Harmonizing 10,000 connectomes: site-invariant representation learning for multi-site analysis of network connectivity and cognitive impairment.

ABSTRACT:

Purpose

Data-driven harmonization can mitigate systematic confounding signals across imaging cohorts caused by variance in scanners and acquisition protocols. As diffusion magnetic resonance imaging data are often acquired with different hardware and software, harmonization is essential for integrating these scattered datasets into a cohesive analysis for improved statistical power. Large-scale, multi-site studies for Alzheimer's disease (AD), a neurodegenerative condition characterized by high data variability and complex pathology, pose the challenge of both site-based and biological variation.

Approach

We learn lower-dimensional representations of structural connectivity invariant to imaging cohort, geographical location, scanner, and acquisition factors. We design a cond

SUBMITTER: Newlin NR 

PROVIDER: S-EPMC12594104 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature