{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T13:12:22Z","timestamp":1789132342041,"version":"build-2803163510"},"reference-count":44,"publisher":"Wiley","issue":"17","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01 NS085391"],"award-info":[{"award-number":["R01 NS085391"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high\u2010pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise\u2010reduction techniques, for example, CompCor, FIX, and ICA\u2010AROMA have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting\u2010state fMRI and, only rarely, in task\u2010related fMRI. Application of noise\u2010reduction techniques to task\u2010related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. Little remains known about the impact of these techniques on the retention of signal in tasks that may be associated with systemic physiological changes. In this paper, we compared two ICA\u2010based, that is FIX and ICA\u2010AROMA, two CompCor\u2010based noise\u2010reduction techniques, that is aCompCor, and tCompCor, and standard preprocessing using a large (<jats:italic>n<\/jats:italic>\u2009=\u2009101) fMRI dataset including noxious heat and non\u2010noxious auditory stimulation. Results show that preprocessing using FIX performs optimally for data obtained using noxious heat, conserving more signals than CompCor\u2010based techniques and ICA\u2010AROMA, while removing only slightly less noise. Similarly, for data obtained during non\u2010noxious auditory stimulation, FIX noise\u2010reduction technique before analysis with a covariate of interest outperforms the other techniques. These results indicate that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise during task\u2010related fMRI.<\/jats:p>","DOI":"10.1002\/hbm.26447","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T09:17:01Z","timestamp":1695806221000},"page":"5523-5546","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Denoising task\u2010related <scp>fMRI<\/scp>: Balancing noise reduction against signal loss"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9872-7739","authenticated-orcid":false,"given":"M. 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K.","family":"Mortensen","sequence":"additional","affiliation":[{"name":"Division of Behavioral Medicine and Clinical Psychology Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"},{"name":"Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Nahman\u2010Averbuch","sequence":"additional","affiliation":[{"name":"Division of Behavioral Medicine and Clinical Psychology Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"},{"name":"Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"},{"name":"Washington University Pain Center, Department of Anesthesiology Washington University School of Medicine  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. 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C.","family":"Coghill","sequence":"additional","affiliation":[{"name":"Division of Behavioral Medicine and Clinical Psychology Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"},{"name":"Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"},{"name":"Department of Pediatrics University of Cincinnati, College of Medicine  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"Andersson J. Jenkinson M. & Smith S. (2007).Non\u2010linear registration aka Spatial normalisation. 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