{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T14:21:35Z","timestamp":1770906095080,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597276","type":"print"},{"value":"9783030597283","type":"electronic"}],"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_42","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T10:03:00Z","timestamp":1601632980000},"page":"428-436","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Deep Pattern Recognition Approach for Inferring Respiratory Volume Fluctuations from fMRI Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7197-1248","authenticated-orcid":false,"given":"Roza G.","family":"Bayrak","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8885-0813","authenticated-orcid":false,"given":"Jorge A.","family":"Salas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2096-8065","authenticated-orcid":false,"given":"Yuankai","family":"Huo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1541-9579","authenticated-orcid":false,"given":"Catie","family":"Chang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"42_CR1","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.neuroimage.2013.04.001","volume":"80","author":"K Murphy","year":"2013","unstructured":"Murphy, K., Birn, R.M., Bandettini, P.A.: Resting-state fMRI confounds and cleanup. Neuroimage 80, 349\u2013359 (2013)","journal-title":"Neuroimage"},{"issue":"4","key":"42_CR2","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.1016\/j.neuroimage.2006.02.048","volume":"31","author":"RM Birn","year":"2006","unstructured":"Birn, R.M., et al.: Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage 31(4), 1536\u20131548 (2006)","journal-title":"Neuroimage"},{"issue":"2","key":"42_CR3","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/j.neuroimage.2007.11.059","volume":"40","author":"RM Birn","year":"2008","unstructured":"Birn, R.M., et al.: The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40(2), 644\u2013654 (2008)","journal-title":"Neuroimage"},{"issue":"4","key":"42_CR4","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1016\/j.neuroimage.2003.11.025","volume":"21","author":"RG Wise","year":"2004","unstructured":"Wise, R.G., et al.: Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. Neuroimage 21(4), 1652\u20131664 (2004)","journal-title":"Neuroimage"},{"key":"42_CR5","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.neuroimage.2018.04.076","volume":"181","author":"MF Glasser","year":"2018","unstructured":"Glasser, M.F., et al.: Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data. NeuroImage 181, 692\u2013717 (2018)","journal-title":"NeuroImage"},{"key":"42_CR6","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1016\/j.neuroimage.2016.09.038","volume":"146","author":"JD Power","year":"2017","unstructured":"Power, J.D., et al.: Sources and implications of whole-brain fMRI signals in humans. Neuroimage 146, 609\u2013625 (2017)","journal-title":"Neuroimage"},{"issue":"1","key":"42_CR7","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1002\/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E","volume":"44","author":"GH Glover","year":"2000","unstructured":"Glover, G.H., Li, T.Q., Ress, D.: Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnet. Reson. Med. 44(1), 162\u2013167 (2000)","journal-title":"Magnet. Reson. Med."},{"issue":"3","key":"42_CR8","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1016\/j.neuroimage.2008.09.029","volume":"44","author":"C Chang","year":"2009","unstructured":"Chang, C., Cunningham, J.P., Glover, G.H.: Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44(3), 857\u2013869 (2009)","journal-title":"Neuroimage"},{"key":"42_CR9","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.neuroimage.2013.01.050","volume":"72","author":"M Falahpour","year":"2013","unstructured":"Falahpour, M., Refai, H., Bodurka, J.: Subject specific BOLD fMRI respiratory and cardiac response functions obtained from global signal. Neuroimage 72, 252\u2013264 (2013)","journal-title":"Neuroimage"},{"key":"42_CR10","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.neuroimage.2014.10.031","volume":"104","author":"AM Golestani","year":"2015","unstructured":"Golestani, A.M., et al.: Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test\u2013retest reliability and effect of fMRI sampling rate. Neuroimage 104, 266\u2013277 (2015)","journal-title":"Neuroimage"},{"key":"42_CR11","doi-asserted-by":"publisher","first-page":"116150","DOI":"10.1016\/j.neuroimage.2019.116150","volume":"202","author":"M Kassinopoulos","year":"2019","unstructured":"Kassinopoulos, M., Mitsis, G.D.: Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration. Neuroimage 202, 116150 (2019)","journal-title":"Neuroimage"},{"issue":"3","key":"42_CR12","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/j.neuroimage.2011.12.028","volume":"60","author":"P Kundu","year":"2012","unstructured":"Kundu, P., et al.: Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage 60(3), 1759\u20131770 (2012)","journal-title":"Neuroimage"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Bright, M.G., et al., Vascular physiology drives functional brain networks. NeuroImage 116907 (2020)","DOI":"10.1016\/j.neuroimage.2020.116907"},{"key":"42_CR14","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.neuroimage.2016.12.018","volume":"154","author":"C Caballero-Gaudes","year":"2017","unstructured":"Caballero-Gaudes, C., Reynolds, R.C.: Methods for cleaning the BOLD fMRI signal. Neuroimage 154, 128\u2013149 (2017)","journal-title":"Neuroimage"},{"issue":"4","key":"42_CR15","doi-asserted-by":"publisher","first-page":"1286","DOI":"10.1016\/j.neuroimage.2007.07.004","volume":"37","author":"EB Beall","year":"2007","unstructured":"Beall, E.B., Lowe, M.J.: Isolating physiologic noise sources with independently determined spatial measures. Neuroimage 37(4), 1286\u20131300 (2007)","journal-title":"Neuroimage"},{"issue":"4","key":"42_CR16","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","volume":"62","author":"DC Van Essen","year":"2012","unstructured":"Van Essen, D.C., et al.: The Human Connectome Project: a data acquisition perspective. Neuroimage 62(4), 2222\u20132231 (2012)","journal-title":"Neuroimage"},{"key":"42_CR17","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 acquisition for improved resting state network imaging. Neuroimage 95, 232\u2013247 (2014)","journal-title":"Neuroimage"},{"key":"42_CR18","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.neuroimage.2013.05.081","volume":"82","author":"X Shen","year":"2013","unstructured":"Shen, X., et al.: Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403\u2013415 (2013)","journal-title":"Neuroimage"},{"issue":"1","key":"42_CR19","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1006\/nimg.2001.0940","volume":"15","author":"RN Henson","year":"2002","unstructured":"Henson, R.N., et al.: Detecting latency differences in event-related BOLD responses: application to words versus nonwords and initial versus repeated face presentations. Neuroimage 15(1), 83\u201397 (2002)","journal-title":"Neuroimage"},{"issue":"1","key":"42_CR20","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1093\/cercor\/bhr099","volume":"22","author":"WR Shirer","year":"2012","unstructured":"Shirer, W.R., et al.: Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22(1), 158\u2013165 (2012)","journal-title":"Cereb. Cortex"},{"issue":"4","key":"42_CR21","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1016\/j.neuroimage.2009.04.048","volume":"47","author":"C Chang","year":"2009","unstructured":"Chang, C., Glover, G.H.: Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI. Neuroimage 47(4), 1381\u20131393 (2009)","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_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:09:13Z","timestamp":1759356553000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59728-3_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597276","9783030597283"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59728-3_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}