Deep learning-based embedding of functional connectivity profiles for precision functional mapping.

ABSTRACT: Spatial similarity of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial similarity is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial similarity is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent spa

SUBMITTER: Tu JC 

PROVIDER: S-EPMC12409741 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature