Decoding personalized motor cortical excitability states from human electroencephalography
| Autor: | Sara J. Hussain, Romain Quentin |
|---|---|
| Přispěvatelé: | University of Texas at Austin [Austin], Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Quentin, Romain |
| Rok vydání: | 2021 |
| Předmět: |
Adult
Computer science Brain activity and meditation medicine.medical_treatment brain stimulation Electromyography Electroencephalography medicine Humans Multidisciplinary medicine.diagnostic_test [SCCO.NEUR]Cognitive science/Neuroscience [SCCO.NEUR] Cognitive science/Neuroscience Motor Cortex Evoked Potentials Motor Transcranial Magnetic Stimulation Neuromodulation (medicine) Transcranial magnetic stimulation machine learning medicine.anatomical_structure Brain state Cortical Excitability Neuroscience Decoding methods Motor cortex |
| Popis: | OBJECTIVE: Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Current state-of-the-art approaches deliver TMS during brain states shown to correlate with motor cortex (M1) excitability at the group level. Here, we hypothesized that machine learning classifiers could successfully discriminate between high and low M1 excitability states in individual subjects using power spectral features obtained from low-density electroencephalography (EEG) signals. METHODS: We analyzed a pre-existing publicly available dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings. Personalized multivariate pattern classification was used to discriminate between brain states during which TMS evoked small and large motor-evoked potentials (MEPs). These brain states were labeled as low and high M1 excitability states, respectively. RESULTS: Personalized classifiers successfully discriminated between low and high M1 excitability states in every subject tested. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states in all subjects. Personalized classifiers trained using EEG features obtained immediately before each TMS pulse performed better than personalized classifiers trained using EEG features obtained from earlier time points. Personalized classifiers generalized weakly but significantly across subjects. CONCLUSION: Individual subjects exhibit unique brain activity patterns that correspond to low and high M1 excitability states. These patterns can be efficiently captured using power spectral features obtained from low-density EEG signals. Deploying individualized classifiers during brain state-dependent TMS may enable effective, fully personalized neuromodulation in the future. |
| Databáze: | OpenAIRE |
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