  Accurate delineation of tumor-adjacent functional brain regions is essential
for planning function-preserving neurosurgery. Functional magnetic resonance
imaging (fMRI) is increasingly used for presurgical counseling and planning.
When analyzing presurgical fMRI data, false negatives are more dangerous to the
patients than false positives because patients are more likely to experience
significant harm from failing to identify functional regions and subsequently
resecting critical tissues. In this paper, we propose a novel spatially
adaptive variable screening procedure to enable effective control of false
negatives while leveraging the spatial structure of fMRI data. Compared to
existing statistical methods in fMRI data analysis, the new procedure directly
controls false negatives at a desirable level and is completely data-driven.
The new method is also substantially different from existing false-negative
control procedures which do not take spatial information into account.
Numerical examples show that the new method outperforms several
state-of-the-art methods in retaining signal voxels, especially the subtle ones
at the boundaries of functional regions, while providing cleaner separation of
functional regions from background noise. Such results could be valuable to
preserve critical tissues in neurosurgery.
