Non-harmonicity in high-frequency components of the intra-operative corticogram to delineate epileptogenic tissue during surgery

Abstract

Objective We aimed to test the potential of auto-regressive model residual modulation (ARRm), an artefact-insensitive method based on non-harmonicity of the high-frequency signal, to identify epileptogenic tissue during surgery. Methods Intra-operative electrocorticography (ECoG) of 54 patients with refractory focal epilepsy were recorded pre- and post-resection at 2048 Hz. The ARRm was calculated in one-minute epochs in which high-frequency oscillations (HFOs; fast ripples, 250–500 Hz; ripples, 80–250 Hz) and spikes were marked. We investigated the pre-resection fraction of HFOs and spikes explained by the ARRm (h 2-index). A general ARRm threshold was set and used to compare the ARRm to surgical outcome in post-resection ECoG (Pearson X 2). Results ARRm was associated strongest with the number of fast ripples in pre-resection ECoG (h 2 = 0.80, P < 0.01), but also with ripples and spikes. An ARRm threshold of 0.47 yielded high specificity (95%) with 52% sensitivity for channels with fast ripples. ARRm values >0.47 were associated with poor outcome at channel and patient level (both P < 0.01) in post-resection ECoG. Conclusions The ARRm algorithm might enable intra-operative delineation of epileptogenic tissue. Significance ARRm is the first unsupervised real-time analysis that could provide an intra-operative, ‘on demand’ interpretation per electrode about the need to remove underlying tissue to optimize the chance of seizure freedom.