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 inNeuroImage (Orlando, Fla.) Vol. 41; no. 4; pp. 1324 - 1335
Main Authors Pell, Gaby S., Briellmann, Regula S., Chan, Chow Huat (Patrick), Pardoe, Heath, Abbott, David F., Jackson, Graeme D.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 15.07.2008
Elsevier Limited
Subjects
Online AccessGet full text
ISSN1053-8119
1095-9572
DOI10.1016/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.
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
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  surname: Pell
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  surname: Briellmann
  fullname: Briellmann, Regula S.
  organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia
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  givenname: Chow Huat (Patrick)
  surname: Chan
  fullname: Chan, Chow Huat (Patrick)
  organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia
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  givenname: Heath
  surname: Pardoe
  fullname: Pardoe, Heath
  organization: Brain Research Institute, Neuroscience Building, Austin Health, Victoria, Australia
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  givenname: David F.
  surname: Abbott
  fullname: Abbott, David F.
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  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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Issue 4
Keywords Controls
Volumes
Gender
Brain structure
Age
Language English
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Snippet Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising...
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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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Title Selection of the control group for VBM analysis: Influence of covariates, matching and sample size
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