Phase-amplitude coupling within MEG data can identify eloquent cortex

Objective.Proper identification of eloquent cortices is essential to minimize post-surgical deficits in patients undergoing resection for epilepsy and tumors. Current methods are subjective, vary across centers, and require significant expertise, underscoring the need for more objective pre-surgical mapping. Phase-amplitude coupling (PAC), the interaction between the phase of low-frequency oscillations and the amplitude of high-frequency activity, has been implicated in task-induced brain activity and may serve as a biomarker for functional mapping. Our objective was to develop a novel PAC-based algorithm to non-invasively identify somatosensory eloquent cortex using magnetoencephalography (MEG) data in epilepsy patients.Approach.We analyzed somatosensory and spontaneous MEG recordings from 30 subjects with drug-resistant epilepsy. PAC was calculated on source-reconstructed data (5-12 Hz for low frequencies and 30-300 Hz for high frequencies), followed by rank-2 tensor decomposition. Density-based clustering compared active brain regions during somatosensory task and spontaneous data at a population level. We employed a linear mixed-effects model to quantify changes in PAC between somatosensory and resting-state data. We developed a patient-specific support vector machine (SVM) classifier to identify active brain regions based on PAC values during the somatosensory task.Main results.Five of six expected brain regions were active during left and right-sided stimulation (p=1.08×10-8, hypergeometric probability test). The mixed-effects model confirmed task-specific PAC in anatomically relevant brain regions (p < 0.01). The SVM classifier gave a specificity of 99.46% and a precision of 66.9%. These results demonstrate that the PAC algorithm reliably identifies somatosensory cortex activation at both individual and population levels with statistical significance.Significance.This study demonstrates the feasibility of using PAC as a non-invasive marker for identifying functionally relevant brain regions during somatosensory task in epilepsy patients. Future work will evaluate its applicability for mapping other eloquent cortices, including language, motor, and auditory areas.