{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:16:43Z","timestamp":1740143803107,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62076059"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11517-022-02558-4","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T06:03:36Z","timestamp":1651817016000},"page":"1897-1913","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Modeling the dynamic brain network representation for autism spectrum disorder diagnosis"],"prefix":"10.1007","volume":"60","author":[{"given":"Peng","family":"Cao","sequence":"first","affiliation":[]},{"given":"Guangqi","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Xiaoli","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Osmar R.","family":"Zaiane","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"issue":"8","key":"2558_CR1","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1515\/revneuro-2020-0043","volume":"31","author":"HS Nogay","year":"2020","unstructured":"Nogay HS, Adeli H (2020) Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Reviews in the Neurosciences 31(8):825\u2013841","journal-title":"Reviews in the Neurosciences"},{"key":"2558_CR2","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.cmpb.2019.06.006","volume":"177","author":"GS Bajestani","year":"2019","unstructured":"Bajestani GS, Behrooz M, Khani AG, Nouri-Baygi M, Mollaei A (2019) Diagnosis of autism spectrum disorder based on complex network features. Computer Methods and Programs in Biomedicine 177:277\u2013283","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"2558_CR3","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.nicl.2017.08.017","volume":"17","author":"AS Heinsfeld","year":"2018","unstructured":"Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F (2018) Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage: Clinical 17:16\u201323","journal-title":"NeuroImage: Clinical"},{"issue":"4","key":"2558_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15585\/mmwr.ss6904a1","volume":"69","author":"MJ Maenner","year":"2020","unstructured":"Maenner MJ, Shaw KA, Baio J et al (2020) Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, united states, 2016. MMWR Surveillance Summaries 69(4):1","journal-title":"MMWR Surveillance Summaries"},{"key":"2558_CR5","doi-asserted-by":"publisher","first-page":"104678","DOI":"10.1016\/j.compbiomed.2021.104678","volume":"136","author":"W Kang","year":"2021","unstructured":"Kang W, Lin L, Zhang B, Shen X, Wu S, Initiative ADN et al (2021) Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer\u2019s disease diagnosis. Computers in Biology and Medicine 136:104678","journal-title":"Computers in Biology and Medicine"},{"key":"2558_CR6","doi-asserted-by":"crossref","unstructured":"Yan Y, Zhu J, Duda M, Solarz E, Sripada C, Koutra D (2019) Groupinn: grouping-based interpretable neural network for classification of limited, noisy brain data. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp 772\u2013782","DOI":"10.1145\/3292500.3330921"},{"issue":"8","key":"2558_CR7","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.euroneuro.2010.03.008","volume":"20","author":"MP Van Den Heuvel","year":"2010","unstructured":"Van Den Heuvel MP, Pol HEH (2010) Exploring the brain network: a review on resting-state fmri functional connectivity. European Neuropsychopharmacology 20(8):519\u2013534","journal-title":"European Neuropsychopharmacology"},{"key":"2558_CR8","doi-asserted-by":"publisher","first-page":"104096","DOI":"10.1016\/j.compbiomed.2020.104096","volume":"127","author":"H Jiang","year":"2020","unstructured":"Jiang H, Cao P, Xu M, Yang J, Zaiane O (2020) Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Computers in Biology and Medicine 127:104096","journal-title":"Computers in Biology and Medicine"},{"key":"2558_CR9","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR)"},{"key":"2558_CR10","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.neuroimage.2013.05.079","volume":"80","author":"RM Hutchison","year":"2013","unstructured":"Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della