{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T05:24:44Z","timestamp":1782105884850,"version":"3.54.5"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164514","type":"print"},{"value":"9783031164521","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_36","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"375-385","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Interpretable Graph Neural Networks for\u00a0Connectome-Based Brain Disorder Analysis"],"prefix":"10.1007","author":[{"given":"Hejie","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqiao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Bai, B., et al.: Why attentions may not be interpretable? In: SIGKDD (2021)","DOI":"10.1145\/3447548.3467307"},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Caspers, J., et al.: Within-and across-network alterations of the sensorimotor network in Parkinson\u2019s disease. Neuroradiology 63, 2073\u20132085 (2021)","DOI":"10.1007\/s00234-021-02731-w"},{"key":"36_CR3","unstructured":"Corso, G., et al.: Principal neighbourhood aggregation for graph nets. In: NeurIPS (2020)"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Cui, H., et al.: BrainGB: a benchmark for brain network analysis with graph neural networks. arXiv preprint arXiv:2204.07054 (2022)","DOI":"10.1109\/BigData55660.2022.10020992"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Cui, H., Lu, Z., Li, P., Yang, C.: On positional and structural node features for graph neural networks on non-attributed graphs. arXiv preprint arXiv:2107.01495 (2021)","DOI":"10.1145\/3511808.3557661"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Das, T.K., et al.: Parietal lobe and disorganisation syndrome in schizophrenia and psychotic bipolar disorder: a bimodal connectivity study. Psychiatry Res. Neuroimaging 303, 111139 (2020)","DOI":"10.1016\/j.pscychresns.2020.111139"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Dyrba, M., et al.: Multimodal analysis of functional and structural disconnection in Alzheimer\u2019s disease using multiple kernel SVM. Hum. Brain Mapp. 36, 2118\u20132131 (2015)","DOI":"10.1002\/hbm.22759"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"van Eimeren, T., et al.: Dysfunction of the default mode network in Parkinson disease: a functional magnetic resonance imaging study. Arch. Neurol. 66, 877\u2013883 (2009)","DOI":"10.1001\/archneurol.2009.97"},{"key":"36_CR9","unstructured":"Fey, M., et al.: Fast graph representation learning with pytorch geometric. In: RLGM@ICLR (2019)"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Figley, T.D., et al.: Probabilistic white matter atlases of human auditory, basal ganglia, language, precuneus, sensorimotor, visual and visuospatial networks. Front. Hum. Neurosci. 11, 306 (2017)","DOI":"10.3389\/fnhum.2017.00306"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Flannery, J.S., et al.: HIV infection is linked with reduced error-related default mode network suppression and poorer medication management abilities. medRxiv.org (2021)","DOI":"10.1101\/2021.04.10.21255223"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Herting, M.M., et al.: Default mode connectivity in youth with perinatally acquired HIV. Medicine (2015)","DOI":"10.1097\/MD.0000000000001417"},{"key":"36_CR13","unstructured":"Jain, S., et al.: Attention is not explanation. In: NAACL-HLT (2019)"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Jie, B., et al.: Sub-network based kernels for brain network classification. In: ACM BCB (2016)","DOI":"10.1145\/2975167.2985687"},{"key":"36_CR15","unstructured":"Kan, X., Cui, H., Lukemire, J., Guo, Y., Yang, C.: FBNetGen: task-aware GNN-based fMRI analysis via functional brain network generation. In: MIDL (2022)"},{"key":"36_CR16","unstructured":"Kan, X., Dai, W., Cui, H., Zhang, Z., Guo, Y., Yang, C.: Brain network transformer. arXiv preprint (2022)"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Kawahara, J., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038\u20131049 (2017)","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Kendi, A.K., et al.: Altered diffusion in the frontal lobe in Parkinson disease. AJNR Am. J. Neuroradiol. 29, 501\u2013505 (2008)","DOI":"10.3174\/ajnr.A0850"},{"key":"36_CR19","unstructured":"Kipf, T.N., et al.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)","DOI":"10.1016\/j.media.2021.102233"},{"key":"36_CR21","unstructured":"Li, Y., et al.: Structural gray matter change early in male patients with HIV. Int. J. Clin. Exp. Med 7, 3362 (2014)"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Multi-view multi-graph embedding for brain network clustering analysis. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11288"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Lu, H., et al.: MPCA: multilinear principal component analysis of tensor objects. IEEE Trans. Neural Netw. 19, 18\u201339 (2008)","DOI":"10.1109\/TNN.2007.901277"},{"key":"36_CR24","unstructured":"Luo, D., et al.: Parameterized explainer for graph neural network. In: NeurIPS (2020)"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Ma, Q., et al.: HIV-associated structural and functional brain alterations in homosexual males. Front. Neurol. (2021)","DOI":"10.3389\/fneur.2021.757374"},{"key":"36_CR26","unstructured":"Maron, H., et al.: Invariant and equivariant graph networks. In: ICLR (2018)"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Martensson, G., et al.: Stability of graph theoretical measures in structural brain networks in Alzheimer\u2019s disease. Sci. Rep. 8, 1\u201315 (2018)","DOI":"10.1038\/s41598-018-29927-0"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"O\u2019Bryan, R.A., et al.: Disturbances of visual motion perception in bipolar disorder. Bipolar Disord. 16, 354\u2013365 (2014)","DOI":"10.1111\/bdi.12173"},{"key":"36_CR29","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)"},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Reavis, E.A., et al.: Structural and functional connectivity of visual cortex in Schizophrenia and bipolar disorder: a graph-theoretic analysis. Schizophr. Bull. Open 1, sgaa056 (2020)","DOI":"10.1093\/schizbullopen\/sgaa056"},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Shao, W., et al.: Clustering on multi-source incomplete data via tensor modeling and factorization. In: PAKDD (2015)","DOI":"10.1007\/978-3-319-18032-8_38"},{"key":"36_CR32","doi-asserted-by":"crossref","unstructured":"Shirer, W.R., et al.: Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22, 158\u2013165 (2012)","DOI":"10.1093\/cercor\/bhr099"},{"key":"36_CR33","doi-asserted-by":"crossref","unstructured":"Tessitore, A., et al.: Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease. Neurology 79, 2226\u20132232 (2012)","DOI":"10.1212\/WNL.0b013e31827689d6"},{"key":"36_CR34","unstructured":"Veli\u010dkovi\u0107, P., et al.: Graph attention networks. In: ICLR (2018)"},{"key":"36_CR35","unstructured":"Veli\u010dkovi\u0107, P., et al.: Deep graph infomax. In: ICLR (2019)"},{"key":"36_CR36","unstructured":"Vu, M.N., et al.: PGM-explainer: probabilistic graphical model explanations for graph neural networks. In: NeurIPS (2020)"},{"key":"36_CR37","doi-asserted-by":"crossref","unstructured":"Xia, M., et al.: BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910 (2013)","DOI":"10.1371\/journal.pone.0068910"},{"key":"36_CR38","doi-asserted-by":"crossref","unstructured":"Yang, Y., et al.: Data-efficient brain connectome analysis via multi-task meta-learning. In: KDD (2022)","DOI":"10.1145\/3534678.3542680"},{"key":"36_CR39","unstructured":"Ying, Z., et al.: GNNExplainer: generating explanations for graph neural networks. In: NeurIPS (2019)"},{"key":"36_CR40","unstructured":"Yuan, H., et al.: Explainability in graph neural networks: a taxonomic survey. arXiv.org (2020)"},{"key":"36_CR41","unstructured":"Yun, S., et al.: Graph transformer networks. In: NeurIPS (2019)"},{"key":"36_CR42","doi-asserted-by":"crossref","unstructured":"Zhan, L., et al.: Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer\u2019s disease. Front. Aging Neurosci. 7, 48 (2015)","DOI":"10.3389\/fnagi.2015.00048"},{"key":"36_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Cui, H., He, L., Sun, L., Yang, C.: Joint embedding of structural and functional brain networks with graph neural networks for mental illness diagnosis. In: EMBC (2022)","DOI":"10.1109\/EMBC48229.2022.9871118"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:47:51Z","timestamp":1710244071000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}