Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.
Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea; Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.
Prog Neuropsychopharmacol Biol Psychiatry. 2024 Aug 30;134:111048. doi: 10.1016/j.pnpbp.2024.111048. Epub 2024 May 31.
Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD).
The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed.
For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores.
Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.
使用非线性方法识别精神障碍患者异常大脑动力学的研究有限。本研究使用多尺度熵（MSE）分析，基于 EEG 研究了精神分裂症（SZ）和双相障碍（BD）患者的大脑动力学。
从 51 名 SZ 患者、51 名 BD 患者和 51 名健康对照者（HCs）中采集闭眼静息状态 EEG 数据。BD 患者进一步分为 1 型（n=23）和 2 型（n=16），然后与 SZ 患者进行比较。使用每个个体的 30 秒无伪迹 EEG 数据，从双侧额、中央和顶枕区评估基于样本熵的 MSE。对 MSE 值与精神症状进行相关性分析。
与 HCs 和 BD 患者相比，SZ 患者在所有区域的较高尺度因素（即 41-70）观察到较高的 MSE 值。此外，左额和顶枕区 MSE 值与 PANSS 评分呈正相关。BD 患者在双侧额中和中央区的中尺度因素（即 13-40）观察到较高的 MSE 值，与 HCs 相比。BD 1 型患者在所有区域的较高尺度因素中观察到较高的 MSE 值，与 BD 2 型患者相比。BD 1 型中，所有左侧区域的 MSE 值与 YMRS 评分之间存在正相关。
精神障碍患者表现出组依赖性 MSE 特征。这些结果表明 MSE 特征可能是反映病理生理特征的有用生物标志物。
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