Neural silences can be localized rapidly using noninvasive scalp EEG

Abstract

A rapid and cost-effective noninvasive tool to detect and characterize suppressed neural activity can be of significant benefit for the diagnosis and treatment of many disorders. We propose a novel algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural "silences", using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a novel hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric patients with lobectomy, using less than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53mm), as well for 70 different simulated regions of silence based on a real human head model (11{+/-}0.5mm vs. 54{+/-}2.2mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.