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White-Matter BOLD Encoding Beyond Marginal Connectivity

Li, M.; Ding, Z.; Gore, J. C.

2026-07-13 neuroscience
10.64898/2026.07.08.737282 bioRxiv
Show abstract

Functional MRI studies have traditionally focused on gray matter, whereas white-matter BOLD signals have often been treated as weak or artifactual. Recent work suggests that white-matter BOLD fluctuations contain reproducible functional information, but most gray-to-white matter analyses rely on marginal functional connectivity, which cannot separate pairwise coupling from shared variance among distributed cortical systems. Here, we used a multivariate cortical encoding framework to test whether spontaneous white-matter BOLD activity can be predicted from distributed cortical gray-matter activity and whether this predictive structure reveals organization beyond marginal connectivity. Resting-state fMRI data from 81 Human Connectome Project young adult participants were analyzed using a strict white-matter mask with no overlap with cortical predictors. For each white-matter voxel, time series from 400 Schaefer cortical parcels were used to predict held-out white-matter BOLD signals with nested leave-one-run-out ridge regression. Cortical activity modestly but reliably predicted white-matter BOLD dynamics, demonstrating consistent cross-validated prediction accuracy across a broad spatial extent of the white matter. Ridge beta fingerprints strongly recapitulated marginal functional connectivity fingerprints, indicating a shared functional backbone, but their first gradients diverged reproducibly. This beta-FC divergence axis organized FC-adjusted prediction residuals and remained robust after controlling for gray-matter proximity, mask-boundary distance, white-matter prevalence, temporal signal variability, spatial coordinates, and spatial autocorrelation. The high-divergence end showed relatively low marginal FC but high FC-adjusted prediction residuals and was enriched for posterior thalamic/optic-radiation and posterior corona-radiata anatomy. These findings suggest that multivariate cortical encoding reveals a tract-organized dimension of white-matter functional coupling not captured by pairwise connectivity alone.

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