{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:13:44Z","timestamp":1778346824507,"version":"3.51.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12071369"],"award-info":[{"award-number":["12071369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176244"],"award-info":[{"award-number":["62176244"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Industry Innovation Chain (Group) of Shaanxi Province","award":["2019ZDLSF02-09-02"],"award-info":[{"award-number":["2019ZDLSF02-09-02"]}]},{"name":"Shaanxi Fundamental Science Research Project for Mathematics and Physics","award":["22JSZ008"],"award-info":[{"award-number":["22JSZ008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s13042-023-01980-w","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T08:01:52Z","timestamp":1696924912000},"page":"1517-1532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A novel autism spectrum disorder identification method: spectral graph network with brain-population graph structure joint learning"],"prefix":"10.1007","volume":"15","author":[{"given":"Sihui","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9547-2585","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feilong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,10]]},"reference":[{"issue":"7432","key":"1980_CR1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/nature11860","volume":"493","author":"DH Ebert","year":"2013","unstructured":"Ebert DH, Greenberg ME (2013) Activity-dependent neuronal signalling and autism spectrum disorder. Nature 493(7432):327\u2013337","journal-title":"Nature"},{"key":"1980_CR2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s13042-022-01554-2","volume":"14","author":"I Monarca","year":"2023","unstructured":"Monarca I, Cibrian FL, Chavez E, Tentori M (2023) Using a small dataset to classify strength-interactions with an elastic display: A case study for the screening of autism spectrum disorder. Int J Mach Learn Cybernet 14:151\u2013169","journal-title":"Int J Mach Learn Cybernet"},{"key":"1980_CR3","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.neuroimage.2016.02.079","volume":"145","author":"MR Arbabshirani","year":"2017","unstructured":"Arbabshirani MR, Plis S, Sui J, Calhoun VD (2017) Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 145:137\u2013165","journal-title":"NeuroImage"},{"issue":"11","key":"1980_CR4","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/S1474-4422(15)00044-7","volume":"14","author":"DH Geschwind","year":"2015","unstructured":"Geschwind DH et al (2015) Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol 14(11):1109\u20131120","journal-title":"Lancet Neurol"},{"key":"1980_CR5","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.cortex.2014.08.011","volume":"63","author":"T Iidaka","year":"2015","unstructured":"Iidaka T (2015) Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63:55\u201367","journal-title":"Cortex"},{"key":"1980_CR6","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.neuroimage.2018.09.043","volume":"184","author":"E Jun","year":"2019","unstructured":"Jun E, Kang E, Choi J, Suk HI (2019) Modeling regional dynamics in low-frequency fluctuation and its application to autism spectrum disorder diagnosis. NeuroImage 184:669\u2013686","journal-title":"NeuroImage"},{"issue":"11","key":"1980_CR7","doi-asserted-by":"crossref","first-page":"5804","DOI":"10.1002\/hbm.23769","volume":"38","author":"TE Kam","year":"2017","unstructured":"Kam TE, Suk HI, Lee SW (2017) Multiple functional networks modeling for autism spectrum disorder diagnosis. Hum Brain Map 38(11):5804\u20135821","journal-title":"Hum Brain Map"},{"key":"1980_CR8","doi-asserted-by":"crossref","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, Craddock RC, Samaras D, Thirion B, Varoquaux G (2017) Deriving reproducible biomarkers from multi-site resting-state data: an Autism-based example. NeuroImage 147:736\u2013745","journal-title":"NeuroImage"},{"key":"1980_CR9","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"1980_CR10","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.neuroimage.2016.01.005","volume":"129","author":"HI Suk","year":"2016","unstructured":"Suk HI, Wee CY, Lee SW, Shen D (2016) State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129:292\u2013307","journal-title":"NeuroImage"},{"key":"1980_CR11","doi-asserted-by":"crossref","unstructured":"Liu Y, He L, Cao B, Yu P, Ragin A, Leow A (2018) Multi-view multi-graph embedding for brain network clustering analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 117\u2013124","DOI":"10.1609\/aaai.v32i1.11288"},{"issue":"7","key":"1980_CR12","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1109\/TMI.2017.2715285","volume":"37","author":"H Huang","year":"2018","unstructured":"Huang H, Hu X, Zhao Y, Makkie M, Dong Q, Zhao S, Guo L, Liu T (2018) Modeling task fMRI data via deep convolutional autoencoder. IEEE Trans Med Imaging 37(7):1551\u20131561","journal-title":"IEEE Trans Med Imaging"},{"key":"1980_CR13","doi-asserted-by":"crossref","unstructured":"Dvornek NC, Ventola P, Pelphrey KA, Duncan JS (2017) Identifying autism from resting-state fMRI using long short-term memory networks. In: Proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, pp 362\u2013370","DOI":"10.1007\/978-3-319-67389-9_42"},{"key":"1980_CR14","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1007\/s13042-018-0887-5","volume":"10","author":"R Saini","year":"2019","unstructured":"Saini R, Kumar P, Kaur B, Roy PP, Dogra DP, Santosh K (2019) Kinect sensor-based interaction monitoring system using the BLSTM neural network in healthcare. Int J Mach Learn Cybernet 10:2529\u20132540","journal-title":"Int J Mach Learn Cybernet"},{"key":"1980_CR15","doi-asserted-by":"crossref","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"},{"key":"1980_CR16","doi-asserted-by":"crossref","first-page":"491","DOI":"10.3389\/fnins.2018.00491","volume":"12","author":"H Li","year":"2018","unstructured":"Li H, Parikh NA, He L (2018) A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurosci 12:491","journal-title":"Front Neurosci"},{"issue":"7","key":"1980_CR17","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1109\/TNNLS.2020.3007943","volume":"32","author":"ZA Huang","year":"2021","unstructured":"Huang ZA, Zhu Z, Yau CH, Tan KC (2021) Identifying autism spectrum disorder from resting-state fMRI using deep belief network. IEEE Trans Neural Netw Learn Syst 32(7):2847\u20132861","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1980_CR18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106320","volume":"151","author":"X Deng","year":"2022","unstructured":"Deng X, Zhang J, Liu R, Liu K (2022) Classifying asd based on time-series fMRI using spatial-temporal transformer. Comput Biol Med 151:106320","journal-title":"Comput Biol Med"},{"key":"1980_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3243000","author":"R Liu","year":"2023","unstructured":"Liu R, Huang ZA, Hu Y, Zhu Z, Wong KC, Tan KC (2023) Spatial-temporal co-attention learning for diagnosis of mental disorders from resting-state fMRI data. IEEE Trans Neural Netw Learn Syst Early Access. https:\/\/doi.org\/10.1109\/TNNLS.2023.3243000","journal-title":"IEEE Trans Neural Netw Learn Syst Early Access"},{"issue":"1","key":"1980_CR20","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"1980_CR21","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1109\/TPAMI.2022.3183143","volume":"45","author":"C Huang","year":"2023","unstructured":"Huang C, Li M, Cao F, Fujita H, Li Z, Wu X (2023) Are graph convolutional networks with random weights feasible? IEEE Trans Pattern Anal Mach Intell 45(3):2751\u20132768","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1980_CR22","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1007\/s13042-020-01155-x","volume":"11","author":"S Liu","year":"2020","unstructured":"Liu S, Li T, Ding H, Tang B, Wang X, Chen Q, Yan J, Zhou Y (2020) A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction. Int J Mach Learn Cybernet 11:2849\u20132856","journal-title":"Int J Mach Learn Cybernet"},{"key":"1980_CR23","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.neunet.2022.06.035","volume":"154","author":"X Zhao","year":"2022","unstructured":"Zhao X, Wu J, Peng H, Beheshti A, Monaghan JJ, McAlpine D, Hernandez-Perez H, Dras M, Dai Q, Li Y et al (2022) Deep reinforcement learning guided graph neural networks for brain network analysis. Neural Netw 154:56\u201367","journal-title":"Neural Netw"},{"key":"1980_CR24","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1007\/s13042-023-01802-z","volume":"14","author":"P Song","year":"2023","unstructured":"Song P, Li J, Fan H, Fan L (2023) DBCGN: Dual branch cascade graph network for skin lesion segmentation. Int J Mach Learn Cybernet 14:2847\u20132865","journal-title":"Int J Mach Learn Cybernet"},{"key":"1980_CR25","volume":"139","author":"C Yang","year":"2021","unstructured":"Yang C, Wang P, Tan J, Liu Q, Li X (2021) Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Comput Biol Med 139:104963","journal-title":"Comput Biol Med"},{"key":"1980_CR26","doi-asserted-by":"crossref","unstructured":"Cao P, Wen G, Li L, Liu X, Yang J, Zaiane O (2021) Temporal graph representation learning for autism spectrum disorder brain networks. In: Proceedings of 2021 IEEE International Conference on Bioinformatics and Biomedicine, pp 1270\u20131275","DOI":"10.1109\/BIBM52615.2021.9669613"},{"key":"1980_CR27","volume":"153","author":"L Liu","year":"2023","unstructured":"Liu L, Wen G, Cao P, Hong T, Yang J, Zhang X, Zaiane OR (2023) BrainTGL: A dynamic graph representation learning model for brain network analysis. Comput Biol Med 153:106521","journal-title":"Comput Biol Med"},{"key":"1980_CR28","volume":"142","author":"G Wen","year":"2022","unstructured":"Wen G, Cao P, Bao H, Yang W, Zheng T, Zaiane O (2022) MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis. Comput Biol Med 142:105239","journal-title":"Comput Biol Med"},{"key":"1980_CR29","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106772","volume":"219","author":"W Yang","year":"2022","unstructured":"Yang W, Wen G, Cao P, Yang J, Zaiane OR (2022) Collaborative learning of graph generation, clustering and classification for brain networks diagnosis. Comput Methods Programs Biomed 219:106772","journal-title":"Comput Methods Programs Biomed"},{"key":"1980_CR30","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot S, Ktena SI, Ferrante E, Lee M, Guerrero R, Glocker B, Rueckert D (2018) Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer\u2019s disease. Med Image Anal 48:117\u2013130","journal-title":"Med Image Anal"},{"key":"1980_CR31","doi-asserted-by":"crossref","unstructured":"Kazi A, Shekarforoush S, Arvind\u00a0Krishna S, Burwinkel H, Vivar G, Kort\u00fcm K, Ahmadi SA, Albarqouni S, Navab N (2019) InceptionGCN: receptive field aware graph convolutional network for disease prediction. In: Proceedings of the 26th international conference on information processing in medical imaging, pp 73\u201385","DOI":"10.1007\/978-3-030-20351-1_6"},{"key":"1980_CR32","volume":"255","author":"B Zhang","year":"2022","unstructured":"Zhang B, Guo X, Lin Q, Wang H, Xu S (2022) Counterfactual inference graph network for disease prediction. Knowled-Based Syst 255:109722","journal-title":"Knowled-Based Syst"},{"key":"1980_CR33","volume":"77","author":"Y Huang","year":"2022","unstructured":"Huang Y, Chung AC (2022) Disease prediction with edge-variational graph convolutional networks. Med Image Anal 77:102375","journal-title":"Med Image Anal"},{"issue":"9","key":"1980_CR34","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TMI.2022.3159264","volume":"41","author":"S Zheng","year":"2022","unstructured":"Zheng S, Zhu Z, Liu Z, Guo Z, Liu Y, Yang Y, Zhao Y (2022) Multi-modal graph learning for disease prediction. IEEE Trans Med Imag 41(9):2207\u20132216","journal-title":"IEEE Trans Med Imag"},{"key":"1980_CR35","doi-asserted-by":"crossref","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. Comput Biol Med 127:104096","journal-title":"Comput Biol Med"},{"key":"1980_CR36","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations"},{"key":"1980_CR37","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems, pp 3844\u20133852"},{"key":"1980_CR38","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning, pp 807\u2013814"},{"key":"1980_CR39","doi-asserted-by":"crossref","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. Med Image Anal 74:102233","DOI":"10.1016\/j.media.2021.102233"},{"issue":"1","key":"1980_CR40","first-page":"545","volume":"35","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Bu J, Ester M, Zhang J, Li Z, Yao C, Dai H, Yu Z, Wang C (2023) Hierarchical multi-view graph pooling with structure learning. IEEE Trans Knowl Data Eng 35(1):545\u2013559","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1980_CR41","unstructured":"Martins A, Astudillo R (2016) From softmax to sparsemax: A sparse model of attention and multi-label classification. In: Proceedings of International Conference on Machine Learning, pp 1614\u20131623"},{"key":"1980_CR42","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of international conference on machine learning, pp 448\u2013456"},{"key":"1980_CR43","unstructured":"Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: Proceedings of International Conference on Machine Learning, pp 1725\u20131735"},{"key":"1980_CR44","first-page":"10","volume":"42","author":"C Craddock","year":"2013","unstructured":"Craddock C, Sikka S, Cheung B, Khanuja R, Ghosh SS, Yan C, Li Q, Lurie D, Vogelstein J, Burns R et al (2013) Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (C-PAC). Front Neuroinform 42:10\u20133389","journal-title":"Front Neuroinform"},{"issue":"3","key":"1980_CR45","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan RS, S\u00e9gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968\u2013980","journal-title":"NeuroImage"},{"key":"1980_CR46","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: International Conference on Learning Representations"},{"key":"1980_CR47","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"issue":"1","key":"1980_CR48","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TPAMI.2022.3145392","volume":"45","author":"AM Carrington","year":"2023","unstructured":"Carrington AM, Manuel DG, Fieguth P, Ramsay TO, Osmani V, Wernly B, Bennett C, Hawken S, Magwood O, Sheikh Y et al (2023) Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation. IEEE Trans Pattern Anal Mach Intell 45(1):329\u2013341","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1980_CR49","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s13042-017-0741-1","volume":"10","author":"L Yang","year":"2019","unstructured":"Yang L, Xu Z (2019) Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning. Int J Mach Learn Cybernet 10:591\u2013601","journal-title":"Int J Mach Learn Cybernet"},{"key":"1980_CR50","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten LVD, Hinton GE (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579\u20132605","journal-title":"J Mach Learn Res"},{"issue":"7","key":"1980_CR51","volume":"8","author":"M Xia","year":"2013","unstructured":"Xia M, Wang J, He Y (2013) BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE 8(7):e68910","journal-title":"PLoS ONE"},{"issue":"5","key":"1980_CR52","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1002\/ana.24391","volume":"77","author":"KA Doyle-Thomas","year":"2015","unstructured":"Doyle-Thomas KA, Lee W, Foster NE, Tryfon A, Ouimet T, Hyde KL, Evans AC, Lewis J, Zwaigenbaum L, Anagnostou E et al (2015) Atypical functional brain connectivity during rest in autism spectrum disorders. Ann Neurol 77(5):866\u2013876","journal-title":"Ann Neurol"},{"key":"1980_CR53","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.neuroimage.2016.10.045","volume":"147","author":"A Abraham","year":"2016","unstructured":"Abraham A, Milham MP, Martino AD, Craddock RC, Samaras D, Thirion B, Varoquaux G (2016) Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147:736\u2013745","journal-title":"NeuroImage"},{"key":"1980_CR54","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.dcn.2017.01.007","volume":"29","author":"AC Linke","year":"2017","unstructured":"Linke AC, Keehn RJJ, Pueschel EB, Fishman I, M\u00fcller RA (2017) Children with ASD show links between aberrant sound processing, social symptoms, and atypical auditory interhemispheric and thalamocortical functional connectivity. Dev Cognit Neurosci 29:117\u2013126","journal-title":"Dev Cognit Neurosci"},{"key":"1980_CR55","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.neunet.2020.03.017","volume":"126","author":"H Shahamat","year":"2020","unstructured":"Shahamat H, Abadeh MS (2020) Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw 126:218\u2013234","journal-title":"Neural Netw"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01980-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01980-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01980-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T13:56:14Z","timestamp":1730296574000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01980-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,10]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1980"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01980-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,10]]},"assertion":[{"value":"29 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2023","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}