{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:18:43Z","timestamp":1780053523215,"version":"3.54.0"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["1R21NS130494"],"award-info":[{"award-number":["1R21NS130494"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["75N92023D00002"],"award-info":[{"award-number":["75N92023D00002"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["75N92023D00003"],"award-info":[{"award-number":["75N92023D00003"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["75N92023D00004"],"award-info":[{"award-number":["75N92023D00004"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["75N92023D00005"],"award-info":[{"award-number":["75N92023D00005"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["75N92023D00006"],"award-info":[{"award-number":["75N92023D00006"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath\u2010hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath\u2010hold compliance often requires external monitoring equipment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a non\u2010invasive and data\u2010driven quality filter for breath\u2010hold compliance using only measurements of head motion during imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Prospective cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>\n                      Longitudinal data from healthy middle\u2010aged subjects enrolled in the Coronary Artery Risk Development in Young Adults Brain MRI Study,\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20091141, 47.1% female.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>3.0 Tesla gradient\u2010echo MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Manual labelling of respiratory belt monitored data was used to determine breath hold compliance during MRI scan. A model to estimate the probability of non\u2010compliance with the breath hold task was developed using measures of head motion. The model's ability to identify scans in which the participant was not performing the breath hold were summarized using performance metrics including sensitivity, specificity, recall, and F1 score. The model was applied to additional unmarked data to assess effects on population measures of CVR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>Sensitivity analysis revealed exclusion of non\u2010compliant scans using the developed model did not affect median cerebrovascular reactivity (Median [q1, q3]\u2009=\u20091.32 [0.96, 1.71]) compared to using manual review of respiratory belt data (1.33 [1.02, 1.74]) while reducing interquartile range.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The final model based on a multi\u2010layer perceptron machine learning classifier estimated non\u2010compliance with an accuracy of 76.9% and an F1 score of 69.5%, indicating a moderate balance between precision and recall for the identification of scans in which the participant was not compliant.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>The developed model provides the probability of non\u2010compliance with a breath\u2010hold task, which could later be used as a quality filter or included in statistical analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>1.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical\u2009Efficacy<\/jats:title>\n                    <jats:p>Stage\u20093.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70105","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T09:39:10Z","timestamp":1757324350000},"page":"183-192","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Breath Hold Task Compliance From Head Motion"],"prefix":"10.1002","volume":"63","author":[{"given":"Timothy B.","family":"Weng","sequence":"first","affiliation":[{"name":"Department of Neurology Dell Medical School, University of Texas at Austin  Austin Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gargi","family":"Porwal","sequence":"additional","affiliation":[{"name":"Department of Neurology Dell Medical School, University of Texas at Austin  Austin Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhivya","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pennsylvania Health System  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Inglis","sequence":"additional","affiliation":[{"name":"Henry H. Wheeler Jr. Brain Imaging Center University of California, Berkeley  Berkeley California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabrina","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"Department of Neurology Dell Medical School, University of Texas at Austin  Austin Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"suffix":"Jr","given":"David R.","family":"Jacobs","sequence":"additional","affiliation":[{"name":"Division of Epidemiology and Community Health University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pamela J.","family":"Schreiner","sequence":"additional","affiliation":[{"name":"Department of Neurology Northwestern University Feinberg School of Medicine  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farzaneh A.","family":"Sorond","sequence":"additional","affiliation":[{"name":"Department of Neurology Northwestern University Feinberg School of Medicine  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Sidney","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Medical Center Program  Oakland California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cora","family":"Lewis","sequence":"additional","affiliation":[{"name":"Department of Epidemiology University of Alabama at Birmingham  Birmingham Alabama USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lenore","family":"Launer","sequence":"additional","affiliation":[{"name":"National Institute on Aging, National Institute of Health LEPS\/IRP\/NIA\/NIH, Laboratory of Epidemiology and Population Sciences  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guray","family":"Erus","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pennsylvania Health System  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilya M.","family":"Nasrallah","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pennsylvania Health System  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. 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