{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:23:09Z","timestamp":1783117389181,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819595747","type":"print"},{"value":"9789819595754","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-9575-4_19","type":"book-chapter","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:03:45Z","timestamp":1783116225000},"page":"247-258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Graph Neural Network Approach to\u00a0Study the\u00a0Human Connectome in\u00a0Male Individuals with\u00a0Autism Spectrum Disorders"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6727-3517","authenticated-orcid":false,"given":"Giuseppe Antonio","family":"Motisi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0900-0421","authenticated-orcid":false,"given":"Francesca","family":"Lizzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9861-6961","authenticated-orcid":false,"given":"Francesca","family":"Mainas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7524-0285","authenticated-orcid":false,"given":"Gianmarco","family":"Tiddia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6250-5739","authenticated-orcid":false,"given":"Sara","family":"Calderoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9446-3967","authenticated-orcid":false,"given":"Piernicola","family":"Oliva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5135-4472","authenticated-orcid":false,"given":"Alessandra","family":"Retico","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20, 61\u201380 (2009)","journal-title":"IEEE Trans. Neural Networks"},{"key":"19_CR2","unstructured":"Battaglia, P.W. et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Ahmedt-Aristizabal, D., Armin, M.A., \u00a0Denman, S., \u00a0Fookes, C., \u00a0Petersson, L.: Graph-based deep learning for medical diagnosis and analysis: Past, present and future. Sensors 21 (2021)","DOI":"10.3390\/s21144758"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Yousefian, A., Shayegh, F., \u00a0Maleki, Z.: Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals. Front. Syst. Neurosci.16 (2023)","DOI":"10.3389\/fnsys.2022.904770"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Zafeiropoulos, N., \u00a0Bitilis, P., Tsekouras, G.E., \u00a0Kotis, K.: Graph neural networks for Parkinson\u2019s disease monitoring and alerting. Sensors (Basel, Switzerland), 23 (2023)","DOI":"10.3390\/s23218936"},{"issue":"5","key":"19_CR6","doi-asserted-by":"publisher","first-page":"5833","DOI":"10.1109\/TPAMI.2022.3209686","volume":"45","author":"A Bessadok","year":"2022","unstructured":"Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5833\u20135848 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR7","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.: The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Di\u00a0Martino, A., et al.: Enhancing studies of the connectome in autism using the autism brain imaging data exchange ii. Sci. Data 4 (2017)","DOI":"10.1038\/sdata.2017.10"},{"key":"19_CR9","unstructured":"Craddock, C., et al.: Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac). In: Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics 2013 (2013)"},{"issue":"2","key":"19_CR10","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: Freesurfer. Neuroimage 62(2), 774\u2013781 (2012). Epub 2012 Jan 10","journal-title":"Neuroimage"},{"issue":"1","key":"19_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2010.06.010","volume":"53","author":"C Destrieux","year":"2010","unstructured":"Destrieux, C., Fischl, B., Dale, A., Halgren, E.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1), 1\u201315 (2010)","journal-title":"Neuroimage"},{"key":"19_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116450","volume":"208","author":"R Pomponio","year":"2020","unstructured":"Pomponio, R., et al.: Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage 208, 116450 (2020)","journal-title":"Neuroimage"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Spera, G., et al.: Evaluation of altered functional connections in male children with autism spectrum disorders on multiple-site data optimized with machine learning. Front. Psych. 10 1\u201314 (2019)","DOI":"10.3389\/fpsyt.2019.00620"},{"key":"19_CR14","unstructured":"Hamilton, W.L., Ying, R.,\u00a0Leskovec, J.: Inductive representation learning on large graphs. Advances in neural information processing systems, (2017)"},{"key":"19_CR15","unstructured":"Lee, J., \u00a0Lee, I., Kang, J.: Self-attention graph pooling. In: International conference on machine learning, volume abs\/1904.08082 of Proceedings of Machine Learning Research, pp. 3734\u20133743. pmlr, PMLR (2019)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Akiba, T., \u00a0Sano, S., Yanase, T., \u00a0Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)","DOI":"10.1145\/3292500.3330701"},{"key":"19_CR17","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TNSRE.2025.3565627","volume":"33","author":"Z Qin","year":"2025","unstructured":"Qin, Z., Li, Y., Song, X., Chai, L.: Classification of neuropsychiatric disorders via brain-region-selected graph convolutional network. IEEE Trans. Neural Syst. Rehabil. Eng. 33, 1664\u20131672 (2025)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"19_CR18","volume":"8","author":"X Yang","year":"2022","unstructured":"Yang, X., Zhang, N., Schrader, P.: A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. Mach. Learn. Appl. 8, 100290 (2022)","journal-title":"Mach. Learn. Appl."},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Alves, C.L., et al.: Multiclass classification of autism spectrum disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis. PLOS ONE 19(10), 1\u201335 (10 2024)","DOI":"10.1371\/journal.pone.0305630"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Plitt, M., Barnes, K.A.,\u00a0Martin, A.: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage: Clinical 7, 359\u2013366 (2015)","DOI":"10.1016\/j.nicl.2014.12.013"},{"issue":"8","key":"19_CR21","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1089\/brain.2019.0689","volume":"9","author":"A Eill","year":"2019","unstructured":"Eill, A., et al.: Functional connectivities are more informative than anatomical variables in diagnostic classification of autism. Brain Connect. 9(8), 604\u2013612 (2019). Epub 2019 Aug 23","journal-title":"Brain Connect."},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Heinsfeld, A.S., Franco, A.R., Craddock, R.C., \u00a0Buchweitz, A.,\u00a0Meneguzzi., F.: Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage: Clin. 17, 16\u201323 (2018)","DOI":"10.1016\/j.nicl.2017.08.017"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Bhavna, K., Ghosh, N., Banerjee, R., \u00a0Roy, D.: A lightweight, end-to-end explainable, and generalized attention-based graph neural network model trained on high-order spatiotemporal organization of dynamic functional connectivity to classify autistics from typically developing. Network Neuroscience, pp. 1\u201329, 09 (2025)","DOI":"10.1162\/NETN.a.32"},{"issue":"3","key":"19_CR24","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1093\/brain\/awl004","volume":"129","author":"M Trimble","year":"2006","unstructured":"Trimble, M., Cavanna, A.: The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129(3), 564\u2013583 (2006)","journal-title":"Brain"},{"issue":"5","key":"19_CR25","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.neuropsychologia.2005.07.017","volume":"44","author":"L Schilbach","year":"2006","unstructured":"Schilbach, L., et al.: Being with virtual others: neural correlates of social interaction. Neuropsychologia 44(5), 718\u2013730 (2006)","journal-title":"Neuropsychologia"},{"issue":"Pt 5","key":"19_CR26","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1093\/brain\/awv051","volume":"138","author":"W Cheng","year":"2015","unstructured":"Cheng, W., Rolls, E.T., 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 138(Pt 5), 1382\u20131393 (2015)","journal-title":"Brain"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Saponaro, S., et al.: Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders. Brain Inform. 11(1), 2 (2024)","DOI":"10.1186\/s40708-023-00217-4"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Lombardo, M.V., \u00a0Chakrabarti, B., Bullmore, E.T., Sadek, S.A.G.\u00a0Pasco, S.J.: Wheelwright, J.\u00a0Suckling, Consortium MRC, AIMS, and S.\u00a0Baron, Cohen. Atypical neural self-representation in autism. Brain, 133(Pt 2):611\u2013624 (2010)","DOI":"10.1093\/brain\/awp306"},{"issue":"2","key":"19_CR29","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1073\/pnas.98.2.676","volume":"98","author":"ME Raichle","year":"2001","unstructured":"Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L.: A default mode of brain function. Proc. Natl. Acad. Sci. 98(2), 676\u2013682 (2001)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"1","key":"19_CR30","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1177\/1073858412440596","volume":"19","author":"ML Seghier","year":"2013","unstructured":"Seghier, M.L.: The angular gyrus: multiple functions and multiple subdivisions. Neuroscientist 19(1), 43\u201361 (2013)","journal-title":"Neuroscientist"},{"issue":"8","key":"19_CR31","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1007\/s00787-017-0964-4","volume":"26","author":"J Liu","year":"2017","unstructured":"Liu, J., et al.: Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping. Europ. Child Adolescent Psych. 26(8), 933\u2013945 (2017)","journal-title":"Europ. Child Adolescent Psych."},{"issue":"1","key":"19_CR32","doi-asserted-by":"publisher","first-page":"20603","DOI":"10.1038\/s41598-022-25051-2","volume":"12","author":"K Krishnamurthy","year":"2022","unstructured":"Krishnamurthy, K., Chan, M.M.Y., Han, Y.M.Y.: Neural substrates underlying effortful control deficit in autism spectrum disorder: a meta-analysis of fMRI studies. Sci. Rep. 12(1), 20603 (2022)","journal-title":"Sci. Rep."},{"issue":"5","key":"19_CR33","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1177\/1362361319886246","volume":"24","author":"BY Lau","year":"2020","unstructured":"Lau, B.Y., et al.: Anxiety in young people with autism spectrum disorder: Common and autism-related anxiety experiences and their associations with individual characteristics. Autism 24(5), 1111\u20131126 (2020)","journal-title":"Autism"},{"issue":"1","key":"19_CR34","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.neuroimage.2004.06.016","volume":"23","author":"N Boddaert","year":"2004","unstructured":"Boddaert, N., et al.: Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage 23(1), 364\u2013369 (2004)","journal-title":"Neuroimage"},{"key":"19_CR35","doi-asserted-by":"crossref","unstructured":"Levitt, J.G., et al.: Cortical sulcal maps in autism. Cerebral Cortex 13(7), 728\u2013735 (07 2003)","DOI":"10.1093\/cercor\/13.7.728"},{"key":"19_CR36","doi-asserted-by":"publisher","first-page":"418","DOI":"10.3389\/fnhum.2017.00418","volume":"11","author":"Y Lee","year":"2017","unstructured":"Lee, Y., Park, B.Y., James, O., Kim, S.G., Park, H.: Autism spectrum disorder related functional connectivity changes in the language network in children, adolescents, and adults. Front. Hum. Neurosci. 11, 418 (2017)","journal-title":"Front. Hum. Neurosci."},{"issue":"12","key":"19_CR37","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.30773\/pi.2022.0174","volume":"19","author":"N Yoon","year":"2022","unstructured":"Yoon, N., et al.: Alterations in social brain network topology at rest in children with autism spectrum disorder. Psych. Investig. 19(12), 1055\u20131068 (2022)","journal-title":"Psych. Investig."}],"container-title":["Lecture Notes in Computer Science","Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-9575-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:03:55Z","timestamp":1783116235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-9575-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819595747","9789819595754"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-9575-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests or other interests that might be perceived to influence the results and\/or discussion reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"BI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"brain2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wi-consortium.org\/conferences\/bi2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}