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A selective overview of feature screening methods with applications to neuroimaging data

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In neuroimaging studies, regression models are frequently used to identify the association of the imaging features and clinical outcome, where the number of imaging features (e.g., hundreds of thousands of voxel‐level predictors) much outweighs the number of subjects in the studies. Classical best subset selection or penalized variable selection methods that perform well for low‐ or moderate‐dimensional data do not scale to ultrahigh‐dimensional neuroimaging data. To reduce the dimensionality, variable screening has emerged as a powerful tool for feature selection in neuroimaging studies. We present a selective review of the recent developments in ultrahigh‐dimensional variable screening, with a focus on their practical performance on the analysis of neuroimaging data with complex spatial correlation structures and high‐dimensionality. We conduct extensive simulation studies to compare the performance on selection accuracy and computational costs between the different methods. We present analyses of resting‐state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange study. This article is categorized under: Applications of Computational Statistics > Computational and Molecular Biology Statistical Learning and Exploratory Methods of the Data Sciences > Image Data Mining Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data
Typical simulated images (200 × 200) and the true spatially varying coefficient function (SVCF) for different settings
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Box plots of PMSE (light blue) and PR2 (red) over 10 cross‐validations for four different imaging measures (fALFF, WDC, ReHo, and LFCD) and four different feature screening methods (SIS, HOLP, CIS, and PartS) using linear models and random forest models. CIS, covariance‐insured screening; fALFF, fractional amplitude of low‐frequency fluctuations; HOLP, high‐dimensional ordinary‐least squares projection; LFCD, local functional connectivity density; PartS, partition‐based screening; PMSE, predicted mean square error; PR2, predicted R2; ReHo, Regional Homogeneity; SIS, sure independence screening; WDC, weighted degree centrality
[ Normal View | Magnified View ]

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Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data
Statistical Learning and Exploratory Methods of the Data Sciences > Image Data Mining
Applications of Computational Statistics > Computational and Molecular Biology

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