{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T08:01:10Z","timestamp":1780128070269,"version":"3.54.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s13042-026-03009-4","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T05:52:30Z","timestamp":1775109150000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-graph convolution network with attention mechanism based on multi-modal data for ASD diagnosis"],"prefix":"10.1007","volume":"17","author":[{"given":"Lizhen","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmei","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"3009_CR1","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.neuroimage.2016.10.045","volume":"147","author":"A Abraham","year":"2017","unstructured":"Abraham A, Milham MP, Di Martino A et al (2017) Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. Neuroimage 147:736\u2013745","journal-title":"Neuroimage"},{"issue":"11","key":"3009_CR2","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.3390\/children11111311","volume":"11","author":"T Abualait","year":"2024","unstructured":"Abualait T, Alabbad M, Kaleem I et al (2024) Autism spectrum disorder in children: early signs and therapeutic interventions. Children 11(11):1311","journal-title":"Children"},{"key":"3009_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2024.110100","volume":"405","author":"S Bandyopadhyay","year":"2024","unstructured":"Bandyopadhyay S, Peddi S, Sarma M et al (2024) Decoding autism: uncovering patterns in brain connectivity through sparsity analysis with RS-FMRI data. J Neurosci Methods 405:110100","journal-title":"J Neurosci Methods"},{"issue":"1","key":"3009_CR4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neuroimage.2007.04.042","volume":"37","author":"Y Behzadi","year":"2007","unstructured":"Behzadi Y, Restom K, Liau J et al (2007) A component based noise correction method (COMPCOR) for bold and perfusion based fMRI. Neuroimage 37(1):90\u2013101","journal-title":"Neuroimage"},{"key":"3009_CR5","first-page":"27","volume":"7","author":"C Craddock","year":"2013","unstructured":"Craddock C, Benhajali Y, Chu C et al (2013) The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Front Neuroinform 7:27","journal-title":"Front Neuroinform"},{"key":"3009_CR6","first-page":"189","volume":"7","author":"C Craddock","year":"2013","unstructured":"Craddock C, Sikka S, Briann C et al (2013) Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (C-PAC). Front Neuroinform 7:189\u2013210","journal-title":"Front Neuroinform"},{"issue":"2","key":"3009_CR7","doi-asserted-by":"publisher","first-page":"57","DOI":"10.31083\/j.jin2102057","volume":"21","author":"J Crucitti","year":"2022","unstructured":"Crucitti J, Hyde C, Enticott P et al (2022) A systematic review of frontal lobe volume in autism spectrum disorder revealing distinct trajectories. J Integr Neurosci 21(2):57","journal-title":"J Integr Neurosci"},{"key":"3009_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106320","volume":"151","author":"X Deng","year":"2022","unstructured":"Deng X, Zhang J, Liu R et al (2022) Classifying ASD based on time-series FMRI using spatial\u2013temporal transformer. Comput Biol Med 151:106320","journal-title":"Comput Biol Med"},{"issue":"3","key":"3009_CR9","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1093\/psyrad\/kkac013","volume":"2","author":"X Duan","year":"2022","unstructured":"Duan X, Chen H (2022) Mapping brain functional and structural abnormities in autism spectrum disorder: moving toward precision treatment. Psychoradiology 2(3):78\u201385","journal-title":"Psychoradiology"},{"key":"3009_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2021.109456","volume":"368","author":"W Feng","year":"2022","unstructured":"Feng W, Liu G, Zeng K et al (2022) A review of methods for classification and recognition of ASD using FMRI data. J Neurosci Methods 368:109456","journal-title":"J Neurosci Methods"},{"key":"3009_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105090","volume":"85","author":"X Gu","year":"2023","unstructured":"Gu X, Xie L, Xia Y et al (2023) Autism spectrum disorder diagnosis using the relational graph attention network. Biomed Signal Process Control 85:105090","journal-title":"Biomed Signal Process Control"},{"key":"3009_CR12","doi-asserted-by":"publisher","first-page":"460","DOI":"10.3389\/fnins.2017.00460","volume":"11","author":"X Guo","year":"2017","unstructured":"Guo X, Dominick KC, Minai AA et al (2017) Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front Neurosci 11:460","journal-title":"Front Neurosci"},{"key":"3009_CR13","doi-asserted-by":"publisher","first-page":"1558081","DOI":"10.3389\/fpsyg.2025.1558081","volume":"16","author":"MVF Holanda","year":"2025","unstructured":"Holanda MVF, Paiva ES, de Souza LN et al (2025) Neurobiological basis of autism spectrum disorder: mini review. Front Psychol 16:1558081","journal-title":"Front Psychol"},{"issue":"4","key":"3009_CR14","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1002\/hbm.20887","volume":"31","author":"KL Hyde","year":"2010","unstructured":"Hyde KL, Samson F, Evans AC et al (2010) Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Hum Brain Mapp 31(4):556\u2013566","journal-title":"Hum Brain Mapp"},{"issue":"1","key":"3009_CR15","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","volume":"8","author":"WE Johnson","year":"2007","unstructured":"Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8(1):118\u2013127","journal-title":"Biostatistics"},{"key":"3009_CR16","first-page":"171","volume":"6","author":"A Klein","year":"2012","unstructured":"Klein A, Tourville J (2012) 31 per hemisphere 101 labeled brain images and a consistent human cortical labeling protocol. Front Brain Imaging Methods 6:171","journal-title":"Front Brain Imaging