{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:24:06Z","timestamp":1782314646960,"version":"3.54.5"},"reference-count":47,"publisher":"IMR Press","issue":"7","license":[{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IITP grant funded by the Korean government","award":["RS-2020\u2013II201361"],"award-info":[{"award-number":["RS-2020\u2013II201361"]}]},{"name":"IITP grant funded by the Korean government","award":["RS-2022\u20130\u2013II220113"],"award-info":[{"award-number":["RS-2022\u20130\u2013II220113"]}]},{"name":"ETRI grant funded by the Korean government","award":["24ZB1100"],"award-info":[{"award-number":["24ZB1100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Integr. Neurosci."],"abstract":"<jats:p>Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variability in developmental progression and distinct neuroanatomical features influenced by sex and age. Recent advances in deep learning models based on functional connectivity (FC) graphs have produced promising results, but they have focused on generalized global activation patterns and failed to capture specialized regional characteristics and accurately assess disease indications. Methods: To overcome these limitations, we propose a novel deep learning method that models FC with multi-head attention, which enables simultaneous modeling of the intricate and variable patterns of brain connectivity associated with ASD, effectively extracting abnormal patterns of brain connectivity. The proposed method not only identifies region-specific correlations but also emphasizes connections at specific, transient time points from diverse perspectives. The extracted FC is transformed into a graph, assigning weighted labels to the edges to reflect the degree of correlation, which is then processed using a graph neural network capable of handling edge labels. Results: Experiments on the autism brain imaging data exchange (ABIDE) I and II datasets, which include a heterogeneous cohort, showed superior performance over the state-of-the-art methods, improving accuracy by up to 3.7%p. The incorporation of multi-head attention in FC analysis markedly improved the distinction between typical brains and those affected by ASD. Additionally, the ablation study validated diverse brain characteristics in ASD patients across different ages and sexes, offering insightful interpretations. Conclusion: These results emphasize the effectiveness of the method in enhancing diagnostic accuracy and its potential in advancing neurological research for ASD diagnosis.<\/jats:p>","DOI":"10.31083\/j.jin2307135","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T08:47:49Z","timestamp":1721378869000},"source":"Crossref","is-referenced-by-count":4,"title":["Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network"],"prefix":"10.31083","volume":"23","author":[{"given":"Hyung-Jun","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Yonsei University, 03722 Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Bae","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Computer Science, Yonsei University, 03722 Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16242","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"J\u00f6nemo J, Abramian D, Eklund A. Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks. Diagnostics. 2023; 13: 2773.","DOI":"10.3390\/diagnostics13172773"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"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. Frontiers in Psychiatry. 2020; 11: 440.","DOI":"10.3389\/fpsyt.2020.00440"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Park K, Cho S. A residual graph convolutional network with spatio-temporal features for autism classification from fMRI brain images. Applied Soft Computing. 2023; 142: 110363.","DOI":"10.1016\/j.asoc.2023.110363"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Barnea-Goraly N, Frazier TW, Piacenza L, Minshew NJ, Keshavan MS, Reiss AL, et al. A preliminary longitudinal volumetric MRI study of amygdala and hippocampal volumes in autism. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 2014; 48: 124\u2013128.","DOI":"10.1016\/j.pnpbp.2013.09.010"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Schumann CM, Hamstra J, Goodlin-Jones BL, Lotspeich LJ, Kwon H, Buonocore MH, et al. The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. The Journal of Neuroscience. 2004; 24: 6392\u20136401.","DOI":"10.1523\/JNEUROSCI.1297-04.2004"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Nordahl CW, Scholz R, Yang X, Buonocore MH, Simon T, Rogers S, et al. Increased rate of amygdala growth in children aged 2 to 4 years with autism spectrum disorders: a longitudinal study. Archives of General Psychiatry. 2012; 69: 53\u201361.","DOI":"10.1001\/archgenpsychiatry.2011.145"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Nordahl CW, Iosif AM, Young GS, Hechtman A, Heath B, Lee JK, et al. High Psychopathology Subgroup in Young Children With Autism: Associations With Biological Sex and Amygdala Volume. Journal of the American Academy of Child and Adolescent Psychiatry. 2020; 59: 1353\u20131363.e2.","DOI":"10.1016\/j.jaac.2019.11.022"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Stoodley CJ. Distinct regions of the cerebellum show gray matter decreases in autism, ADHD, and developmental dyslexia. Frontiers in Systems Neuroscience. 2014; 8: 92.","DOI":"10.3389\/fnsys.2014.00092"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017; 542: 348\u2013351.","DOI":"10.1038\/nature21369"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Ohta H, Nordahl CW, Iosif AM, Lee A, Rogers S, Amaral DG. Increased Surface Area, but not Cortical Thickness, in a Subset of Young Boys With Autism Spectrum Disorder. Autism Research. 2016; 9: 232\u2013248.","DOI":"10.1002\/aur.1520"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Thion MS, Low D, Silvin A, Chen J, Grisel P, Schulte-Schrepping J, et al. Microbiome Influences Prenatal and Adult Microglia in a Sex-Specific Manner. Cell. 2018; 172: 500\u2013516.e16.","DOI":"10.1016\/j.cell.2017.11.042"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Erny D, Hrab\u011b de Angelis AL, Jaitin D, Wieghofer P, Staszewski O, David E, et al. Host microbiota constantly control maturation and function of microglia in the CNS. Nature Neuroscience. 2015; 18: 965\u2013977.","DOI":"10.1038\/nn.4030"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"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"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Chen Z, Qing J, Xiang T, Yue WL, Zhou JH. Seeing beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023; 22710\u201322720.","DOI":"10.1109\/CVPR52729.2023.02175"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Abrol A, Fu Z, Du Y, Calhoun VD. Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer\u2019s Disease Progression. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2019; 2019: 4409\u20134413.","DOI":"10.1109\/EMBC.2019.8856500"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Shao L, Fu C, You Y, Fu D. Classification of ASD based on fMRI data with deep learning. Cognitive Neurodynamics. 2021; 15: 961\u2013974.","DOI":"10.1007\/s11571-021-09683-0"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Wadhera T, Mahmud M. Computational Model of Functional Connectivity Distance Predicts Neural Alterations. IEEE Transactions on Cognitive and Developmental Systems. 2023.","DOI":"10.1109\/TCDS.2023.3320243"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Wadhera T, Kakkar D. Social cognition and functional brain network in autism spectrum disorder: Insights from EEG graph-theoretic measures. Biomedical Signal Processing and Control. 2021; 67: 102556.","DOI":"10.1016\/j.bspc.2021.102556"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, et al. Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry. 2013; 70: 869\u2013879.","DOI":"10.1001\/jamapsychiatry.2013.104"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Lynch CJ, Uddin LQ, Supekar K, Khouzam A, Phillips J, Menon V. Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits. Biological Psychiatry. 2013; 74: 212\u2013219.","DOI":"10.1016\/j.biopsych.2012.12.013"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Uddin LQ, Supekar K, Menon V. Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience. 2013; 7: 458.","DOI":"10.3389\/fnhum.2013.00458"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Supekar K, Musen M, Menon V. Development of large-scale functional brain networks in children. PLoS Biology. 2009; 7: e1000157.","DOI":"10.1371\/journal.pbio.1000157"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Moon H, Bu S, Cho S. A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons. Neurocomputing. 2023; 530: 60\u201368.","DOI":"10.1016\/j.neucom.2023.01.091"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Pan C, Yu H, Fei X, Zheng X, Yu R. Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification. Frontiers in Neuroscience. 2022; 16: 965937.","DOI":"10.3389\/fnins.2022.965937"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Takagi Y, Nishimoto S. High-resolution image reconstruction with latent diffusion models from human brain activity. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023; 14453\u201314463.","DOI":"10.1109\/CVPR52729.2023.01389"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"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. Frontiers in Neuroscience. 2017; 11: 460.","DOI":"10.3389\/fnins.2017.00460"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Zhao Y, Ge F, Zhang S, Liu T. 3D Deep Convolutional Neural Network Revealed the Value of Brain Network Overlap in Differentiating Autism Spectrum Disorder from Healthy Controls. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018. 2018; 2: 172\u2013180.","DOI":"10.1007\/978-3-030-00931-1_20"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP. Clinical applications of the functional connectome. NeuroImage. 2013; 80: 527\u2013540.","DOI":"10.1016\/j.neuroimage.2013.04.083"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Ahmed MR, Zhang Y, Liu Y, Liao H. Single Volume Image Generator and Deep Learning-Based ASD Classification. IEEE Journal of Biomedical and Health Informatics. 2020; 24: 3044\u20133054.","DOI":"10.1109\/JBHI.2020.2998603"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"El-Gazzar A, Quaak M, Cerliani L, Bloem P, van Wingen G, Mani Thomas R. A Hybrid 3DCNN and 3DC-LSTM Based Model for 4D Spatio-Temporal fMRI Data: an ABIDE Autism Classification Study. or 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. 2019; 147: 95\u2013102.","DOI":"10.1007\/978-3-030-32695-1_11"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"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. Frontiers in Neuroinformatics. 2019; 13: 70.","DOI":"10.3389\/fninf.2019.00070"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Almuqhim F, Saeed F. ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data. Frontiers in Computational Neuroscience. 2021; 15: 654315.","DOI":"10.3389\/fncom.2021.654315"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Dvornek NC, Ventola P, Duncan JS. COMBINING PHENOTYPIC AND RESTING-STATE FMRI DATA FOR AUTISM CLASSIFICATION WITH RECURRENT NEURAL NETWORKS. Proceedings. IEEE International Symposium on Biomedical Imaging. 2018; 2018: 725\u2013728.","DOI":"10.1109\/ISBI.2018.8363676"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Deng X, Zhang J, Liu R, Liu K. Classifying ASD based on time-series fMRI using spatial-temporal transformer. Computers in Biology and Medicine. 2022; 151: 106320.","DOI":"10.1016\/j.compbiomed.2022.106320"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Wang C, Xiao Z, Wu J. Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data. Physica Medica. 2019; 65: 99\u2013105.","DOI":"10.1016\/j.ejmp.2019.08.010"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"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. Journal of Digital Imaging. 2019; 32: 899\u2013918.","DOI":"10.1007\/s10278-019-00196-1"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. IEEE Transactions on Bio-medical Engineering. 2021; 68: 3628\u20133637.","DOI":"10.1109\/TBME.2021.3080259"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Ktena SI, Parisot S, Ferrante E, Rajchl M, Lee M, Glocker B, et al. Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks. Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017. 2017; 147: 469\u2013477.","DOI":"10.1007\/978-3-319-66182-7_54"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Anirudh R, Thiagarajan JJ. Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification. ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019; 3197\u20133201.","DOI":"10.1109\/ICASSP.2019.8683547"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Chen L, Huang Y, Liao B, Nie K, Dong S, Hu J. Graph Learning Approaches for Graph with Noise: Application to Disease Prediction in Population Graph. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2020; 2724\u20132729.","DOI":"10.1109\/BIBM49941.2020.9313348"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Park K, Bu S, Cho S. Learning Dynamic Connectivity with Residual-Attention Network for Autism Classification in 4D fMRI Brain Images. Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2021. 2021; 8: 387\u2013396.","DOI":"10.1007\/978-3-030-91608-4_38"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"Bu S, Cho S. Triplet-trained graph transformer with control flow graph for few-shot malware classification. Information Sciences. 2023; 649: 119598.","DOI":"10.1016\/j.ins.2023.119598"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Li W, Wang S, Liu G. Transformer-based Model for fMRI Data: ABIDE Results. 2022 7Th International Conference on Computer and Communication Systems (ICCCS). 2022; 162\u2013167.","DOI":"10.1109\/ICCCS55155.2022.9845999"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Kang L, Chen J, Huang J, Jiang J. Autism spectrum disorder recognition based on multi-view ensemble learning with multi-site fMRI. Cognitive Neurodynamics. 2023; 17: 345\u2013355.","DOI":"10.1007\/s11571-022-09828-9"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"Sotero RC, Sanchez-Bornot JM, Iturria-Medina Y. Improving fMRI-based Autism Spectrum Disorder Classification with Random Walks-informed Feature Extraction and Selection. Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications. 2023.","DOI":"10.1145\/3632047.3632054"},{"key":"ref46","doi-asserted-by":"crossref","unstructured":"Gaur M, Chaturvedi K, Vishwakarma DK, Ramasamy S, Prasad M. Self-supervised ensembled learning for autism spectrum classification. Research in Autism Spectrum Disorders. 2023; 107: 102223.","DOI":"10.1016\/j.rasd.2023.102223"},{"key":"ref47","doi-asserted-by":"crossref","unstructured":"Chen Z, Ji J, Liang Y. Convolutional neural network with an element-wise filter to classify dynamic functional connectivity. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. 2019; 643\u2013646.","DOI":"10.1109\/BIBM47256.2019.8983245"}],"container-title":["Journal of Integrative Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.imrpress.com\/journal\/JIN\/23\/7\/10.31083\/j.jin2307135","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T16:37:58Z","timestamp":1732725478000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.imrpress.com\/journal\/JIN\/23\/7\/10.31083\/j.jin2307135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,17]]},"references-count":47,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024]]}},"alternative-id":["S0219-6352(24)00763-0"],"URL":"https:\/\/doi.org\/10.31083\/j.jin2307135","relation":{},"ISSN":["0219-6352","1757-448X"],"issn-type":[{"value":"0219-6352","type":"print"},{"value":"1757-448X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,17]]},"article-number":"135"}}