Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.

ABSTRACT: Popular methods for analyzing the brain's functional connectome examine statistical associations between pairs of atlas-defined brain regions, viewing the strength of these links as independent values. However, edges within a standard connectivity matrix, i.e., correlations between individual regions or nodes, are not independent. They are part of an interconnected system. Here, we propose that consideration of both independent, linear relationships (as in standard approaches such as linear kernel ridge regression and connectome-based predictive modeling) as well as higher order statistical associations - such as tertiary interactions between matrix components and global features of the matrix space - will enhance identification of meaningful individual differences. To test this, we adopt

SUBMITTER: Riley S 

PROVIDER: S-EPMC12633399 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature