{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:23:34Z","timestamp":1743049414783,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030332259"},{"type":"electronic","value":"9783030332266"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-33226-6_8","type":"book-chapter","created":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T15:24:59Z","timestamp":1572449099000},"page":"66-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Brain Hemodynamic Response Patterns via Deep Recurrent Autoencoder"],"prefix":"10.1007","author":[{"given":"Shijie","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yan","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Yaowu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Junwei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Li","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Tianming","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1038\/nature06976","volume":"453","author":"NK Logothetis","year":"2008","unstructured":"Logothetis, N.K.: What we can do and what we cannot do with fMRI. Nature 453, 869\u2013878 (2008)","journal-title":"Nature"},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1002\/hbm.460020402","volume":"2","author":"KJ Friston","year":"1994","unstructured":"Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., Frackowiak, R.S.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189\u2013210 (1994)","journal-title":"Hum. Brain Mapp."},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/S0730-725X(99)00028-4","volume":"17","author":"AH Andersen","year":"1999","unstructured":"Andersen, A.H., Gash, D.M., Avison, M.J.: Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. Magn. Reson. Imaging 17, 795\u2013815 (1999)","journal-title":"Magn. Reson. Imaging"},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1097\/00004728-199903000-00016","volume":"23","author":"BB Biswal","year":"1999","unstructured":"Biswal, B.B., Ulmer, J.L.: Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. J. Comput. Assist. Tomogr. 23, 265\u2013271 (1999)","journal-title":"J. Comput. Assist. Tomogr."},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1109\/TBME.2014.2369495","volume":"62","author":"J Lv","year":"2015","unstructured":"Lv, J., et al.: Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function. IEEE Trans. Biomed. Eng. 62, 1120\u20131131 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1109\/TMI.2015.2418734","volume":"34","author":"S Zhao","year":"2015","unstructured":"Zhao, S., et al.: Supervised dictionary learning for inferring concurrent brain networks. IEEE Trans. Med. Imaging 34, 2036\u20132045 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/TBME.2018.2831186","volume":"66","author":"W Zhang","year":"2018","unstructured":"Zhang, W., et al.: Experimental comparisons of sparse dictionary learning and independent component analysis for brain network inference from fMRI data. IEEE Trans. Biomed. Eng. 66, 289\u2013299 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"8_CR8","unstructured":"Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. Computer Science (2015)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Cui, Y., et al.: Identifying brain networks at multiple time scales via deep recurrent neural network. IEEE J. Biomed. Health Inform. (2018)","DOI":"10.1109\/JBHI.2018.2882885"},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/BF00332918","volume":"59","author":"H Bourlard","year":"1988","unstructured":"Bourlard, H., Kamp, Y.: Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59, 291\u2013294 (1988)","journal-title":"Biol. Cybern."},{"key":"8_CR11","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"}],"container-title":["Lecture Notes in Computer Science","Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33226-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:15:34Z","timestamp":1728519334000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33226-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030332259","9783030332266"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33226-6_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MBIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Multimodal Brain Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","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":"mbia2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/crystal.uta.edu\/~mbia\/","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":"16","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":"16","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":"100% - 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","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":"3","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":"No","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"}]}}