Di Giovanni, D. A. | Search bioRxiv

bioRxiv2026

Person-specific intrinsic-timescale topographies persist from rest into sentence processing

Language processing does not occur in a vacuum. Language comprehension and production have its own temporal organization, and it unfolds against ongoing neural. We examined whether the base organization persists from rest into sentence processing, and we further examined how it relates to evoked responses and semantic decisions. Sentences crossed linguistic argument versus adjunct completions with semantic plausibility. Anomalies elicited a centroparietal late positivity from 600 to 900 ms (+0.628 microvolt, 95% CI 0.349-0.907). Channel-wise autocorrelation timescales formed person-specific topographies: own-person rest-task similarity exceeded mismatched-person similarity (Fisher-z advantage = 0.463, permutation P = 5.00 x 10^-6), identifying 78.6% of participants against 7.1% chance. The advantage survived Cz re-referencing and surface-Laplacian transformation. In separate ERP predictor tests, longer absolute task timescales accompanied a smaller anomaly effect (cross-fitted Spearman rho = -0.486, Holm-adjusted P = 0.030). Stronger topographic continuity also accompanied greater anomaly-related decision slowing, although the behavioral association fell short of correction across six endpoints (rho = 0.547, familywise P = 0.054). Supporting analyses evaluated larger eligible samples and an eyes-closed subset. The findings place persistent, person-specific temporal organization within sentence processing and link absolute task timescale to the neural response to semantic anomaly.

neuroscience↗

bioRxiv2026

VRPTR prediction of individual language activation and uncertainty from resting state fMRI

Resting-state connectivity can predict task-evoked fMRI activation, but correspondence with an individual task map may partly reflect a shared population pattern. We evaluated the Variational Resting-state-to-Task Prediction TransformeR (VRPTR), a three-dimensional encoder-decoder combining a compressed Transformer bottleneck, variational latent sampling, and multiscale skip connections. Models were trained in 360 healthy adults from the WU-Minn Human Connectome Project and evaluated in 40 held-out participants from the same dataset for the story-versus-math language contrast. VRPTR achieved mean voxel-map Pearson r=0.642 and Dice AUC=0.519, exceeding compact volumetric BrainSurfCNN-like and SWIFUN-like comparators by {Delta}r=0.0376 and 0.0335, respectively. A template fixed from the 360 training maps yielded residual correlation 0.364; VRPTR improved correlation but not mean absolute or root mean squared error over this template. A matched factorial ablation associated Transformer processing with improved map correlation ({Delta}r=0.0168); the variational main effect did not survive multiple-comparison correction. Component swaps indicated that subject-specific activation patterns depended more on multiscale skip features than on the bottleneck representation. Latent sampling produced a modest association between posterior standard deviation and voxelwise absolute error (mean Spearman {rho}=0.091). Raw 95% intervals covered only 4.7% of observed values. Five-fold calibration within the held-out cohort increased coverage to 94.8%, and spatially varying uncertainty modestly improved probabilistic scores over a constant-width model. Uncertainty also increased under input degradation. These findings support the contribution of Transformer processing to language-map prediction and distinguish it from subject-specific spatial conditioning and relative uncertainty estimation. Independent validation of calibrated uncertainty and evaluation in clinical cohorts remain necessary.

neuroscience↗