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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
PMID: 27630538
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.
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.
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.
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.
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.
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.
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.
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.
Wang J, Wang Q, Wang S, Shen D.
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Review publications discussing ABIDE in the context of large-scale data-sharing efforts
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