Methods"},{"issue":"4","key":"3009_CR17","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.3390\/ijms25042423","volume":"25","author":"J Lamanna","year":"2024","unstructured":"Lamanna J, Meldolesi J (2024) Autism spectrum disorder: brain areas involved, neurobiological mechanisms, diagnoses and therapies. Int J Mol Sci 25(4):2423","journal-title":"Int J Mol Sci"},{"key":"3009_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2022.109732","volume":"383","author":"C Li","year":"2023","unstructured":"Li C, Zhang T, Li J (2023) Identifying autism spectrum disorder in resting-state FNIRS signals based on multiscale entropy and a two-branch deep learning network. J Neurosci Methods 383:109732","journal-title":"J Neurosci Methods"},{"issue":"5","key":"3009_CR19","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1089\/cmb.2015.0189","volume":"23","author":"Y Li","year":"2016","unstructured":"Li Y, Chen CY, Wasserman WW (2016) Deep feature selection: theory and application to identify enhancers and promoters. J Comput Biol 23(5):322\u2013336","journal-title":"J Comput Biol"},{"key":"3009_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104714","volume":"157","author":"Y Ma","year":"2024","unstructured":"Ma Y, Mu X, Zhang T et al (2024) MAFT-SO: a novel multi-atlas fusion template based on spatial overlap for ASD diagnosis. J Biomed Inform 157:104714","journal-title":"J Biomed Inform"},{"key":"3009_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106337","volume":"95","author":"M Mengi","year":"2024","unstructured":"Mengi M, Malhotra D (2024) SSMDA: Semi-supervised multi-source domain adaptive autism prediction model using neuroimaging. Biomed Signal Process Control 95:106337","journal-title":"Biomed Signal Process Control"},{"key":"3009_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104686","volume":"84","author":"M Mishra","year":"2023","unstructured":"Mishra M, Umesh CP (2023) A classification framework for autism spectrum disorder detection using sMRI: optimizer based ensemble of deep convolution neural network with on-the-fly data augmentation. Biomed Signal Process Control 84:104686","journal-title":"Biomed Signal Process Control"},{"key":"3009_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102475","volume":"136","author":"AS Qasim","year":"2023","unstructured":"Qasim AS, Lianhua C, Phoebe CYP (2023) DeepMNF: deep multimodal neuroimaging framework for diagnosing autism spectrum disorder. Artif Intell Med 136:102475","journal-title":"Artif Intell Med"},{"key":"3009_CR24","first-page":"815","volume-title":"International conference on complex networks and their applications","author":"Z Rakhimberdina","year":"2020","unstructured":"Rakhimberdina Z, Murata T (2020) Linear graph convolutional model for diagnosing brain disorders. International conference on complex networks and their applications. Springer, Cham, pp 815\u2013826"},{"issue":"6","key":"3009_CR25","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1007\/s11571-021-09683-0","volume":"15","author":"L Shao","year":"2021","unstructured":"Shao L, Fu C, You Y et al (2021) Classification of ASD based on fMRI data with deep learning. Cogn Neurodyn 15(6):961\u2013974","journal-title":"Cogn Neurodyn"},{"key":"3009_CR26","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1186\/s12859-023-05495-7","volume":"24","author":"L Shao","year":"2023","unstructured":"Shao L, Fu C, Chen X (2023) A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder. BMC Bioinformatics 24:363","journal-title":"BMC Bioinformatics"},{"key":"3009_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainresbull.2023.110826","volume":"206","author":"T Tang","year":"2024","unstructured":"Tang T, Li C, Zhang S et al (2024) A hybrid graph network model for asd diagnosis based on resting-state eeg signals. Brain Res Bull 206:110826","journal-title":"Brain Res Bull"},{"key":"3009_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainresbull.2025.111290","volume":"224","author":"P Wang","year":"2025","unstructured":"Wang P, Wen X, Lei Y et al (2025) Mcdgln: masked connection-based dynamic graph learning network for autism spectrum disorder. Brain Res Bull 224:111290","journal-title":"Brain Res Bull"},{"issue":"7","key":"3009_CR29","doi-asserted-by":"publisher","first-page":"11254","DOI":"10.1038\/ncomms11254","volume":"7","author":"N Yahata","year":"2016","unstructured":"Yahata N, Morimoto J, Hashimoto R et al (2016) A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat Commun 7(7):11254","journal-title":"Nat Commun"},{"key":"3009_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110723","volume":"196","author":"M Zeraati","year":"2025","unstructured":"Zeraati M, Davoodi A (2025) Asd-graphnet: a novel graph learning approach for autism spectrum disorder diagnosis using fmri data. Comput Biol Med 196:110723","journal-title":"Comput Biol Med"},{"key":"3009_CR31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3389\/fninf.2018.00003","volume":"12","author":"Y Zhou","year":"2018","unstructured":"Zhou Y, Lishan Q, Weikai L et al (2018) Simultaneous estimation of low-and high-order functional connectivity for identifying mild cognitive impairment. Front Neuroinform 12:3","journal-title":"Front Neuroinform"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03009-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03009-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03009-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T07:02:48Z","timestamp":1780124568000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03009-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["3009"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03009-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"21 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"248"}}