Penna S, Duyn JH, Glover GH, Gonzalez-Castillo J et al (2013) Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360\u2013378","journal-title":"Neuroimage"},{"issue":"8","key":"2558_CR11","doi-asserted-by":"publisher","first-page":"2241","DOI":"10.1109\/TBME.2019.2957921","volume":"67","author":"M Wang","year":"2019","unstructured":"Wang M, Lian C, Yao D, Zhang D, Liu M, Shen D (2019) Spatial-temporal dependency modeling and network hub detection for functional MRI analysis via convolutional-recurrent network. IEEE Transactions on Biomedical Engineering 67(8):2241\u20132252","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"2558_CR12","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.media.2018.03.013","volume":"47","author":"B Jie","year":"2018","unstructured":"Jie B, Liu M, Shen D (2018) Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease. Medical Image Analysis 47:81\u201394","journal-title":"Medical Image Analysis"},{"key":"2558_CR13","doi-asserted-by":"publisher","first-page":"102063","DOI":"10.1016\/j.media.2021.102063","volume":"71","author":"M Wang","year":"2021","unstructured":"Wang M, Huang J, Liu M, Zhang D (2021) Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI. Medical Image Analysis 71:102063","journal-title":"Medical Image Analysis"},{"key":"2558_CR14","doi-asserted-by":"crossref","unstructured":"Gadgil S, Zhao Q, Pfefferbaum A, Sullivan, EV, Adeli, E, Pohl, KM (2020) Spatio-temporal graph convolution for resting-state fmri analysis. In: International conference on medical image computing and computer-assisted intervention. Springer pp 528\u2013538","DOI":"10.1007\/978-3-030-59728-3_52"},{"key":"2558_CR15","doi-asserted-by":"crossref","unstructured":"Azevedo T, Campbell A, Romero-Garcia R, Passamonti L, Bethlehem RA, Li\u00f2 P, Toschi N (2020) A deep graph neural network architecture for modelling spatio-temporal dynamics in resting-state functional MRI data, bioRxiv","DOI":"10.1101\/2020.11.08.370288"},{"key":"2558_CR16","doi-asserted-by":"crossref","unstructured":"Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G (2021) Functional connectivity-based prediction of autism on site harmonized ABIDE dataset. IEEE Transactions on Biomedical Engineering","DOI":"10.1109\/TBME.2021.3080259"},{"issue":"11","key":"2558_CR17","doi-asserted-by":"publisher","first-page":"4213","DOI":"10.1002\/hbm.24241","volume":"39","author":"M Yu","year":"2018","unstructured":"Yu M, Linn KA, Cook PA, Phillips ML, McInnis M, Fava M, Trivedi MH, Weissman MM, Shinohara RT, Sheline YI (2018) Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Human brain mapping 39(11):4213\u20134227","journal-title":"Human brain mapping"},{"issue":"3","key":"2558_CR18","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1109\/TMI.2019.2933160","volume":"39","author":"M Wang","year":"2019","unstructured":"Wang M, Zhang D, Huang J, Yap P-T, Shen D, Liu M (2019) Identifying autism spectrum disorder with multi-site fmri via low-rank domain adaptation. IEEE Transactions on Medical Imaging 39(3):644\u2013655","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2558_CR19","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.neuropsychologia.2017.06.008","volume":"102","author":"RK Kana","year":"2017","unstructured":"Kana RK, Sartin EB, Stevens C Jr, Deshpande HD, Klein C, Klinger MR, Klinger LG (2017) Neural networks underlying language and social cognition during self-other processing in autism spectrum disorders. Neuropsychologia 102:116\u2013123","journal-title":"Neuropsychologia"},{"issue":"5","key":"2558_CR20","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1007\/s11682-016-9604-8","volume":"11","author":"JP Hegarty","year":"2017","unstructured":"Hegarty JP, Ferguson BJ, Zamzow RM, Rohowetz LJ, Johnson JD, Christ SE, Beversdorf DQ (2017) Beta-adrenergic antagonism modulates functional connectivity in the default mode network of individuals with and without autism spectrum disorder. Brain Imaging and Behavior 11(5):1278\u20131289","journal-title":"Brain Imaging and Behavior"},{"issue":"4","key":"2558_CR21","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1002\/hbm.22252","volume":"35","author":"SD Washington","year":"2014","unstructured":"Washington SD, Gordon EM, Brar J, Warburton S, Sawyer AT, Wolfe A, Mease-Ference ER, Girton L, Hailu A, Mbwana J et al (2014) Dysmaturation of the default mode network in autism. Human Brain Mapping 35(4):1284\u20131296","journal-title":"Human Brain Mapping"},{"key":"2558_CR22","unstructured":"Kudo T, Maeda E, Matsumoto Y (2004) An application of boosting to graph classification. Advances in Neural Information Processing Systems 17"},{"key":"2558_CR23","doi-asserted-by":"crossref","unstructured":"Cao B, He L, Wei X, Xing M, Yu PS, Klumpp H, Leow AD (2017) t-bne: tensor-based brain network embedding. In: Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, pp 189\u2013197","DOI":"10.1137\/1.9781611974973.22"},{"key":"2558_CR24","doi-asserted-by":"publisher","first-page":"70","DOI":"10.3389\/fninf.2019.00070","volume":"13","author":"T Eslami","year":"2019","unstructured":"Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F (2019) ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Frontiers in Neuroinformatics 13:70","journal-title":"Frontiers in Neuroinformatics"},{"key":"2558_CR25","doi-asserted-by":"crossref","unstructured":"Yao D, Liu M, Wang M, Lian C, Wei J, Sun L, Sui J, Shen D (2019) Triplet graph convolutional network for multi-scale analysis of functional connectivity using functional MRI. In: International workshop on graph learning in medical imaging. Springer, pp 70\u201378","DOI":"10.1007\/978-3-030-35817-4_9"},{"key":"2558_CR26","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neuroimage.2014.07.033","volume":"103","author":"RP Monti","year":"2014","unstructured":"Monti RP, Hellyer P, Sharp D, Leech R, Anagnostopoulos C, Montana G (2014) Estimating time-varying brain connectivity networks from functional MRI time series. NeuroImage 103:427\u2013443","journal-title":"NeuroImage"},{"issue":"7","key":"2558_CR27","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.1109\/TBME.2018.2880428","volume":"66","author":"B Cai","year":"2018","unstructured":"Cai B, Zhang G, Zhang A, Stephen JM, Wilson TW, Calhoun VD, Wang Y-P (2018) Capturing dynamic connectivity from resting state fMRI using time-varying graphical lasso. IEEE Transactions on Biomedical Engineering 66(7):1852\u20131862","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"2558_CR28","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/s11682-015-9408-2","volume":"10","author":"C-Y Wee","year":"2016","unstructured":"Wee C-Y, Yang S, Yap P-T, Shen D (2016) Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging and Behavior 10(2):342\u2013356","journal-title":"Brain Imaging and Behavior"},{"key":"2558_CR29","doi-asserted-by":"crossref","unstructured":"Tang W, Lu Z, Dhillon IS (2009) Clustering with multiple graphs. In: 2009 ninth IEEE international conference on data mining. IEEE, pp 1016\u20131021","DOI":"10.1109\/ICDM.2009.125"},{"issue":"6","key":"2558_CR30","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino A, Yan C-G, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M et al (2014) The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry 19(6):659\u2013667","journal-title":"Molecular Psychiatry"},{"key":"2558_CR31","doi-asserted-by":"crossref","unstructured":"Cameron C, Sharad S, Brian C, Ranjeet K, Satrajit G, Chaogan Y, Qingyang L, Daniel L, Joshua V, Randal B, Stanley C, Maarten M, Clare K, Adriana DM, Francisco C, Michael M (2013) Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (c-pac). Frontiers in Neuroinformatics 7","DOI":"10.3389\/conf.fninf.2013.09.00042"},{"key":"2558_CR32","doi-asserted-by":"crossref","unstructured":"Kreutzer JS, DeLuca J, Caplan B (2011) Encyclopedia of clinical neuropsychology. Springer","DOI":"10.1007\/978-0-387-79948-3"},{"key":"2558_CR33","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.neuroimage.2017.12.052","volume":"169","author":"SI Ktena","year":"2018","unstructured":"Ktena SI, Parisot S, Ferrante E, Rajchl M, Lee M, Glocker B, Rueckert D (2018) Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169:431\u2013442","journal-title":"NeuroImage"},{"key":"2558_CR34","doi-asserted-by":"crossref","unstructured":"Ma Y, Wang S, Aggarwal CC, Tang J (2019) Graph convolutional networks with eigenpooling. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp 723\u2013731","DOI":"10.1145\/3292500.3330982"},{"key":"2558_CR35","unstructured":"Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, et\u00a0al (2018) Relational inductive biases, deep learning, and graph networks. arXiv:1806.01261"},{"key":"2558_CR36","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","volume":"146","author":"J Kawahara","year":"2017","unstructured":"Kawahara J, Brown CJ, Miller SP, Booth BG, Chau V, Grunau RE, Zwicker JG, Hamarneh G (2017) BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146:1038\u20131049","journal-title":"NeuroImage"},{"key":"2558_CR37","doi-asserted-by":"publisher","first-page":"102233","DOI":"10.1016\/j.media.2021.102233","volume":"74","author":"X Li","year":"2021","unstructured":"Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS (2021) BrainGNN: interpretable brain graph neural network for fMRI analysis. Medical Image Analysis 74:102233","journal-title":"Medical Image Analysis"},{"key":"2558_CR38","doi-asserted-by":"crossref","unstructured":"Parisot S, Ktena SI, Ferrante E, Lee M, Moreno RG, Glocker B, Rueckert D (2017) Spectral graph convolutions for population-based disease prediction. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 177\u2013185","DOI":"10.1007\/978-3-319-66179-7_21"},{"issue":"9","key":"2558_CR39","doi-asserted-by":"publisher","first-page":"5107","DOI":"10.1093\/cercor\/bhaa105","volume":"30","author":"KE Lawrence","year":"2020","unstructured":"Lawrence KE, Hernandez LM, Bowman HC, Padgaonkar NT, Fuster E, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA et al (2020) Sex differences in functional connectivity of the salience, default mode, and central executive networks in youth with asd. Cerebral Cortex 30(9):5107\u20135120","journal-title":"Cerebral Cortex"},{"issue":"10","key":"2558_CR40","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/j.tics.2011.08.003","volume":"15","author":"V Menon","year":"2011","unstructured":"Menon V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences 15(10):483\u2013506","journal-title":"Trends in Cognitive Sciences"},{"key":"2558_CR41","doi-asserted-by":"publisher","first-page":"e47427","DOI":"10.7554\/eLife.47427","volume":"8","author":"MV Lombardo","year":"2019","unstructured":"Lombardo MV, Eyler L, Moore A, Datko M, Barnes CC, Cha D, Courchesne E, Pierce K (2019) Default mode-visual network hypoconnectivity in an autism subtype with pronounced social visual engagement difficulties. elife 8:e47427","journal-title":"elife"},{"issue":"8","key":"2558_CR42","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.biopsych.2015.08.029","volume":"79","author":"MB Nebel","year":"2016","unstructured":"Nebel MB, Eloyan A, Nettles CA, Sweeney KL, Ament K, Ward RE, Choe AS, Barber AD, Pekar JJ, Mostofsky SH (2016) Intrinsic visual-motor synchrony correlates with social deficits in autism. Biological Psychiatry 79(8):633\u2013641","journal-title":"Biological Psychiatry"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02558-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02558-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02558-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T03:38:46Z","timestamp":1656041926000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02558-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,6]]},"references-count":42,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["2558"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02558-4","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2022,5,6]]},"assertion":[{"value":"17 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}