{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T13:33:44Z","timestamp":1758807224006,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872335"},{"type":"electronic","value":"9783030872342"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87234-2_59","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"625-636","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes"],"prefix":"10.1007","author":[{"given":"Niharika Shimona","family":"D\u2019Souza","sequence":"first","affiliation":[]},{"given":"Mary Beth","family":"Nebel","sequence":"additional","affiliation":[]},{"given":"Deana","family":"Crocetti","sequence":"additional","affiliation":[]},{"given":"Joshua","family":"Robinson","sequence":"additional","affiliation":[]},{"given":"Stewart","family":"Mostofsky","sequence":"additional","affiliation":[]},{"given":"Archana","family":"Venkataraman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"1","key":"59_CR1","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s12031-007-0029-0","volume":"34","author":"Y Assaf","year":"2008","unstructured":"Assaf, Y., Pasternak, O.: Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J. Mol. Neurosci. 34(1), 51\u201361 (2008)","journal-title":"J. Mol. Neurosci."},{"issue":"1","key":"59_CR2","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neuroimage.2006.09.018","volume":"34","author":"TE Behrens","year":"2007","unstructured":"Behrens, T.E., et al.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34(1), 144\u2013155 (2007)","journal-title":"NeuroImage"},{"issue":"1","key":"59_CR3","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neuroimage.2007.04.042","volume":"37","author":"Y Behzadi","year":"2017","unstructured":"Behzadi, Y., et al.: A component based noise correction method (CompCor) for bold and perfusion based FMRI. NeuroImage 37(1), 90\u2013101 (2017)","journal-title":"NeuroImage"},{"key":"59_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/978-3-030-59728-3_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"A Bessadok","year":"2020","unstructured":"Bessadok, A., Mahjoub, M.A., Rekik, I.: Topology-aware generative adversarial network for joint prediction of multiple brain graphs from a single brain graph. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 551\u2013561. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_54"},{"issue":"3","key":"59_CR5","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1177\/1073191112446655","volume":"19","author":"WB Bilker","year":"2012","unstructured":"Bilker, W.B., et al.: Development of abbreviated nine-item forms of the Raven\u2019s standard progressive matrices test. Assessment 19(3), 354\u2013369 (2012)","journal-title":"Assessment"},{"key":"59_CR6","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fninf.2020.00025","volume":"14","author":"G Castellazzi","year":"2020","unstructured":"Castellazzi, G., et al.: A machine learning approach for the differential diagnosis of Alzheimer and vascular dementia fed by MRI selected features. Front. Neuroinformatics 14, 25 (2020)","journal-title":"Front. Neuroinformatics"},{"issue":"1","key":"59_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"OY Ch\u00e9n","year":"2019","unstructured":"Ch\u00e9n, O.Y., et al.: Resting-state brain information flow predicts cognitive flexibility in humans. Sci. Rep. 9(1), 1\u201316 (2019)","journal-title":"Sci. Rep."},{"issue":"1","key":"59_CR8","doi-asserted-by":"publisher","first-page":"4741","DOI":"10.1038\/s41598-018-23051-9","volume":"8","author":"SH Chu","year":"2018","unstructured":"Chu, S.H., et al.: Function-specific and enhanced brain structural connectivity mapping via joint modeling of diffusion and functional MRI. Sci. Rep. 8(1), 4741 (2018)","journal-title":"Sci. Rep."},{"key":"59_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Z., et al.: Deep manifold learning of symmetric positive definite matrices with application to face recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.11232"},{"key":"59_CR10","doi-asserted-by":"crossref","unstructured":"Duncan, J.: Frontal lobe function and general intelligence: why it matters. Cortex J. Devoted Study Nervous Syst. Behav. 41(2), 215\u2013217 (2005)","DOI":"10.1016\/S0010-9452(08)70896-7"},{"key":"59_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/978-3-030-32248-9_79","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"NS D\u2019Souza","year":"2019","unstructured":"D\u2019Souza, N.S., Nebel, M.B., Wymbs, N., Mostofsky, S., Venkataraman, A.: Integrating neural networks and dictionary learning for multidimensional clinical characterizations from functional connectomics data. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 709\u2013717. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_79"},{"key":"59_CR12","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","volume":"80","author":"V Essen","year":"2013","unstructured":"Essen, V., et al.: The WU-MINN human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","journal-title":"Neuroimage"},{"issue":"9","key":"59_CR13","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1038\/nrn2201","volume":"8","author":"MD Fox","year":"2007","unstructured":"Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neuro. 8(9), 700\u2013711 (2007)","journal-title":"Nat. Rev. Neuro."},{"issue":"3","key":"59_CR14","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s00429-017-1539-3","volume":"223","author":"M Fukushima","year":"2018","unstructured":"Fukushima, M., et al.: Structure-function relationships during segregated and integrated network states of human brain functional connectivity. Brain Struct. Funct. 223(3), 1091\u20131106 (2018)","journal-title":"Brain Struct. Funct."},{"key":"59_CR15","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neuroimage.2013.05.011","volume":"81","author":"K Hahn","year":"2013","unstructured":"Hahn, K., et al.: Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer\u2019s disease\u2014revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence. Neuroimage 81, 96\u2013109 (2013)","journal-title":"Neuroimage"},{"issue":"6","key":"59_CR16","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1073\/pnas.0811168106","volume":"106","author":"CJ Honey","year":"2009","unstructured":"Honey, C.J., et al.: Predicting human resting-state functional connectivity from structural connectivity. Proc. of the Nat. Acad. Sci. 106(6), 2035\u20132040 (2009)","journal-title":"Proc. of the Nat. Acad. Sci."},{"key":"59_CR17","doi-asserted-by":"crossref","unstructured":"Huang, Z., Van Gool, L.: A Riemannian network for SPD matrix learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10866"},{"issue":"2","key":"59_CR18","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., et al.: FSL. NeuroImage 62(2), 782\u2013790 (2012)","journal-title":"NeuroImage"},{"key":"59_CR19","unstructured":"Karras, T., et al.: Training generative adversarial networks with limited data. arXiv preprint arXiv:2006.06676 (2020)"},{"key":"59_CR20","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., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038\u20131049 (2017)","journal-title":"NeuroImage"},{"key":"59_CR21","unstructured":"Kiar, G., et al.: ndmg: Neurodata\u2019s MRI graphs pipeline. Zenodo (2016)"},{"key":"59_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"59_CR23","first-page":"534","volume":"13","author":"D Le Bihan","year":"2001","unstructured":"Le Bihan, D., et al.: Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging Official J. Int. Soc. Magn. Reson. Med. 13(4), 534\u2013546 (2001)","journal-title":"J. Magn. Reson. Imaging Official J. Int. Soc. Magn. Reson. Med."},{"key":"59_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"YA LeCun","year":"2012","unstructured":"LeCun, Y.A., Bottou, L., Orr, G.B., M\u00fcller, K.-R.: Efficient BackProp. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 9\u201348. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_3"},{"issue":"10","key":"59_CR25","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.3174\/ajnr.A3263","volume":"34","author":"MH Lee","year":"2013","unstructured":"Lee, M.H., et al.: Resting-state FMRI: a review of methods and clinical applications. Am. J. Neuroradiol. 34(10), 1866\u20131872 (2013)","journal-title":"Am. J. Neuroradiol."},{"key":"59_CR26","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.cmpb.2015.11.012","volume":"125","author":"L Lin","year":"2016","unstructured":"Lin, L., et al.: Predicting healthy older adult\u2019s brain age based on structural connectivity networks using artificial neural networks. Comput. Meth. Prog. Biomed. 125, 8\u201317 (2016)","journal-title":"Comput. Meth. Prog. Biomed."},{"key":"59_CR27","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Community-preserving graph convolutions for structural and functional joint embedding of brain networks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1163\u20131168. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9005586"},{"issue":"3","key":"59_CR28","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s40708-015-0019-x","volume":"2","author":"S Liu","year":"2015","unstructured":"Liu, S., et al.: Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform. 2(3), 167\u2013180 (2015)","journal-title":"Brain Inform."},{"key":"59_CR29","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"10","key":"59_CR30","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.: Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15(10), 483\u2013506 (2011)","journal-title":"Trends Cogn. Sci."},{"key":"59_CR31","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neuroimage.2015.02.001","volume":"111","author":"A Mess\u00e9","year":"2015","unstructured":"Mess\u00e9, A., et al.: Predicting functional connectivity from structural connectivity via computational models using MRI: an extensive comparison study. NeuroImage 111, 65\u201375 (2015)","journal-title":"NeuroImage"},{"issue":"3","key":"59_CR32","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1017\/S1355617706060437","volume":"12","author":"SH Mostofsky","year":"2006","unstructured":"Mostofsky, S.H., et al.: Developmental dyspraxia is not limited to imitation in children with autism spectrum disorders. J. Int. Neuropsychol. Soc. JINS 12(3), 314 (2006)","journal-title":"J. Int. Neuropsychol. Soc. JINS"},{"issue":"4","key":"59_CR33","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1586\/erp.12.29","volume":"12","author":"N Payakachat","year":"2012","unstructured":"Payakachat, N., et al.: Autism spectrum disorders: a review of measures for clinical, health services and cost-effectiveness applications. Expert Rev. Pharmacoecon. Outcomes Res. 12(4), 485\u2013503 (2012)","journal-title":"Expert Rev. Pharmacoecon. Outcomes Res."},{"issue":"12","key":"59_CR34","doi-asserted-by":"publisher","first-page":"4407","DOI":"10.1523\/JNEUROSCI.3335-10.2011","volume":"31","author":"C Sestieri","year":"2011","unstructured":"Sestieri, C., et al.: Episodic memory retrieval, parietal cortex, and the default mode network: functional and topographic analyses. J. Neurosci. 31(12), 4407\u20134420 (2011)","journal-title":"J. Neurosci."},{"key":"59_CR35","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neuroimage.2013.05.039","volume":"80","author":"SM Smith","year":"2013","unstructured":"Smith, S.M., et al.: Resting-state FMRI in the human connectome project. Neuroimage 80, 144\u2013168 (2013)","journal-title":"Neuroimage"},{"key":"59_CR36","doi-asserted-by":"publisher","first-page":"235","DOI":"10.3389\/fnhum.2013.00235","volume":"7","author":"J Sui","year":"2013","unstructured":"Sui, J., et al.: Combination of resting state fMRI, DTI, and sMRI data to discriminate schizophrenia by N-way MCCA+ JICA. Front. Human Neurosci. 7, 235 (2013)","journal-title":"Front. Human Neurosci."},{"key":"59_CR37","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Manifold alignment using procrustes analysis. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1120\u20131127 (2008)","DOI":"10.1145\/1390156.1390297"},{"key":"59_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-030-00755-3_9","volume-title":"Connectomics in NeuroImaging","author":"E Wong","year":"2018","unstructured":"Wong, E., Anderson, J.S., Zielinski, B.A., Fletcher, P.T.: Riemannian regression and classification models of brain networks applied to autism. In: Wu, G., Rekik, I., Schirmer, M.D., Chung, A.W., Munsell, B. (eds.) CNI 2018. LNCS, vol. 11083, pp. 78\u201387. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00755-3_9"},{"key":"59_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-59728-3_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Wang, L., Zhu, D.: Recovering brain structural connectivity from functional connectivity via Multi-GCN based generative adversarial network. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 53\u201361. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_6"},{"key":"59_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-030-59728-3_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Zhan, L., Thompson, P., Wang, Y.: Deep representation learning for multimodal brain networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 613\u2013624. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_60"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87234-2_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:39:37Z","timestamp":1673311177000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87234-2_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872335","9783030872342"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87234-2_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}