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.

翻译：病灶-症状映射研究揭示了大脑不同区域参与认知各层面的机制。此类研究通常通过对自然发生脑损伤（如中风或脑肿瘤）患者进行行为测试来完成，由此产生高维观测数据：其中病灶状态（存在/缺失）呈非均匀分布，某些体素在极少数（甚至没有）受试者中存在病灶。在此情境下，大规模单变量假设检验存在严重的功效异质性——许多检验先验已知几乎或完全缺乏统计效力。多重检验方法的最新进展允许研究者根据辅助信息（如功效异质性信息）对假设进行加权。本文提出将p值加权方法应用于基于体素的病灶-症状映射研究。权重通过病灶状态分布与空间信息构建，采用多种常用方法估计各假设检验的不同非零先验概率。我们提出单调最小权重准则，该准则仅需最少的先验功效信息。本方法在依赖性模拟数据及一项探究脑卒中幸存者语言障碍严重程度相关脑区的失语症研究中得到验证。结果表明，所提方法具备稳健的误差控制能力并能提升统计功效。此外，我们展示了如何利用权重识别因功效不足而无法得出确定结论的脑区。