{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:41:01Z","timestamp":1759358461006,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597276"},{"type":"electronic","value":"9783030597283"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59728-3_43","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T10:03:00Z","timestamp":1601632980000},"page":"437-447","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism"],"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":"Nicholas","family":"Wymbs","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":[[2020,9,29]]},"reference":[{"issue":"3","key":"43_CR1","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1109\/TIP.2010.2076294","volume":"20","author":"MV Afonso","year":"2010","unstructured":"Afonso, M.V., Bioucas-Dias, J.M., Figueiredo, M.A.: An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Trans. Image Process 20(3), 681\u2013695 (2010)","journal-title":"IEEE Trans. Image Process"},{"issue":"6","key":"43_CR2","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s10278-018-0093-8","volume":"31","author":"MA Aghdam","year":"2018","unstructured":"Aghdam, M.A., Sharifi, A., Pedram, M.M.: Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J. Digit. Imaging 31(6), 895\u2013903 (2018)","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"43_CR3","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). https:\/\/doi.org\/10.1007\/s12031-007-0029-0","journal-title":"J. Mol. Neurosci."},{"issue":"11\u201312","key":"43_CR4","doi-asserted-by":"publisher","first-page":"3015","DOI":"10.1016\/j.laa.2008.01.029","volume":"428","author":"A Banerjee","year":"2008","unstructured":"Banerjee, A., Jost, J.: On the spectrum of the normalized graph Laplacian. Linear Algebra Appl. 428(11\u201312), 3015\u20133022 (2008)","journal-title":"Linear Algebra Appl."},{"issue":"1","key":"43_CR5","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., Berg, H.J., Jbabdi, S., Rushworth, M.F., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1), 144\u2013155 (2007)","journal-title":"Neuroimage"},{"issue":"1","key":"43_CR6","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neuroimage.2007.04.042","volume":"37","author":"Y Behzadi","year":"2007","unstructured":"Behzadi, Y., et al.: A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37(1), 90\u2013101 (2007)","journal-title":"Neuroimage"},{"issue":"7","key":"43_CR7","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1016\/j.neubiorev.2013.04.008","volume":"37","author":"IJ Bennett","year":"2013","unstructured":"Bennett, I.J., Rypma, B.: Advances in functional neuroanatomy: a review of combined DTI and fMRI studies in healthy younger and older adults. Neurosci. Biobehav. Rev. 37(7), 1201\u20131210 (2013)","journal-title":"Neurosci. Biobehav. Rev."},{"key":"43_CR8","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.neuroimage.2017.03.045","volume":"160","author":"J Cabral","year":"2017","unstructured":"Cabral, J., Kringelbach, M.L., Deco, G.: Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: models and mechanisms. NeuroImage 160, 84\u201396 (2017)","journal-title":"NeuroImage"},{"issue":"5","key":"43_CR9","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1109\/TMI.2017.2786553","volume":"37","author":"B Cai","year":"2017","unstructured":"Cai, B., Zille, P., Stephen, J.M., Wilson, T.W., Calhoun, V.D., Wang, Y.P.: Estimation of dynamic sparse connectivity patterns from resting state fMRI. IEEE Trans. Med. Imaging 37(5), 1224\u20131234 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"43_CR10","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.nicl.2014.07.003","volume":"5","author":"E Damaraju","year":"2014","unstructured":"Damaraju, E., et al.: Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clin. 5, 298\u2013308 (2014)","journal-title":"NeuroImage: Clin."},{"issue":"10","key":"43_CR11","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1111\/j.1469-8749.2007.00734.x","volume":"49","author":"M Dziuk","year":"2007","unstructured":"Dziuk, M., Larson, J.G., Apostu, A., Mahone, E.M., Denckla, M.B., Mostofsky, S.H.: Dyspraxia in autism: association with motor, social, and communicative deficits. Dev. Med. Child Neurol. 49(10), 734\u2013739 (2007)","journal-title":"Dev. Med. Child Neurol."},{"key":"43_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-030-00931-1_19","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"NS D\u2019Souza","year":"2018","unstructured":"D\u2019Souza, N.S., Nebel, M.B., Wymbs, N., Mostofsky, S., Venkataraman, A.: A generative-discriminative basis learning framework to predict clinical severity from resting state functional MRI data. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 163\u2013171. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_19"},{"key":"43_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/978-3-030-20351-1_47","volume-title":"Information Processing in Medical Imaging","author":"NS D\u2019Souza","year":"2019","unstructured":"D\u2019Souza, N.S., Nebel, M.B., Wymbs, N., Mostofsky, S., Venkataraman, A.: A coupled manifold optimization framework to jointly model the functional connectomics and behavioral data spaces. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 605\u2013616. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_47"},{"key":"43_CR14","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. (ed.) MICCAI 2019. LNCS, vol. 11766, pp. 709\u2013717. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_79"},{"issue":"4","key":"43_CR15","first-page":"782","volume":"3","author":"R Everson","year":"1998","unstructured":"Everson, R.: Orthogonal, but not orthonormal, procrustes problems. Adv. Comput. Math. 3(4), 782\u2013790 (1998)","journal-title":"Adv. Comput. Math."},{"issue":"2","key":"43_CR16","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., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782\u2013790 (2012)","journal-title":"Neuroimage"},{"key":"43_CR17","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":"43_CR18","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization (2015)"},{"issue":"10","key":"43_CR19","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.3174\/ajnr.A3263","volume":"34","author":"MH Lee","year":"2013","unstructured":"Lee, M.H., Smyser, C.D., Shimony, J.S.: Resting-state fMRI: a review of methods and clinical applications. Am. J. Neuroradiol. 34(10), 1866\u20131872 (2013)","journal-title":"Am. J. Neuroradiol."},{"issue":"2","key":"43_CR20","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1109\/TSP.2002.807002","volume":"51","author":"JH Manton","year":"2003","unstructured":"Manton, J.H., Mahony, R., Hua, Y.: The geometry of weighted low-rank approximations. IEEE Trans. Sig. Process 51(2), 500\u2013514 (2003)","journal-title":"IEEE Trans. Sig. Process"},{"issue":"3","key":"43_CR21","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1017\/S1355617706060437","volume":"12","author":"SH Mostofsky","year":"2006","unstructured":"Mostofsky, S.H., Dubey, P., Jerath, V.K., Jansiewicz, E.M., Goldberg, M.C., Denckla, M.B.: Developmental dyspraxia is not limited to imitation in children with autism spectrum disorders. J. Int. Neuropsychol. Soc. 12(3), 314\u2013326 (2006)","journal-title":"J. Int. Neuropsychol. Soc."},{"issue":"8","key":"43_CR22","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.biopsych.2015.08.029","volume":"79","author":"MB Nebel","year":"2016","unstructured":"Nebel, M.B., et al.: Intrinsic visual-motor synchrony correlates with social deficits in autism. Biol. Psychiatry 79(8), 633\u2013641 (2016)","journal-title":"Biol. Psychiatry"},{"issue":"4","key":"43_CR23","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. Pharmacoeconomics Outcomes Res. 12(4), 485\u2013503 (2012)","journal-title":"Expert Rev. Pharmacoeconomics Outcomes Res."},{"issue":"4","key":"43_CR24","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.rasd.2013.01.002","volume":"7","author":"LB Pouw","year":"2013","unstructured":"Pouw, L.B., Rieffe, C., Stockmann, L., Gadow, K.D.: The link between emotion regulation, social functioning, and depression in boys with ASD. Res. Autism Spectrum Disord. 7(4), 549\u2013556 (2013)","journal-title":"Res. Autism Spectrum Disord."},{"key":"43_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-319-10443-0_23","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"T Price","year":"2014","unstructured":"Price, T., Wee, C.-Y., Gao, W., Shen, D.: Multiple-network classification of childhood autism using functional connectivity dynamics. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 177\u2013184. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10443-0_23"},{"key":"43_CR26","doi-asserted-by":"publisher","first-page":"101966","DOI":"10.1016\/j.nicl.2019.101966","volume":"24","author":"L Rabany","year":"2019","unstructured":"Rabany, L., et al.: Dynamic functional connectivity in schizophrenia and autism spectrum disorder: convergence, divergence and classification. NeuroImage Clin. 24, 101966 (2019)","journal-title":"NeuroImage Clin."},{"key":"43_CR27","doi-asserted-by":"publisher","first-page":"897","DOI":"10.3389\/fnhum.2014.00897","volume":"8","author":"B Rashid","year":"2014","unstructured":"Rashid, B., Damaraju, E., Pearlson, G.D., Calhoun, V.D.: Dynamic connectivity states estimated from resting fMRI identify differences among schizophrenia, bipolar disorder, and healthy control subjects. Front. Hum. Neurosci. 8, 897 (2014)","journal-title":"Front. Hum. Neurosci."},{"issue":"1","key":"43_CR28","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/BF02591723","volume":"25","author":"RB Schnabel","year":"1983","unstructured":"Schnabel, R.B., Toint, P.L.: Forcing sparsity by projecting with respect to a non-diagonally weighted Frobenius norm. Math. Program. 25(1), 125\u2013129 (1983). https:\/\/doi.org\/10.1007\/BF02591723","journal-title":"Math. Program."},{"issue":"3","key":"43_CR29","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.neuroimage.2008.07.063","volume":"43","author":"P Skudlarski","year":"2008","unstructured":"Skudlarski, P., Jagannathan, K., Calhoun, V.D., Hampson, M., Skudlarska, B.A., Pearlson, G.: Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. Neuroimage 43(3), 554\u2013561 (2008)","journal-title":"Neuroimage"},{"issue":"34","key":"43_CR30","doi-asserted-by":"publisher","first-page":"12569","DOI":"10.1073\/pnas.0800005105","volume":"105","author":"D Sridharan","year":"2008","unstructured":"Sridharan, D., et al.: A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Nat. Acad. Sci. 105(34), 12569\u201312574 (2008)","journal-title":"Proc. Nat. Acad. Sci."},{"key":"43_CR31","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. Hum. Neurosci. 7, 235 (2013)","journal-title":"Front. Hum. Neurosci."},{"issue":"1","key":"43_CR32","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273\u2013289 (2002)","journal-title":"Neuroimage"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59728-3_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:05:25Z","timestamp":1759356325000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59728-3_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597276","9783030597283"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59728-3_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 due to the COVID-19 pandemic.","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)"}}]}}