Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data.

ABSTRACT: Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term. Corresponding to previously developed methods that inform the estimation of the regression coefficients, we incorporate additional information via a Laplacian matrix based on the proximi

SUBMITTER: Steiner A 

PROVIDER: S-EPMC9555077 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature