<p>Transcranial magnetic stimulation (TMS) is utilized as a treatment method for a variety of neurological and psychiatric disorders. The standard dose parameter for administration of TMS is known as the resting motor threshold (RMT). Between individuals, the RMT has traditionally been thought to reflect differences in neuroanatomy; however, the functional state of the brain has also been shown to have an influence on this value. In this study, 19 mild traumatic brain injury participants (mTBI) resting state electroencephalography (EEG) was obtained before undergoing TMS treatment. Various time frequency and power spectra values were derived from this EEG data to examine the relationship between RMT and neurophysiology. These EEG connectivity metrics were then used to establish relationships with RMT alone and accounting for differences in individual neuroanatomy. We found that the incorporation of EEG functional connectivity improved multiple regression model predictions of inter-participant RMT variability than neuroanatomy alone. Future investigations into the evaluation of EEG in predicting RMT variability should include a larger participant population with a healthy control. </p>
<p>Transcranial magnetic stimulation (TMS) is utilized as a treatment method for a variety of neurological and psychiatric disorders. The standard dose parameter for administration of TMS is known as the resting motor threshold (RMT). Between individuals, the RMT has traditionally been thought to reflect differences in neuroanatomy; however, the functional state of the brain has also been shown to have an influence on this value. In this study, 19 mild traumatic brain injury participants (mTBI) resting state electroencephalography (EEG) was obtained before undergoing TMS treatment. Various time frequency and power spectra values were derived from this EEG data to examine the relationship between RMT and neurophysiology. These EEG connectivity metrics were then used to establish relationships with RMT alone and accounting for differences in individual neuroanatomy. We found that the incorporation of EEG functional connectivity improved multiple regression model predictions of inter-participant RMT variability than neuroanatomy alone. Future investigations into the evaluation of EEG in predicting RMT variability should include a larger participant population with a healthy control. </p>
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