Reactivation of Motor-Related Gamma Activity in Human NREM Sleep.

Models of memory consolidation posit a central role for reactivation of brain activity patterns during sleep, especially in non-Rapid Eye Movement (NREM) sleep. While such "replay" of recent waking experiences has been well-demonstrated in rodents, electrophysiological evidence of reactivation in human sleep is still largely lacking. In this intracranial study in patients with epilepsy (N = 9) we explored the spontaneous electroencephalographic reactivation during sleep of spatial patterns of brain activity evoked by motor learning. We first extracted the gamma-band (60-140 Hz) patterns underlying finger movements during a tapping task and underlying no-movement during a short rest period just prior to the task, and trained a binary classifier to discriminate between motor movements vs. rest. We then used the trained model on NREM sleep data immediately after the task and on NREM sleep during a control sleep period preceding the task. Compared with the control sleep period, we found, at the subject level, an increase in the detection rate of motor-related patterns during sleep following the task, but without association with performance changes. These data provide electrophysiological support for the reoccurrence in NREM sleep of the neural activity related to previous waking experience, i.e. that a basic tenet of the reactivation theory does occur in human sleep.

Cross-correlation based µECoG waveform tracking.

Clinical electrodes for epileptic seizure monitoring traditionally require a tradeoff between coverage area and spatial resolution. However, with multiplexed, flexible array devices, high spatial resolution is possible over large surface areas. This high resolution data, recorded from 360 electrodes or more, is difficult to review manually for subtle patterns. Here we develop innovative methods for visualizing micro-electrocorticography (µECoG) datasets. The data contains seizure and non-seizure dynamics that can be used to better understand how seizures begin, progress, and end. Novel visualization techniques allow the researcher to better understand the data by arranging it in accessible ways. This paper presents tools to visualize a seizure waveform's velocity and location over a given window of time.