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dc.contributor.authorReddy, Neha A.
dc.contributor.authorZvolanek, Kristina M.
dc.contributor.authorMoia, Stefano
dc.contributor.authorCaballero-Gaudes, César
dc.contributor.authorBright, Molly G.
dc.date.accessioned2024-11-11T16:01:05Z
dc.date.available2024-11-11T16:01:05Z
dc.date.issued2024
dc.identifier.citationReddy, N.A., Zvolanek, K.M., Moia, S., Caballero-Gaudes, C., & Bright, M.G. (2024). Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA. Imaging Neuroscience, 2024 2: 1–30. Doi:10.1162/imag_a_00057es_ES
dc.identifier.citationImaging Neuroscience
dc.identifier.issn2837-6056
dc.identifier.urihttp://hdl.handle.net/10810/70419
dc.descriptionAvailable online 7 december 2023es_ES
dc.description.abstractMotor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson’s disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models’ performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage.es_ES
dc.description.sponsorshipN.A.R. and K.M.Z. were supported by the National Institutes of Health under a training program (T32EB025766). K.M.Z was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number F31HL166079. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was supported by the Center for Translational Imaging at Northwestern University. This research was also supported by the Spanish Ministry of Economy and Competitiveness (Ramon y Cajal Fellowship, RYC-2017- 21845), the Basque Government (BERC 2018-2021 and PIBA_2019_104), and the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019-105520GB-100).es_ES
dc.language.isoenges_ES
dc.publisherMIT PRESSes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/RYC-2017- 21845es_ES
dc.relationinfo:eu-repo/grantAgreement/GV/BERC2018-2021es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/PID2019-105520GB-100es_ES
dc.relationinfo:eu-repo/grantAgreement/GV/PIBA_2019_104es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectBOLD fMRIes_ES
dc.subjectmotor taskes_ES
dc.subjecttask-correlated head motiones_ES
dc.subjectmulti-echoes_ES
dc.subjectindependent component analysises_ES
dc.titleDenoising task-correlated head motion from motor-task fMRI data with multi-echo ICAes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2023 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.es_ES
dc.relation.publisherversionhttps://direct.mit.edu/images_ES
dc.identifier.doi10.1162/imag_a_00057


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