False Discovery Rate Control for Lesion-Symptom Mapping with Heterogeneous data via Weighted P-values

TL;DR

This paper introduces a p-value weighting method for lesion-symptom mapping that accounts for data heterogeneity, improving statistical power and error control in neuroimaging studies of brain lesions.

Contribution

It proposes a novel weighted p-value approach using lesion distribution and spatial info, with a minimum weight criterion requiring minimal prior power knowledge.

Findings

Method demonstrates robust error control on simulated data.

Increases detection power in aphasia brain region analysis.

Identifies regions with inconclusive results due to low power.

Abstract

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side-information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping (VLSM) studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power.