Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning.

ABSTRACT: The brain's multi-scale hierarchical organization supports functional segregation and integration. Characterizing the hierarchy of individualized multi-scale functional networks (FNs) is crucial for understanding these fundamental brain processes. It provides promising opportunities for both basic neuroscience and translational research in neuropsychiatric illness. However, current methods typically compute individualized FNs at a single scale and are not equipped to quantify any possible hierarchical organization. To address this limitation, we present a self-supervised deep learning (DL) framework that simultaneously computes multi-scale FNs and characterizes their across-scale hierarchical structure at the individual level. Our method learns intrinsic representations of fMRI data in a l

SUBMITTER: Li H 

PROVIDER: S-EPMC12026987 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature