NeuroImageNeurosciencePeer-reviewed journal article
Immediate TMS-EEG responses reveal motor cortex excitability
Antonietta StangoiD, Agnese ZazioiD, Guido BarchiesiiD, Natale Salvatore BonfiglioiD, Elisa DogniniiD, Eleonora MarcantoniiD, Marta BortolettoiD
Abstract
BackgroundCombined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is widely used to probe cortical excitability at the network level, but technical challenges have prevented its application to investigate local excitability of the stimulated area. A recent study revealed immediate TMS-evoked potentials (i-TEPs) after primary motor cortex (M1) stimulation, suggesting that it may represent a local response. Here, we aimed at testing if this activity is physiological in nature and what it represents. MethodsWe analyzed a TMS-EEG dataset from 28 healthy participants recorded at 9.6 kHz including two M1 stimulation conditions with opposite biphasic current directions. We localized the brain sources of i-TEPs, calculated the immediate TMS-related power (i-TRP) to distinguish between two oscillatory components that may contribute to i-TEPs, and investigated the relationship between i-TRP and motor-evoked potentials (MEPs). In an additional recording, we stimulated a control site evoking a muscular response to understand the contribution of the TMS-related muscle artifact. ResultsResults confirmed i-TEPs with similar characteristics as previously described. The i-TRP revealed strong activity in two ranges 600-800 Hz and 100-200 Hz; The former was positively associated with MEPs amplitude for both current direction conditions. Moreover, i-TEPs were localized in the precentral gyrus of the stimulated hemisphere and the muscular response generated by the control stimulation site differed from i-TEPs and i-TRP. DiscussionThese findings provide first evidence on the physiological nature of i-TEPs and i-TRP following M1 stimulation and that i-TRP represents a direct measure of excitability of the stimulated cortex.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 8
- FWCI
- 2.87
- Field percentile
- top 9%
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