PT  - JOURNAL ARTICLE
AU  - Atalanti A. Mastakouri
AU  - Bernhard Schölkopf
AU  - Moritz Grosse-Wentrup
TI  - Stratification of behavioral response to transcranial current stimulation by resting-state electrophysiology
AID  - 10.1101/2020.01.27.921668
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.27.921668
4099  - http://biorxiv.org/content/early/2020/01/31/2020.01.27.921668.short
4100  - http://biorxiv.org/content/early/2020/01/31/2020.01.27.921668.full
AB  - Transcranial alternating current stimulation (tACS) enables the non-invasive, focal stimulation of brain areas in desired frequencies, intensities and spatial configurations. These attributes have raised tACS to a widely used tool in cognitive neuroscience and a promising treatment in the field of motor rehabilitation. Nevertheless, considerable heterogeneity of its behavioral effects has been reported across individuals. We present a machine learning pipeline for predicting the behavioral response to 70 Hz contralateral motor cortex-tACS from Electroencephalographic resting-state activity preceding the stimulation. Specifically, we show in a cross-over study design that high-gamma (90–160 Hz) resting-state activity predicts arm-speed response to the stimulation in a concurrent reaching task. Moreover, we show in a prospective study that the behavioral effect size of stimulation significantly increases after the stratification of subjects with our prediction method. Finally, we discuss a plausible neurophysiological mechanism that links high resting-state gamma power in motor areas to stimulation response. As such, we provide a method that can reliably distinguish responders from non-responders to tACS, prior to the stimulation treatment. This contribution could eventually bring us a step closer towards translating non-invasive brain stimulation from a research tool into a safe and effective clinical treatment.