS Consortium, Lai MC, Chakravarty MM.
Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder.
Cereb Cortex. 2021; 31:3338-3352. doi: 10.1093/cercor/bhab015.

Rolison M, Lacadie C, Chawarska K, Spann M, Scheinost D.
Atypical Intrinsic Hemispheric Interaction Associated with Autism Spectrum Disorder Is Present within the First Year of Life.
Cereb Cortex. 2021; None:None. doi: 10.1093/cercor/bhab284.

Pagani M, Barsotti N, Bertero A, Trakoshis S, Ulysse L, Locarno A, Miseviciute I, De Felice A, Canella C, Supekar K, Galbusera A, Menon V, Tonini R, Deco G, Lombardo MV, Pasqualetti M, Gozzi A.
mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity.
Nat Commun. 2021; 12:6084. doi: 10.1038/s41467-021-26131-z.

Spronk M, Keane BP, Ito T, Kulkarni K, Ji JL, Anticevic A, Cole MW.
A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction.
Cereb Cortex. 2021; 31:547-561. doi: 10.1093/cercor/bhaa242.

Moradimanesh Z, Khosrowabadi R, Eshaghi Gordji M, Jafari GR.
Altered structural balance of resting-state networks in autism.
Sci Rep. 2021; 11:1966. doi: 10.1038/s41598-020-80330-0.

Tanaka SC, Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, Takamura M, Yoshihara Y, Kunimatsu A, Okada N, Hashimoto R, Okada G, Sakai Y, Morimoto J, Narumoto J, Shimada Y, Mano H, Yoshida W, Seymour B, Shimizu T, Hosomi K, Saitoh Y, Kasai K, Kato N, Takahashi H, Okamoto Y, Yamashita O, Kawato M, Imamizu H.
A multi-site, multi-disorder resting-state magnetic resonance image database.
Sci Data. 2021; 8:227. doi: 10.1038/s41597-021-01004-8.

Benkarim O, Paquola C, Park BY, Hong SJ, Royer J, Vos de Wael R, Lariviere S, Valk S, Bzdok D, Mottron L, C Bernhardt B.
Connectivity alterations in autism reflect functional idiosyncrasy.
Commun Biol. 2021; 4:1078. doi: 10.1038/s42003-021-02572-6.

Saberi M, Khosrowabadi R, Khatibi A, Misic B, Jafari G.
Topological impact of negative links on the stability of resting-state brain network.
Sci Rep. 2021; 11:2176. doi: 10.1038/s41598-021-81767-7.

Germann J, Gouveia FV, Brentani H, Bedford SA, Tullo S, Chakravarty MM, Devenyi GA.
Involvement of the habenula in the pathophysiology of autism spectrum disorder.
Sci Rep. 2021; 11:21168. doi: 10.1038/s41598-021-00603-0.

Reardon AM, Li K, Hu XP.
Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning.
Front Comput Neurosci. 2021; 15:762781. doi: 10.3389/fncom.2021.762781.

Okamoto N, Akama H.
Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling.
Front Neuroinform. 2021; 15:709179. doi: 10.3389/fninf.2021.709179.

Gao K, Fan Z, Su J, Zeng LL, Shen H, Zhu J, Hu D.
Deep Transfer Learning for Cerebral Cortex Using Area-Preserving Geometry Mapping.
Cereb Cortex. 2022; 32:2972-2984. doi: 10.1093/cercor/bhab394.

Sun K, Simon S.
FDRN: A fast deformable registration network for medical images.
Med Phys. 2021 Oct;48(10):6453-6463. doi: 10.1002/mp.15011.

Zheng W, Zhao Z, Zhang Z, Liu T, Zhang Y, Fan J, Wu D.
Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder.
Hum Brain Mapp. 2021; 42:660-675. doi: 10.1002/hbm.25251.

Chu Y, Wang G, Cao L, Qiao L, Liu M.
Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI.
Front Neuroinform. 2021; 15:802305. doi: 10.3389/fninf.2021.802305.

Delisle PL, Anctil-Robitaille B, Desrosiers C, Lombaert H.
Realistic image normalization for multi-Domain segmentation.
Med Image Anal. 2021; 74:102191. doi: 10.1016/j.media.2021.102191.

ElNakieb Y, Ali MT, Elnakib A, Shalaby A, Soliman A, Mahmoud A, Ghazal M, Barnes GN, El-Baz A.
The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results.
Sensors (Basel). 2021; 21:None. doi: 10.3390/s21248171.

Ji J, Yao Y.
Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification.
IEEE/ACM Trans Comput Biol Bioinform. 2021; 18:2327-2338. doi: 10.1109/TCBB.2020.2989315.

Deng Z, Wang S.
Sex differentiation of brain structures in autism: Findings from a gray matter asymmetry study.
Autism Res. 2021 Jun;14(6):1115-1126. doi: 10.1002/aur.2506.

Ma L, Yuan T, Li W, Guo L, Zhu D, Wang Z, Liu Z, Xue K, Wang Y, Liu J, Man W, Ye Z, Liu F, Wang J.
Dynamic Functional Connectivity Alterations and Their Associated Gene Expression Pattern in Autism Spectrum Disorders.
Front Neurosci. 2021; 15:794151. doi: 10.3389/fnins.2021.794151.

Li L, He C, Jian T, Guo X, Xiao J, Li Y, Chen H, Kang X, Chen H, Duan X.
Attenuated link between the medial prefrontal cortex and the amygdala in children with autism spectrum disorder: Evidence from effective connectivity within the "social brain".
Prog Neuropsychopharmacol Biol Psychiatry. 2021; 111:110147. doi: 10.1016/j.pnpbp.2020.110147.

Yao S, Zhou M, Zhang Y, Zhou F, Zhang Q, Zhao Z, Jiang X, Xu X, Becker B, Kendrick KM.
Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder.
Autism Res. 2021; 14:1609-1620. doi: 10.1002/aur.2523.

Yao S, Zhou M, Zhang Y, Zhou F, Zhang Q, Zhao Z, Jiang X, Xu X, Becker B, Kendrick KM.
Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge.
Medical Image Analysis, Volume 70, 2021, 101972, ISSN 1361-8415. doi: doi.org/10.1016/j.media.2021.101972.

Li C, Ning M, Fang P, Xu H.
Sex differences in structural brain asymmetry of children with autism spectrum disorders.
J Integr Neurosci. 2021; 20:331-340. doi: 10.31083/j.jin2002032.

Artiles O, Saeed F.
A Multi-Factorial Assessment of Functional Human Autistic Spectrum Brain Network Analysis.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2021; 2021:3526-3531. doi: 10.1109/bibm52615.2021.9669679.

Moreau C, Huguet G, Urchs S, Kumar K, Douard E, Sharmarke H, Orban P, Linden D, Maillard A, Lippe S, Bearden C, Thompson PM, Bellec P, Jacquemont S.
The General Impact of Haploinsufficiency on Brain Connectivity Underlies the Pleiotropic Effect of Neuropsychiatric CNVS.
Biological Psychiatry, Volume 89, Issue 9, Supplement, 2021, Page S40, ISSN 0006-3223. doi: doi.org/10.1016/j.biopsych.2021.02.115.

Crimi A, Dodero L, Sambataro F, Murino V, Sona D.
Structurally constrained effective brain connectivity.
Neuroimage. 2021; 239:118288. doi: 10.1016/j.neuroimage.2021.118288.

Seguin D, Pac S, Wang J, Nicolson R, Martinez-Trujillo J, Duerden EG.
Amygdala subnuclei development in adolescents with autism spectrum disorder: Association with social communication and repetitive behaviors.
Brain Behav. 2021; 11:e2299. doi: 10.1002/brb3.2299.

Kupis L, Goodman ZT, Kircher L, Romero C, Dirks B, Chang C, Nomi JS, Uddin LQ.
Altered patterns of brain dynamics linked with body mass index in youth with autism.
Autism Res. 2021; 14:873-886. doi: 10.1002/aur.2488.

Dekhil O, Shalaby A, Soliman A, Mahmoud A, Kong M, Barnes G, Elmaghraby A, El-Baz A.
Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns.
Med Image Anal. 2021; 68:101899. doi: 10.1016/j.media.2020.101899.

Huang Y, Yu S, Wilson G, Park J, Cheng M, Kong X, Lu T, Kong J.
Altered Extended Locus Coeruleus and Ventral Tegmental Area Networks in Boys with Autism Spectrum Disorders: A Resting-State Functional Connectivity Study.
Neuropsychiatr Dis Treat. 2021; 17:1207-1216. doi: 10.2147/NDT.S301106.

Wang K, Li K, Niu X.
Altered Functional Connectivity in a Triple-Network Model in Autism With Co-occurring Attention Deficit Hyperactivity Disorder.
Front Psychiatry. 2021 Dec 2;12:736755. doi: 10.3389/fpsyt.2021.736755.

Du Y, Fu Z, Xing Y, Lin D, Pearlson G, Kochunov P, Hong LE, Qi S, Salman M, Abrol A, Calhoun VD.
Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder.
Commun Biol. 2021; 4:1073. doi: 10.1038/s42003-021-02592-2.

Park S, Haak KV, Cho HB, Valk SL, Bethlehem RAI, Milham MP, Bernhardt BC, Di Martino A, Hong SJ.
Atypical Integration of Sensory-to-Transmodal Functional Systems Mediates Symptom Severity in Autism.
Front Psychiatry. 2021; 12:699813. doi: 10.3389/fpsyt.2021.699813.

Al-Hiyali MI, Yahya N, Faye I, Hussein AF.
Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network.
Sensors (Basel). 2021; 21:None. doi: 10.3390/s21165256.

Xiao J, Chen H, Shan X, He C, Li Y, Guo X, Chen H, Liao W, Uddin LQ, Duan X.
Linked Social-Communication Dimensions and Connectivity in Functional Brain Networks in Autism Spectrum Disorder.
Cereb Cortex. 2021; 31:3899-3910. doi: 10.1093/cercor/bhab057.

Zhao F, Zhang X, Thung KH, Mao N, Lee SW, Shen D.
Constructing Multi-View High-Order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder.
IEEE Trans Biomed Eng. 2022; 69:1237-1250. doi: 10.1109/TBME.2021.3122813.

Xie Q, Zhang X, Rekik I, Chen X, Mao N, Shen D, Zhao F.
Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder.
PeerJ. 2021; 9:e11692. doi: 10.7717/peerj.11692.

Chen D, Jia T, Zhang Y, Cao M, Loth E, Lo CZ, Cheng W, Liu Z, Gong W, Sahakian BJ, Feng J.
Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals.
Front Hum Neurosci. 2021; 15:657857. doi: 10.3389/fnhum.2021.657857.

Haghighat H, Mirzarezaee M, Araabi BN, Khadem A.
Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity.
Brain Topogr. 2021; 34:306-322. doi: 10.1007/s10548-021-00831-7.

Sun JW, Fan R, Wang Q, Wang QQ, Jia XZ, Ma HB.
Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification.
Brain Res. 2021; 1757:147299. doi: 10.1016/j.brainres.2021.147299.

Chen L, Chen Y, Zheng H, Zhang B, Wang F, Fang J, Li Y, Chen Q, Zhang S.
Changes in the topological organization of the default mode network in autism spectrum disorder.
Brain Imaging Behav. 2021; 15:1058-1067. doi: 10.1007/s11682-020-00312-8.

Sserwadda A, Rekik I.
Topology-guided cyclic brain connectivity generation using geometric deep learning.
J Neurosci Methods. 2021; 353:108988. doi: 10.1016/j.jneumeth.2020.108988.

Graña M, Silva M.
Impact of Machine Learning Pipeline Choices in Autism Prediction From Functional Connectivity Data.
Int J Neural Syst. 2021; 31:2150009. doi: 10.1142/S012906572150009X.

Zhao F, Chen Z, Rekik I, Liu P, Mao N, Lee SW, Shen D.
A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks.
Front Neurosci. 2021; 15:651574. doi: 10.3389/fnins.2021.651574.

Bathelt J, Geurts HM.
Difference in default mode network subsystems in autism across childhood and adolescence.
Autism. 2021; 25:556-565. doi: 10.1177/1362361320969258.

Long J, Lu F, Guo X, Pang Y, Yang S, Chen H, He B.
Parcellation of the thalamus by using a dual-segment method based on resting-state functional connectivity: An application on autism spectrum disorder.
Neurosci Lett. 2021; 742:135518. doi: 10.1016/j.neulet.2020.135518.

Fu Z, Sui J, Turner JA, Du Y, Assaf M, Pearlson GD, Calhoun VD.
Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder.
Hum Brain Mapp. 2021; 42:80-94. doi: 10.1002/hbm.25205.

Tavares V, Fernandes LA, Antunes M, Ferreira H, Prata D.
Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder.
J Autism Dev Disord. 2022; 52:3088-3101. doi: 10.1007/s10803-021-05191-6.

Ma ZH, Lu B, Li X, Mei T, Guo YQ, Yang L, Wang H, Tang XZ, Ji ZZ, Liu JR, Xu LZ, Yang YL, Cao QJ, Yan CG, Liu J.
Atypicalities in the developmental trajectory of cortico-striatal functional connectivity in autism spectrum disorder.
Autism. 2022; 26:1108-1122. doi: 10.1177/13623613211041904.

Shi C, Xin X, Zhang J.
Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data.
Brain Sci. 2021; 11:None. doi: 10.3390/brainsci11050603.

Liang Y, Liu B, Zhang H.
A Convolutional Neural Network Combined With Prototype Learning Framework for Brain Functional Network Classification of Autism Spectrum Disorder.
IEEE Trans Neural Syst Rehabil Eng. 2021; 29:2193-2202. doi: 10.1109/TNSRE.2021.3120024.

Ribeiro AH, Vidal MC, Sato JR, Fujita A.
Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder.
Entropy (Basel). 2021; 23:None. doi: 10.3390/e23091204.

Liu G, Shi L, Qiu J, Lu W.
Two neuroanatomical subtypes of males with autism spectrum disorder revealed using semi-supervised machine learning.
Mol Autism. 2022; 13:9. doi: 10.1186/s13229-022-00489-3.

Gupta S, Chan YH, Rajapakse JC.
Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot.
Neurocomputing, Volume 453, 2021, Pages 326-336, ISSN 0925-2312. doi: doi.org/10.1016/j.neucom.2020.04.152.

Wang M, Huang J, Liu M, Zhang D.
Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI.
Med Image Anal. 2021; 71:102063. doi: 10.1016/j.media.2021.102063.

Asadi N, Olson IR, Obradovic Z.
The backbone network of dynamic functional connectivity.
Netw Neurosci. 2021; 5:851-873. doi: 10.1162/netn_a_00209.

Du Y, Hao H, Xing Y, Niu J, Calhoun VD.
A Transdiagnostic Biotype Detection Method for Schizophrenia and Autism Spectrum Disorder Based on Graph Kernel.
Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021:3241-3244. doi: 10.1109/EMBC46164.2021.9629618.

Cifre I, Miller Flores MT, Penalba L, Ochab JK, Chialvo DR.
Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed.
Front Neurosci. 2021; 15:700171. doi: 10.3389/fnins.2021.700171.

Burak Gürbüz M, Rekik I.
MGN-Net: A multi-view graph normalizer for integrating heterogeneous biological network populations.
Med Image Anal. 2021; 71:102059. doi: 10.1016/j.media.2021.102059.

Lu H, Liu S, Wei H, Chen Chao, Geng X.
Deep multi-kernel auto-encoder network for clustering brain functional connectivity data.
Neural Networks, Volume 135, 2021, Pages 148-157, ISSN 0893-6080. doi: doi.org/10.1016/j.neunet.2020.12.005.

Matsubara T, Kusano K, Tashiro T, Ukai K, Uehara K.
Deep Generative Model of Individual Variability in fMRI Images of Psychiatric Patients.
IEEE Trans Biomed Eng. 2021; 68:592-605. doi: 10.1109/TBME.2020.3008707.

Zhang H, Li R, Wen X, Li Q, Wu X.
Altered Time-Frequency Feature in Default Mode Network of Autism Based on Improved Hilbert-Huang Transform.
IEEE J Biomed Health Inform. 2021; 25:485-492. doi: 10.1109/JBHI.2020.2993109.

Wang L, Li K, Hu XP.
Graph convolutional network for fmri analysis based on connectivity neighborhood.
Netw Neurosci. 2021 Feb 1;5(1):83-95. doi: 10.1162/netn_a_00171.

Sun L, Xue Y, Zhang Y, Qiao L, Zhang L, Liu M.
Estimating sparse functional connectivity networks via hyperparameter-free learning model.
Artif Intell Med. 2021; 111:102004. doi: 10.1016/j.artmed.2020.102004.

Duffy BA, Zhao L, Sepehrband F, Min J, Wang DJ, Shi Y, Toga AW, Kim H, Alzheimer's Disease Neuroimaging Initiative.
Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.
Neuroimage. 2021; 230:117756. doi: 10.1016/j.neuroimage.2021.117756.

Mengucci C, Remondini D, Castellani G, Giampieri E.
WISDoM: Characterizing Neurological Time Series With the Wishart Distribution.
Front Neuroinform. 2020; 14:611762. doi: 10.3389/fninf.2020.611762.

Naghashzadeh M, Yazdi M, Zolghadrasli A.
Classification of autism spectrum disorders individuals and controls using phase and envelope features from resting-state fMRI data.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10:1, 55-66, doi: 10.1080/21681163.2021.1972343.

Zhao Y, Ossowski J, Wang X, Li S, Devinsky O, Martin SP, Pardoe HR.
Localized motion artifact reduction on brain MRI using deep learning with effective data augmentation techniques.
2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 2021, pp. 1-9, doi: 10.1109/IJCNN52387.2021.9534191.

Yalçin A, Rekik I.
A diagnostic unified classification model for classifying multi-sized and multi-modal brain graphs using graph alignment.
J Neurosci Methods. 2021; 348:109014. doi: 10.1016/j.jneumeth.2020.109014.

Yang X, Zhang N, Schrader P.
A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity
Machine Learning with Applications. 2022 Jun 15;8:100290. doi: 10.1016/j.mlwa.2022.100290.

Feng Y, Kang X, Wang H, Cong J, Zhuang W, Xue K, Li F, Yao D, Xu P, Zhang T.
The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network
Cereb Cortex. 2023 Jan 5;33(3):764-776. doi: 10.1093/cercor/bhac100.

Peng L, Liu X, Ma D, Chen X, Xu X, Gao X.
The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification
Front Neurosci. 2022 May 6:16:913377. doi: 10.3389/fnins.2022.913377. eCollection 2022.

Fernandez L, Burmester A, Duque JD, Silk TJ, Hyde CE, Kirkovski M, Enticott PG, Caeyenberghs K.
Examination of Cerebellar Grey-Matter Volume in Children with Neurodevelopmental Disorders: a Coordinated Analysis Using the ACAPULCO Algorithm
Cerebellum. 2023 Dec;22(6):1243-1249. doi: 10.1007/s12311-022-01503-3. Epub 2022 Dec 9.

Li T, Hoogman M, Roth Mota N, Buitelaar JK; ENIGMA-ASD working group; Vasquez AA, Franke B, van Rooij D.
Dissecting the heterogeneous subcortical brain volume of autism spectrum disorder using community detection
Autism Res. 2022 Jan;15(1):42-55. doi: 10.1002/aur.2627. Epub 2021 Oct 27.

Moreau CA, Kumar K, Harvey A, Huguet G, Urchs SGW, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S.
Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions
Brain. 2023 Apr 19;146(4):1686-1696. doi: 10.1093/brain/awac315.

Yue X, Zhang G, Li X, Shen Y, Wei W, Bai Y, Luo Y, Wei H, Li Z, Zhang X, Wang M.
Brain Functional Alterations in Prepubertal Boys With Autism Spectrum Disorders
Front Hum Neurosci. 2022 May 19:16:891965. doi: 10.3389/fnhum.2022.891965.

Hwang IS, Hong SB.
Association between body mass index and subcortical volume in pre-adolescent children with autism spectrum disorder: An exploratory study
Autism Res. 2022 Dec;15(12):2238-2249. doi: 10.1002/aur.2834. Epub 2022 Oct 18.

Moreau CA, Kumar K, Harvey A, Huguet G, Urchs SGW, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S.
Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions
Brain. 2023 Apr 19;146(4):1686-1696. doi: 10.1093/brain/awac315.

Polk M, Ikuta T.
Disrupted functional connectivity between the nucleus accumbens and posterior cingulate cortex in autism spectrum disorder
Neuroreport. 2022 Jan, 33(2):43-47. doi: 10.1097/WNR.0000000000001742.

Wang M, Wang L, Yang B, Yuan L, Wang X, Potenza MN, Dong GH.
Disrupted dynamic network reconfiguration of the brain functional networks of individuals with autism spectrum disorder
Brain Commun. 2022 Aug, 4(4):fcac177. doi: 10.1093/braincomms/fcac177.

Li T, Hoogman M, Roth Mota N, Buitelaar JK, ENIGMA-ASD working group, Vasquez AA, Franke B, van Rooij D.
Dissecting the heterogeneous subcortical brain volume of autism spectrum disorder using community detection
Autism Res. 2022 Jan, 15(1):42-55. doi: 10.1002/aur.2627. Epub 2021 Oct 27.

Ma L, Liu M, Xue K, Ye C, Man W, Cheng M, Liu Z, Zhu D, Liu F, Wang J
Abnormal Regional Spontaneous Brain Activities in White Matter in Patients with Autism Spectrum Disorder
Neuroscience. 2022, 10:490:1-10. doi: 10.1016/j.neuroscience.2022.02.022. Epub 2022 Feb 23

Khadem-Reza ZK, Zare H
Evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging
Egypt J Neurol Psychiatry Neurosurg, 2022 58:135. doi: 10.1186/s41983-022-00576-5

Jain V, Selvaraj A, Mittal R, Rani P, Kilpattu Ramaniharan A, Agastinose Ronickom JF.
Automated Diagnosis of Autism Spectrum Disorder Condition Using Shape Based Features Extracted from Brainstem.
Stud Health Technol Inform. 2022; 294:53-57. doi: 10.3233/SHTI220395.

Wang Y, Liu J, Xiang Y, Wang J, Chen Q, Chong J.
MAGE: Automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional networks and ensemble learning.
Neurocomputing, Volume 469, 2022, Pages 346-353, ISSN 0925-2312. doi: doi.org/10.1016/.

Weerasekera A, Ion-Mărgineanu A, Nolan G, Mody M.
Subcortical Brain Morphometry Differences between Adults with Autism Spectrum Disorder and Schizophrenia.
Brain Sci. 2022; 12:None. doi: 10.3390/brainsci12040439.

Chen Y, Yan J, Jiang M, Zhang T, Zhao Z, Zhao W, Zheng J, Yao D, Zhang R, Kendrick KM, Jiang X.
Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.
IEEE Trans Neural Netw Learn Syst. 2022; PP:None. doi: 10.1109/TNNLS.2022.3154755.

Denier N, Steinberg G, van Elst LT, Bracht T.
The role of head circumference and cerebral volumes to phenotype male adults with autism spectrum disorder.
Brain Behav. 2022; 12:e2460. doi: 10.1002/brb3.2460.

Mellema CJ, Nguyen KP, Treacher A, Montillo A.
Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.
Sci Rep. 2022; 12:3057. doi: 10.1038/s41598-022-06459-2.

Lin P, Zang S, Bai Y, Wang H.
Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model.
Front Hum Neurosci. 2022; 16:774921. doi: 10.3389/fnhum.2022.774921.

Ali MT, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Ghazal M, Yousaf J, Abu Khalifeh H, Casanova M, Barnes G, El-Baz A.
The Role of Structure MRI in Diagnosing Autism.
Diagnostics (Basel). 2022; 12:None. doi: 10.3390/diagnostics12010165.

Chandra A, Verma S, Raghuvanshi AS, Bodhey NK.
CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net.
Biocybernetics and Biomedical Engineering, Volume 42, Issue 1, 2022, Pages 187-203, ISSN 0208-5216. doi: doi.org/10.1016/j.bbe.2021.12.008.

Xie Y, Xu Z, Xia M, Liu J, Shou X, Cui Z, Liao X, He Y.
Alterations in Connectome Dynamics in Autism Spectrum Disorder: A Harmonized Mega- and Meta-analysis Study Using the Autism Brain Imaging Data Exchange Dataset.
Biol Psychiatry. 2022; 91:945-955. doi: 10.1016/j.biopsych.2021.12.004.

Shan X, Uddin LQ, Xiao J, He C, Ling Z, Li L, Huang X, Chen H, Duan X.
Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model.
Biol Psychiatry. 2022; 91:967-976. doi: 10.1016/j.biopsych.2022.01.011.

Yang W, Wen G, Cao P, Yang J, Zaiane OR.
Collaborative learning of graph generation, clustering and classification for brain networks diagnosis.
Comput Methods Programs Biomed. 2022; 219:106772. doi: 10.1016/j.cmpb.2022.106772.

Elumalai P, Yadav Y, Williams N, Saucan E, Jost J, Samal A.
Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder.
Sci Rep. 2022; 12:8295. doi: 10.1038/s41598-022-12171-y.

Yin W, Li L, Wu FX.
A semi-supervised autoencoder for autism disease diagnosis.
Neurocomputing, Volume 483, 2022, Pages 140-147, ISSN 0925-2312. doi: doi.org/10.1016/j.neucom.2022.02.017.

Benkarim O, Paquola C, Park BY, Kebets V, Hong SJ, Vos de Wael R, Zhang S, Yeo BTT, Eickenberg M, Ge T, Poline JB, Bernhardt BC, Bzdok D.
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.
PLoS Biol. 2022; 20:e3001627. doi: 10.1371/journal.pbio.3001627.

Lu Z, Wang J, Mao R, Lu M, Shi J.
Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems.
IEEE/ACM Trans Comput Biol Bioinform. 2023; 20:476-488. doi: 10.1109/TCBB.2022.3163140.

Zhao L, Sun YK, Xue SW, Luo H, Lu XD, Zhang LH.
Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach.
Front Neuroinform. 2022; 16:761942. doi: 10.3389/fninf.2022.761942.

Lam YS, Li J, Ke Y, Yung WH.
Variational dimensions of cingulate cortex functional connectivity and implications in neuropsychiatric disorders.
Cereb Cortex. 2022; 32:5682-5697. doi: 10.1093/cercor/bhac045.

Cao P, Wen G, Liu X, Yang J, Zaiane OR.
Modeling the dynamic brain network representation for autism spectrum disorder diagnosis.
Med Biol Eng Comput. 2022; 60:1897-1913. doi: 10.1007/s11517-022-02558-4.

Wen G, Cao P, Bao H, Yang W, Zheng T, Zaiane O.
MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis.
Comput Biol Med. 2022 Mar;142:105239. doi: 10.1016/j.compbiomed.2022.105239.

Supekar K, Ryali S, Yuan R, Kumar D, de Los Angeles C, Menon V.
Robust, Generalizable, and Interpretable Artificial Intelligence-Derived Brain Fingerprints of Autism and Social Communication Symptom Severity.
Biol Psychiatry. 2022; 92:643-653. doi: 10.1016/j.biopsych.2022.02.005.

Han T, Gong X, Feng F, Zhang J, Sun Z, Zhang Yu.
Privacy preserving mutli-source domain adaptaion for medical data.
IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2, pp. 842-853, Feb. 2023, doi: 10.1109/JBHI.2022.3175071.

Ji J, Zhang Y.
Functional Brain Network Classification Based on Deep Graph Hashing Learning.
IEEE Trans Med Imaging. 2022; 41:2891-2902. doi: 10.1109/TMI.2022.3173428.

Choi H, Byeon K, Park BY, Lee JE, Valk SL, Bernhardt B, Martino AD, Milham M, Hong SJ, Park H.
Diagnosis-informed connectivity subtyping discovers subgroups of autism with reproducible symptom profiles.
Neuroimage. 2022; 256:119212. doi: 10.1016/j.neuroimage.2022.119212.

Jia H, Wu X, Wang E.
Aberrant dynamic functional connectivity features within default mode network in patients with autism spectrum disorder: evidence from dynamical conditional correlation.
Cogn Neurodyn. 2022; 16:391-399. doi: 10.1007/s11571-021-09723-9.

Zhang A, Liu L, Chang S, Shi L, Li P, Shi J, Lu L, Bao Y, Liu J.
Connectivity-Based Brain Network Supports Restricted and Repetitive Behaviors in Autism Spectrum Disorder Across Development.
Front Psychiatry. 2022; 13:874090. doi: 10.3389/fpsyt.2022.874090.

Yue X, Zhang G, Li X, Shen Y, Wei W, Bai Y, Luo Y, Wei H, Li Z, Zhang X, Wang M.
Abnormal Dynamic Functional Network Connectivity in Adults with Autism Spectrum Disorder.
Clin Neuroradiol. 2022; 32:1087-1096. doi: 10.1007/s00062-022-01173-y.

Chen YY, Uljarevic M, Neal J, Greening S, Yim H, Lee TH.
Excessive Functional Coupling With Less Variability Between Salience and Default Mode Networks in Autism Spectrum Disorder.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2022; 7:876-884. doi: 10.1016/j.bpsc.2021.11.016.

Berto S, Treacher AH, Caglayan E, Luo D, Haney JR, Gandal MJ, Geschwind DH, Montillo AA, Konopka G.
Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder.
Nat Commun. 2022; 13:3328. doi: 10.1038/s41467-022-31053-5.

Wilson KC, Kornisch M, Ikuta T.
Disrupted functional connectivity of the primary auditory cortex in autism.
Psychiatry Res Neuroimaging. 2022; 324:111490. doi: 10.1016/j.pscychresns.2022.111490.

Li J, Chen X, Zheng R, Chen A, Zhou Y, Ruan J.
Altered Cerebellum Spontaneous Activity in Juvenile Autism Spectrum Disorders Associated with Clinical Traits.
J Autism Dev Disord. 2022; 52:2497-2504. doi: 10.1007/s10803-021-05167-6.

Han T, Gong X, Feng F, Zhang J, Sun Z, Zhang Y.
Privacy-Preserving Multi-Source Domain Adaptation for Medical Data.
IEEE J Biomed Health Inform. 2023; 27:842-853. doi: 10.1109/JBHI.2022.3175071.

Demirci N, Holland MA.
Cortical thickness systematically varies with curvature and depth in healthy human brains.
Hum Brain Mapp. 2022; 43:2064-2084. doi: 10.1002/hbm.25776.

Bathelt J, Geurts HM, Borsboom D.
More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism.
Netw Neurosci. 2022 Jun 1;6(2):445-466. doi: 10.1162/netn_a_00222.

Traut N, Heuer K, Lemaître G, Beggiato A, Germanaud D, Elmaleh M, Bethegnies A, Bonnasse-Gahot L, Cai W, Chambon S, Cliquet F, Ghriss A, Guigui N, de Pierrefeu A, Wang M, Zantedeschi V, Boucaud A, van den Bossche J, Kegl B, Delorme R, Bourgeron T, Toro R, Varoquaux G.
Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery.
Neuroimage. 2022; 255:119171. doi: 10.1016/j.neuroimage.2022.119171.

Olivé G, Slušná D, Vaquero L, Muchart-López J, Rodríguez-Fornells A, Hinzen W.
Structural connectivity in ventral language pathways characterizes non-verbal autism.
Brain Struct Funct. 2022; 227:1817-1829. doi: 10.1007/s00429-022-02474-1.

Raval V, Nguyen KP, Pinho M, Dewey RB, Trivedi M, Montillo AA.
Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI.
Neuroinformatics. 2022; 20:879-896. doi: 10.1007/s12021-022-09565-8.

Reardon AM, Li K, Langley J, Hu XP.
Subtyping Autism Spectrum Disorder Via Joint Modeling of Clinical and Connectomic Profiles.
Brain Connect. 2022; 12:193-205. doi: 10.1089/brain.2020.0997.

Duan Y, Zhao W, Luo C, Liu X, Jiang H, Tang Y, Liu C, Yao D.
Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning.
Front Hum Neurosci. 2021; 15:765517. doi: 10.3389/fnhum.2021.765517.

Chen B.
A preliminary study of atypical cortical change ability of dynamic whole-brain functional connectivity in autism spectrum disorder.
Int J Neurosci. 2022; 132:213-225. doi: 10.1080/00207454.2020.1806837.

Chen B.
A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys.
Clin EEG Neurosci. 2022; 53:3-11. doi: 10.1177/15500594211026282.

Li G, Chen MH, Li G, Wu D, Lian C, Sun Q, Rushmore RJ, Wang L.
Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism.
J Autism Dev Disord. 2022; None:None. doi: 10.1007/s10803-022-05535-w.

Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T.
Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2022; None:None. doi: 10.1016/j.bpsc.2022.01.007.

Talesh Jafadideh A, Mohammadzadeh Asl B.
Rest-fMRI based comparison study between autism spectrum disorder and typically control using graph frequency bands.
Comput Biol Med. 2022; 146:105643. doi: 10.1016/j.compbiomed.2022.105643.

Hanik M, Demirtaş MA, Gharsallaoui MA, Rekik I.
Predicting cognitive scores with graph neural networks through sample selection learning.
Brain Imaging Behav. 2022; 16:1123-1138. doi: 10.1007/s11682-021-00585-7.

Nebel MB, Lidstone DE, Wang L, Benkeser D, Mostofsky SH, Risk BB.
Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?
Neuroimage. 2022; 257:119296. doi: 10.1016/j.neuroimage.2022.119296.

Liu S, Ge F, Zhao L, Wang T, Ni D, Liu T.
NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns.
Med Image Anal. 2022; 77:102316. doi: 10.1016/j.media.2021.102316.

Zhao L, Xue SW, Sun YK, Lan Z, Zhang Z, Xue Y, Wang X, Jin Y.
Altered dynamic functional connectivity of insular subregions could predict symptom severity of male patients with autism spectrum disorder.
J Affect Disord. 2022; 299:504-512. doi: 10.1016/j.jad.2021.12.093.

Zhao F, Han Z, Cheng D, Mao N, Chen X, Li Y, Fan D, Liu P.
Hierarchical Synchronization Estimation of Low- and High-Order Functional Connectivity Based on Sub-Network Division for the Diagnosis of Autism Spectrum Disorder.
Front Neurosci. 2021; 15:810431. doi: 10.3389/fnins.2021.810431.

Chen Y, Liu A, Fu X, Wen J, Chen X.
An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification.
Front Neurosci. 2022 Feb 4;15:828512. doi: 10.3389/fnins.2021.828512.

Zhao HC, Lv R, Zhang GY, He LM, Cai XT, Sun Q, Yan CY, Bao XY, Lv XY, Fu B.
Alterations of Prefrontal-Posterior Information Processing Patterns in Autism Spectrum Disorders.
Front Neurosci. 2022; 15:768219. doi: 10.3389/fnins.2021.768219.

Haghighat H, Mirzarezaee M, Araabi BN, Khadem A.
An age-dependent Connectivity-based computer aided diagnosis system for Autism Spectrum Disorder using Resting-state fMRI.
Biomedical Signal Processing and Control, Volume 71, Part A, 2022, 103108, ISSN 1746-8094. doi: doi.org/10.1016/j.bspc.2021.103108.

Blume J, Kahathuduwa C, Mastergeorge A.
Intrinsic Structural Connectivity of the Default Mode Network and Behavioral Correlates of Executive Function and Social Skills in Youth with Autism Spectrum Disorders.
J Autism Dev Disord. 2023; 53:1930-1941. doi: 10.1007/s10803-022-05460-y.

Wang J, Zhang F, Jia X, Wang X, Zhang H, Ying S, Wang Q, Shi J, Shen D.
Multi-Class ASD Classification via Label Distribution Learning with Class-Shared and Class-Specific Decomposition.
Med Image Anal. 2022; 75:102294. doi: 10.1016/j.media.2021.102294.

Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TGM, Belger A, Calhoun VD.
A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder.
Hum Brain Mapp. 2022; 43:3887-3903. doi: 10.1002/hbm.25890.

Lawrence KE, Hernandez LM, Fuster E, Padgaonkar NT, Patterson G, Jung J, Okada NJ, Lowe JK, Hoekstra JN, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA, McPartland JC, Webb SJ, Pelphrey KA, Green SA, Bookheimer SY, Geschwind DH, Dapretto M, GENDAAR Consortium.
Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect.
Brain. 2022; 145:378-387. doi: 10.1093/brain/awab204.

Supekar K, de Los Angeles C, Ryali S, Cao K, Ma T, Menon V.
Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism.
Br J Psychiatry. 2022; None:1-8. doi: 10.1192/bjp.2022.13.

Santana CP, de Carvalho EA, Rodrigues ID, Bastos GS, de Souza AD, de Brito LL.
rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.
Sci Rep. 2022; 12:6030. doi: 10.1038/s41598-022-09821-6.

Jiang X, Zhou Y, Zhang Y, Zhang L, Qiao L, De Leone R.
Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.
Front Neurosci. 2022; 16:872848. doi: 10.3389/fnins.2022.872848.

Aglinskas A, Hartshorne JK, Anzellotti S.
Contrastive machine learning to study the structure of neuroanatomical variation within autism.
Science. 2022 Jun 3;376(6597):1070-1074. doi: 10.1126/science.abm2461.

Huang Y, Chung ACS.
Disease prediction with edge-variational graph convolutional networks.
Med Image Anal. 2022; 77:102375. doi: 10.1016/j.media.2022.102375.

Wang Y, Fu Y, Luo X.
Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders.
Front Neurosci. 2022; 16:900330. doi: 10.3389/fnins.2022.900330.

Kim S, Kim YE, Song I, Ujihara Y, Kim N, Jiang YH, Yin HH, Lee TH, Kim IH.
Neural circuit pathology driven by Shank3 mutation disrupts social behaviors.
Cell Rep. 2022; 39:110906. doi: 10.1016/j.celrep.2022.110906.

Sha Z, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Bernhardt B, Bolte S, Busatto GF, Calderoni S, Calvo R, Daly E, Deruelle C, Duan M, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, Fitzgerald J, Floris DL, Franke B, Freitag CM, Gallagher L, Glahn DC, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, King JA, Lazaro L, Luna B, McGrath J, Medland SE, Muratori F, Murphy DGM, Neufeld J, O'Hearn K, Oranje B, Parellada M, Pariente JC, Postema MC, Remnelius KL, Retico A, Rosa PGP, Rubia K, Shook D, Tammimies K, Taylor MJ, Tosetti M, Wallace GL, Zhou F, Thompson PM, Fisher SE, Buitelaar JK, Francks C.
Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium.
Mol Psychiatry. 2022; 27:2114-2125. doi: 10.1038/s41380-022-01452-7.

Zhao M, Yan W, Luo N, Zhi D, Fu Z, Du Y, Yu S, Jiang T, Calhoun VD, Sui J.
An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.
Med Image Anal. 2022; 78:102413. doi: 10.1016/j.media.2022.102413.

Panda R, Kalmady SV, Greiner R.
Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study.
Front Neuroinform. 2022; 16:805117. doi: 10.3389/fninf.2022.805117.

Henschel L, Kügler D, Reuter M.
FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI.
Neuroimage. 2022; 251:118933. doi: 10.1016/j.neuroimage.2022.118933.

Shi CL, Xin XW, Zhang JC.
Domain adaptation based on rough adjoint inconsistency and optimal transport for identifying autistic patients.
Comput Methods Programs Biomed. 2022; 215:106615. doi: 10.1016/j.cmpb.2021.106615.

Saponaro S, Giuliano A, Bellotti R, Lombardi A, Tangaro S, Oliva P, Calderoni S, Retico A.
Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset.
Neuroimage Clin. 2022; 35:103082. doi: 10.1016/j.nicl.2022.103082.

Sun F, Chen Y, Gao Q, Zhao Z.
Abnormal gray matter structure in children and adolescents with high-functioning autism spectrum disorder.
Psychiatry Res Neuroimaging. 2022; 327:111564. doi: 10.1016/j.pscychresns.2022.111564.

Wei L, Zhang Y, Zhai W, Wang H, Zhang J, Jin H, Feng J, Qin Q, Xu H, Li B, Liu J.
Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders.
Front Neurosci. 2022; 16:987248. doi: 10.3389/fnins.2022.987248.

Jia H, Wu X, Wu Z, Wang E.
Aberrant dynamic minimal spanning tree parameters within default mode network in patients with autism spectrum disorder.
Front Psychiatry. 2022; 13:860348. doi: 10.3389/fpsyt.2022.860348.

Hao Z, Shi Y, Huang L, Sun J, Li M, Gao Y, Li J, Wang Q, Zhan L, Ding Q, Jia X, Li H.
The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study.
Front Neurosci. 2022; 16:927556. doi: 10.3389/fnins.2022.927556.

Sun H, He Q, Qi S, Yao Y, Teng Y.
Improving the level of autism discrimination with augmented data by GraphRNN.
Comput Biol Med. 2022; 150:106141. doi: 10.1016/j.compbiomed.2022.106141.

Qiao J, Wang R, Liu H, Xu G, Wang Z.
Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer's disease and autism spectrum disorder.
Front Aging Neurosci. 2022; 14:912895. doi: 10.3389/fnagi.2022.912895.

Yang S, Jin D, Liu J, He Y.
Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study.
Brain Sci. 2022; 12:None. doi: 10.3390/brainsci12070883.

Gao Y, Sun J, Cheng L, Yang Q, Li J, Hao Z, Zhan L, Shi Y, Li M, Jia X, Li H.
Altered resting state dynamic functional connectivity of amygdala subregions in patients with autism spectrum disorder: A multi-site fMRI study.
J Affect Disord. 2022; 312:69-77. doi: 10.1016/j.jad.2022.06.011.

Shi C, Xin X, Zhang J.
A novel multigranularity feature-selection method based on neighborhood mutual information and its application in autistic patient identification.
Biomedical Signal Processing and Control, Volume 78, 2022, 103887, ISSN 1746-8094. doi: doi.org/10.1016/j.bspc.2022.103887.

Deng X, Zhang J, Liu R, Liu K.
Classifying ASD based on time-series fMRI using spatial-temporal transformer.
Comput Biol Med. 2022; 151:106320. doi: 10.1016/j.compbiomed.2022.106320.

Jiang W, Liu S, Zhang H, Sun X, Wang SH, Zhao J, Yan J.
CNNG: A Convolutional Neural Networks With Gated Recurrent Units for Autism Spectrum Disorder Classification.
Front Aging Neurosci. 2022; 14:948704. doi: 10.3389/fnagi.2022.948704.

Pan J, Lin H, Dong Y, Wang Y, Ji Y.
MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder.
Comput Biol Med. 2022 Sep;148:105823. doi: 10.1016/j.compbiomed.2022.105823.

Guo X, Zhai G, Liu J, Cao Y, Zhang X, Cui D, Gao L.
Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder.
Mol Autism. 2022; 13:52. doi: 10.1186/s13229-022-00535-0.

Yan J, Chen Y, Xiao Z, Zhang S, Jiang M, Wang T, Zhang T, Lv J, Becker B, Zhang R, Zhu D, Han J, Yao D, Kendrick KM, Liu T, Jiang X.
Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs).
Med Image Anal. 2022; 80:102518. doi: 10.1016/j.media.2022.102518.

Cheng L, Zhan L, Huang L, Zhang H, Sun J, Huang G, Wang Y, Li M, Li H, Gao Y, Jia X.
The atypical functional connectivity of Broca's area at multiple frequency bands in autism spectrum disorder.
Brain Imaging Behav. 2022; 16:2627-2636. doi: 10.1007/s11682-022-00718-6.

Chu Y, Ren H, Qiao L, Liu M.
Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification.
Brain Sci. 2022 Oct 20;12(10):1413. doi: 10.3390/brainsci12101413.

Kwon H, Kim JI, Son SY, Jang YH, Kim BN, Lee HJ, Lee JM.
Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.
Front Neurosci. 2022; 16:935431. doi: 10.3389/fnins.2022.935431.

Hao X, An Q, Li J, Min H, Guo Y, Yu M, Qin J.
Exploring high-order correlations with deep-broad learning for autism spectrum disorder diagnosis.
Front Neurosci. 2022; 16:1046268. doi: 10.3389/fnins.2022.1046268.

Zhang F, Wei Y, Liu J, Wang Y, Xi W, Pan Y.
Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder.
Comput Biol Med. 2022; 148:105854. doi: 10.1016/j.compbiomed.2022.105854.

Alamdari SB, Sadeghi Damavandi M, Zarei M, Khosrowabadi R.
Cognitive theories of autism based on the interactions between brain functional networks.
Front Hum Neurosci. 2022; 16:828985. doi: 10.3389/fnhum.2022.828985.

Huang Y, Vangel M, Chen H, Eshel M, Cheng M, Lu T, Kong J.
The Impaired Subcortical Pathway From Superior Colliculus to the Amygdala in Boys With Autism Spectrum Disorder.
Front Integr Neurosci. 2022 Jun 17;16:666439. doi: 10.3389/fnint.2022.666439.

Dafflon J, F Da Costa P, Váša F, Monti RP, Bzdok D, Hellyer PJ, Turkheimer F, Smallwood J, Jones E, Leech R.
A guided multiverse study of neuroimaging analyses.
Nat Commun. 2022; 13:3758. doi: 10.1038/s41467-022-31347-8.

Talesh Jafadideh A, Mohammadzadeh Asl B.
Structural filtering of functional data offered discriminative features for autism spectrum disorder.
PLoS One. 2022; 17:e0277989. doi: 10.1371/journal.pone.0277989.

Chen T, Yuan M, Tang J, Lu L.
Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare.
Front Public Health. 2022; 10:896967. doi: 10.3389/fpubh.2022.896967.

Qi S, Sui J, Pearlson G, Bustillo J, Perrone-Bizzozero NI, Kochunov P, Turner JA, Fu Z, Shao W, Jiang R, Yang X, Liu J, Du Y, Chen J, Zhang D, Calhoun VD.
Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network.
Nat Commun. 2022; 13:4929. doi: 10.1038/s41467-022-32513-8.

Wang M, Zhang D, Huang J, Liu M, Liu Q.
Consistent connectome landscape mining for cross-site brain disease identification using functional MRI.
Med Image Anal. 2022; 82:102591. doi: 10.1016/j.media.2022.102591.

Mahmood U, Fu Z, Ghosh S, Calhoun V, Plis S.
Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI.
Neuroimage. 2022; 264:119737. doi: 10.1016/j.neuroimage.2022.119737.

Tong X, Xie H, Carlisle N, Fonzo GA, Oathes DJ, Jiang J, Zhang Y.
Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity.
Transl Psychiatry. 2022; 12:367. doi: 10.1038/s41398-022-02134-2.

Urchs SGW, Tam A, Orban P, Moreau C, Benhajali Y, Nguyen HD, Evans AC, Bellec P.
Functional connectivity subtypes associate robustly with ASD diagnosis.
Elife. 2022; 11:None. doi: 10.7554/eLife.56257.

Li L, Su X, Zheng Q, Xiao J, Huang XY, Chen W, Yang K, Nie L, Yang X, Chen H, Shi S, Duan X.
Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder.
Hum Brain Mapp. 2022; 43:4722-4732. doi: 10.1002/hbm.25986.

Pourmotahari F, Doosti H, Borumandnia N, Tabatabaei SM, Alavi Majd H.
Group-level comparison of brain connectivity networks.
BMC Med Res Methodol. 2022; 22:273. doi: 10.1186/s12874-022-01712-8.

Zhao F, Li N, Pan H, Chen X, Li Y, Zhang H, Mao N, Cheng D.
Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis.
Front Hum Neurosci. 2022 Jul 15;16:918969. doi: 10.3389/fnhum.2022.918969.

Whi W, Ha S, Kang H, Lee DS.
Hyperbolic disc embedding of functional human brain connectomes using resting-state fMRI.
Netw Neurosci. 2022; 6:745-764. doi: 10.1162/netn_a_00243.

Hettwer MD, Larivière S, Park BY, van den Heuvel OA, Schmaal L, Andreassen OA, Ching CRK, Hoogman M, Buitelaar J, van Rooij D, Veltman DJ, Stein DJ, Franke B, van Erp TGM, ENIGMA ADHD Working Group, ENIGMA Autism Working Group, ENIGMA Bipolar Disorder Working Group, ENIGMA Major Depression Working Group, ENIGMA OCD Working Group, ENIGMA Schizophrenia Working Group, Jahanshad N, Thompson PM, Thomopoulos SI, Bethlehem RAI, Bernhardt BC, Eickhoff SB, Valk SL.
Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders.
Nat Commun. 2022; 13:6851. doi: 10.1038/s41467-022-34367-6.

Peng L, Wang N, Xu J, Zhu X, Li X.
GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis.
IEEE Trans Med Imaging. 2022; PP:None. doi: 10.1109/TMI.2022.3201974.

Li S, Tang Z, Jin N, Yang Q, Liu G, Liu T, Hu J, Liu S, Wang P, Hao J, Zhang Z, Zhang X, Li J, Wang X, Li Z, Wang Y, Yang B, Ma L.
Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder Using Deep Learning.
Int J Neural Syst. 2022 Sep;32(9):2250044. doi: 10.1142/S0129065722500447.

Liang L, Dong G, Li C, Wen D, Zhao Y, Li J.
Improving Autism Spectrum Disorder Prediction by Fusion of Multiple Measures of Resting-State Functional MRI Data.
Annu Int Conf IEEE Eng Med Biol Soc. 2022; 2022:1851-1854. doi: 10.1109/EMBC48229.2022.9871167.

Kang L, Chen J, Huang J, Jiang J.
Autism spectrum disorder recognition based on multi-view ensemble learning with multi-site fMRI.
Cogn Neurodyn. 2023; 17:345-355. doi: 10.1007/s11571-022-09828-9.

Ji J, Li J.
Deep Forest With Multi-Channel Message Passing and Neighborhood Aggregation Mechanisms for Brain Network Classification.
IEEE J Biomed Health Inform. 2022; 26:5608-5618. doi: 10.1109/JBHI.2022.3199505.

Kunda M, Zhou S, Gong G, Lu H.
Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity.
IEEE Trans Med Imaging. 2023; 42:55-65. doi: 10.1109/TMI.2022.3203899.

Karavallil Achuthan S, Coburn KL, Beckerson ME, Kana RK.
Amplitude of low frequency fluctuations during resting state fMRI in autistic children.
Autism Res. 2023; 16:84-98. doi: 10.1002/aur.2846.

Li L, Wen G, Cao P, Liu X, R Zaiane O, Yang J.
Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis.
Int J Comput Assist Radiol Surg. 2023; 18:663-673. doi: 10.1007/s11548-022-02780-3.

Bayer JMM, Dinga R, Kia SM, Kottaram AR, Wolfers T, Lv J, Zalesky A, Schmaal L, Marquand A.
Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models.
Neuroimage. 2022; 264:119699. doi: 10.1016/j.neuroimage.2022.119699.

Talesh Jafadideh A, Mohammadzadeh Asl B.
Topological analysis of brain dynamics in autism based on graph and persistent homology.
Computers in Biology and Medicine, Volume 150, 2022, 106202, ISSN 0010-4825. doi: doi.org/10.1016/j.compbiomed.2022.106202.

Liu R, Huang ZA, Hu Y, Zhu Z, Wong KC, Tan KC.
Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.
IEEE Trans Neural Netw Learn Syst. 2022; PP:None. doi: 10.1109/TNNLS.2022.3219551.

Huang ZA, Hu Y, Liu R, Xue X, Zhu Z, Song L, Tan KC.
Federated Multi-Task Learning for Joint Diagnosis of Multiple Mental Disorders on MRI Scans.
IEEE Trans Biomed Eng. 2023; 70:1137-1149. doi: 10.1109/TBME.2022.3210940.

Kim E, Kim S, Kim Y, Cha H, Lee HJ, Lee T, Chang Y.
Connectome-based predictive models using resting-state fMRI for studying brain aging.
Exp Brain Res. 2022; 240:2389-2400. doi: 10.1007/s00221-022-06430-7.

Haghighat H, Mirzarezaee M, Araabi BN, Khadem A.
A sex-dependent computer-aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI.
J Neural Eng. 2022; 19:None. doi: 10.1088/1741-2552/ac86a4.

Jha RR, Bhardwaj A, Garg D, Bhavsar A, Nigam A.
MHATC: Autism Spectrum Disorder Identification Utilizing Multi-Head Attention Encoder Along with Temporal Consolidation Modules.
Annu Int Conf IEEE Eng Med Biol Soc. 2022; 2022:337-341. doi: 10.1109/EMBC48229.2022.9871130.

Ma H, Cao Y, Li M, Zhan L, Xie Z, Huang L, Gao Y, Jia X.
Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study.
Hum Brain Mapp. 2023; 44:1094-1104. doi: 10.1002/hbm.26141.

Qin C, Zhu X, Ye L, Peng L, Li L, Wang J, Ma J, Liu T.
Autism detection based on multiple time scale model.
J Neural Eng. 2022; 19:None. doi: 10.1088/1741-2552/ac8b39.

Zhang H, Song R, Wang L, Zhang L, Wang D, Wang C, Zhang W.
Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks.
IEEE Trans Med Imaging. 2023; 42:444-455. doi: 10.1109/TMI.2022.3219260.

Wang H, Jiang X, De Leone R, Zhang Y, Qiao L, Zhang L.
Extracting BOLD signals based on time-constrained multiset canonical correlation analysis for brain functional network estimation and classification.
Brain Res. 2022; 1775:147745. doi: 10.1016/j.brainres.2021.147745.

Zhang Y, Peng B, Xue Z, Bao J, Li BK, Liu Y, Liu Y, Sheng M, Pang C, Dai Y.
Self-Paced Learning and Privileged Information based Cascaded Multi-column Classification algorithm for ASD diagnosis.
Annu Int Conf IEEE Eng Med Biol Soc. 2021; 2021:3281-3284. doi: 10.1109/EMBC46164.2021.9630150.

Sadiq A, Al-Hiyali MI, Yahya N, Tang TB, Khan DM.
Non-Oscillatory Connectivity Approach for Classification of Autism Spectrum Disorder Subtypes Using Resting-State fMRI
IEEE Access. 2022; 10:14049 - 14061. doi: 10.1109/ACCESS.2022.3146719

Devika K, Mahapatra D, Subramanian R, Oruganti VRM.
Outlier-Based Autism Detection Using Longitudinal Structural MRI
IEEE Access. 2022; 10:27794 - 27808. doi: 10.1109/ACCESS.2022.3157613.

Al-Hiyali MI, Yahya N, Faye I, Al-Quraishi MS, Al-Ezzi A.
Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
Appl. Sci. 2022, 12(18), 9339; doi: 10.3390/app12189339

Wang M, Guo J, Wang Y, Yu M, Guo J.
Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN
IEEE Trans Neural Syst Rehabil Eng. 2023;31:3664-3674. doi: 10.1109/TNSRE.2023.3314516. Epub 2023 Sep 20.

Lei D, Zhang T, Wu Y, Li W, Li X.
Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation
Med Biol Eng Comput. 2023 Nov;61(11):2829-2842. doi: 10.1007/s11517-023-02859-2. Epub 2023 Jul 24.

Xie J, Zhang W, Shen Y, Wei W, Bai Y, Zhang G, Meng N, Yue X, Wang X, Zhang X, Wang M.
Abnormal spontaneous brain activity in females with autism spectrum disorders
Front Neurosci. 2023 Jul 14;17:1189087. doi: 10.3389/fnins.2023.1189087.

Shao L, Fu C, Chen X.
A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder
BMC Bioinformatics. 2023 Sep 27;24(1):363. doi: 10.1186/s12859-023-05495-7.

Cheng Y, Liu L, Gu X, Lu Z, Xia Y, Chen J, Tang L.
Graph fusion prediction of autism based on attentional mechanisms
J Biomed Inform. 2023 Oct;146:104484. doi: 10.1016/j.jbi.2023.104484. Epub 2023 Sep 1.

Artiles O, Al Masry Z, Saeed F.
Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data
Neuroinformatics. 2023 Oct;21(4):651-668. doi: 10.1007/s12021-023-09639-1. Epub 2023 Aug 15.

Gaur M, Chaturvedi K, Vishwakarma DK, Ramasamy S, Prasad M.
Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data
Res Autism Spectr Disord. 2023 Sep 1;107:102223. doi: 10.1016/j.rasd.2023.102223

Hao Y, Xu H, Xia M, Yan C, Zhang Y, Zhou D, Kärkkäinen T, Nickerson LD, Li H, Cong F.
Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data
Eur J Neurosci. 2023 Sep;58(6):3466-3487. doi: 10.1111/ejn.16120. Epub 2023 Aug 30.

Alharthi AG, Alzahrani SM.
Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers
Brain Sci. 2023 Nov 10;13(11):1578. doi: 10.3390/brainsci13111578.

Dhinagar NJ, Santhalingam V, Lawrence KE, Laltoo E, Thompson PM.
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI
Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul:2023:1-6. doi: 10.1109/EMBC40787.2023.10340852.

Watanabe D, Watanabe T.
Distinct Frontoparietal Brain Dynamics Underlying the Co-Occurrence of Autism and ADHD
eNeuro. 2023 Jul 13;10(7):ENEURO.0146-23.2023. doi: 10.1523/ENEURO.0146-23.2023. Print 2023 Jul.

Sidulova M, Park CH.
Conditional Variational Autoencoder for Functional Connectivity Analysis of Autism Spectrum Disorder Functional Magnetic Resonance Imaging Data: A Comparative Study
Bioengineering (Basel). 2023 Oct 16;10(10):1209. doi: 10.3390/bioengineering10101209.

Sidulova M, Park CH.
Conditional Variational Autoencoder for Functional Connectivity Analysis of Autism Spectrum Disorder Functional Magnetic Resonance Imaging Data: A Comparative Study
Bioengineering (Basel). 2023 Oct 16;10(10):1209. doi: 10.3390/bioengineering10101209.

Bhattacharya A, Manoj G, Gupta V, Gadda AA, Vedantham D, Prince AA, Rani P, Ramaniharan AK, Ronickom JF.
Comparative evaluation of geometrical, Zernike moments, and volumetric features of the corpus callosum for discrimination of ASD using machine learning algorithms
Int J Biomed Eng Technol. 2023 Oct 30:43(3):275-296. doi: 10.1504/IJBET.2023.134588.

Jing J, Klugah-Brown B, Xia S, Sheng M, Biswal BB.
Comparative analysis of group information-guided independent component analysis and independent vector analysis for assessing brain functional network characteristics in autism spectrum disorder
Front Neurosci. 2023 Oct 19:17:1252732. doi: 10.3389/fnins.2023.1252732. eCollection 2023.

Almuqhim F, Saeed F.
ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2023 Dec:2023:2837-2843. doi: 10.1109/bibm58861.2023.10385743. Epub 2024 Jan 18.

Samanta A, Sarma M, Samanta D.
ALERT: Atlas-Based Low Estimation Rank Tensor Approach to Detect Autism Spectrum Disorder
Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul:2023:1-4. doi: 10.1109/EMBC40787.2023.10340610.

Chandra A, Verma S, Raghuvanshi AS, Bodhey NK.
PCcS-RAU-Net: Automated parcellated Corpus callosum segmentation from brain MRI images using modified residual attention U-Net
Biocybern Biomed Eng. 2023 Apr-Jun;43(2):403-427. doi: 10.1016/j.bbe.2023.02.003

Wang M, Guo J, Wang Y, Yu M, Guo J.
Multimodal Autism Spectrum Disorder Diagnosis Method Based on DeepGCN
IEEE Trans Neural Syst Rehabil Eng. 2023:31:3664-3674. doi: 10.1109/TNSRE.2023.3314516. Epub 2023 Sep 20.

Li C, Li T, Chen Y, Zhang C, Ning M, Qin R, Li L, Wang X, Chen L.
Sex differences of the triple network model in children with autism: A resting-state fMRI investigation of effective connectivity
Autism Res. 2023 Sep;16(9):1693-1706. doi: 10.1002/aur.2991. Epub 2023 Aug 10.

Xie Y, Sun J, Man W, Zhang Z, Zhang N.
Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance
Mol Autism. 2023 Jul 28;14(1):27. doi: 10.1186/s13229-023-00558-1.

Liu S, Liang B, Wang S, Li B, Pan L, Wang SH.
NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification
IEEE Open J Eng Med Biol. 2023 Apr 26:5:428-433. doi: 10.1109/OJEMB.2023.3267612. eCollection 2024.

Ma Y, Wang Q, Cao L, Li L, Zhang C, Qiao L, Liu M.
Multi-Scale Dynamic Graph Learning for Brain Disorder Detection With Functional MRI
IEEE Trans Neural Syst Rehabil Eng. 2023:31:3501-3512. doi: 10.1109/TNSRE.2023.3309847. Epub 2023 Sep 4.

Zhao F, Ye S, Zhang M, Lv K, Qiao X, Li Y, Mao N, Ren Y, Zhang M.
Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder
Front Hum Neurosci. 2023 Nov 22:17:1257987. doi: 10.3389/fnhum.2023.1257987. eCollection 2023.

Kurucu MC, Rekik I; Alzheimer’s Disease Neuroimaging Initiative.
Graph neural network based unsupervised influential sample selection for brain multigraph population fusion
Comput Med Imaging Graph. 2023 Sep:108:102274. doi: 10.1016/j.compmedimag.2023.102274. Epub 2023 Jul 21.

Dhamale TD, Bhandari SU, Harpale VK.
Fusion of Features: A Technique to Improve Autism Spectrum Disorder Detection Using Brain MRI Images
Biomed Pharmacol J Dec 2023;16(4):2443-2455. doi: 10.13005/bpj/2819.

Ghosal N, Basu S, Bhaumik D.
Detection of sparse differential dependent functional brain connectivity
Stat Med. 2023 Nov 10;42(25):4664-4680. doi: 10.1002/sim.9882. Epub 2023 Aug 30.

Ryan M, Glonek G, Tuke J, Humphries M.
Capturing functional connectomics using Riemannian partial least squares
Sci Rep. 2023 Oct 13;13(1):17386. doi: 10.1038/s41598-023-44687-2.

Jain V, Singh Sengar S, Agastinose Ronickom JF.
Age-Specific Diagnostic Classification of ASD Using Deep Learning Approaches
Stud Health Technol Inform. 2023 Oct 20:309:267-271. doi: 10.3233/SHTI230794.

Gupta V, Manoj G, Bhattacharya A, Singh Sengar S, Mishra R, Kar BR, Srivastava C, Agastinose Ronickom JF.
A Framework to Diagnose Autism Spectrum Disorder Using Morphological Connectivity of sMRI and XGBoost
Stud Health Technol Inform. 2023 Oct 20:309:33-37. doi: 10.3233/SHTI230734.

Zhang C, Meng X, Liu Q, Wu S, Wang L, Ning H.
FedBrain: A robust multi-site brain network analysis framework based on federated learning for brain disease diagnosis
Neurocomputing. 2023 Nov 28:559:126791. doi: 10.1016/j.neucom.2023.126791.

Li Y, Zhou F, Li R, Gu J, He J.
Exploring the correlation between genetic transcription and multi-temporal developmental autism spectrum disorder using resting-state functional magnetic resonance imaging
Front Neurosci. 2023 Jun 29:17:1219753. doi: 10.3389/fnins.2023.1219753. eCollection 2023.

Yang J, Hu M, Hu Y, Zhang Z, Zhong J.
Diagnosis of Autism Spectrum Disorder (ASD) Using Recursive Feature Elimination-Graph Neural Network (RFE-GNN) and Phenotypic Feature Extractor (PFE)
Sensors (Basel). 2023 Dec 6;23(24):9647. doi: 10.3390/s23249647.

Feng M, Xu J.
Detection of ASD Children through Deep-Learning Application of fMRI
Children (Basel). 2023 Oct 5;10(10):1654. doi: 10.3390/children10101654.

Li F, Lin Q, Zhao X, Hu Z.
Description length guided nonlinear unified Granger causality analysis
Netw Neurosci. 2023 Oct 1;7(3):1109-1128. doi: 10.1162/netn_a_00316. eCollection 2023.

Yang J, Wang F, Li Z, Yang Z, Dong X, Han Q.
Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders
Front Neurosci. 2023 Aug 31:17:1257982. doi: 10.3389/fnins.2023.1257982. eCollection 2023.

Chen K, Zhuang W, Zhang Y, Yin S, Liu Y, Chen Y, Kang X, Ma H, Zhang T.
Alteration of the large-scale white-matter functional networks in autism spectrum disorder
Cereb Cortex. 2023 Dec 9;33(24):11582-11593. doi: 10.1093/cercor/bhad392.

Zhu J, Jiao Y, Chen R, Wang XH, Han Y.
Aberrant dynamic and static functional connectivity of the striatum across specific low-frequency bands in patients with autism spectrum disorder
Psychiatry Res Neuroimaging. 2023 Dec:336:111749. doi: 10.1016/j.pscychresns.2023.111749. Epub 2023 Nov 10.

Zhang S, Chen X, Shen X, Ren B, Yu Z, Yang H, Jiang X, Shen D, Zhou Y, Zhang XY.
A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders
Med Image Anal. 2023 Dec:90:102932. doi: 10.1016/j.media.2023.102932. Epub 2023 Aug 22.

Ali MT, Gebreil A, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Sleman A, Giridharan GA, Barnes G, Elbaz AS.
A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework
Sci Rep. 2023 Oct 9;13(1):17048. doi: 10.1038/s41598-023-43478-z.

Wang M, Ma Z, Wang Y, Liu J, Guo J.
A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder
PLoS One. 2023 Dec 8;18(12):e0295621. doi: 10.1371/journal.pone.0295621. eCollection 2023.

Qiang N, Gao J, Dong Q, Li J, Zhang S, Liang H, Sun Y, Ge B, Liu Z, Wu Z, Liu T, Yue H, Zhao S.
A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks
Behav Brain Res. 2023 Aug 24:452:114603. doi: 10.1016/j.bbr.2023.114603. Epub 2023 Jul 27.

ElNakieb Y, Ali MT, Elnakib A, Shalaby A, Mahmoud A, Soliman A, Barnes GN, El-Baz A.
Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study
Bioengineering (Basel). 2023 Jan 2;10(1):56. doi: 10.3390/bioengineering10010056.

Rakshe C, Kunneth S, Sundaram S, Agastinose Ronickom JF.
Diagnostic Classification of ASD Using Fractal Functional Connectivity of fMRI and Logistic Regression
Stud Health Technol Inform. 2023 Jun 29:305:60-63. doi: 10.3233/SHTI230424.

Lewis JD, Fonov VS, Collins DL.
Bloody noise: The impact of blood-flow artifacts on registration
Hum Brain Mapp. 2023 Oct 1;44(14):4914-4926. doi: 10.1002/hbm.26426. Epub 2023 Jul 29.

Mishra M, Pati UC.
A classification framework for Autism Spectrum Disorder detection using sMRI: Optimizer based ensemble of deep convolution neural network with on-the-fly data augmentation
Biomed Signal Process Control. 2023 Jul;84:104686. doi: 10.1016/j.bspc.2023.104686.

Bhandage V, Rao MK, Muppidi S, Maram B.
Autism spectrum disorder classification using Adam war strategy optimization enabled deep belief network
Biomed Signal Process Control. 2023 Sep;86(A):104914. doi: 10.1016/j.bspc.2023.104914.

Gu X, Xie L, Xia Y, Cheng Y, Liu L, Tang L.
Autism spectrum disorder diagnosis using the relational graph attention network
Biomed Signal Process Control. 2023 Aug;85:105090. doi: 10.1016/j.bspc.2023.105090

Guo X, Zhai G, Liu J, Zhang X, Zhang T, Cui D, Zhou R, Gao L.
Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder
Autism Res. 2023 Dec;16(12):2275-2290. doi: 10.1002/aur.3037. Epub 2023 Oct 10.

Lu H, Wang S, Xue Z, Liu J, Niu X, Gao L, Guo X.
Decreased functional concordance in male children with autism spectrum disorder
Autism Res. 2023 Dec;16(12):2263-2274. doi: 10.1002/aur.3035. Epub 2023 Oct 3.

Lee J, Kang E, Heo DW, Suk HI.
Site-Invariant Meta-Modulation Learning for Multisite Autism Spectrum Disorders Diagnosis
IEEE Trans Neural Netw Learn Syst. 2023 Sep 14:PP. doi: 10.1109/TNNLS.2023.3311195.

Kim M, Leonardsen E, Rutherford S, Selbæk G, Persson K, Steen NE, Smeland OB, Ueland T, Richard G, Beckmann CF, Marquand AF, Andreassen OA, Westlye LT, Wolfers T, Moberget T.
Interpretation of deep non-linear factorization for autism
Nat. Mental Health 2, 1196–1207 (2024). doi: 10.1038/s44220-024-00297-z

Chen B, Yin B, Ke H.
Interpretation of deep non-linear factorization for autism
Front Psychiatry. 2023 Jun 22:14:1199113. doi: 10.3389/fpsyt.2023.1199113. eCollection 2023.

Segal A, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A.
Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders
Nat Neurosci. 2023 Sep;26(9):1613-1629. doi: 10.1038/s41593-023-01404-6. Epub 2023 Aug 14.

Koc E, Kalkan H, Bilgen S.
Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images
Autism Res Treat. 2023 Dec 20:2023:4136087. doi: 10.1155/2023/4136087. eCollection 2023.

Dong Q, Li J, Ju Y, Xiao C, Li K, Shi B, Zheng W, Zhang Y.
Altered Relationship between Functional Connectivity and Fiber-Bundle Structure in High-Functioning Male Adults with Autism Spectrum Disorder
Brain Sci. 2023 Jul 20;13(7):1098. doi: 10.3390/brainsci13071098.

Bedford SA, Ortiz-Rosa A, Schabdach JM, Costantino M, Tullo S, Piercy T; Lifespan Brain Chart Consortium; Lai MC, Lombardo MV, Di Martino A, Devenyi GA, Chakravarty MM, Alexander-Bloch AF, Seidlitz J, Baron-Cohen S, Bethlehem RAI.
The impact of quality control on cortical morphometry comparisons in autism
Imaging Neurosci (Camb). 2023 Oct 6:1:1-21. doi: 10.1162/imag_a_00022. eCollection 2023 Oct 1.

Wang X, Chu Y, Wang Q, Cao L, Qiao L, Zhang L, Liu M.
Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection
Hum Brain Mapp. 2023 Dec 1;44(17):5672-5692. doi: 10.1002/hbm.26469. Epub 2023 Sep 5.

Hu Y, Huang ZA, Liu R, Xue X, Sun X, Song L, Tan KC.
Source Free Semi-Supervised Transfer Learning for Diagnosis of Mental Disorders on fMRI Scans
IEEE Trans Pattern Anal Mach Intell. 2023 Nov;45(11):13778-13795. doi: 10.1109/TPAMI.2023.3298332. Epub 2023 Oct 3.

Zhang C, Ma Y, Qiao L, Zhang L, Liu M.
Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification
Biology (Basel). 2023 Jul 8;12(7):971. doi: 10.3390/biology12070971.

Gopinath K, Desrosiers C, Lombaert H.
Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation
Med Image Anal. 2023 Dec:90:102974. doi: 10.1016/j.media.2023.102974. Epub 2023 Sep 22.

Jönemo J, Abramian D, Eklund A.
Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks
Diagnostics (Basel). 2023 Aug 27;13(17):2773. doi: 10.3390/diagnostics13172773.

Wan B, Hong SJ, Bethlehem RAI, Floris DL, Bernhardt BC, Valk SL.
Diverging asymmetry of intrinsic functional organization in autism
Mol Psychiatry. 2023 Oct;28(10):4331-4341. doi: 10.1038/s41380-023-02220-x. Epub 2023 Aug 16.

Ma X, Zhou W, Zheng H, Ye S, Yang B, Wang L, Wang M, Dong GH.
Connectome-based prediction of the severity of autism spectrum disorder
Psychoradiology. 2023 Nov 27:3:kkad027. doi: 10.1093/psyrad/kkad027.

Jin J, Wang XQ, Yang X, Zhao N, Feng ZJ, Zang YF, Yuan LX.
Abnormal individualized peak functional connectivity toward potential repetitive transcranial magnetic stimulation treatment of autism spectrum disorder
Hum Brain Mapp. 2023 Nov;44(16):5450-5459. doi: 10.1002/hbm.26455. Epub 2023 Sep 11.

Serra G, Mainas F, Golosio B, Retico A, Oliva P.
Effect of data harmonization of multicentric dataset in ASD/TD classification
Brain Inform. 2023 Nov 25;10(1):32. doi: 10.1186/s40708-023-00210-x.

Rakshe C, Kunneth S, Sundaram S, Agastinose Ronickom JF.
Diagnostic Classification of ASD Using Fractal Functional Connectivity of fMRI and Logistic Regression
Stud Health Technol Inform. 2023 Jun 29:305:60-63. doi: 10.3233/SHTI230424.

Ratnaik R, Rakshe C, Kumar M, Agastinose Ronickom JF.
Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression
Stud Health Technol Inform. 2023 Jun 29:305:64-67. doi: 10.3233/SHTI230425.

Alves CL, Toutain TGLO, de Carvalho Aguiar P, Pineda AM, Roster K, Thielemann C, Porto JAM, Rodrigues FA.
Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
Sci Rep. 2023 May 18;13(1):8072. doi: 10.1038/s41598-023-34650-6.

Yousefian A, Shayegh F, Maleki Z.
Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals
Front Syst Neurosci. 2023 Feb 2:16:904770. doi: 10.3389/fnsys.2022.904770. eCollection 2022.

Zhu H, Wang J, Zhao YP, Lu M, Shi J.
Contrastive Multi-View Composite Graph Convolutional Networks Based on Contribution Learning for Autism Spectrum Disorder Classification
IEEE Trans Biomed Eng. 2023 Jun;70(6):1943-1954. doi: 10.1109/TBME.2022.3232104. Epub 2023 May 19.

Jiang X, Yan J, Zhao Y, Jiang M, Chen Y, Zhou J, Xiao Z, Wang Z, Zhang R, Becker B, Zhu D, Kendrick KM, Liu T.
Characterizing functional brain networks via Spatio-Temporal Attention 4D Convolutional Neural Networks (STA-4DCNNs)
Neural Netw. 2023 Jan:158:99-110. doi: 10.1016/j.neunet.2022.11.004. Epub 2022 Nov 10.

Lu Q, Chen J, Wang Y, Huang L, Jiang Z, Nguchu BA, Chen S, Qiu B, Wang X.
Cerebellar Structural Abnormality in Autism Spectrum Disorder: A Magnetic Resonance Imaging Study
Psychiatry Investig. 2023 Apr;20(4):334-340. doi: 10.30773/pi.2022.0254. Epub 2023 Apr 20.

Liu L, Wen G, Cao P, Hong T, Yang J, Zhang X, Zaiane OR.
BrainTGL: A dynamic graph representation learning model for brain network analysis
Comput Biol Med. 2023 Feb:153:106521. doi: 10.1016/j.compbiomed.2022.106521. Epub 2023 Jan 6.

Li Q, Zhao W, Palaniyappan L, Guo S.
Atypical hemispheric lateralization of brain function and structure in autism: a comprehensive meta-analysis study
Psychol Med. 2023 Oct;53(14):6702-6713. doi: 10.1017/S0033291723000181. Epub 2023 Feb 17.

Peng L, Chen Z, Gao X.
Altered rich-club organization of brain functional network in autism spectrum disorder
Biofactors. 2023 May-Jun;49(3):612-619. doi: 10.1002/biof.1933. Epub 2023 Feb 13.

Khandan Khadem-Reza Z, Shahram MA, Zare H.
Altered resting-state functional connectivity of the brain in children with autism spectrum disorder
Radiol Phys Technol. 2023 Jun;16(2):284-291. doi: 10.1007/s12194-023-00717-2. Epub 2023 Apr 11.

Wang C, Yang L, Lin Y, Wang C, Tian P.
Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis
Front Integr Neurosci. 2023 Jan 19:16:922577. doi: 10.3389/fnint.2022.922577. eCollection 2022.

Ma D, Peng L, Gao X.
Adaptive noise depression for functional brain network estimation
Front Psychiatry. 2023 Jan 10:13:1100266. doi: 10.3389/fpsyt.2022.1100266. eCollection 2022.

Wang Z, He M, Lv Y, Ge E, Zhang S, Qiang N, Liu T, Zhang F, Li X, Ge B.
Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism
Cereb Cortex. 2023 Jun 20;33(13):8405-8420. doi: 10.1093/cercor/bhad125.

Xu MX, Ju XD.
Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis
Brain Sci. 2023 May 10;13(5):779. doi: 10.3390/brainsci13050779.

Zhang X, Shams SP, Yu H, Wang Z, Zhang Q.
A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis
Diagnostics (Basel). 2023 Jan 6;13(2):218. doi: 10.3390/diagnostics13020218.

Zhao Y, Yang Z, Ding Z, Su J.
A Riemannian Framework for Structurally Curated Functional Clustering of Brain White Matter Fibers
IEEE Trans Med Imaging. 2023 Aug;42(8):2414-2424. doi: 10.1109/TMI.2023.3252269. Epub 2023 Aug 1.

Manikantan K, Jaganathan S.
A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks
Diagnostics (Basel). 2023 Mar 16;13(6):1143. doi: 10.3390/diagnostics13061143.

Benabdallah FZ, Drissi El Maliani A, Lotfi D, El Hassouni M.
A Convolutional Neural Network-Based Connectivity Enhancement Approach for Autism Spectrum Disorder Detection
J Imaging. 2023 May 31;9(6):110. doi: 10.3390/jimaging9060110.

Ratnaik R, Rakshe C, Kumar M, Agastinose Ronickom JF.
Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression
Stud Health Technol Inform. 2023 Jun 29:305:64-67. doi: 10.3233/SHTI230425.

Moreau CA, Harvey A, Kumar K, Huguet G, Urchs SGW, Douard EA, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Rolland T, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Labbe A, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S.
Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry
Biol Psychiatry. 2023 Jan 1;93(1):45-58. doi: 10.1016/j.biopsych.2022.08.024. Epub 2022 Sep 2.

Haghighat H.
A sex-dependent functional-effective connectivity model for diagnostic classification of Autism Spectrum Disorder using resting-state fMRI
Biomedical Signal Processing and Control. 2023 Aug, 85:104837. doi: 10.1016/j.bspc.2023.104837.

Sharma B, Nowikow C, DeMatteo C, Noseworthy MD, Timmons BW.
Sex-specific differences in resting-state functional brain activity in pediatric concussion
Sci Rep. 2023 Feb, 13(1):3284. doi: 10.1038/s41598-023-30195-w.

Ilioska I, Oldehinkel M, Llera A, Chopra S, Looden T, Chauvin R, Van Rooij D, Floris DL, Tillmann J, Moessnang C, Banaschewski T, Holt RJ, Loth E, Charman T, Murphy DGM, Ecker C, Mennes M, Beckmann CF, Fornito A, Buitelaar JK.
Connectome-wide Mega-analysis Reveals Robust Patterns of Atypical Functional Connectivity in Autism
Biol Psychiatry. 2023 Jul, 94(1):29-39. doi: 10.1016/j.biopsych.2022.12.018. Epub 2022 Dec 23.

Ren P, Bi Q, Pang W, Wang M, Zhou Q, Ye X, Li L, Xiao L.
Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI
Behav Brain Res. 2023 Jul, 449:114458. doi: 10.1016/j.bbr.2023.114458. Epub 2023 Apr 29.

Yang B, Wang M, Zhou W, Wang X, Chen S, Yuan LX, Dong GH.
Edge-centric functional network analyses reveal disrupted network configuration in autism spectrum disorder
J Affect Disord. 2023 Sep, 336:74-80. doi: 10.1016/j.jad.2023.05.025. Epub 2023 May 16.

Yang B, Wang M, Zhou W, Wang X, Chen S, Potenza MN, Yuan LX, Dong GH.
Disrupted network integration and segregation involving the default mode network in autism spectrum disorder
J Affect Disord. 2023 Feb, 323:309-319. doi: 10.1016/j.jad.2022.11.083. Epub 2022 Nov 28.

Liu R, Huang ZA, Hu Y, Zhu Z, Wong KC, Tan KC.
Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data
IEEE Trans Neural Netw Learn Syst. 2024 Aug, 35(8):10591-10605. doi: 10.1109/TNNLS.2023.3243000. Epub 2024 Aug 5.

Li Y, Li R, Wang N, Gu J, Gao J.
Gender effects on autism spectrum disorder: a multi-site resting-state functional magnetic resonance imaging study of transcriptome-neuroimaging
Front Neurosci. 2023 Jun, 20:17:1203690. doi: 10.3389/fnins.2023.1203690. eCollection 2023.

Abbas SQ, Chi L, Chen YP.
DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder
Artif Intell Med. 2023 Feb, 136:102475. doi: 10.1016/j.artmed.2022.102475. Epub 2022 Dec 21

Liu M, Zhang H, Shi F, Shen D
Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity
IEEE Trans Neural Netw Learn Syst. 2024, 35(11):15182-15194. doi: 10.1109/TNNLS.2023.3282961. Epub 2024 Oct 29.

Wang Z, He M, Lv Y, Ge E, Zhang S, Qiang N, Liu T, Zhang F, Li X, Ge B
Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism
Cereb Cortex. 2023 Jun, 33(13):8405-8420. doi: 10.1093/cercor/bhad125.

Kirkovski M, Singh M, Dhollander T, Fuelscher I, Hyde CE, Albein-Urios N, Donaldson PH, Enticott PG
An Investigation of Age-related Neuropathophysiology in Autism Spectrum Disorder Using Fixel-based Analysis of Corpus Callosum White Matter Micro- and Macrostructure
J Autism Dev Disord. 2024, 54(6):2198-2210. doi: 10.1007/s10803-023-05980-1. Epub 2023 Apr 20.

Blume J, Dhanasekara CS, Kahathuduwa CN, Mastergeorge AM
Central Executive and Default Mode Networks: An Appraisal of Executive Function and Social Skill Brain-Behavior Correlates in Youth with Autism Spectrum Disorder
J Autism Dev Disord. 2024, 54(5):1882-1896. doi: 10.1007/s10803-023-05961-4. Epub 2023 Mar 29.

Buch AM, Vértes PE, Seidlitz J, Kim SH, Grosenick L, Liston C
Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder
Nat Neurosci. 2023 Apr;26(4):650-663. doi: 10.1038/s41593-023-01259-x. Epub 2023 Mar 9.

Cong J, Zhuang W, Liu Y, Yin S, Jia H, Yi C, Chen K, Xue K, Li F, Yao D, Xu P, Zhang T
Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis
Hum Brain Mapp. 2023 Apr 15;44(6):2279-2293. doi: 10.1002/hbm.26209. Epub 2023 Jan 20.

Yang Y, Ye C, Ma T
A deep connectome learning network using graph convolution for connectome-disease association study
Neural Netw. 2023, 164:91-104. doi: 10.1016/j.neunet.2023.04.025. Epub 2023 Apr 22.

Zhuang W, Jia H, Liu Y, Cong J, Chen K, Yao D, Kang X, Xu P, Zhang T
Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity
Autism Res. 2023 Aug;16(8):1512-1526. doi: 10.1002/aur.2974. Epub 2023 Jun 27.

Lin Q, Shi Y, Huang H, Jiao B, Kuang C, Chen J, Rao Y, Zhu Y, Liu W, Huang R, Lin J, Ma L
Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder
Eur Child Adolesc Psychiatry. 2024, 33(2):369-380. doi: 10.1007/s00787-023-02165-0. Epub 2023 Feb 17.

Chen Y, Luo J, Chen S, Lin Q, Kuang C, Rao Y, Zhang X, Huang Y, Ma L, Lin J
Altered cortical gyrification, sulcal depth, and fractal dimension in the autism spectrum disorder comorbid attention-deficit/hyperactivity disorder than the autism spectrum disorder
Neuroreport. 2023, 34(2):93-101. doi: 10.1097/WNR.0000000000001864. Epub 2022 Dec 17

Wang C, Zhang L, Zhang J, Qiao L, Liu M
Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification
J Pers Med. 2023, 13(2):251. doi: 10.3390/jpm13020251.

Wang Z, Xu Y, Peng D, Gao J, Lu F
Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression
Cereb Cortex. 2023, 33(10):6407-6419. doi: 10.1093/cercor/bhac513.

Guo X, Zhang X, Chen H, Zhai G, Cao Y, Zhang T, Gao L
Exploring the heterogeneity of brain structure in autism spectrum disorder based on individual structural covariance network
Cereb Cortex. 2023, 33(12):7311-7321. doi: 10.1093/cercor/bhad040.

Yuan B, Wang M, Wu X, Cheng P, Zhang R, Zhang R, Yu S, Zhang J, Du Y, Wang X, Qiu Z
Identification of de novo Mutations in the Chinese Autism Spectrum Disorder Cohort via Whole-Exome Sequencing Unveils Brain Regions Implicated in Autism
Neurosci Bull, 2023, 39(10):1469-1480. doi: 10.1007/s12264-023-01037-6. Epub 2023 Mar 7.

Rasero J, Jimenez-Marin A, Diez I, Toro R, Hasan MT, Cortes JM
The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals
Biol Psychiatry. 2023, 94(10):804-813. doi: 10.1016/j.biopsych.2023.04.014. Epub 2023 Apr 22.

Long J, Lu F, Yang S, Zhang Q, Chen X, Pang Y, Wang M, He B, Liu H, Duan X, Chen H, Ye S, Chen H
Different functional connectivity optimal frequency in autism compared with healthy controls and the relationship with social communication deficits: Evidence from gene expression and behavior symptom analyses
Hum Brain Mapp, 2023, 44(1):258-268. doi: 10.1002/hbm.26011. Epub 2022 Jul 13.

Hwang G, Wen J, Sotardi S, Brodkin ES, Chand GB, Dwyer DB, Erus G, Doshi J, Singhal P, Srinivasan D, Varol E, Sotiras A, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Shou H, Fan Y, Di Martino A, Koutsouleris N, Gur RE, Gur RC, Satterthwaite TD, Wolf DH, Davatzikos C
Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population
JAMA psychiatry, 2023, 80(5):498-507. doi: 10.1001/jamapsychiatry.2023.0409.

Hollestein V, Poelmans G, Forde NJ, Beckmann CF, Ecker C, Mann C, Schäfer T, Moessnang C, Baumeister S, Banaschewski T, Bourgeron T, Loth E, Dell'Acqua F, Murphy DGM, Puts NA, Tillmann J, Charman T, Jones EJH, Mason L, Ambrosino S, Holt R, Bölte S, Buitelaar JK, Naaijen J
Excitatory/inhibitory imbalance in autism: the role of glutamate and GABA gene-sets in symptoms and cortical brain structure
Translational psychiatry, 2023, 13(1):18 doi: 10.1038/s41398-023-02317-5.

Floris DL, Peng H, Warrier V, Lombardo MV, Pretzsch CM, Moreau C, Tsompanidis A, Gong W, Mennes M, Llera A, van Rooij D, Oldehinkel M, Forde NJ, Charman T, Tillmann J, Banaschewski T, Moessnang C, Durston S, Holt RJ, Ecker C, Dell'Acqua F, Loth E, Bourgeron T, Murphy DGM, Marquand AF, Lai MC, Buitelaar JK, Baron-Cohen S, Beckmann CF; APEX Group; EU-AIMS LEAP Group
The Link Between Autism and Sex-Related Neuroanatomy, and Associated Cognition and Gene Expression
American Journal of Psychiatry, 2023, 180(1):50-64 doi: 10.1176/appi.ajp.20220194.

Sun A, Wang J, Zhang J.
Identifying autism spectrum disorder using edge-centric functional connectivity
Cerebral Cortex, 2023, 33:8122–8130 doi: 10.1093/cercor/bhad103.

Wang R, Chaudharia P, Davatzikos C.
Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies
Proceedings of the National Academy of Sciences of the United States of America, 2023, 120(6):2211613120. doi: 10.1073/pnas.2211613120.

Hong SJ, Mottron L, Park BY, Benkarim O, Valk SL, Paquola C, Larivière S, Vos de Wael R, Degré-Pelletier J, Soulieres I, Ramphal B, Margolis A, Milham M, Di Martino A, Bernhardt BC.
A convergent structure-function substrate of cognitive imbalances in autism.
Cereb Cortex. 2023; 33:1566-1580. doi: 10.1093/cercor/bhac156.

Khundrakpam B, Bhutani N, Vainik U, Gong J, Al-Sharif N, Dagher A, White T, Evans AC.
A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD.
Mol Psychiatry. 2023 Mar;28(3):1210-1218. doi: 10.1038/s41380-022-01916-w.

Xu H, Hao Y, Zhang Y, Zhou D, Kärkkäinen T, Nickerson LD, Li H, Cong F.
Harmonization of multi-site functional MRI data with dual-projection based ICA model
Front Neurosci. 2023 Jul 20:17:1225606. doi: 10.3389/fnins.2023.1225606. eCollection 2023.

Sigar P, Uddin LQ, Roy D.
Altered global modular organization of intrinsic functional connectivity in autism arises from atypical node-level processing.
Autism Res. 2023; 16:66-83. doi: 10.1002/aur.2840.

Liu X, Wu J, Li W, Liu Q, Tian L, Huang H.
Domain Adaptation via Low Rank and Class Discriminative Representation for Autism Spectrum Disorder identification: A Multi-site fMRI Study
IEEE Trans Neural Syst Rehabil Eng. 2023 Jan 9:PP. doi: 10.1109/TNSRE.2022.3233656.

Cui W, Du J, Sun M, Zhu S, Zhao S, Peng Z, Tan L, Li Y.
Dynamic multi-site graph convolutional network for autism spectrum disorder identification
Comput Biol Med. 2023 May:157:106749. doi: 10.1016/j.compbiomed.2023.106749. Epub 2023 Mar 9.

Guo X, Cao Y, Liu J, Zhang X, Zhai G, Chen H, Gao L.
Dysregulated dynamic time-varying triple-network segregation in children with autism spectrum disorder.
Cereb Cortex. 2023 Apr 25;33(9):5717-5726. doi: 10.1093/cercor/bhac454.

Blum D, Hepp T, Belov V, Goya-Maldonado R, la Fougère C, Reimold M.
Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases.
Hum Brain Mapp. 2023 May;44(7):2802-2814. doi: 10.1002/hbm.26246. Epub 2023 Mar 22.

Pan H, Mao Y, Liu P, Li Y, Wei G, Qiao X, Ren Y, Zhao F.
Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis
Med Phys. 2023 Oct;50(10):6269-6282. doi: 10.1002/mp.16406. Epub 2023 Apr 7.

Tong W, Li YX, Zhao XY, Chen QQ, Gao YB, Li P.
fMRI-Based Brain Disease Diagnosis: A Graph Network Approach
IEEE Trans Med Robot Bionics. 2023 May;5(2):312-322. doi: 10.1109/TMRB.2023.3270481.

Moreau CA, Harvey A, Kumar K, Huguet G, Urchs SGW, Douard EA, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Rolland T, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Labbe A, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S.
Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry
Biol Psychiatry. 2023 Jan 1;93(1):45-58. doi: 10.1016/j.biopsych.2022.08.024. Epub 2022 Sep 2.

Mendes SL, Pinaya WHL, Pan PM, Jackowski AP, Bressan RA, Sato JR.
Generalizability of 3D CNN models for age estimation in diverse youth populations using structural MRI
Sci Rep. 2023 Apr 27;13(1):6886. doi: 10.1038/s41598-023-33920-7.

Li C, Chen W, Li X, Li T, Chen Y, Zhang C, Ning M, Wang X.
Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms.
Front Neurosci. 2023 Jan 25:16:1077908. doi: 10.3389/fnins.2022.1077908. eCollection 2022.

Zhang Z, Li K, Hu X.
Mapping nonlinear brain dynamics by phase space embedding with fMRI data
Biomed Signal Process Control. 2023 Apr;82:104521. doi: 10.1016/j.bspc.2022.104521.

Tang Y, Tong G, Xiong X, Zhang C, Zhang H, Yang Y.
Multi-site diagnostic classification of Autism spectrum disorder using adversarial deep learning on resting-state fMRI
Biomed Signal Process Control. 2023 Aug;85:104892. doi: 10.1016/j.bspc.2023.104892.

RethikumariAmma KN, Ranjana P.
Pivotal region and optimized deep neuro fuzzy network for autism spectrum disorder detection
Biomed Signal Process Control. 2023 May;83:104634. doi: 10.1016/j.bspc.2023.104634

Zhang J, Fang S, Yao Y, Li F, Luo Q.
Parsing the heterogeneity of brain-symptom associations in autism spectrum disorder via random forest with homogeneous canonical correlation
J Affect Disord. 2023 Aug 15:335:36-43. doi: 10.1016/j.jad.2023.04.102. Epub 2023 May 6.

Wang Y, Long H, Zhou Q, Bo T, Zheng J.
PLSNet: Position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting
Comput Biol Med. 2023 Sep:163:107184. doi: 10.1016/j.compbiomed.2023.107184. Epub 2023 Jun 17.

Yue X, Shen Y, Li Y, Zhang G, Li X, Wei W, Bai Y, Shang Y, Xie J, Luo Z, Wang X, Zhang X, Wang M.
Regional Dynamic Neuroimaging Changes of Adults with Autism Spectrum Disorder
Neuroscience. 2023 Jul 15:523:132-139. doi: 10.1016/j.neuroscience.2023.04.016. Epub 2023 Jun 2.

Sun F, Chen Y, Huang Y, Yan J, Chen Y.
Relationship between gray matter structure and age in children and adolescents with high-functioning autism spectrum disorder
Front Hum Neurosci. 2023 Jan 6:16:1039590. doi: 10.3389/fnhum.2022.1039590. eCollection 2022.

Wang G, Zhang L, Qiao L.
The effect of node features on GCN-based brain network classification: an empirical study
PeerJ. 2023 Mar 20:11:e14835. doi: 10.7717/peerj.14835. eCollection 2023.

Jha RR, Kumar BVR, Pathak SK, Schneider W, Bhavsar A, Nigam A.
Undersampled single-shell to MSMT fODF reconstruction using CNN-based ODE solver
Comput Methods Programs Biomed. 2023 Mar:230:107339. doi: 10.1016/j.cmpb.2023.107339. Epub 2023 Jan 6./p>

Xue Y, Xue H, Fang P, Zhu S, Qiao L, An Y.
Dynamic functional connections analysis with spectral learning for brain disorder detection
Artif Intell Med. 2024 Nov;157:102984. doi: 10.1016/j.artmed.2024.102984. Epub 2024 Sep 17.

Zheng K, Yu S, Chen B.
CI-GNN: A Granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis
Neural Netw. 2024 Apr;172:106147. doi: 10.1016/j.neunet.2024.106147. Epub 2024 Jan 26.

Liu Y, Wang H, Ding Y.
The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding
Interdiscip Sci. 2024 Mar;16(1):141-159. doi: 10.1007/s12539-023-00592-w. Epub 2023 Dec 7.

Wagstyl K, Adler S, Seidlitz J, Vandekar S, Mallard TT, Dear R, DeCasien AR, Satterthwaite TD, Liu S, Vértes PE, Shinohara RT, Alexander-Bloch A, Geschwind DH, Raznahan A.
Transcriptional cartography integrates multiscale biology of the human cortex
Elife. 2024 Feb 7;12:RP86933. doi: 10.7554/eLife.86933.

Meijer J, Hebling Vieira B, Elleaume C, Baranczuk-Turska Z, Langer N, Floris DL.
Toward understanding autism heterogeneity: Identifying clinical subgroups and neuroanatomical deviations
J Psychopathol Clin Sci. 2024 Nov;133(8):667-677. doi: 10.1037/abn0000914.

Harkness K, Bray S, Murias K.
The role of stimulant washout status in functional connectivity of default mode and fronto-parietal networks in children with neurodevelopmental conditions
Res Dev Disabil. 2024 Mar;146:104691. doi: 10.1016/j.ridd.2024.104691. Epub 2024 Feb 9.

Shan X, Uddin LQ, Ma R, Xu P, Xiao J, Li L, Huang X, Feng Y, He C, Chen H, Duan X.
Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder
Biol Psychiatry. 2024 May 1;95(9):870-880. doi: 10.1016/j.biopsych.2023.09.012. Epub 2023 Sep 22.

Shan X, Wang P, Yin Q, Li Y, Wang X, Feng Y, Xiao J, Li L, Huang X, Chen H, Duan X.
Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles
Eur Child Adolesc Psychiatry. 2025 Jan;34(1):169-179. doi: 10.1007/s00787-024-02476-w. Epub 2024 Jun 11.

Khan NA, Shang X.
ASD-GANNet: A Generative Adversarial Network-Inspired Deep Learning Approach for the Classification of Autism Brain Disorder
Brain Sci. 2024 Jul 29;14(8):766. doi: 10.3390/brainsci14080766.

Wang Z, Zheng L, Yang L, Yin S, Yu S, Chen K, Zhang T, Wang H, Zhang T, Zhang Y.
Structural and functional whole brain changes in autism spectrum disorder at different age stages
Eur Child Adolesc Psychiatry. 2024 Oct 9. doi: 10.1007/s00787-024-02585-6. Epub ahead of print.

Mengi M, Malhotra D.
SSMDA: Semi-supervised multi-source domain adaptive autism prediction model using neuroimaging
Biomedical Signal Processing and Control. 2024 Sep 1;95:106337. doi: 10.1016/j.bspc.2024.106337.

Qian S, Yang Q, Cai C, Dong J, Cai S.
Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study.
Brain Sci. 2024 May 17;14(5):507. doi: 10.3390/brainsci14050507.

Li C, Wang J, Zhou Y, Li T, Wu B, Yuan X, Li L, Qin R, Liu H, Chen L, Wang X.
Sex-related patterns of functional brain networks in children and adolescents with autism spectrum disorder
Autism Res. 2024 Jul;17(7):1344-1355. doi: 10.1002/aur.3180.

Gao L, Cao Y, Zhang Y, Liu J, Zhang T, Zhou R, Guo X.
Sex differences in the flexibility of dynamic network reconfiguration of autism spectrum disorder based on multilayer network
Brain Imaging Behav. 2024 Oct;18(5):1172-1185. doi: 10.1007/s11682-024-00907-5. Epub 2024 Aug 30.

Pourmotahari F, Borumandnia N, Tabatabaei SM, Alavimajd H.
Secondary analysis: Graph analysis of brain connectivity network in autism spectrum disorder
J Res Med Sci. 2024 Jan 30;29:2. doi: 10.4103/jrms.jrms_428_22.

Ma H, Xu Y, Tian L.
RS-MAE: Region-State Masked Autoencoder for Neuropsychiatric Disorder Classifications Based on Resting-State fMRI
IEEE Trans Neural Netw Learn Syst. 2024 Sep 26;PP. doi: 10.1109/TNNLS.2024.3449949. Epub ahead of print.

Kliemann D, Galdi P, Van De Water AL, Egger B, Jarecka D, Adolphs R, Ghosh SS.
Resting-State Functional Connectivity of the Amygdala in Autism: A Preregistered Large-Scale Study
Am J Psychiatry. 2024 Dec 1;181(12):1076-1085. doi: 10.1176/appi.ajp.20230249. Epub 2024 Aug 29.

Chen J, Zhang H, Zou Q, Liao B, Bi XA.
Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction
Interdiscip Sci. 2024 Sep;16(3):755-768. doi: 10.1007/s12539-024-00629-8. Epub 2024 Apr 29.

Sachdeva J, Mittal R, Mehta J, Jain R, Ranjan A.
Resolving autism spectrum disorder (ASD) through brain topologies using fMRI dataset with multi-layer perceptron (MLP)
Psychiatry Res Neuroimaging. 2024 Sep;343:111858. doi: 10.1016/j.pscychresns.2024.111858. Epub 2024 Jul 6.

Wang Y, Long H, Bo T, Zheng J.
Residual graph transformer for autism spectrum disorder prediction
Comput Methods Programs Biomed. 2024 Apr;247:108065. doi: 10.1016/j.cmpb.2024.108065. Epub 2024 Feb 19.

Shang J, Shen E, Yu Y, Jin A, Wang X, Xiang D.
Relationship between abnormal intrinsic functional connectivity of subcortices and autism symptoms in high-functioning adults with autism spectrum disorder
Psychiatry Res Neuroimaging. 2024 Jan;337:111762. doi: 10.1016/j.pscychresns.2023.111762. Epub 2023 Nov 23.

Wang L, Qin Y, Yang S, Jin D, Zhu Y, Li X, Li W, Wang Y, Jin C.
Posterior default mode network is associated with the social performance in male children with autism spectrum disorder: A dynamic causal modeling analysis based on triple-network model
Hum Brain Mapp. 2024 Jun 1;45(8):e26750. doi: 10.1002/hbm.26750.

Ma K, Wen X, Zhu Q, Zhang D.
Ordinal Pattern Tree: A New Representation Method for Brain Network Analysis
IEEE Trans Med Imaging. 2024 Apr;43(4):1526-1538. doi: 10.1109/TMI.2023.3342047. Epub 2024 Apr 3.

Wang W, Xiao L, Qu G, Calhoun VD, Wang YP, Sun X.
Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis
Med Image Anal. 2024 May;94:103144. doi: 10.1016/j.media.2024.103144. Epub 2024 Mar 19.

Nogay HS, Adeli H.
Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning
J Med Syst. 2024 Jan 22;48(1):15. doi: 10.1007/s10916-023-02032-0.

d'Oleire Uquillas F, Sefik E, Li B, Trotter MA, Steele KA, Seidlitz J, Gesue R, Latif M, Fasulo T, Zhang V, Kislin M, Verpeut JL, Cohen JD, Sepulcre J, Wang SS, Gomez J.
Multimodal evidence for cerebellar influence on cortical development in autism: structural growth amidst functional disruption
Mol Psychiatry. 2024 Oct 11. doi: 10.1038/s41380-024-02769-1. Epub ahead of print.

Alves CL, Martinelli T, Sallum LF, Rodrigues FA, Toutain TGLO, Porto JAM, Thielemann C, Aguiar PMC, Moeckel M.
Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis
PLoS One. 2024 Oct 17;19(10):e0305630. doi: 10.1371/journal.pone.0305630.

Zhang H, Chen J, Liao B, Wu FX, Bi XA.
Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification
Interdiscip Sci. 2024 Jun;16(2):455-468. doi: 10.1007/s12539-024-00625-y. Epub 2024 Apr 4.

Zhao F, Feng F, Ye S, Mao Y, Chen X, Li Y, Ning M, Zhang M.
Multi-head self-attention mechanism-based global feature learning model for ASD diagnosis
Biomedical Signal Processing and Control. 2024 May 1;91:106090. doi: 10.1016/j.bspc.2024.106090

Ma Y, Mu X, Zhang T, Zhao Y.
MAFT-SO: A novel multi-atlas fusion template based on spatial overlap for ASD diagnosis
J Biomed Inform. 2024 Sep;157:104714. doi: 10.1016/j.jbi.2024.104714. Epub 2024 Aug 24.

Fang J, Zhang DF, Xie K, Xu L, Bi XA.
Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification
Interdiscip Sci. 2024 Dec;16(4):936-950. doi: 10.1007/s12539-024-00651-w. Epub 2024 Sep 10.

Zhao F, Lv K, Ye S, Chen X, Chen H, Fan S, Mao N, Ren Y.
Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis
PeerJ. 2024 Apr 9;12:e17078. doi: 10.7717/peerj.17078.

Deng S, Tan S, Guo C, Liu Y, Li X.
Impaired effective functional connectivity in the social preference of children with autism spectrum disorder
Front Neurosci. 2024 May 30;18:1391191. doi: 10.3389/fnins.2024.1391191.

Thadikemalla VSG, Focke NK, Tummala S.
A 3D Sparse Autoencoder for Fully Automated Quality Control of Affine Registrations in Big Data Brain MRI Studies
J Imaging Inform Med. 2024 Feb;37(1):412-427. doi: 10.1007/s10278-023-00933-7. Epub 2024 Jan 10.

Liang S, Chen T, Ma J, Ren S, Lu X, Du W.
Identification of mild cognitive impairment using multimodal 3D imaging data and graph convolutional networks
Phys Med Biol. 2024 Nov 19;69(23). doi: 10.1088/1361-6560/ad8c94.

Yang Y, Tang D, Wang Z, Liu Y, Chen F, Jie B, Ni T, Xu C, Li J, Wang C.
Identification of high-functioning autism spectrum disorders based on gray-white matter functional network connectivity
J Psychiatr Res. 2024 Oct;178:107-113. doi: 10.1016/j.jpsychires.2024.08.006. Epub 2024 Aug 6.

Zhang X, Gao Y, Zhang Y, Li F, Li H, Lei F.
Identification of Autism Spectrum Disorder Using Topological Data Analysis
J Imaging Inform Med. 2024 Jun;37(3):1023-1037. doi: 10.1007/s10278-024-01002-3. Epub 2024 Feb 13.

Ma C, Li W, Ke S, Lv J, Zhou T, Zou L.
Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network
Med Biol Eng Comput. 2024 Jul;62(7):2133-2144. doi: 10.1007/s11517-024-03060-9. Epub 2024 Mar 8.

Kavitha Nair R, Ranjana P.
HYBRID OPTIMIZATION-ENABLED FUNCTIONAL CONNECTIVITY-BASED PIVOTAL REGION EXTRACTION and TRANSFER LEARNING for AUTISM SPECTRUM DISORDER DETECTION
Biomedical Engineering: Applications, Basis and Communications. 2024 Aug 21;36(04):2450018. doi: 10.4015/S1016237224500182.

Xu G, Geng G, Wang A, Li Z, Liu Z, Liu Y, Hu J, Wang W, Li X.
Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning
Autism Res. 2024 Oct;17(10):1962-1973. doi: 10.1002/aur.3183. Epub 2024 Jun 24.

Shi Y, Gong Y, Guan Y, Tang J.
Generation and discrimination of autism MRI images based on autoencoder
Front Psychiatry. 2024 Oct 14;15:1395243. doi: 10.3389/fpsyt.2024.1395243.

Kornisch M, Gonzalez C, Ikuta T.
Functional connectivity of the posterior cingulate cortex in autism spectrum disorder
Psychiatry Res Neuroimaging. 2024 Aug;342:111848. doi: 10.1016/j.pscychresns.2024.111848. Epub 2024 Jun 13.

Gv S, Paul PM, Gudimindla H, Rachapudi V.
Fractional whale driving training-based optimization enabled transfer learning for detecting autism spectrum disorder
Comput Biol Chem. 2024 Dec;113:108200. doi: 10.1016/j.compbiolchem.2024.108200. Epub 2024 Aug 30.

Zolghadr Z, Batouli SAH, Alavi Majd H, Shafaghi L, Mehrabi Y.
fMRI-Based Multi-class DMDC Model Efficiently Decodes the Overlaps between ASD and ADHD
Basic Clin Neurosci. 2024 May-Jun;15(3):367-382. doi: 10.32598/bcn.2023.4302.1. Epub 2024 May 1.

Jha RR, Muralie A, Daroch M, Bhavsar A, Nigam A.
Enhancing Autism Spectrum Disorder identification in multi-site MRI imaging: A multi-head cross-attention and multi-context approach for addressing variability in un-harmonized data
Artif Intell Med. 2024 Nov;157:102998. doi: 10.1016/j.artmed.2024.102998. Epub 2024 Oct 16.

Song R, Cao P, Wen G, Zhao P, Huang Z, Zhang X, Yang J, Zaiane OR.
BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis
Med Image Anal. 2024 Aug;96:103211. doi: 10.1016/j.media.2024.103211. Epub 2024 May 22.

Chen W, Yang J, Sun Z, Zhang X, Tao G, Ding Y, Gu J, Bu J, Wang H.
DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data
Transl Psychiatry. 2024 Sep 14;14(1):375. doi: 10.1038/s41398-024-02972-2

Hussain MA, LaMay D, Grant E, Ou Y.
Deep learning of structural MRI predicts fluid, crystallized, and general intelligence
Sci Rep. 2024 Nov 14;14(1):27935. doi: 10.1038/s41598-024-78157-0.

Liu J, Cui W, Chen Y, Ma Y, Dong Q, Cai R, Li Y, Hu B.
Deep Fusion of Multi-Template Using Spatio-Temporal Weighted Multi-Hypergraph Convolutional Networks for Brain Disease Analysis
IEEE Trans Med Imaging. 2024 Feb;43(2):860-873. doi: 10.1109/TMI.2023.3325261. Epub 2024 Feb 2.

Alfakih A, Xia Z, Ali B, Mamoon S, Lu J.
Deep Causality Variational Autoencoder Network for Identifying the Potential Biomarkers of Brain Disorders
IEEE Trans Neural Syst Rehabil Eng. 2024;32:112-121. doi: 10.1109/TNSRE.2023.3344995. Epub 2024 Jan 15.

Bandyopadhyay S, Peddi S, Sarma M, Samanta D.
Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data
J Neurosci Methods. 2024 May;405:110100. doi: 10.1016/j.jneumeth.2024.110100. Epub 2024 Feb 29.

Cengiz K, Rekik I.
Cortical morphological networks for profiling autism spectrum disorder using tensor component analysis
Front Neurol. 2024 Jul 4;15:1391950. doi: 10.3389/fneur.2024.1391950.

Weber CF, Kebets V, Benkarim O, Lariviere S, Wang Y, Ngo A, Jiang H, Chai X, Park BY, Milham MP, Di Martino A, Valk S, Hong SJ, Bernhardt BC.
Contracted functional connectivity profiles in autism
Mol Autism. 2024 Sep 11;15(1):38. doi: 10.1186/s13229-024-00616-2.

Park BY, Benkarim O, Weber CF, Kebets V, Fett S, Yoo S, Martino AD, Milham MP, Misic B, Valk SL, Hong SJ, Bernhardt BC.
Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism
Neuroimage. 2024 Jan;285:120481. doi: 10.1016/j.neuroimage.2023.120481. Epub 2023 Dec 2.

Li L, Zheng Q, Xue Y, Bai M, Mu Y.
Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder
Eur Child Adolesc Psychiatry. 2024 Dec;33(12):4313-4324. doi: 10.1007/s00787-024-02474-y. Epub 2024 May 30.

Li A, Yang Y, Cui H, Yang C.
BrainSTEAM: A Practical Pipeline for Connectome-based fMRI Analysis towards Subject Classification
Pac Symp Biocomput. 2024;29:53-64. doi: 10.1142/9789811286421_0005.

Nguyen HD, Clément M, Mansencal B, Coupé P.
Brain structure ages-A new biomarker for multi-disease classification
Hum Brain Mapp. 2024 Jan;45(1):e26558. doi: 10.1002/hbm.26558.

Dong C, Sun D.
Brain network classification based on dynamic graph attention information bottleneck
Comput Methods Programs Biomed. 2024 Jan;243:107913. doi: 10.1016/j.cmpb.2023.107913. Epub 2023 Nov 7.

Lu H, Dong Q, Gao L, Xue Z, Niu X, Zhou R, Guo X.
Sex heterogeneity of dynamic brain activity and functional connectivity in autism spectrum disorder
Autism Res. 2024 Sep;17(9):1796-1809. doi: 10.1002/aur.3227. Epub 2024 Sep 7.

Aghaei A, Ebrahimi Moghaddam M; Alzheimer’s Disease Neuroimaging Initiative.
Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection
Brain Inform. 2024 Jun 4;11(1):16. doi: 10.1186/s40708-024-00230-1.

Zheng K, Yu S, Chen L, Dang L, Chen B.
BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping
Neuroimage. 2024 Apr 15;292:120594. doi: 10.1016/j.neuroimage.2024.120594. Epub 2024 Apr 1.

Lu H, Wang S, Gao L, Xue Z, Liu J, Niu X, Zhou R, Guo X.
Links between brain structure and function in children with autism spectrum disorder by parallel independent component analysis
Brain Imaging Behav. 2025 Feb;19(1):124-137. doi: 10.1007/s11682-024-00957-9. Epub 2024 Nov 20.

Mezrioui NE, Aloui K, Nait-Ali A, Naceur MS.
Automatic characterization of cerebral MRI images for the detection of autism spectrum disorders
Intelligence-Based Medicine. 2024 Jan 1;9:100127. doi: 10.1016/j.ibmed.2023.100127

Gao L, Wang Z, Long Y, Zhang X, Su H, Yu Y, Hong J.
Autism spectrum disorders detection based on multi-task transformer neural network
BMC Neurosci. 2024 Jun 13;25(1):27. doi: 10.1186/s12868-024-00870-3.

Warren SL, Khan DM, Moustafa AA.
Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example
Brain Behav. 2024 Jun;14(6):e3554. doi: 10.1002/brb3.3554.

Zhang J, Guo J, Lu D, Cao Y.
ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis
Sci Rep. 2024 Jun 13;14(1):13696. doi: 10.1038/s41598-024-64299-8.

Enns K, Ferdous KT, Balasubramanian S, Ghosh S, Srinivasan V, Thomo A.
Are brain networks classifiable?
Network Modeling Analysis in Health Informatics and Bioinformatics. 2024 Aug 22;13(1):44.

Jiang A, Ma X, Li S, Wang L, Yang B, Wang S, Li M, Dong G.
Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach
Psychol Med. 2024 Jul;54(9):2042-2053. doi: 10.1017/S0033291724000138. Epub 2024 Apr 2.

Zhou Y, Jia G, Ren Y, Ren Y, Xiao Z, Wang Y.
Advancing ASD identification with neuroimaging: a novel GARL methodology integrating Deep Q-Learning and generative adversarial networks
BMC Med Imaging. 2024 Jul 25;24(1):186. doi: 10.1186/s12880-024-01360-y.

Liu X, Wang Z, Liu S, Gong L, Sosa PAV, Becker B, Jung TP, Dai XJ, Wan F.
Activation network improves spatiotemporal modelling of human brain communication processes
Neuroimage. 2024 Jan;285:120472. doi: 10.1016/j.neuroimage.2023.120472. Epub 2023 Nov 23.

Zhang H, Peng D, Tang S, Bi A, Long Y.
Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder
Bioengineering (Basel). 2024 Aug 30;11(9):882. doi: 10.3390/bioengineering11090882.

Dun J, Wang J, Li J, Yang Q, Hang W, Lu X, Ying S, Shi J.
A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification
IEEE J Biomed Health Inform. 2024 Oct 8;PP. doi: 10.1109/JBHI.2024.3476076.

Wang C, Xiao Z, Xu Y, Zhang Q, Chen J.
A novel approach for ASD recognition based on graph attention networks
Front Comput Neurosci. 2024 Apr 10;18:1388083. doi: 10.3389/fncom.2024.1388083.

Ding Y, Zhang T, Cao W, Zhang L, Xu X.
A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification
Cereb Cortex. 2024 Aug 1;34(8):bhae341. doi: 10.1093/cercor/bhae341.

Faraji R, Ganji Z, Khandan Khadem Z, Akbari-Lalimi H, Eidy F, Zare H.
Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement?
Iran J Child Neurol. 2024 Winter;18(1):93-118. doi: 10.22037/IJCN.V18i1.43294. Epub 2024 Jan 18.

Qiu L, Zhai J.
A hybrid CNN-SVM model for enhanced autism diagnosis
PLoS One. 2024 May 14;19(5):e0302236. doi: 10.1371/journal.pone.0302236.

Wang T, Ding Z, Yang X, Chen Y, Lu C, Sun Y.
A brain structure learning-guided multi-view graph representation learning for brain network analysis
Quant Imaging Med Surg. 2024 Sep 1;14(9):6294-6310. doi: 10.21037/qims-24-578. Epub 2024 Aug 17.

Gonzalez J, Múnera N, Alvarez-Jimenez C, Velasco N, Romero E.
An exploration of structural brain differences in Autism Spectrum Disorders: A multi-parcellation and multi-age analysis
Biomedical Signal Processing and Control. 2024 Jun 1;92:106043.

Jang Y, Choi H, Yoo S, Park H, Park BY.
Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning
Behav Brain Funct. 2024 Jan 24;20(1):2. doi: 10.1186/s12993-024-00228-z.

Namgung JY, Mun J, Park Y, Kim J, Park BY.
Sex differences in autism spectrum disorder using class imbalance adjusted functional connectivity
Neuroimage. 2024 Dec 15;304:120956. doi: 10.1016/j.neuroimage.2024.120956. Epub 2024 Nov 26.

Yoo S, Jang Y, Hong SJ, Park H, Valk SL, Bernhardt BC, Park BY.
Whole-brain structural connectome asymmetry in autism
Neuroimage. 2024 Mar;288:120534. doi: 10.1016/j.neuroimage.2024.120534. Epub 2024 Feb 8.

Park Y, Lee MJ, Yoo S, Kim CY, Namgung JY, Park Y, Park H, Lee EC, Yoon YD, Paquola C, Bernhardt BC, Park BY.
GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox
Neuroimage. 2024 May 1;291:120595. doi: 10.1016/j.neuroimage.2024.120595. Epub 2024 Mar 29.

Ruan L, Chen G, Yao M, Li C, Chen X, Luo H, Ruan J, Zheng Z, Zhang D, Liang S, Lü M.
Brain functional gradient and structure features in adolescent and adult autism spectrum disorders
Hum Brain Mapp. 2024 Aug 1;45(11):e26792. doi: 10.1002/hbm.26792.

Guo X, Zhang X, Liu J, Zhai G, Zhang T, Zhou R, Lu H, Gao L.
Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder
Prog Neuropsychopharmacol Biol Psychiatry. 2024 Apr 20:131:110956. doi: 10.1016/j.pnpbp.2024.110956. Epub 2024 Feb 1.

Abu-Doleh A, Abu-Qasmieh IF, Al-Quran HH, Masad IS, Banyissa LR, Ahmad MA.
Recognition of autism in subcortical brain volumetric images using autoencoding-based region selection method and Siamese Convolutional Neural Network
Int J Med Inform. 2025 Feb:194:105707. doi: 10.1016/j.ijmedinf.2024.105707. Epub 2024 Nov 16.

Yamaguchi H, Sugihara G, Shimizu M, Yamashita Y.
Generative artificial intelligence model for simulating structural brain changes in schizophrenia
Front Psychiatry. 2024 Oct 4:15:1437075. doi: 10.3389/fpsyt.2024.1437075. eCollection 2024.

Moon HJ, Cho SB.
Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network
J Integr Neurosci. 2024 Jul 17;23(7):135. doi: 10.31083/j.jin2307135.

Wei L, Xu X, Su Y, Lan M, Wang S, Zhong S.
Abnormal multimodal neuroimaging patterns associated with social deficits in male autism spectrum disorder
Hum Brain Mapp. 2024 Sep;45(13):e70017. doi: 10.1002/hbm.70017.

Li S, Zhang R.
A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction
Neural Netw. 2024 Jul:175:106285. doi: 10.1016/j.neunet.2024.106285. Epub 2024 Apr 1.

Ma Y, Cui W, Liu J, Guo Y, Chen H, Li Y.
A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis
IEEE Trans Med Imaging. 2024 Mar;43(3):1045-1059. doi: 10.1109/TMI.2023.3327283. Epub 2024 Mar 5.

Zheng W, Bao C, Mu R, Wang J, Li T, Zhao Z, Yao Z, Hu B.
Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction
Hum Brain Mapp. 2024 Oct;45(14):e70032. doi: 10.1002/hbm.70032.

Kim YG, Ravid O, Zheng X, Kim Y, Neria Y, Lee S, He X, Zhu X.
Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder
Front Psychiatry. 2024 May 20:15:1397093. doi: 10.3389/fpsyt.2024.1397093.

Sung G, Park E.
Aggregate and transfer knowledge of functional connectivity of brain for detecting autism spectrum disorder for multi-site research
Biomedical Signal Processing and Control 2024;93:1746-8094. doi: 10.1016/j.bspc.2024.106068.

Kang E, Heo DW, Lee J, Suk HI.
A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis
IEEE Trans Med Imaging. 2024 Apr;43(4):1377-1387. doi: 10.1109/TMI.2023.3337074. Epub 2024 Apr 3.

Jung W, Jeon E, Kang E, Suk HI.
EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning
IEEE Trans Med Imaging. 2024 Apr;43(4):1400-1411. doi: 10.1109/TMI.2023.3337362.

Gao J, Xu Y, Li Y, Lu F, Wang Z.
Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model
Cereb Cortex. 2024 Jan 31;34(2):bhad521. doi: 10.1093/cercor/bhad521.

Zhang C, Ma Y, Qiao L, Zhang L, Liu M.
Learning functional brain networks with heterogeneous connectivities for brain disease identification
Neural Netw. 2024 Dec:180:106660. doi: 10.1016/j.neunet.2024.106660. Epub 2024 Aug 22.

Wang H, Liu Y, Ding Y.
Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm
Neuroinformatics. 2024 Jul;22(3):285-296. doi: 10.1007/s12021-024-09662-w.

Jahani A, Jahani I, Khadem A, Braden BB, Delrobaei M, MacIntosh BJ.
Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder
Sci Rep. 2024 Aug 29;14(1):20120. doi: 10.1038/s41598-024-71174-z.

Mao J, Liu J, Tian X, Pan Y, Trucco E, Lin H.
Towards Integrating Federated Learning with Split Learning via Spatio-temporal Graph Framework for Brain Disease Prediction
IEEE Trans Med Imaging. 2024 Nov 7:PP. doi: 10.1109/TMI.2024.3493195.

Yang J, Xu X, Sun M, Ruan Y, Sun C, Li W, Gao X.
Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data
Cereb Cortex. 2024 Jan 14;34(1):bhad477. doi: 10.1093/cercor/bhad477.

Huynh N, Yan D, Ma Y, Wu S, Long C, Sami MT, Almudaifer A, Jiang Z, Chen H, Dretsch MN, Denney TS, Deshpande R, Deshpande G.
The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification.
Brain Sci. 2024 Apr 30;14(5):456. doi: 10.3390/brainsci14050456.

Qing P, Zhang X, Liu Q, Huang L, Xu D, Le J, Kendrick KM, Lai H, Zhao W.
Structure-function coupling in white matter uncovers the hypoconnectivity in autism spectrum disorder
Mol Autism. 2024 Oct 4;15(1):43. doi: 10.1186/s13229-024-00620-6.

Mendoza C, Román C, Mangin JF, Hernández C, Guevara P.
Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter
Front Neurosci. 2024 Apr 26:18:1394681. doi: 10.3389/fnins.2024.1394681. eCollection 2024.

Honnorat N, Seshadri S, Killiany R, Blangero J, Glahn DC, Fox P, Habes M.
Riemannian frameworks for the harmonization of resting-state functional MRI scans
Med Image Anal. 2024 Jan:91:103043. doi: 10.1016/j.media.2023.103043. Epub 2023 Nov 25.

Zheng J, Cheng Y, Wu X, Li X, Fu Y, Yang Z.
Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks
Front Neurosci. 2024 May 24:18:1405734. doi: 10.3389/fnins.2024.1405734. eCollection 2024.

Zhao S, Lv Q, Zhang G, Zhang J, Wang H, Zhang J, Wang M, Wang Z.
Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response
Neurosci Bull. 2024 Nov;40(11):1667-1680. doi: 10.1007/s12264-024-01224-z.

Wang XH, Wu P, Li L.
Predicting individual autistic symptoms for patients with autism spectrum disorder using interregional morphological connectivity
Psychiatry Res Neuroimaging. 2024 Jul:341:111822. doi: 10.1016/j.pscychresns.2024.111822. Epub 2024 Apr 19.

Libedinsky I, Helwegen K, Boonstra J, Simón LG, Gruber M, Repple J, Kircher T, Dannlowski U, van den Heuvel MP.
Polyconnectomic scoring of functional connectivity patterns across eight neuropsychiatric and three neurodegenerative disorders
Biol Psychiatry. 2024 Oct 16:S0006-3223(24)01665-2. doi: 10.1016/j.biopsych.2024.10.007.

Zhang J, Zhang Z, Sun H, Ma Y, Yang J, Chen K, Yu X, Qin T, Zhao T, Zhang J, Chu C, Wang J.
Personalized functional network mapping for autism spectrum disorder and attention-deficit/hyperactivity disorder
Transl Psychiatry. 2024 Feb 12;14(1):92. doi: 10.1038/s41398-024-02797-z.

Klaus J, Stoodley CJ, Schutter DJLG.
Neurodevelopmental trajectories of cerebellar grey matter associated with verbal abilities in males with autism spectrum disorder
Dev Cogn Neurosci. 2024 Jun:67:101379. doi: 10.1016/j.dcn.2024.101379.

Zhou W, Sun M, Xu X, Ruan Y, Sun C, Li W, Gao X.
Multipattern graph convolutional network-based autism spectrum disorder identification
Cereb Cortex. 2024 Mar 1;34(3):bhae064. doi: 10.1093/cercor/bhae064.

Kong Y, Zhang X, Wang W, Zhou Y, Li Y, Yuan Y.
Multi-Scale Spatial-Temporal Attention Networks for Functional Connectome Classification
IEEE Trans Med Imaging. 2024 Aug 22:PP. doi: 10.1109/TMI.2024.3448214.

Liu X, Hasan MR, Gedeon T, Hossain MZ.
MADE-for-ASD: A multi-atlas deep ensemble network for diagnosing Autism Spectrum Disorder
Comput Biol Med. 2024 Nov:182:109083. doi: 10.1016/j.compbiomed.2024.109083. Epub 2024 Sep 3.

Shui L, Shibata D, Chan KCG, Zhang W, Sung J, Haynor DR; Alzheimer’s Disease Neuroimaging Initiative.
Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization
J Alzheimers Dis. 2024;98(3):969-986. doi: 10.3233/JAD-231149.

Wang Q, Wang W, Fang Y, Yap PT, Zhu H, Li HJ, Qiao L, Liu M.
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI
IEEE Trans Biomed Eng. 2024 Aug;71(8):2391-2401. doi: 10.1109/TBME.2024.3370415. Epub 2024 Jul 18.

Jensen AM, DeWitt P, Bettcher BM, Wrobel J, Kechris K, Ghosh D.
Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics
PLoS Comput Biol. 2024 Nov 11;20(11):e1012524. doi: 10.1371/journal.pcbi.1012524. eCollection 2024 Nov.

Ji Y, Silva RF, Adali T, Wen X, Zhu Q, Jiang R, Zhang D, Qi S, Calhoun VD.
Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders
Neuroimage Clin. 2024:43:103663. doi: 10.1016/j.nicl.2024.103663. Epub 2024 Aug 28.

Li J, Zheng W, Fu X, Zhang Y, Yang S, Wang Y, Zhang Z, Hu B, Xu G.
Individual Deviation-Based Functional Hypergraph for Identifying Subtypes of Autism Spectrum Disorder
Brain Sci. 2024 Jul 24;14(8):738. doi: 10.3390/brainsci14080738.

Kim Y, Fisher ZF, Pipiras V.
Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity
Biom J. 2024 Dec;66(8):e202300370. doi: 10.1002/bimj.202300370.

Dhar D, Chaturvedi M, Sehwag S, Malhotra C, Udit, Saraf C, Chakrabarty M.
Gray Matter Volume Correlates of Co-Occurring Depression in Autism Spectrum Disorder
J Autism Dev Disord. 2024 Oct 23. doi: 10.1007/s10803-024-06602-0.

Mei T, Llera A, Forde NJ, van Rooij D, Floris DL, Beckmann CF, Buitelaar JK.
Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort
Mol Autism. 2024 Jan 17;15(1):3. doi: 10.1186/s13229-024-00583-8.

Itahashi T, Yamashita A, Takahara Y, Yahata N, Aoki YY, Fujino J, Yoshihara Y, Nakamura M, Aoki R, Okimura T, Ohta H, Sakai Y, Takamura M, Ichikawa N, Okada G, Okada N, Kasai K, Tanaka SC, Imamizu H, Kato N, Okamoto Y, Takahashi H, Kawato M, Yamashita O, Hashimoto RI.
Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism
Mol Psychiatry. 2024 Sep 28. doi: 10.1038/s41380-024-02759-3.

Bedford SA, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok A, Suckling J, Anagnostou E, Lerch JP, Taylor M, Nicolson R, Stelios G, Crosbie J, Schachar R, Kelley E, Jones J, Arnold PD, Courchesne E, Pierce K, Eyler LT, Campbell K, Barnes CC, Seidlitz J, Alexander-Bloch AF, Bullmore ET, Baron-Cohen S, Bethlehem RAI; MRC AIMS Consortium; Lifespan Brain Chart Consortium.
Brain-Charting Autism and Attention-Deficit/Hyperactivity Disorder Reveals Distinct and Overlapping Neurobiology
Biol Psychiatry. 2025 Mar 1;97(5):517-530. doi: 10.1016/j.biopsych.2024.07.024. Epub 2024 Aug 14.

D'Souza NS, Wang H, Giovannini A, Foncubierta-Rodriguez A, Beck KL, Boyko O, Syeda-Mahmood TF.
Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond
Med Image Anal. 2024 Apr:93:103064. doi: 10.1016/j.media.2023.103064. Epub 2023 Dec 27.

Gregorich M, Simpson SL, Heinze G.
Flexible parametrization of graph-theoretical features from individual-specific networks for prediction
Stat Med. 2024 Jun 15;43(13):2592-2606. doi: 10.1002/sim.10091. Epub 2024 Apr 25.

Dufumier B, Gori P, Petiton S, Louiset R, Mangin JF, Grigis A, Duchesnay E
Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry
Neuroimage. 2024 Aug 1:296:120665. doi: 10.1016/j.neuroimage.2024.120665. Epub 2024 Jun 6.

Chen D, Liu M, Shen Z, Yao L, Zhao X, Song Z, Yuan H, Wang Q, Zhang L.
Exploring Multiconnectivity and Subdivision Functions of Brain Network via Heterogeneous Graph Network for Cognitive Disorder Identification
IEEE Trans Neural Netw Learn Syst. 2024 Oct 25:PP. doi: 10.1109/TNNLS.2024.3481667.

Guan Z, Yu J, Shi Z, Liu X, Yu R, Lai T, Yang C, Dong H, Chen R, Wei L.
Dynamic graph transformer network via dual-view connectivity for autism spectrum disorder identification
Comput Biol Med. 2024 May:174:108415. doi: 10.1016/j.compbiomed.2024.108415. Epub 2024 Apr 7.

Bravo Balsa L, Abu-Akel A, Mevorach C.
Dynamic functional connectivity in the right temporoparietal junction captures variations in male autistic trait expression
Autism Res. 2024 Apr;17(4):702-715. doi: 10.1002/aur.3117. Epub 2024 Mar 8.

Saponaro S, Lizzi F, Serra G, Mainas F, Oliva P, Giuliano A, Calderoni S, Retico A.
Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders
Brain Inform. 2024 Jan 9;11(1):2. doi: 10.1186/s40708-023-00217-4.

Xu J, Bian Q, Li X, Zhang A, Ke Y, Qiao M, Zhang W, Khang Jeremy Sim W, Gulyas B.
Contrastive Graph Pooling for Explainable Classification of Brain Networks
IEEE Trans Med Imaging. 2024 Sep;43(9):3292-3305. doi: 10.1109/TMI.2024.3392988. Epub 2024 Sep 3.

Wang S, Sun Z, Martinez-Tejada LA, Yoshimura N.
Comparison of autism spectrum disorder subtypes based on functional and structural factors
Front Neurosci. 2024 Oct 4:18:1440222. doi: 10.3389/fnins.2024.1440222. eCollection 2024.

Yang Y, Ye C, Su G, Zhang Z, Chang Z, Chen H, Chan P, Yu Y, Ma T.
BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning
IEEE Trans Med Imaging. 2024 Nov;43(11):4004-4016. doi: 10.1109/TMI.2024.3414476. Epub 2024 Nov 4.

Zhan L, Gao Y, Huang L, Zhang H, Huang G, Wang Y, Sun J, Xie Z, Li M, Jia X, Cheng L, Yu Y.
Brain functional connectivity alterations of Wernicke's area in individuals with autism spectrum conditions in multi-frequency bands: A mega-analysis
Heliyon. 2024 Feb 14;10(4):e26198. doi: 10.1016/j.heliyon.2024.e26198. eCollection 2024 Feb 29.

Li Y, Gu J, Li R, Yi H, He J, Gao J.
Sensory and motor cortices parcellations estimated via distance-weighted sparse representation with application to autism spectrum disorder
Prog Neuropsychopharmacol Biol Psychiatry. 2024 Dec 20:135:111125. doi: 10.1016/j.pnpbp.2024.111125. Epub 2024 Aug 22.

Li Y, Li R, Gu J, Yi H, He J, Lu F, Gao J.
Enhanced group-level dorsolateral prefrontal cortex subregion parcellation through functional connectivity-based distance-constrained spectral clustering with application to autism spectrum disorder
Cereb Cortex. 2024 Jan 31;34(2):bhae020. doi: 10.1093/cercor/bhae020.

Bezgin G, Lewis JD, Fonov VS, Collins DL, Evans AC.
Atypical co-development of the thalamus and cortex in autism: Evidence from age-related white-gray contrast change
Hum Brain Mapp. 2024 Apr;45(5):e26584. doi: 10.1002/hbm.26584.

Qiu L, Liang C, Kochunov P, Hutchison KE, Sui J, Jiang R, Zhi D, Vergara VM, Yang X, Zhang D, Fu Z, Bustillo JR, Qi S, Calhoun VD.
Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging
Transl Psychiatry. 2024 Aug 7;14(1):326. doi: 10.1038/s41398-024-03035-2.

Wu B, Guo Y, Kang J.
Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process
J Am Stat Assoc. 2024;119(545):422-433. doi: 10.1080/01621459.2022.2123336. Epub 2022 Nov 28.

Zhou R, Sun C, Sun M, Ruan Y, Li W, Gao X.
Altered intra- and inter-network connectivity in autism spectrum disorder
Aging (Albany NY). 2024 Jun 10;16(11):10004-10015. doi: 10.18632/aging.205913. Epub 2024 Jun 10.

Chen Y, Yang C, Gao B, Chen K, Jao Keehn RJ, Müller RA, Yuan LX, You Y.
Altered functional connectivity of unimodal sensory and multisensory integration networks is related to symptom severity in autism spectrum disorders
Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Nov 2:S2451-9022(24)00313-6. doi: 10.1016/j.bpsc.2024.10.014.

Chen L, Abate M, Fredericks M, Guo Y, Tao Z, Zhang X.
Age-related differences in the intrinsic connectivity of the hippocampus and ventral temporal lobe in autistic individuals
Front Hum Neurosci. 2024 Jun 13:18:1394706. doi: 10.3389/fnhum.2024.1394706. eCollection 2024.

Yang C, Wang XK, Ma SZ, Lee NY, Zhang QR, Dong WQ, Zang YF, Yuan LX.
Abnormal functional connectivity of the reward network is associated with social communication impairments in autism spectrum disorder: A large-scale multi-site resting-state fMRI study
J Affect Disord. 2024 Feb 15:347:608-618. doi: 10.1016/j.jad.2023.12.013. Epub 2023 Dec 7.

Dong Q, Cai H, Li Z, Liu J, Hu B.
A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis
IEEE J Biomed Health Inform. 2024 Aug;28(8):4854-4865. doi: 10.1109/JBHI.2024.3396457. Epub 2024 Aug 6.

Yan W, Pearlson GD, Fu Z, Li X, Iraji A, Chen J, Sui J, Volkow ND, Calhoun VD.
A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants
Biol Psychiatry. 2024 Apr 1;95(7):699-708. doi: 10.1016/j.biopsych.2023.09.017. Epub 2023 Sep 26.

Garcia M, Kelly C.
3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data.
PLoS One. 2024 Oct 21;19(10):e0276832. doi: 10.1371/journal.pone.0276832. eCollection 2024.

Weerasekera A, Ion-Mărgineanu A, Nolan GP, Mody M.
Subcortical-cortical white matter connectivity in adults with autism spectrum disorder and schizophrenia patients
Psychiatry Res Neuroimaging. 2024 Jun:340:111806. doi: 10.1016/j.pscychresns.2024.111806. Epub 2024 Mar 7.