{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T09:01:36Z","timestamp":1767085296728,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031438943"},{"type":"electronic","value":"9783031438950"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43895-0_68","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"723-733","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Explainable Geometric-Weighted Graph Attention Network for\u00a0Identifying Functional Networks Associated with\u00a0Gait Impairment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1959-5302","authenticated-orcid":false,"given":"Favour","family":"Nerrise","sequence":"first","affiliation":[]},{"given":"Qingyu","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3424-7143","authenticated-orcid":false,"given":"Kathleen L.","family":"Poston","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5416-5159","authenticated-orcid":false,"given":"Kilian M.","family":"Pohl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-7763","authenticated-orcid":false,"given":"Ehsan","family":"Adeli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"1","key":"68_CR1","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1137\/050637996","volume":"29","author":"V Arsigny","year":"2007","unstructured":"Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM J. Matrix Anal. Appl. 29(1), 328\u2013347 (2007)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"68_CR2","unstructured":"Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? arXiv preprint arXiv:2105.14491 (2021)"},{"issue":"12","key":"68_CR3","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.1007\/s00234-021-02731-w","volume":"63","author":"J Caspers","year":"2021","unstructured":"Caspers, J., et al.: Within-and across-network alterations of the sensorimotor network in Parkinson\u2019s disease. Neuroradiology 63(12), 2073\u20132085 (2021)","journal-title":"Neuroradiology"},{"key":"68_CR4","first-page":"13260","volume":"33","author":"G Corso","year":"2020","unstructured":"Corso, G., Cavalleri, L., Beaini, D., Li\u00f2, P., Veli\u010dkovi\u0107, P.: Principal neighbourhood aggregation for graph nets. NeurIPS 33, 13260\u201313271 (2020)","journal-title":"NeurIPS"},{"key":"68_CR5","doi-asserted-by":"crossref","unstructured":"Cui, H., et al.: Braingb: a benchmark for brain network analysis with graph neural networks. IEEE TMI 2022 (2022)","DOI":"10.1109\/BigData55660.2022.10020992"},{"key":"68_CR6","doi-asserted-by":"publisher","unstructured":"Cui, H., Dai, W., Zhu, Y., Li, X., He, L., Yang, C.: Interpretable graph neural networks for connectome-based brain disorder analysis. In: MICCAI 2022, pp. 375\u2013385. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_36","DOI":"10.1007\/978-3-031-16452-1_36"},{"key":"68_CR7","doi-asserted-by":"crossref","unstructured":"Dodero, L., Minh, H.Q., Biagio, M.S., Murino, V., Sona, D.: Kernel-based classification for brain connectivity graphs on the riemannian manifold of positive definite matrices. In: 2015 IEEE ISBI, pp. 42\u201345 (2015)","DOI":"10.1109\/ISBI.2015.7163812"},{"key":"68_CR8","doi-asserted-by":"publisher","unstructured":"Endo, M., Poston, K.L., Sullivan, E.V., Fei-Fei, L., Pohl, K.M., Adeli, E.: GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation. MICCAI, pp. 130\u2013139 (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_13","DOI":"10.1007\/978-3-031-16452-1_13"},{"key":"68_CR9","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428 (2019)"},{"key":"68_CR10","unstructured":"Goetz, C.G., et al.: The MDS-sponsored revision of the unified Parkinson\u2019s disease rating scale. Official MDS Dutch Translation (2019)"},{"issue":"3","key":"68_CR11","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1007\/s11682-021-00585-7","volume":"16","author":"M Hanik","year":"2022","unstructured":"Hanik, M., Demirta\u015f, M.A., Gharsallaoui, M.A., Rekik, I.: Predicting cognitive scores with graph neural networks through sample selection learning. Brain Imaging Behav. 16(3), 1123\u20131138 (2022)","journal-title":"Brain Imaging Behav."},{"key":"68_CR12","doi-asserted-by":"crossref","unstructured":"Kawahara, J., et al.: Convolutional neural networks for brain net-works; towards predicting neurodevelopment. Neu-roImage (2017)","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"68_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"68_CR14","doi-asserted-by":"crossref","unstructured":"Klingenberg, W.: Contributions to Riemannian geometry in the large. Ann. Math. 69(3), 654\u2013666 (1959)","DOI":"10.2307\/1970029"},{"issue":"17","key":"68_CR15","first-page":"1","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1\u20135 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"68_CR16","doi-asserted-by":"crossref","unstructured":"Li, K., Su, W., Li, S.H., Jin, Y., Chen, H.B.: Resting state fMRI: a valuable tool for studying cognitive dysfunction in pd. Parkinson\u2019s Disease 2018 (2018)","DOI":"10.1155\/2018\/6278649"},{"key":"68_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102233","volume":"74","author":"x Li","year":"2021","unstructured":"Li, x, et al.: Braingnn: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)","journal-title":"Med. Image Anal."},{"issue":"12263","key":"68_CR18","first-page":"637","volume":"2020","author":"M Lu","year":"2020","unstructured":"Lu, M., et al.: Vision-based estimation of MDS-UPDRS gait scores for assessing Parkinson\u2019s disease motor severity. MICCAI 2020(12263), 637\u2013647 (2020)","journal-title":"MICCAI"},{"key":"68_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102179","volume":"73","author":"M Lu","year":"2021","unstructured":"Lu, M.: Quantifying Parkinson\u2019s disease motor severity under uncertainty using MDS-UPDRS videos. Med. Image Anal. 73, 102179 (2021)","journal-title":"Med. Image Anal."},{"key":"68_CR20","doi-asserted-by":"crossref","unstructured":"Olmos, J., Galvis, J., Mart\u00ednez, F.: Gait patterns coded as Riemannian mean covariances to support Parkinson\u2019s disease diagnosis. In: IBERAMIA, pp. 3\u201314 (2023)","DOI":"10.1007\/978-3-031-22419-5_1"},{"issue":"3","key":"68_CR21","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1002\/ana.24585","volume":"79","author":"KL Poston","year":"2016","unstructured":"Poston, K.L., et al.: Compensatory neural mechanisms in cognitively unimpaired Parkinson disease. Ann. Neurol. 79(3), 448\u2013463 (2016)","journal-title":"Ann. Neurol."},{"key":"68_CR22","doi-asserted-by":"crossref","unstructured":"Ruan, X., et al.: Impaired topographical organization of functional brain networks in parkinson\u2019s disease patients with freezing of gait. Front. Aging Neurosci. 12, 580564 (2020)","DOI":"10.3389\/fnagi.2020.580564"},{"issue":"3","key":"68_CR23","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059\u20131069 (2010)","journal-title":"Neuroimage"},{"key":"68_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118271","volume":"239","author":"M Shahbazi","year":"2021","unstructured":"Shahbazi, M., Shirali, A., Aghajan, H., Nili, H.: Using distance on the Riemannian manifold to compare representations in brain and in models. Neuroimage 239, 118271 (2021)","journal-title":"Neuroimage"},{"key":"68_CR25","doi-asserted-by":"crossref","unstructured":"Togo, H., Nakamura, T., Wakasugi, N., Takahashi, Y., Hanakawa, T.: Interactions across emotional, cognitive and subcortical motor networks underlying freezing of gait. NeuroImage: Clin. 37, 103342 (2023)","DOI":"10.1016\/j.nicl.2023.103342"},{"issue":"20","key":"68_CR26","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. stat 1050(20), 10\u201348550 (2017)","journal-title":"Graph attention networks. stat"},{"key":"68_CR27","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.parkreldis.2016.01.016","volume":"24","author":"G Vervoot","year":"2016","unstructured":"Vervoot, G., et al.: Functional connectivity alterations in the motor and fronto-parietal network relate to behavioral heterogeneity in parkinson\u2019s disease. Parkinsonism Related Disorders 24, 48\u201355 (2016)","journal-title":"Parkinsonism Related Disorders"},{"issue":"3","key":"68_CR28","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1089\/brain.2012.0073","volume":"2","author":"S Whitfield-Gabrieli","year":"2012","unstructured":"Whitfield-Gabrieli, S., Nieto-Castanon, A.: Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain connectivity 2(3), 125\u2013141 (2012)","journal-title":"Brain connectivity"},{"key":"68_CR29","doi-asserted-by":"crossref","unstructured":"Willis, A., et al.: Incidence of Pakinson disease in north America. NPJ Parkinson\u2019s Disease 8(1), 170 (2022)","DOI":"10.1038\/s41531-022-00410-y"},{"key":"68_CR30","doi-asserted-by":"crossref","unstructured":"Wong, E., Anderson, J.S., Zielinski, B.A., Fletcher, P.T.: Riemannian regression and classification models of brain networks applied to autism. In: CNI 2018, Held in Conjunction with MICCAI 2018,pp. 78\u201387 (2018)","DOI":"10.1007\/978-3-030-00755-3_9"},{"key":"68_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117464","volume":"225","author":"K You","year":"2021","unstructured":"You, K., Park, H.J.: Re-visiting Riemannian geometry of symmetric positive definite matrices for the analysis of functional connectivity. Neuroimage 225, 117464 (2021)","journal-title":"Neuroimage"},{"key":"68_CR32","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fnins.2019.00080","volume":"13","author":"H Zhu","year":"2019","unstructured":"Zhu, H., et al.: Abnormal dynamic functional connectivity associated with subcortical networks in Parkinson\u2019s disease: a temporal variability perspective. Front. Neurosci. 13, 80 (2019)","journal-title":"Front. Neurosci."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43895-0_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:35:28Z","timestamp":1710167728000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43895-0_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438943","9783031438950"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43895-0_68","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}