A Data-Driven Method to Map the Functional Organisation of Human Brain White Matter

Yifei Sun; James M. Shine; Robert D. Sanders; Robin F. H. Cash; Sharon L. Naismith; Fernando Calamante; Jinglei Lv

q-bio.NCFeb 21, 2026

A Data-Driven Method to Map the Functional Organisation of Human Brain White Matter

Authors: Yifei Sun, James M. Shine, Robert D. Sanders, Robin F. H. Cash, Sharon L. Naismith, Fernando Calamante, Jinglei Lv

Organizations: School of Biomedical Engineering, The University of Sydney, Sydney, 2050, Australia. · Brain and Mind Centre, The University of Sydney, Sydney, 2050, Australia. · School of Medical Science, The University of Sydney, Sydney, 2050, Australia. · Sydney Medical School, The University of Sydney, Sydney, 2050, Australia. ·

Abstract

The white matter of the brain is organised into axonal bundles that support long-range neural communication. Although diffusion MRI (dMRI) enables detailed mapping of these pathways through tractography, how white matter pathways directly facilitate large-scale neural synchronisation remains poorly understood. We developed a data-driven framework that integrates dMRI and functional MRI (fMRI) to model the dynamic coupling supported by white matter tracks. Specifically, we employed track dynamic functional connectivity (Track-DFC) to characterise functional coupling of remote grey matter connected by individual white matter tracks. Using independent component analysis followed by k-medoids clustering, we derived functionally-coherent clusters of white matter tracks from the Human Connectome Project young adult cohort. When applied to the HCP ageing cohort, these clusters exhibited widespread age-related declines in both functional coupling strength and temporal variability. Importantly, specific clusters encompassing pathways linking control, default mode, attention, and visual systems significantly mediated the relationship between age and cognitive performance. Together, these findings depict the functional organisation of white matter tracks and provide a powerful tool to study brain ageing and cognitive decline.

Functional Connectivity

Functional connectivity measures statistical relationships between brain regions to study how neural systems coordinate.

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BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation

Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu

New Jersey Institute of Technology · University of Oxford

Functional Connectivity Temporal GNNs Dynamic Graph Learning Temporal Link Prediction Brain Age Prediction

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Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the brain transiently synchronise and desynchronise their activity in a highly structured manner, even when an individual is at rest. These functional connectivity dynamics may be related to behaviour and neuropsychiatric disease. To model these dynamics, temporal brain connectivity representations are essential, as they reflect evolving interactions between brain regions and provide insight into transient neural states and network reconfigurations. However, conventional graph neural networks (GNNs) often struggle to capture long-range temporal dependencies in dynamic fMRI data. To address this challenge, we propose BrainATCL, an unsupervised, nonparametric framework for adaptive temporal brain connectivity learning, enabling functional link prediction and age estimation. Our method dynamically adjusts the lookback window for each snapshot based on the rate of newly added edges. Graph sequences are subsequently encoded using a GINE-Mamba2 backbone to learn spatial-temporal representations of dynamic functional connectivity in resting-state fMRI data of 1,000 participants from the Human Connectome Project. To further improve spatial modeling, we incorporate brain structure and function-informed edge attributes, i.e., the left/right hemispheric identity and subnetwork membership of brain regions, enabling the model to capture biologically meaningful topological patterns. We evaluate our BrainATCL on two tasks: functional link prediction and age estimation. The experimental results demonstrate superior performance and strong generalization, including in cross-session prediction scenarios.

Abstract

Functional Connectivity

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