Li Jiaqing, Ping An-An, Zhou Yalan, Su Tangfeng, Li Xiaoli, Xu Sanqing
Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Front Pediatr. 2024 Jun 5;12:1406772. doi: 10.3389/fped.2024.1406772. eCollection 2024.
West syndrome (WS) is a devastating epileptic encephalopathy with onset in infancy and early childhood. It is characterized by clustered epileptic spasms, developmental arrest, and interictal hypsarrhythmia on electroencephalogram (EEG). Hypsarrhythmia is considered the hallmark of WS, but its visual assessment is challenging due to its wide variability and lack of a quantifiable definition. This study aims to analyze the EEG patterns in WS and identify computational diagnostic biomarkers of the disease.
Linear and non-linear features derived from EEG recordings of 31 WS patients and 20 age-matched controls were compared. Subsequently, the correlation of the identified features with structural and genetic abnormalities was investigated.
WS patients showed significantly elevated alpha-band activity (0.2516 vs. 0.1914, < 0.001) and decreased delta-band activity (0.5117 vs. 0.5479, < 0.001), particularly in the occipital region, as well as globally strengthened theta-band activity (0.2145 vs. 0.1655, < 0.001) in power spectrum analysis. Moreover, wavelet-bicoherence analysis revealed significantly attenuated cross-frequency coupling in WS patients. Additionally, bi-channel coherence analysis indicated minor connectivity alterations in WS patients. Among the four non-linear characteristics of the EEG data (i.e., approximate entropy, sample entropy, permutation entropy, and wavelet entropy), permutation entropy showed the most prominent global reduction in the EEG of WS patients compared to controls (1.4411 vs. 1.5544, < 0.001). Multivariate regression results suggested that genetic etiologies could influence the EEG profiles of WS, whereas structural factors could not.
A combined global strengthening of theta activity and global reduction of permutation entropy can serve as computational EEG biomarkers for WS. Implementing these biomarkers in clinical practice may expedite diagnosis and treatment in WS, thereby improving long-term outcomes.
韦斯特综合征（WS）是一种严重的癫痫性脑病，发病于婴儿期和儿童早期。其特征为成串的癫痫痉挛、发育停滞以及脑电图（EEG）上的发作间期高峰失律。高峰失律被认为是WS的标志，但因其高度变异性和缺乏可量化定义，对其进行视觉评估具有挑战性。本研究旨在分析WS患者的脑电图模式，并确定该疾病的计算诊断生物标志物。
比较了31例WS患者和20例年龄匹配对照的脑电图记录得出的线性和非线性特征。随后，研究了所确定特征与结构和基因异常的相关性。
在功率谱分析中，WS患者显示出显著升高的α波段活动（0.2516对0.1914，<0.001）和降低的δ波段活动（0.5117对0.5479，<0.001），特别是在枕叶区域，以及整体增强的θ波段活动（0.2145对0.1655，<0.001）。此外，小波双相干分析显示WS患者的跨频率耦合显著减弱。另外，双通道相干分析表明WS患者存在轻微的连接性改变。在脑电图数据的四个非线性特征（即近似熵、样本熵、排列熵和小波熵）中，与对照组相比，排列熵在WS患者的脑电图中显示出最显著的整体降低（1.4411对1.5544，<0.001）。多变量回归结果表明，遗传病因可影响WS的脑电图特征，而结构因素则不能。
θ活动的整体增强和排列熵的整体降低相结合可作为WS的计算脑电图生物标志物。在临床实践中应用这些生物标志物可能会加快WS的诊断和治疗，从而改善长期预后。
