S-Space College of Medicine/School of Medicine (의과대학/대학원) Dept. of Neurology (신경과학교실) Journal Papers (저널논문_신경과학교실)
Changes in brain complexity during valproate treatment in patients with partial epilepsy
- Kim, Jae-Moon; Jung, Ki-Young; Choi, Chung-Mi
- Issue Date
- Neuropsychobiology, Vol.45 No.2, pp. 106-112
- 복합학; EEG; Nonlinear analysis; Brain complexity; Valproate; Spatial linear mode complexity; Spectral analysis
- Objective: The effect of valproate (VPA) on human electroencephalography (EEG) was studied using nonlinear dynamics analysis to investigate changes in brain complexity. Methods: We propose a spatial linear mode complexity (SLMC) measure to quantify the complexity of spatial linear modes in multichannel EEGs. Nine patients with complex partial seizures who had not previously been exposed to antiepileptic drugs (AEDs) were included in this study. Eighteen-channel EEG data were collected before and after VPA therapy. Changes in brain complexity were examined using the proposed SLMC measure, which reflects brain complexity. Fifteen normal, healthy subjects were included as a control group. To compare SLMC with spectral analysis, we performed spectral analysis within the conventional frequency bands. Results: Spectral analysis showed that the patient group had decreased relative power of the alpha(2) band in the T7, P3, O1 and C4 leads before VPA treatment and an increased relative theta power in the O1 lead relative to the control group. However, no significant changes occurred in any lead at any frequency band after VPA treatment. The mean SLMC value was significantly lower in the patient group before treatment than in the control group (p = 0.026). The average SLMC value for all patients increased after treatment and neared that of the control group, although statistical significance was not attained (p = 0.074). Conclusions: These results suggest that epilepsy patients have interictal abnormalities that are demonstrated by reduced brain complexity, and that VPA partially reverses this trend. Nonlinear analysis of EEG data may be useful in evaluating the effect of AEDs. Copyright (C) 2002 S. Karger AG, Basel.
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