Abstract| Volume 16, ISSUE 1, P119-120, January 2023

EEG brain networks identified by Hidden Markov model and their relation to TMS-evoked MEP amplitudes

      Recent work using time delay embedded hidden Markov model applied to magnetoencephalography resting-state data revealed a distinct set of brain states, with each state engaging a specific set of cortical regions and featuring different functional roles. Here, we apply this approach to EEG-TMS data to the purpose of understanding whether the characteristics of EEG brain states occurring before the stimulation impact response to TMS. Our results show that specific brain states, with a motor network spatial and spectral signature, feature larger Motor Evoked Potential amplitude. These findings enable brain-state-dependent TMS based on real-time excitability and connectivity information, a critical step towards individualized therapeutic brain stimulation.
      Research Category and Technology and Methods
      Translational Research: 7. Responsive (Closed-Loop) Stimulation
      Keywords: EEG-TMS, Hidden Markov model, Brain-state-dependent TMS, Motor cortex