Hassan M, Chaton L, Benquet P, Delval A, Leroy C, Plomhause L, Moonen A J H, Duits A A, Leentjens A F G, van Kranen-Mastenbroek V, Defebvre L, Derambure P, Wendling F, Dujardin K
INSERM, U1099, F-35000 Rennes, France; University of Rennes 1, LTSI, F-35000 Rennes, France.
CHU Lille, Clinical Neurophysiology Department, F-59000 Lille, France.
Neuroimage Clin. 2017 Mar 6;14:591-601. doi: 10.1016/j.nicl.2017.03.002. eCollection 2017.
Cognitive deficits in Parkinson's disease are thought to be related to altered functional brain connectivity. To date, cognitive-related changes in Parkinson's disease have never been explored with dense-EEG with the aim of establishing a relationship between the degree of cognitive impairment, on the one hand, and alterations in the functional connectivity of brain networks, on the other hand. This study was aimed at identifying altered brain networks associated with cognitive phenotypes in Parkinson's disease using dense-EEG data recorded during rest with eyes closed. Three groups of Parkinson's disease patients (N = 124) with different cognitive phenotypes coming from a data-driven cluster analysis, were studied: G1) cognitively intact patients (63), G2) patients with mild cognitive deficits (46) and G3) patients with severe cognitive deficits (15). Functional brain networks were identified using a dense-EEG source connectivity method. Pairwise functional connectivity was computed for 68 brain regions in different EEG frequency bands. Network statistics were assessed at both global (network topology) and local (inter-regional connections) level. Results revealed progressive disruptions in functional connectivity between the three patient groups, typically in the alpha band. Differences between G1 and G2 ( < 0.001, corrected using permutation test) were mainly frontotemporal alterations. A statistically significant correlation (ρ = 0.49, < 0.001) was also obtained between a proposed network-based index and the patients' cognitive score. Global properties of network topology in patients were relatively intact. These findings indicate that functional connectivity decreases with the worsening of cognitive performance and loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.
帕金森病的认知缺陷被认为与大脑功能连接的改变有关。迄今为止，从未通过密集脑电图来探索帕金森病中与认知相关的变化，目的是一方面建立认知障碍程度与另一方面大脑网络功能连接改变之间的关系。本研究旨在使用闭眼休息时记录的密集脑电图数据，识别与帕金森病认知表型相关的大脑网络改变。对来自数据驱动聚类分析的三组具有不同认知表型的帕金森病患者（N = 124）进行了研究：G1）认知正常患者（63例），G2）轻度认知缺陷患者（46例）和G3）重度认知缺陷患者（15例）。使用密集脑电图源连接方法识别大脑功能网络。计算了不同脑电图频段中68个脑区的成对功能连接。在全局（网络拓扑）和局部（区域间连接）水平评估网络统计量。结果显示三组患者之间的功能连接逐渐受到破坏，通常在α频段。G1和G2之间的差异（<0.001，使用置换检验校正）主要是额颞叶改变。在一个提出的基于网络的指标与患者认知评分之间也获得了统计学显著相关性（ρ = 0.49，<0.001）。患者网络拓扑的全局属性相对完整。这些发现表明，功能连接随着认知表现的恶化而降低，额颞叶连接的丧失可能是帕金森病认知障碍的一个有前景的神经标志物。
