Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex
- PMID: 29191438
- DOI: 10.1016/j.brs.2017.11.016
Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex
Abstract
Background: Rapidly changing excitability states in an oscillating neuronal network can explain response variability to external stimulation, but if repetitive stimulation of always the same high- or low-excitability state results in long-term plasticity of opposite direction has never been explored in vivo.
Objective/hypothesis: Different phases of the endogenous sensorimotor μ-rhythm represent different states of corticospinal excitability, and repetitive transcranial magnetic stimulation (rTMS) of always the same high- vs. low-excitability state results in long-term plasticity of different direction.
Methods: State-dependent electroencephalography-triggered transcranial magnetic stimulation (EEG-TMS) was applied to target the EEG negative vs. positive peak of the sensorimotor μ-rhythm in healthy subjects using a millisecond resolution real-time digital signal processing system. Corticospinal excitability was indexed by motor evoked potential amplitude in a hand muscle.
Results: EEG negative vs. positive peak of the endogenous sensorimotor μ-rhythm represent high- vs. low-excitability states of corticospinal neurons. More importantly, otherwise identical rTMS (200 triple-pulses at 100 Hz burst frequency and ∼1 Hz repetition rate), triggered consistently at this high-excitability vs. low-excitability state, leads to long-term potentiation (LTP)-like vs. no change in corticospinal excitability.
Conclusions: Findings raise the intriguing possibility that real-time information of instantaneous brain state can be utilized to control efficacy of plasticity induction in humans.
Keywords: Brain-state dependent stimulation; Corticospinal excitability; EEG-TMS; LTP-like plasticity; Repetitive transcranial magnetic stimulation; μ-Rhythm.
Copyright © 2017 Elsevier Inc. All rights reserved.
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