{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:02:26Z","timestamp":1743127346474,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"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_27","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"287-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Cognitive Scores by Joint Use of Movie-Watching fMRI Connectivity and Eye Tracking via Attention-CensNet"],"prefix":"10.1007","author":[{"given":"Jiaxing","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyang","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibin","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaonai","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwei","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Thiebaut de Schotten, M., Forkel, S.J.: The emergent properties of the connected brain. Science, 378(6619), 505\u2013510 (2022)","key":"27_CR1","DOI":"10.1126\/science.abq2591"},{"key":"27_CR2","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neuroimage.2019.04.016","volume":"196","author":"J Li","year":"2019","unstructured":"Li, J., et al.: Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 196, 126\u2013141 (2019)","journal-title":"Neuroimage"},{"key":"27_CR3","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.neuroimage.2016.12.002","volume":"147","author":"W Huijbers","year":"2017","unstructured":"Huijbers, W., Van Dijk, K.R.A., Boenniger, M.M., Stirnberg, R., Breteler, M.M.: Less head motion during MRI under task than resting-state conditions. Neuroimage 147, 111\u2013120 (2017)","journal-title":"Neuroimage"},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.neuroimage.2013.05.033","volume":"80","author":"DM Barch","year":"2013","unstructured":"Barch, D.M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169\u2013189 (2013)","journal-title":"Neuroimage"},{"issue":"8","key":"27_CR5","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1016\/j.tics.2019.05.004","volume":"23","author":"S Sonkusare","year":"2019","unstructured":"Sonkusare, S., Breakspear, M., Guo, C.: Naturalistic stimuli in neuroscience: critically acclaimed. Trends Cogn. Sci. 23(8), 699\u2013714 (2019)","journal-title":"Trends Cogn. Sci."},{"issue":"5664","key":"27_CR6","doi-asserted-by":"publisher","first-page":"1634","DOI":"10.1126\/science.1089506","volume":"303","author":"U Hasson","year":"2004","unstructured":"Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., Malach, R.: Intersubject synchronization of cortical activity during natural vision. Science 303(5664), 1634\u20131640 (2004)","journal-title":"Science"},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neuroimage.2017.03.064","volume":"160","author":"ES Finn","year":"2017","unstructured":"Finn, E.S., Scheinost, D., Finn, D.M., Shen, X., Papademetris, X., Constable, R.T.: Can brain state be manipulated to emphasize individual differences in functional con-nectivity? Neuroimage 160, 140\u2013151 (2017)","journal-title":"Neuroimage"},{"key":"27_CR8","doi-asserted-by":"publisher","first-page":"117963","DOI":"10.1016\/j.neuroimage.2021.117963","volume":"235","author":"ES Finn","year":"2021","unstructured":"Finn, E.S., Bandettini, P.A.: Movie-watching outperforms rest for functional connectivity-based prediction of behavior. Neuroimage 235, 117963 (2021)","journal-title":"Neuroimage"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"116276","DOI":"10.1016\/j.neuroimage.2019.116276","volume":"206","author":"T He","year":"2020","unstructured":"He, T., et al.: Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and de-mographics. Neuroimage 206, 116276 (2020)","journal-title":"Neuroimage"},{"doi-asserted-by":"crossref","unstructured":"Gal, S., Coldham, Y., Bernstein-Eliav, M.: Act natural: functional connectivity from naturalistic stimuli fMRI outperforms resting-state in predicting brain activity. bioRxiv (2021)","key":"27_CR10","DOI":"10.1101\/2021.11.01.466749"},{"key":"27_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41593-022-01059-9","volume":"25","author":"T He","year":"2022","unstructured":"He, T., et al.: Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nature Neurosci. 25, 1\u201310 (2022)","journal-title":"Nature Neurosci."},{"issue":"8","key":"27_CR12","doi-asserted-by":"publisher","first-page":"2384","DOI":"10.3390\/s20082384","volume":"20","author":"JZ Lim","year":"2020","unstructured":"Lim, J.Z., Mountstephens, J., Teo, J.: Emotion recognition using eye-tracking: taxonomy, review and current challenges. Sensors 20(8), 2384 (2020)","journal-title":"Sensors"},{"issue":"3423","key":"27_CR13","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1126\/science.132.3423.349","volume":"132","author":"EH Hess","year":"1960","unstructured":"Hess, E.H., Polt, J.M.: Pupil size as related to interest value of visual stimuli. Science 132(3423), 349\u2013350 (1960)","journal-title":"Science"},{"issue":"1","key":"27_CR14","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1006\/obhd.1996.0087","volume":"68","author":"GL Lohse","year":"1996","unstructured":"Lohse, G.L., Johnson, E.J.: A comparison of two process tracing methods for choice tasks. Organ. Behav. Hum. Decis. Process. 68(1), 28\u201343 (1996)","journal-title":"Organ. Behav. Hum. Decis. Process."},{"issue":"3","key":"27_CR15","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1093\/cercor\/bhz157","volume":"30","author":"J Son","year":"2020","unstructured":"Son, J., et al.: Evaluating fMRI-based estimation of eye gaze during naturalistic viewing. Cereb. Cortex 30(3), 1171\u20131184 (2020)","journal-title":"Cereb. Cortex"},{"doi-asserted-by":"crossref","unstructured":"Gao, J., et al.: Prediction of cognitive scores by movie-watching FMRI connectivity and eye movement via spectral graph convolutions. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2022)","key":"27_CR16","DOI":"10.1109\/ISBI52829.2022.9761565"},{"doi-asserted-by":"crossref","unstructured":"Jiang, X., Ji, P., Li, S.: CensNet: convolution with edge-node switching in graph neural networks. In: IJCAI, pp. 2656\u20132662 (2019)","key":"27_CR17","DOI":"10.24963\/ijcai.2019\/369"},{"doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","key":"27_CR18","DOI":"10.1109\/CVPR.2018.00745"},{"unstructured":"Elam, J.: https:\/\/www.humanconnectome.org\/study\/hcp-young-adult\/article\/first-release-of-7t-mr-image-data. Accessed 20 June 2016","key":"27_CR19"},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.neuroimage.2014.03.034","volume":"95","author":"L Griffanti","year":"2014","unstructured":"Griffanti, L., et al.: ICA-based artefact removal and accelerated fMRI ac-quisition for improved resting state network imaging. Neuroimage 95, 232\u2013247 (2014)","journal-title":"Neuroimage"},{"doi-asserted-by":"crossref","unstructured":"Glasser, M.F., et al., Wu-Minn HCP Consortium: The minimal preprocessing pipelines for the human connectome project. Neuroimage, 80, 105\u2013124 (2013)","key":"27_CR21","DOI":"10.1016\/j.neuroimage.2013.04.127"},{"issue":"1","key":"27_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2010.06.010","volume":"53","author":"C Destrieux","year":"2010","unstructured":"Destrieux, C., Fischl, B., Dale, A., Halgren, E.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1), 1\u201315 (2010)","journal-title":"Neuroimage"},{"key":"27_CR23","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":"27_CR24","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.dcn.2013.01.001","volume":"5","author":"C Tye","year":"2013","unstructured":"Tye, C., et al.: Neurophysiological responses to faces and gaze direction differentiate children with ASD, ADHD and ASD + ADHD. Dev. Cogn. Neurosci. 5, 71\u201385 (2013)","journal-title":"Dev. Cogn. 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_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:31:10Z","timestamp":1710167470000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43895-0_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438943","9783031438950"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43895-0_27","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)"}}]}}