Stacked regressions and structured variance partitioning for interpretable brain maps.

ABSTRACT: Relating brain activity associated with a complex stimulus to different properties of that stimulus is a powerful approach for constructing functional brain maps. However, when stimuli are naturalistic, their properties are often correlated (e.g., visual and semantic features of natural images, or different layers of a convolutional neural network that are used as features of images). Correlated properties can act as confounders for each other and complicate the interpretability of brain maps, and can impact the robustness of statistical estimators. Here, we present an approach for brain mapping based on two proposed methods: stacking different encoding models and structured variance partitioning. Our stacking algorithm combines encoding models that each use as input a featur

SUBMITTER: Lin R 

PROVIDER: S-EPMC10168225 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature