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 March 2022.

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.

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.

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.

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.

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/ Epub 2016 Aug 23.

Chaddad A, Desrosiers C, Hassan L, Tanougast C.
Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder.
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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/

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/

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.
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.

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 11, 93 (2021). doi: 10.1038/s41398-021-01221-0.

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.

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

Last updated on March 2022.

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.

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.

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.

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.