{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T16:40:07Z","timestamp":1758559207950,"version":"3.44.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Traumatic brain injury (TBI) often causes lasting cognitive impairments and requires accurate predictions for personalized rehabilitation. This study evaluated the performance of multi-task deep learning models with and without slice-wise data augmentation. 3D Cerebrovascular reactivity (CVR) maps derived from resting-state fMRI at 3 months post injury along with clinical characteristics were used to predict neuropsychological outcomes at 12 months post-injury in patients with moderate to severe TBI. The model with slice-wise augmentation of 3D CVR maps outperformed the model without augmentation, achieving a mean absolute error of 9.18 \u00b1 1.30. The study demonstrated that slice-wise augmentation of 3D CVR maps enhances the performance of multi-task deep learning models for predicting neuropsychological outcomes one year after TBI. *CORRIGENDUM on final page*<\/jats:p>","DOI":"10.3233\/shti251203","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:44:22Z","timestamp":1754567062000},"source":"Crossref","is-referenced-by-count":0,"title":["Slice-Wise Augmentation for Multi-Task Learning to Predict Neuropsychological Outcomes After Traumatic Brain Injury"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0749-0125","authenticated-orcid":false,"given":"Wonpil","family":"Jang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junbeom","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yechan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonghun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junghoon J.","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Molecular, Cellular, and Biomedical Sciences, CUNY School of Medicine, The City College of New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joon Yul","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T16:17:18Z","timestamp":1758557838000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251203","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}