Abstract
Transcranial alternating current stimulation (tACS) enables the non-invasive 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 stimulation 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 distinguish responders from non-responders to tACS, prior to the stimulation treatment. This contribution could eventually bring us a step closer towards translating tACS into a safe and effective clinical treatment tool.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This work was fully funded by the Max Planck Institute for Intelligent Systems.
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