Kim Min Seung, Park Sanguk, Park Ukeob, Kang Seung Wan, Kang Suk Yun
Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea.
iMediSync, Inc., Seoul, Korea.
J Mov Disord. 2024 Jul;17(3):304-312. doi: 10.14802/jmd.24038. Epub 2024 Jun 10.
Fatigue is a common, debilitating nonmotor symptom of Parkinson's disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could objectively measure fatigue and to explore the pathophysiology of fatigue in PD.
We studied 32 de novo PD patients who underwent EEG. We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectra and graphs, including the global efficiency, characteristic path length, clustering coefficient, small-worldness, local efficiency, degree centrality, closeness centrality, and betweenness centrality.
No significant differences in absolute or relative power were detected between PD patients without or with fatigue (all p > 0.02, Bonferroni-corrected). According to our network analysis, brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD patients without fatigue (p < 0.0001, p = 0.0011, and p = 0.0007, respectively, Bonferroni-corrected).
Our study suggests that PD patients with fatigue have less efficient networks in the frontal area than PD patients without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD patients with fatigue is assumed to be compensatory. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.
疲劳是帕金森病(PD)常见的、使人衰弱的非运动症状,但其机制尚不清楚。我们旨在确定脑电图(EEG)是否能客观测量疲劳,并探索PD中疲劳的病理生理学。
我们研究了32例新发PD患者,这些患者接受了脑电图检查。我们通过EEG功率谱和图表,包括全局效率、特征路径长度、聚类系数、小世界性质、局部效率、度中心性、紧密中心性和中介中心性,比较了19例无疲劳的PD患者和13例有疲劳的PD患者之间的脑活动。
无疲劳和有疲劳的PD患者之间在绝对或相对功率上未检测到显著差异(所有p>0.02,经Bonferroni校正)。根据我们的网络分析,脑网络效率因频段而异。一般来说,额叶区域θ和δ频段的脑网络效率更高,而在颞叶区域,无疲劳的PD患者α1频段效率较低(分别为p<0.0001、p = 0.0011和p = 0.0007,经Bonferroni校正)。
我们的研究表明,有疲劳的PD患者额叶区域的网络效率低于无疲劳的PD患者。这些发现可能解释了为什么疲劳在作为额纹状体疾病的PD中很常见。有疲劳的PD患者颞叶区域效率增加被认为是一种代偿。在揭示与疲劳相关的脑机制方面,使用图论的脑网络分析比功率谱分析更有价值。