Predicting an individual's functional connectivity from their structural connectome: Evaluation of evidence, recommendations, and future prospects.

ABSTRACT: Several recent studies have optimized deep neural networks to learn high-dimensional relationships linking structural and functional connectivity across the human connectome. However, the extent to which these models recapitulate individual-specific characteristics of resting-state functional brain networks remains unclear. A core concern relates to whether current individual predictions outperform simple benchmarks such as group averages and null conditions. Here, we consider two measures to statistically evaluate whether functional connectivity predictions capture individual effects. We revisit our previously published functional connectivity predictions for 1,000 healthy adults and provide multiple lines of evidence supporting that our predictions successfully capture subtle individual-

SUBMITTER: Zalesky A 

PROVIDER: S-EPMC11674402 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature