Automatic methods for cortex-wide layer identification of electrophysiological signals reveals a cortical motif for the expression of neuronal rhythms

Abstract

Cerebral cortex is composed of 6 anatomical layers. How these layers contribute to computations that give rise to cognition remains a challenge in neuroscience. Part of this challenge is to reliably identify laminar markers from in-vivo neurophysiological data. Classic methods for laminar identification are based on assumptions which are often violated and require expert users to identify the pattern, potentially introducing bias. We recorded local field potentials (LFP) with probes containing 16 or 32 electrodes that span all cortical layers in frontal, parietal, and visual cortex in monkeys. We describe two novel methods to identify layers in a fully automatic and quantitative way. The first method represents relative power across electrodes from as a 2-dimensional image, and maximizes image similarity across probes. The second method leverages ensemble machine learning to maximize classification accuracy of LFP data to a laminar label. Both methods detect consistent patterns, and the image similarity approach reveals a cortex-wide motif of laminar expression for delta/theta, alpha/beta and gamma rhythms. Delta/theta (1-4 Hz) and gamma (50-150 Hz) power peak in superficial layers 2/3, and alpha/beta (10-30 Hz) power peaks in deep layers 5/6.