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 Diego Perez.

Empirical Studies

Last updated on May 2023.

ABIDE I Announcing Manuscript

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.

ABIDE II Announcing Manuscript

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.

Lin HY, Ni HC, Tseng WI, Gau SS.
Characterizing intrinsic functional connectivity in relations to impaired self-regulation in high-functioning male youth with autism spectrum disorder.
Autism. 2020 Jul;24(5):1201-1216. doi: 10.1177/1362361319888104.

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.

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.

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.

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.

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 Yijun, 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.

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. 2021; 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.

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.

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.

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.

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.

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.

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.

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.


Review publications discussing ABIDE in the context of large-scale data-sharing efforts

Last updated on May 2023.

Uddin, L. Q., Supekar, K., & Menon, V.
Reconceptualizing functional brain connectivity in autism from a developmental perspective.
Front Hum Neurosci. 2013 Aug 7;7:458. doi: 10.3389/fnhum.2013.00458. eCollection 2013.

Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P.
Making data sharing work: The FCP/INDI experience.
Neuroimage. 2013 Nov 15;82:683-91. doi: 10.1016/j.neuroimage.2012.10.064. Epub 2012 Oct 30.

Di Martino, A., Fair, D. A., Kelly, C., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., Craddock, R.C., Luna, B., Leventhal, B.L., Zuo, X.N., & Milham, M. P.
Unraveling the miswired connectome: a developmental perspective.
Neuron. 2014 Sep 17;83(6):1335-53. doi: 10.1016/j.neuron.2014.08.050.

Kana, R. K., Uddin, L. Q., Kenet, T., Chugani, D., & Müller, R. A.
Brain connectivity in autism.
Front Hum Neurosci. 2014 Jun 2;8:349. doi: 10.3389/fnhum.2014.00349. eCollection 2014.

Turner, J. A.
The rise of large-scale imaging studies in psychiatry.
Gigascience. 2014 Nov 25;3:29. doi: 10.1186/2047-217X-3-29. eCollection 2014.

Maximo, J. O., Cadena, E. J., & Kana, R. K.
The implications of brain connectivity in the neuropsychology of autism.
Neuropsychol Rev. 2014 Mar;24(1):16-31. doi: 10.1007/s11065-014-9250-0. Epub 2014 Feb 5.

Rane, P., Cochran, D., Hodge, S. M., Haselgrove, C., Kennedy, D. N., & Frazier, J. A.
Connectivity in autism: A review of MRI connectivity studies.
Harv Rev Psychiatry. 2015 Jul-Aug;23(4):223-44. doi: 10.1097/HRP.0000000000000072.

Lainhart, J. E.
Brain imaging research in autism spectrum disorders: in search of neuropathology and health across the lifespan.
Curr Opin Psychiatry. 2015 Mar;28(2):76-82. doi: 10.1097/YCO.0000000000000130.

Wintermark, M., Coombs, L., Druzgal, T. J., Field, A. S., Filippi, C. G., Hicks, R., Horton, R., Lui, Y. W., Law, M., Mukherjee, P., Norbash, A., Riedy, G., Sanelli, P. C., Stone, J. R., Sze, G., Tilkin, M., Whitlow, C. T., Wilde, E. A., York, G., Provenzale, J. M.; American College of Radiology Head Injury Institute.
Traumatic brain injury imaging research roadmap.
AJNR Am J Neuroradiol. 2015 Mar;36(3):E12-23. doi: 10.3174/ajnr.A4254. Epub 2015 Feb 5.

Book, G. A., Stevens, M. C., Assaf, M., Glahn, D. C., & Pearlson, G. D.
Neuroimaging data sharing on the neuroinformatics database platform.
Neuroimage. 2016 Jan 1;124(Pt B):1089-92. doi: 10.1016/j.neuroimage.2015.04.022. Epub 2015 Apr 16.

Uddin LQ, Dajani DR, Voorhies W, Bednarz H, Kana RK.
Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder.
Transl Psychiatry. 2017; 7:e1218. doi: 10.1038/tp.2017.164.

Mertz L.
Sharing Data to Solve the Autism Riddle: An Interview with Adriana Di Martino and Michael Milham of ABIDE.
IEEE Pulse. 2017 Nov-Dec;8(6):6-9. doi: 10.1109/MPUL.2017.2750819.

Al-Jawahiri R, Milne E.
Resources available for autism research in the big data era: a systematic review.
PeerJ. 2017; 5:e2880. doi: 10.7717/peerj.2880.

Yahata N,Kasai K,Kawato M.
Computational neuroscience approach to biomarkers and treatments for mental disorders.
Psychiatry Clin Neurosci. 2017; 71:215-237. doi: 10.1111/pcn.12502.

Uddin LQ, Dajani DR, Voorhies W, Bednarz H, Kana RK.
Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder.
Transl Psychiatry. 2017; 7:e1218. doi: 10.1038/tp.2017.164.

Milham MP, Craddock RC, Son JJ, Fleischmann M, Clucas J, Xu H, Koo B, Krishnakumar A, Biswal BB, Castellanos FX, Colcombe S, Di Martino A, Zuo XN, Klein A.
Assessment of the impact of shared brain imaging data on the scientific literature.
Nat Commun. 2018; 9:2818. doi: 10.1038/s41467-018-04976-1.

Holiga Š, Hipp JF, Chatham CH, Garces P, Spooren W, D'Ardhuy XL, Bertolino A, Bouquet C, Buitelaar JK, Bours C, Rausch A, Oldehinkel M, Bouvard M, Amestoy A, Caralp M, Gueguen S, Ly-Le Moal M, Houenou J, Beckmann CF, Loth E, Murphy D, Charman T, Tillmann J, Laidi C, Delorme R, Beggiato A, Gaman A, Scheid I, Leboyer M, d'Albis MA, Sevigny J, Czech C, Bolognani F, Honey GD, Dukart J.
Patients with autism spectrum disorders display reproducible functional connectivity alterations.
Sci Transl Med. 2019; 11:None. doi: 10.1126/scitranslmed.aat9223.

Postema MC, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Filho GB, Calderoni S, Calvo R, Daly E, Deruelle C, Di Martino A, Dinstein I, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, Feng X, Fitzgerald J, Floris DL, Freitag CM, Gallagher L, Glahn DC, Gori I, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, King JA, Kong XZ, Lazaro L, Lerch JP, Luna B, Martinho MM, McGrath J, Medland SE, Muratori F, Murphy CM, Murphy DGM, O'Hearn K, Oranje B, Parellada M, Puig O, Retico A, Rosa P, Rubia K, Shook D, Taylor MJ, Tosetti M, Wallace GL, Zhou F, Thompson PM, Fisher SE, Buitelaar JK, Francks C.
Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets.
Nat Commun. 2019 Oct 31;10(1):4958. doi: 10.1038/s41467-019-13005-8. Erratum in: Nat Commun. 2021 Dec 8;12(1):7260.

Dvornek NC, Li X, Zhuang J, Duncan JS.
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI.
Mach Learn Med Imaging. 2019; 11861:382-390. doi: 10.1007/978-3-030-32692-0_44.

Holiga Š, Hipp JF, Chatham CH, Garces P, Spooren W, D'Ardhuy XL, Bertolino A, Bouquet C, Buitelaar JK, Bours C, Rausch A, Oldehinkel M, Bouvard M, Amestoy A, Caralp M, Gueguen S, Ly-Le Moal M, Houenou J, Beckmann CF, Loth E, Murphy D, Charman T, Tillmann J, Laidi C, Delorme R, Beggiato A, Gaman A, Scheid I, Leboyer M, d'Albis MA, Sevigny J, Czech C, Bolognani F, Honey GD, Dukart J.
Patients with autism spectrum disorders display reproducible functional connectivity alterations.
Sci Transl Med. 2019; 11:None. doi: 10.1126/scitranslmed.aat9223.

Zhuang J, Dvornek NC, Li X, Ventola P, Duncan JS.
Invertible Network for Classification and Biomarker Selection for ASD.
Med Image Comput Comput Assist Interv. 2019; 11766:700-708. doi: 10.1007/978-3-030-32248-9_78.

Zuo XN.
Editorial: Mapping the Miswired Connectome in Autism Spectrum Disorder.
J Am Acad Child Adolesc Psychiatry. 2020; 59:348-349. doi: 10.1016/j.jaac.2020.01.001.

