O-43 Data-driven selection of active iEEG channels during verbal memory task performance

Abstract

Background Specific brain regions activated during a memory task can be accurately determined using spectral power and amplitude from intracranial electroencephalography (iEEG) signals sampled from the overlying electrodes implanted across the neocortex. We developed a data-driven method for active electrode determination and compared our results with a previously used approach based on a predefined arbitrary threshold. Material and methods We assessed neurophysiological activity of 57 epilepsy patients during a verbal short-term memory task, in which lists of 12 common nouns were presented for subsequent freely recall. Normalized amplitude of the high gamma frequency component of the signal was extracted from the word presentation epochs. A family of exponential functions was used to automatically determine patient-specific threshold values for selecting active channels. Results Proportion of active electrodes determined with our data-driven method was lower (2056 of 5857 channels – 35.28%) than for the method using a predefined threshold (3865 channels – 66.33%). There was 67.70% overlap between the channels selected with the two. Methods We observed significant differences between the two populations of selected active electrodes obtained with these methods, including the profile of average spectral power response during word presentation and memory effect differences between words that were subsequently recalled and forgotten. Conclusions The proposed method offers a fully automatic, patient-specific selection of active electrodes sampling from brain areas activated by verbal memory processing. Our results suggest improved sensitivity and specificity of active electrode selection that can potentially be applied to other cognitive, perceptual, and motor tasks.