Characterizing EEG cortical dynamics and connectivity with responses to single pulse electrical stimulation (SPES)

G Alarc�n, D Jim�nez-Jim�nez, A Valent�n…�- …�journal of neural�…, 2018 - World Scientific
International journal of neural systems, 2018World Scientific
Objectives: To model cortical connections in order to characterize their oscillatory behavior
and role in the generation of spontaneous electroencephalogram (EEG). Methods: We
studied averaged responses to single pulse electrical stimulation (SPES) from the non-
epileptogenic hemisphere of five patients assessed with intracranial EEG who became
seizure free after contralateral temporal lobectomy. Second-order control system equations
were modified to characterize the systems generating a given response. SPES responses�…
Objectives: To model cortical connections in order to characterize their oscillatory behavior and role in the generation of spontaneous electroencephalogram (EEG). Methods: We studied averaged responses to single pulse electrical stimulation (SPES) from the non-epileptogenic hemisphere of five patients assessed with intracranial EEG who became seizure free after contralateral temporal lobectomy. Second-order control system equations were modified to characterize the systems generating a given response. SPES responses were modeled as responses to a unit step input. EEG power spectrum was calculated on the 20s preceding SPES. Results: 121 channels showed responses to 32 stimulation sites. A single system could model the response in 41.3% and two systems were required in 58.7%. Peaks in the frequency response of the models tended to occur within the frequency range of most activity on the spontaneous EEG. Discrepancies were noted between activity predicted by models and activity recorded in the spontaneous EEG. These discrepancies could be explained by the existence of alpha rhythm or interictal epileptiform discharges. Conclusions: Cortical interactions shown by SPES can be described as control systems which can predict cortical oscillatory behavior. The method is unique as it describes connectivity as well as dynamic interactions.
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