Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy

This study was undertaken to reliably estimate individual-specific resting-state cortical networks and determine whether language network topography can predict task-based language dominance in drug-resistant epilepsy.

We utilized a multisession hierarchical Bayesian model (MS-HBM) trained on drug-resistant epilepsy patients to map high-quality individual-specific cortical networks in this population (n = 65) with only 6-24 min of resting-state functional magnetic resonance imaging (fMRI). We compared the quality of networks to MS-HBM models trained on healthy participants from the human connectome project (n = 40) and tested the generalizability of the model in an independent cohort of drug-resistant epilepsy participants (n = 26). Resting-state language network topography was then used to predict task-based language dominance.

Ninety-one participants with drug-resistant epilepsy (National Institutes of Health, n = 65; University of Iowa, n = 26) were included: 61 (67.0%) temporal lobe epilepsy, 29 (31.9%) extratemporal lobe epilepsy, and one (1.1%) undetermined seizure onset zone. The mean age was 33.0 ± 11.4 years, and 50 (54.9%) were male. There were 40 healthy participants with a mean age of 29.0 ± 4.0 years, and 16 (40.0%) were male. MS-HBM trained on drug-resistant epilepsy estimated individual-specific networks that more accurately capture cortical functional organization than group-average networks or MS-HBM trained on healthy participants. The trained MS-HBM model generalized to an independent cohort of drug-resistant epilepsy participants with concurrent intracranial electrical stimulation and fMRI. Critically, cortical evoked fMRI activity aligned more closely with individual-specific networks than with group-average networks. Furthermore, individual-specific language network topography significantly predicted task-based language dominance, achieving high accuracy for left (area under the curve [AUC] = .82), bilateral (AUC = .72), and right (AUC = .83) dominance.

These results demonstrate that MS-HBM captures functionally meaningful network reorganization in drug-resistant epilepsy and enables accurate, individual-level prediction of language lateralization, with direct implications for presurgical functional mapping.