Prediction of Stimulation-Defined Eloquent Cortex Using Graph-Theoretical Connectivity from Electrocorticography...

Electrical stimulation mapping (ESM) is the clinical gold standard for identifying eloquent cortex during presurgical evaluation but is time-intensive, constrained by incomplete cortical sampling, and limited by patient tolerance. We investigated whether features derived from electrocorticography (ECoG), including functional connectivity measures, can provide complementary information for identifying functionally critical cortex and examined how predictive performance varies across functional domains, behavioral tasks, and data quantity.

Fourteen patients undergoing intracranial monitoring for epilepsy surgery performed speech production tasks while ECoG was recorded independently of stimulation mapping. Graph-theoretic functional connectivity features derived from high-gamma activity (70-150 Hz), combined with anatomical region encoding, were used to train machine learning classifiers, and predictive performance was evaluated using leave-one-subject-out validation with ESM-defined functional deficits used as ground-truth outcomes.

Fourteen patients were included. Performance differed across functional domains, with motor-critical electrodes identified with the highest accuracy (ROC-AUC 0.929 ± 0.061; PR-AUC 0.755 ± 0.191), followed by speech arrest (ROC-AUC 0.793 ± 0.103; PR-AUC 0.550 ± 0.196), whereas language-critical electrodes were more difficult to robustly predict (ROC-AUC 0.761 ± 0.160; PR-AUC 0.385 ± 0.167). Additional signal-derived features provided limited benefit beyond anatomical and connectivity features. Performance also varied across tasks and with the number of available trials, with motor prediction remaining stable across trial counts and speech arrest prediction improving with increasing trial counts up to approximately 10 trials before plateauing. In addition, evaluation at the level of stimulation pairs increased sensitivity, with only modest changes in overall performance.

Graph-theoretic connectivity analysis of ECoG provides complementary information for identifying stimulation-defined functional criticality and supports presurgical mapping. Differences in predictive performance across functional domains likely reflect underlying neurophysiologic organization, with connectivity providing the clearest improvement for speech arrest prediction. Combined-label prediction strategies increased sensitivity at the expense of specificity, reflecting a clinically relevant tradeoff between broad detection of critical cortex and precise localization. These findings suggest that connectivity-informed approaches may help guide task selection and improve mapping efficiency while complementing electrical stimulation mapping.