Causal decoding of individual cortical excitability states

Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalogram (EEG). For example, the amplitude of motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) of motor cortex changes from trial to trial. To date, individual estimation of the cortical processes leading to this excitability fluctuation has not been possible. Here, we propose a data-driven method to derive individually optimized EEG classifiers in healthy humans using a supervised learning approach that relates pre-TMS EEG activity dynamics to MEP amplitude. Our approach enables considering multiple brain regions and frequency bands, without defining them a priori, whose compound phase-pattern information determines the excitability. The individualized classifier leads to an increased classification accuracy of cortical excitability states from 57% to 67% when compared to mu-oscillation phase extracted by standard fixed spatial fillers. Results show that, for the used TMS protocol, excitability fluctuates predominantly in the mu-oscillation range, and relevant cortical areas cluster around the stimulated motor cortex, but between subjects there is variability in relevant power spectra, phases, and cortical regions. This novel decoding method allows causal investigation of the cortical excitability state, which is critical also for individualizing therapeutic brain stimulation.

Automated summary

The study proposes a method for personalized EEG classification to accurately identify brain excitability states. Results show that excitability fluctuates predominantly in the mu-oscillation range, but there is variability in relevant power spectra, phases, and cortical regions among different subjects.