Selection of the control group for VBM analysis: Influence of covariates, matching and sample size
Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising this variance are inclusion of covariates and matching of control and patient groups. We address two major questions: What are the optimal co...
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Published in | NeuroImage (Orlando, Fla.) Vol. 41; no. 4; pp. 1324 - 1335 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
15.07.2008
Elsevier Limited |
Subjects | |
Online Access | Get full text |
ISSN | 1053-8119 1095-9572 |
DOI | 10.1016/j.neuroimage.2008.02.050 |
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Abstract | Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising this variance are inclusion of covariates and matching of control and patient groups. We address two major questions: What are the optimal covariates in the VBM design? When a large pool of controls are available, is it better to choose a subset of matched control subjects at the expense of numbers, or include all available controls?
We used regression analysis in a group of 176 controls to determine the contribution of gender, age, and total intracranial volume (TIV) to volume variation. We then used different matching and covariate strategies to determine the optimal design for VBM detection of abnormality in epilepsy patients with hippocampal sclerosis.
In the regression analysis, focal gender effects disappeared with inclusion of TIV as an additional regressor. Age had a small but unique contribution to focal volume changes. In the VBM analysis of HS patients, detection of abnormalities was strongly influenced by choice of covariates. The optimal combination was different for grey and white matter (for grey matter: TIV; for temporal lobe white matter: TIV, age and gender). A control group size of 70–90 subjects allowed optimal detection of volume loss in the hippocampus and thalamus. At these group sizes, matched control groups did not consistently prove superior to deliberately “unmatched” groups of the same size. The optimal detection of volume loss was obtained with all available control subjects. |
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AbstractList | Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising this variance are inclusion of covariates and matching of control and patient groups. We address two major questions: What are the optimal covariates in the VBM design? When a large pool of controls are available, is it better to choose a subset of matched control subjects at the expense of numbers, or include all available controls?
We used regression analysis in a group of 176 controls to determine the contribution of gender, age, and total intracranial volume (TIV) to volume variation. We then used different matching and covariate strategies to determine the optimal design for VBM detection of abnormality in epilepsy patients with hippocampal sclerosis.
In the regression analysis, focal gender effects disappeared with inclusion of TIV as an additional regressor. Age had a small but unique contribution to focal volume changes. In the VBM analysis of HS patients, detection of abnormalities was strongly influenced by choice of covariates. The optimal combination was different for grey and white matter (for grey matter: TIV; for temporal lobe white matter: TIV, age and gender). A control group size of 70–90 subjects allowed optimal detection of volume loss in the hippocampus and thalamus. At these group sizes, matched control groups did not consistently prove superior to deliberately “unmatched” groups of the same size. The optimal detection of volume loss was obtained with all available control subjects. Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising this variance are inclusion of covariates and matching of control and patient groups. We address two major questions: What are the optimal covariates in the VBM design? When a large pool of controls are available, is it better to choose a subset of matched control subjects at the expense of numbers, or include all available controls? We used regression analysis in a group of 176 controls to determine the contribution of gender, age, and total intracranial volume (TIV) to volume variation. We then used different matching and covariate strategies to determine the optimal design for VBM detection of abnormality in epilepsy patients with hippocampal sclerosis. In the regression analysis, focal gender effects disappeared with inclusion of TIV as an additional regressor. Age had a small but unique contribution to focal volume changes. In the VBM analysis of HS patients, detection of abnormalities was strongly influenced by choice of covariates. The optimal combination was different for grey and white matter (for grey matter: TIV; for temporal lobe white matter: TIV, age and gender). A control group size of 70-90 subjects allowed optimal detection of volume loss in the hippocampus and thalamus. At these group sizes, matched control groups did not consistently prove superior to deliberately "unmatched" groups of the same size. The optimal detection of volume loss was obtained with all available control subjects.Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising this variance are inclusion of covariates and matching of control and patient groups. We address two major questions: What are the optimal covariates in the VBM design? When a large pool of controls are available, is it better to choose a subset of matched control subjects at the expense of numbers, or include all available controls? We used regression analysis in a group of 176 controls to determine the contribution of gender, age, and total intracranial volume (TIV) to volume variation. We then used different matching and covariate strategies to determine the optimal design for VBM detection of abnormality in epilepsy patients with hippocampal sclerosis. In the regression analysis, focal gender effects disappeared with inclusion of TIV as an additional regressor. Age had a small but unique contribution to focal volume changes. In the VBM analysis of HS patients, detection of abnormalities was strongly influenced by choice of covariates. The optimal combination was different for grey and white matter (for grey matter: TIV; for temporal lobe white matter: TIV, age and gender). A control group size of 70-90 subjects allowed optimal detection of volume loss in the hippocampus and thalamus. At these group sizes, matched control groups did not consistently prove superior to deliberately "unmatched" groups of the same size. The optimal detection of volume loss was obtained with all available control subjects. Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising this variance are inclusion of covariates and matching of control and patient groups. We address two major questions: What are the optimal covariates in the VBM design? When a large pool of controls are available, is it better to choose a subset of matched control subjects at the expense of numbers, or include all available controls? We used regression analysis in a group of 176 controls to determine the contribution of gender, age, and total intracranial volume (TIV) to volume variation. We then used different matching and covariate strategies to determine the optimal design for VBM detection of abnormality in epilepsy patients with hippocampal sclerosis. In the regression analysis, focal gender effects disappeared with inclusion of TIV as an additional regressor. Age had a small but unique contribution to focal volume changes. In the VBM analysis of HS patients, detection of abnormalities was strongly influenced by choice of covariates. The optimal combination was different for grey and white matter (for grey matter: TIV; for temporal lobe white matter: TIV, age and gender). A control group size of 70-90 subjects allowed optimal detection of volume loss in the hippocampus and thalamus. At these group sizes, matched control groups did not consistently prove superior to deliberately "unmatched" groups of the same size. The optimal detection of volume loss was obtained with all available control subjects. |
Author | Briellmann, Regula S. Jackson, Graeme D. Pell, Gaby S. Pardoe, Heath Abbott, David F. Chan, Chow Huat (Patrick) |
Author_xml | – sequence: 1 givenname: Gaby S. surname: Pell fullname: Pell, Gaby S. organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia – sequence: 2 givenname: Regula S. surname: Briellmann fullname: Briellmann, Regula S. organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia – sequence: 3 givenname: Chow Huat (Patrick) surname: Chan fullname: Chan, Chow Huat (Patrick) organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia – sequence: 4 givenname: Heath surname: Pardoe fullname: Pardoe, Heath organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia – sequence: 5 givenname: David F. surname: Abbott fullname: Abbott, David F. organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia – sequence: 6 givenname: Graeme D. surname: Jackson fullname: Jackson, Graeme D. email: BRI@brain.org.au organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18467131$$D View this record in MEDLINE/PubMed |
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Copyright | 2008 Elsevier Inc. Copyright Elsevier Limited Jul 15, 2008 |
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Keywords | Controls Volumes Gender Brain structure Age |
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SubjectTerms | Adolescent Adult Age Aged Aging - physiology Analysis of Variance Brain - anatomy & histology Brain - physiology Brain structure Controls Data Interpretation, Statistical Epilepsy Epilepsy - genetics Epilepsy - pathology Epilepsy - physiopathology Female Gender Hippocampus - pathology Hippocampus - physiopathology Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging Male Methods Middle Aged Patients Regression Analysis Sample Size Sclerosis Sex Characteristics Studies Volumes |
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