Spatially adaptive variable screening in presurgical functional magnetic resonance imaging data analysis.

Abstract

Accurate delineation of functional brain regions adjacent to tumors is imperative for planning neurosurgery that preserves critical functions. Functional magnetic resonance imaging (fMRI) plays an increasingly pivotal role in presurgical counseling and planning. In the analysis of presurgical fMRI data, the impact of false negatives on patients surpasses that of false positives because failure to identify functional regions and unintentionally resecting critical tissues can result in severe harm to patients. This paper introduces a novel metric, the Bayesian missed discovery rate (BMDR), designed for controlling false negatives within the voxel-specific mixture model. Building on the BMDR metric, we propose a new variable screening procedure that not only ensures effective control of false negatives but also capitalizes on the spatial structure of fMRI data. In comparison to existing statistical methods in fMRI data analysis, our new procedure directly regulates false negatives at a desirable level and is entirely data-driven. Moreover, it significantly differs from current false-negative control procedures by incorporating spatial information. Numerical examples demonstrate that the new method outperforms several state-of-the-art methods in retaining signal voxels, particularly the subtle ones at the boundaries of functional regions, while achieving a cleaner separation of functional regions from background noise. These findings hold promising implications for planning function-preserving neurosurgery.