Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI.

ABSTRACT: Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impr

SUBMITTER: Mellema CJ 

PROVIDER: S-EPMC11583961 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature