Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points
Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring (“motion scrubbing”). In motion regression, various...
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| Published in | Human brain mapping Vol. 35; no. 5; pp. 1981 - 1996 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
Blackwell Publishing Ltd
01.05.2014
Wiley-Liss John Wiley & Sons, Inc John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring (“motion scrubbing”). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal‐to‐noise ratio. Motion censoring decreased variance in parameter estimates within‐ and across‐subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Hum Brain Mapp 35:1981–1996, 2014. © 2013 Wiley Periodicals, Inc. |
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| AbstractList | Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring (“motion scrubbing”). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal‐to‐noise ratio. Motion censoring decreased variance in parameter estimates within‐ and across‐subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Hum Brain Mapp 35:1981–1996, 2014. © 2013 Wiley Periodicals, Inc. Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring (“motion scrubbing”). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal‐to‐noise ratio. Motion censoring decreased variance in parameter estimates within‐ and across‐subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Hum Brain Mapp 35:1981–1996, 2014 . © 2013 Wiley Periodicals, Inc . Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Hum Brain Mapp 35:1981-1996, 2014. © 2013 Wiley Periodicals, Inc. [PUBLICATION ABSTRACT] Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement.Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. |
| Author | Siegel, Joshua S. Power, Jonathan D. Church, Jessica A. Schlaggar, Bradley L. Dubis, Joseph W. Vogel, Alecia C. Petersen, Steven E. |
| AuthorAffiliation | 1 Department of Neurology Washington University School of Medicine St. Louis Missouri 3 Department of Pediatrics Washington University School of Medicine St. Louis Missouri 5 Department of Psychology Washington University Saint Louis Missouri 6 Department of Neurosurgery Washington University School of Medicine St. Louis Missouri 7 Department of Biomedical Engineering Washington University Saint Louis Missouri 4 Department of Anatomy and Neurobiology Washington University School of Medicine St. Louis Missouri 2 Department of Radiology Washington University School of Medicine St. Louis Missouri |
| AuthorAffiliation_xml | – name: 2 Department of Radiology Washington University School of Medicine St. Louis Missouri – name: 6 Department of Neurosurgery Washington University School of Medicine St. Louis Missouri – name: 7 Department of Biomedical Engineering Washington University Saint Louis Missouri – name: 1 Department of Neurology Washington University School of Medicine St. Louis Missouri – name: 5 Department of Psychology Washington University Saint Louis Missouri – name: 3 Department of Pediatrics Washington University School of Medicine St. Louis Missouri – name: 4 Department of Anatomy and Neurobiology Washington University School of Medicine St. Louis Missouri |
| Author_xml | – sequence: 1 givenname: Joshua S. surname: Siegel fullname: Siegel, Joshua S. email: siegelj@wusm.wustl.edu organization: Department of Neurology, Washington University School of Medicine, Missouri, St. Louis – sequence: 2 givenname: Jonathan D. surname: Power fullname: Power, Jonathan D. organization: Department of Neurology, Washington University School of Medicine, Missouri, St. Louis – sequence: 3 givenname: Joseph W. surname: Dubis fullname: Dubis, Joseph W. organization: Department of Neurology, Washington University School of Medicine, Missouri, St. Louis – sequence: 4 givenname: Alecia C. surname: Vogel fullname: Vogel, Alecia C. organization: Department of Neurology, Washington University School of Medicine, Missouri, St. Louis – sequence: 5 givenname: Jessica A. surname: Church fullname: Church, Jessica A. organization: Department of Neurology, Washington University School of Medicine, Missouri, St. Louis – sequence: 6 givenname: Bradley L. surname: Schlaggar fullname: Schlaggar, Bradley L. organization: Department of Neurology, Washington University School of Medicine, St. Louis, Missouri – sequence: 7 givenname: Steven E. surname: Petersen fullname: Petersen, Steven E. organization: Department of Neurology, Washington University School of Medicine, St. Louis, Missouri |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28377217$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/23861343$$D View this record in MEDLINE/PubMed |
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| Snippet | Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects... |
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| SubjectTerms | Adolescent Adult Algorithms Biological and medical sciences Brain - blood supply Brain - physiology Child Child Development - physiology Cohort Studies data quality Electrodiagnosis. Electric activity recording Female fMRI general linear model GLM head movement Head Movements - physiology Humans Image Processing, Computer-Assisted Investigative techniques, diagnostic techniques (general aspects) Magnetic Resonance Imaging Male Medical sciences Motion Nervous system noise Oxygen - blood Radiodiagnosis. Nmr imagery. Nmr spectrometry Regression Analysis scrubbing Sensory Thresholds - physiology task Young Adult |
| Title | Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points |
| URI | https://api.istex.fr/ark:/67375/WNG-8CGP66N8-S/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhbm.22307 https://www.ncbi.nlm.nih.gov/pubmed/23861343 https://www.proquest.com/docview/1514322878 https://www.proquest.com/docview/1515648262 https://pubmed.ncbi.nlm.nih.gov/PMC3895106 |
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