Nogay HS, Adeli H.
Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging.
Rev Neurosci. 2020 Aug 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. doi: 10.1515/revneuro-2020-0043.

DeSalvo MN.
Motion-Dependent Effects of Functional Magnetic Resonance Imaging Preprocessing Methodology on Global Functional Connectivity.
Brain Connect. 2020; 10:578-584. doi: 10.1089/brain.2020.0854.

Zuo XN.
Editorial: Mapping the Miswired Connectome in Autism Spectrum Disorder.
J Am Acad Child Adolesc Psychiatry. 2020; 59:348-349. doi: 10.1016/j.jaac.2020.01.001.

DeSalvo MN.
Motion-Dependent Effects of Functional Magnetic Resonance Imaging Preprocessing Methodology on Global Functional Connectivity.
Brain Connect. 2020; 10:578-584. doi: 10.1089/brain.2020.0854.

Elvsåshagen T, Bahrami S, van der Meer D, Agartz I, Alnæs D, Barch DM, Baur-Streubel R, Bertolino A, Beyer MK, Blasi G, Borgwardt S, Boye B, Buitelaar J, Bøen E, Celius EG, Cervenka S, Conzelmann A, Coynel D, Di Carlo P, Djurovic S, Eisenacher S, Espeseth T, Fatouros-Bergman H, Flyckt L, Franke B, Frei O, Gelao B, Harbo HF, Hartman CA, Håberg A, Heslenfeld D, Hoekstra PJ, Høgestøl EA, Jonassen R, Jönsson EG, Karolinska Schizophrenia Project (KaSP) consortium, Kirsch P, Kłoszewska I, Lagerberg TV, Landrø NI, Le Hellard S, Lesch KP, Maglanoc LA, Malt UF, Mecocci P, Melle I, Meyer-Lindenberg A, Moberget T, Nordvik JE, Nyberg L, Connell KSO, Oosterlaan J, Papalino M, Papassotiropoulos A, Pauli P, Pergola G, Persson K, de Quervain D, Reif A, Rokicki J, van Rooij D, Shadrin AA, Schmidt A, Schwarz E, Selbæk G, Soininen H, Sowa P, Steen VM, Tsolaki M, Vellas B, Wang L, Westman E, Ziegler GC, Zink M, Andreassen OA, Westlye LT, Kaufmann T.
The genetic architecture of human brainstem structures and their involvement in common brain disorders.
Nat Commun. 2020; 11:4016. doi: 10.1038/s41467-020-17376-1.

Gharehgazlou A, Freitas C, Ameis SH, Taylor MJ, Lerch JP, Radua J, Anagnostou E.
Cortical Gyrification Morphology in Individuals with ASD and ADHD across the Lifespan: A Systematic Review and Meta-Analysis.
Cereb Cortex. 2021; 31:2653-2669. doi: 10.1093/cercor/bhaa381.

Uddin LQ.
Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder.
Biol Psychiatry. 2021; 89:172-183. doi: 10.1016/j.biopsych.2020.05.010.

Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P, Khadem A, Alizadehsani R, Zare A, Kong Y, Khosravi A, Nahavandi S, Hussain S, Acharya UR, Berk M.
Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.
Comput Biol Med. 2021; 139:104949. doi: 10.1016/j.compbiomed.2021.104949.

Quaak M, van de Mortel L, Thomas RM, van Wingen G.
Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.
Neuroimage Clin. 2021; 30:102584. doi: 10.1016/j.nicl.2021.102584.

Moreau CA, Raznahan A, Bellec P, Chakravarty M, Thompson PM, Jacquemont S.
Dissecting autism and schizophrenia through neuroimaging genomics.
Brain. 2021; 144:1943-1957. doi: 10.1093/brain/awab096.

Liu M, Li B, Hu D.
Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review.
Front Neurosci. 2021; 15:697870. doi: 10.3389/fnins.2021.697870.

Kennedy D, Ghosh S, Poline JB, Keator D, Halchenko Y, Martone M, Grethe J.
IQ in Typical Development: A Mega-Analysis of the Historical Literature.
Biological Psychiatry, Volume 89, Issue 9, Supplement, 2021, Page S150, ISSN 0006-3223. doi: doi.org/10.1016/j.biopsych.2021.02.385..