Back

Resting-State Network Dynamics and Language Lateralization in Patients with Brain Arteriovenous Malformations

Di Giovanni, D. A.; Chen, J.-K.; Tampieri, D.; La Piana, R.; Klein, D.; Collins, D. L.

2026-08-13 neurology
10.64898/2026.08.12.26360285 medRxiv
Show abstract

Background and PurposeBrain arteriovenous malformations may be associated with atypical language lateralization, but whether individual variation in task-derived hemispheric dominance is reflected in time-varying intrinsic connectivity is unclear. We examined task-based language lateralization and resting-state dynamic connectivity in unruptured, untreated brain arteriovenous malformations and controls. MethodsThirty patients and 23 controls underwent language-task fMRI and resting-state fMRI. Language lateralization indices were derived from threshold-swept activation maps. Resting-state time series were modeled with hidden Markov models and canonical clustering across three atlases, yielding fractional occupancy, mean dwell time, and flexibility. The prespecified primary analysis used Schaefer-100 with four canonical states. ResultsPatients showed reduced leftward language lateralization compared with controls, most clearly in left-sided lesions. Canonical dynamic summary metrics did not differ robustly between groups after false-discovery-rate correction. Within-group partial least squares models showed that language lateralization was associated with dynamic state metrics in both groups. In patients, stronger leftward lateralization was linked mainly to flexibility; in controls, it was linked more consistently to longer dwell time. Exploratory perfusion analysis did not show a clear relationship between gross hemispheric perfusion asymmetry and language lateralization. ConclusionsDynamic resting-state features tracked individual variation in language lateralization despite limited group-level differences in dynamic state usage. These findings provide proof-of-concept evidence of brain-behavior coupling rather than an AVM-specific dynamic biomarker or a validated clinical prediction tool.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.