{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:37:02Z","timestamp":1763545022482,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":25,"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_7","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T10:03:00Z","timestamp":1601632980000},"page":"62-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["From Connectomic to Task-Evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-State Functional Connectivity"],"prefix":"10.1007","author":[{"given":"Gia H.","family":"Ngo","sequence":"first","affiliation":[]},{"given":"Meenakshi","family":"Khosla","sequence":"additional","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":[[2020,9,29]]},"reference":[{"key":"7_CR1","unstructured":"Biswal, B.B., et al.: Toward discovery science of human brain function. Proc. Natl. Acad. Sci. 107(10), 4734\u20134739 (2010)"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Kelly, C., Biswal, B.B., Cameron Craddock, R., Xavier Castellanos, F., Milham, M.P.: Characterizing variation in the functional connectome: promise and pitfalls. Trends Cogn. Sci. 16(3), 181\u2013188 (2012)","DOI":"10.1016\/j.tics.2012.02.001"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Khosla, M., Jamison, K., Ngo, G.H., Kuceyeski, A., Sabuncu, M.R.: Machine learning in resting-state fMRI analysis. Magn. Reson. Imaging 64, 101\u2013121 (2019)","DOI":"10.1016\/j.mri.2019.05.031"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Dosenbach, N.U.F., et al.: Prediction of individual brain maturity using fMRI. Science 329(5997), 1358\u20131361 (2010)","DOI":"10.1126\/science.1194144"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Finn, E.S., et al.: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18(11), 1664 (2015)","DOI":"10.1038\/nn.4135"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Tavor, I., Jones, O.P., Mars, R.B., Smith, S.M., Behrens, T.E., Jbabdi, S.: Task-free MRI predicts individual differences in brain activity during task performance. Science 352(6282), 216\u2013220 (2016)","DOI":"10.1126\/science.aad8127"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Cole, M.W., Ito, T., Bassett, D.S., Schultz, D.H.: Activity flow over resting-state networks shapes cognitive task activations. Nat. Neurosci. 19(12), 1718 (2016)","DOI":"10.1038\/nn.4406"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Glasser, M.F., et al.: The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105\u2013124 (2013)","DOI":"10.1016\/j.neuroimage.2013.04.127"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R.: Ensemble learning with 3d convolutional neural networks for functional connectome-based prediction. Neuroimage 199, 651\u2013662 (2019)","DOI":"10.1016\/j.neuroimage.2019.06.012"},{"key":"7_CR10","unstructured":"Chiyu, M.J., Huang, J., Kashinath, K., Prabhat, P.M., Niessner, M.: Spherical CNNs on unstructured grids. In: International Conference on Learning Representations (2019)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Van Essen, D.C., Glasser, M.F., Dierker, D.L., Harwell, J., Coalson, T.: Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22(10), 2241\u20132262 (2012)","DOI":"10.1093\/cercor\/bhr291"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8(4), 272\u2013284 (1999)","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Accurate nonlinear mapping between MNI volumetric and freesurfer surface coordinate systems. Hum. Brain Mapp. 39(9), 3793\u20133808 (2018)","DOI":"10.1002\/hbm.24213"},{"key":"7_CR14","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":"7_CR15","unstructured":"Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014\u20132023 (2016)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Baumgardner, J.R., Frederickson, P.O.: Icosahedral discretization of the two-sphere. SIAM J. Numer. Anal. 22(6), 1107\u20131115 (1985)","DOI":"10.1137\/0722066"},{"key":"7_CR17","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop (2015)"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Smith, S.M., et al.: Resting-state fMRI in the human connectome project. Neuroimage 80, 144\u2013168 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.039"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Barch, D.M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169\u2013189 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.033"},{"issue":"1","key":"7_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-25089-1","volume":"8","author":"E Amico","year":"2018","unstructured":"Amico, E., Go\u00f1i, J.: The quest for identifiability in human functional connectomes. Sci. Rep. 8(1), 1\u201314 (2018)","journal-title":"Sci. Rep."},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Yeo, B.T.T., et al.: Functional specialization and flexibility in human association cortex. Cereb. Cortex 25(10), 3654\u20133672 (2015)","DOI":"10.1093\/cercor\/bhu217"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Ngo, G.H., et al.: Beyond consensus: embracing heterogeneity in curated neuroimaging meta-analysis. NeuroImage 200, 142\u2013158 (2019)","DOI":"10.1016\/j.neuroimage.2019.06.037"}],"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_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:05:50Z","timestamp":1759356350000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59728-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597276","9783030597283"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59728-3_7","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)"}}]}}