Mapping Lifespan Functional Connectivity with Vision Transformer and Attention-Guided Trajectory Analysis

The science

Original title

Mapping Lifespan Functional Connectivity with Vision Transformer and Attention-Guided Trajectory Analysis.

Authors

Cho K, Choi J, Park M et al.

Journal

International journal of neural systems

Published

Oct 27, 2026

Study type

Study

PMID

42935467 ↗

DOI

10.1142/S0129065727500304 ↗

Abstract

Brain aging involves widespread reorganization of functional networks, yet characterizing these changes across the lifespan remains challenging. This study presents an innovative application of a Vision Transformer (ViT) to resting-state fMRI (rs-fMRI) functional connectivity for age group classification using the Human Connectome Project (HCP) Lifespan datasets. A ViT model was fine-tuned on [Formula: see text] ROI-to-ROI connectivity matrices to perform three binary age group classifications (Young versus Old, Adult versus Aging, Young versus Aging). The model achieved accuracies of 91%, 71%, and 93%, respectively, under five-fold cross-validation. To interpret model decisions, attention weight maps from the final transformer encoder layer were extracted using attention rollout and thresholded with a scree plot analysis, identifying 48 high-salience connections across 28 brain regions. These features were analyzed across the lifespan using generalized additive models (GAM), and k-means clustering of the trajectories revealed three dominant lifespan connectivity patterns: Age-Declining, Mid-Life Peaking, and Age-Increasing. This study demonstrates the viability of the ViT for functional connectome analysis and shows that attention-based interpretability combined with GAM trajectory analysis can enrich insights into age-related changes in brain network connectivity, offering a framework for interpretable brain-age modeling.

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