{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T09:22:19Z","timestamp":1769332939859,"version":"3.49.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030008888","type":"print"},{"value":"9783030008895","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-00889-5_16","type":"book-chapter","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T10:26:49Z","timestamp":1537352809000},"page":"137-145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["3D Convolutional Neural Networks for Classification of Functional Connectomes"],"prefix":"10.1007","author":[{"given":"Meenakshi","family":"Khosla","sequence":"first","affiliation":[]},{"given":"Keith","family":"Jamison","sequence":"additional","affiliation":[]},{"given":"Amy","family":"Kuceyeski","sequence":"additional","affiliation":[]},{"given":"Mert R.","family":"Sabuncu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,20]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.neuroimage.2016.10.045","volume":"147","author":"A Abraham","year":"2017","unstructured":"Abraham, A., et al.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147, 736\u2013745 (2017)","journal-title":"NeuroImage"},{"key":"16_CR2","unstructured":"Brown, C.J., Hamarneh, G.: Machine learning on human connectome data from MRI. CoRR, 1611.08699 (2016)"},{"issue":"8","key":"16_CR3","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1002\/hbm.21333","volume":"33","author":"CR Cameron","year":"2012","unstructured":"Cameron, C.R., et al.: A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33(8), 1914\u20131928 (2012)","journal-title":"Hum. Brain Mapp."},{"key":"16_CR4","unstructured":"Craddock, C., et al.: The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Front. Neuroinformatics (2013)"},{"issue":"3","key":"16_CR5","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3), 968\u2013980 (2006)","journal-title":"NeuroImage"},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"170010","DOI":"10.1038\/sdata.2017.10","volume":"4","author":"A Di Martino","year":"2017","unstructured":"Di Martino, A., et al.: Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. Data 4, 170010 (2017)","journal-title":"Sci. Data"},{"issue":"5997","key":"16_CR7","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1126\/science.1194144","volume":"329","author":"NUF Dosenbach","year":"2010","unstructured":"Dosenbach, N.U.F., Nardos, B., Cohen, A.L., et al.: Prediction of individual brain maturity using fMRI. Science 329(5997), 1358\u20131361 (2010)","journal-title":"Science"},{"issue":"4","key":"16_CR8","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1016\/j.neuroimage.2004.12.034","volume":"25","author":"SB Eickhoff","year":"2005","unstructured":"Eickhoff, S.B., et al.: A new spm toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25(4), 1325\u20131335 (2005)","journal-title":"NeuroImage"},{"key":"16_CR9","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.nicl.2017.08.017","volume":"17","author":"AS Heinsfeld","year":"2018","unstructured":"Heinsfeld, A.S., et al.: Identification of autism spectrum disorder using deep learning and the abide dataset. NeuroImage Clin. 17, 16\u201323 (2018)","journal-title":"NeuroImage Clin."},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Kaiser, M.: A Tutorial in Connectome Analysis: Topological and Spatial Features of Brain Networks. ArXiv e-prints, May 2011","DOI":"10.1016\/j.neuroimage.2011.05.025"},{"key":"16_CR11","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":"16_CR12","doi-asserted-by":"crossref","unstructured":"Kong, R., et al.: Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex (2018)","DOI":"10.1101\/213041"},{"issue":"3","key":"16_CR13","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1002\/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8","volume":"10","author":"JL Lancaster","year":"2000","unstructured":"Lancaster, J.L., Woldorff, M.G., et al.: Automated talairach atlas labels for functional brain mapping. Hum. Brain Mapp. 10(3), 120\u2013131 (2000)","journal-title":"Hum. Brain Mapp."},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Mennes, M., et al.: Resting state functional connectivity correlates of inhibitory control in children with ADHD. Front Psychiatry (2012)","DOI":"10.3389\/fpsyt.2011.00083"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.neuroimage.2014.03.028","volume":"96","author":"J Muschelli","year":"2014","unstructured":"Muschelli, J., Nebel, M.B., et al.: Reduction of motion-related artifacts in resting state fMRI using aCompCor. NeuroImage 96, 22\u201335 (2014)","journal-title":"NeuroImage"},{"key":"16_CR16","unstructured":"Niepert, M., et al.: Learning convolutional neural networks for graphs. In: Proceedings of Machine Learning Research, New York, USA, June 2016"},{"issue":"6","key":"16_CR17","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.bpsc.2017.04.004","volume":"2","author":"A Padmanabhan","year":"2017","unstructured":"Padmanabhan, A., et al.: The default mode network in autism. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2(6), 476\u2013486 (2017)","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"16_CR18","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.nicl.2014.12.013","volume":"7","author":"M Plitt","year":"2015","unstructured":"Plitt, M., et al.: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage Clin. 7, 359\u2013366 (2015)","journal-title":"NeuroImage Clin."},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.neuroimage.2013.08.048","volume":"84","author":"JD Power","year":"2014","unstructured":"Power, J.D., Mitra, A., et al.: Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage 84, 320\u2013341 (2014)","journal-title":"NeuroImage"},{"key":"16_CR20","unstructured":"Simonyan, K., et al.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR, 1312.6034 (2013)"},{"issue":"1","key":"16_CR21","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"},{"key":"16_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/978-3-642-15705-9_25","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2010","author":"G Varoquaux","year":"2010","unstructured":"Varoquaux, G., Baronnet, F., Kleinschmidt, A., Fillard, P., Thirion, B.: Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 200\u2013208. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15705-9_25"}],"container-title":["Lecture Notes in Computer Science","Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00889-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:03:32Z","timestamp":1695081812000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00889-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030008888","9783030008895"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00889-5_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"20 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DLMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Deep Learning in Medical Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dlmia2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cs.adelaide.edu.au\/~dlmia4\/","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":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","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":"39","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":"46% - 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":"2.5","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":"n\/a","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}