Toward a better measure of functional laterality: Comparing and refining laterality indices in resting-state functional connectivity

Abstract

Systematic investigations into the lateralized human brain have revealed a bivariate functional architecture that underpins distinct cognitive processes. This architecture manifests through inter- and intra-hemispheric lateralization, captured respectively by neural integration and segregation. In this study, we conducted a comprehensive evaluation of multiple quantitative laterality metrics in resting-state fMRI connectivity, using conceptual models to illustrate how inter- and intra-hemispheric correlations shape functional lateralization. We further highlight the critical influence of factors such as correlation sign, correlation coefficient distribution, and statistical thresholding methodology on the interpretation of functional connectivity-based laterality indices. Our findings show that, in our dataset, laterality metrics based on positive-only functional connectivity with a lenient connection-level threshold most consistently capture established relationships between functional brain lateralization and performance in language and visuospatial domains.