Parsimonious model for mass-univariate vertexwise analysis.

ABSTRACT: Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertexwise analyses. Approach: We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise gray matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. Results

SUBMITTER: Couvy-Duchesne B 

PROVIDER: S-EPMC9122091 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature