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

ObjectiveTo reliably estimate individual-specific resting-state cortical networks and determine if language network topography can predict task-based language dominance in drug-resistant epilepsy. MethodsWe utilised a multi-session 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 to 24 minutes of resting-state 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. ResultsNinety-one participants with drug-resistant epilepsy were included: 61 (67.0%) temporal lobe epilepsy, 29 (31.9%) extra-temporal lobe epilepsy, and 1 (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 (AUC = 0.82), bilateral (AUC = 0.72), and right (AUC = 0.83) dominance. SignificanceThese 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. Key PointsO_LIExisting resting-state fMRI methods have limited ability to predict language dominance for epilepsy surgery. C_LIO_LIPrecision functional mapping techniques allow reliable estimation of inter-individual variability in large-scale resting-state networks. C_LIO_LIMS-HBM can map high-quality individual-specific cortical networks in drug-resistant epilepsy using only 6 to 24 minutes of data. C_LIO_LIIndividual-specific language network topography predicted task-based language dominance well in individual patients. C_LI