Patients with Parkinson's disease with mild cognitive impairment (PD-M) progress to dementia more frequently than those with normal cognition (PD-N), but the underlying neurobiology remains unclear. This study aimed to define the specific morphological brain network alterations in PD-M, and explore their potential diagnostic value. Twenty-four PD-M patients, 17 PD-N patients, and 29 healthy controls (HC) underwent a structural MRI scan. Similarity between interregional gray matter volume distributions was used to construct individual morphological brain networks. These were analyzed using graph theory and network-based statistics (NBS), and their relationship to neuropsychological tests was assessed. Support vector machine (SVM) was used to perform individual classification. Globally, compared with HC, PD-M showed increased local efficiency (p = .001) in their morphological networks, while PD-N showed decreased normalized path length (p = .008). Locally, similar nodal deficits were found in the rectus and lingual gyrus, and cerebellum of both PD groups relative to HC; additionally in PD-M nodal deficits involved several frontal and parietal regions, correlated with cognitive scores. NBS found that similar connections were involved in the default mode and cerebellar networks of both PD groups (to a greater extent in PD-M), while PD-M, but not PD-N, showed altered connections involving the frontoparietal network. Using connections identified by NBS, SVM allowed discrimination with high accuracy between PD-N and HC (90%), PD-M and HC (85%), and between the two PD groups (65%). These results suggest that default mode and cerebellar disruption characterizes PD, more so in PD-M, whereas frontoparietal disruption has diagnostic potential.

中文翻译：

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伴有和不伴有轻度认知障碍的早期帕金森病的拓扑收敛和发散形态学灰质网络

患有轻度认知障碍 (PD-M) 的帕金森病患者比认知正常的患者 (PD-N) 更频繁地发展为痴呆，但潜在的神经生物学仍不清楚。本研究旨在确定 PD-M 中特定的形态学脑网络改变，并探索其潜在的诊断价值。24 名 PD-M 患者、17 名 PD-N 患者和 29 名健康对照 (HC) 接受了结构 MRI 扫描。区域间灰质体积分布之间的相似性用于构建个体形态学脑网络。使用图论和基于网络的统计（NBS）对这些进行了分析，并评估了它们与神经心理学测试的关系。支持向量机（SVM）用于执行个体分类。在全球范围内，与 HC 相比，PD-M 表现出更高的局部效率（p  = .001) 在他们的形态网络中，而 PD-N 显示归一化路径长度 ( p = .008)。在局部，与 HC 相比，两个 PD 组的直肌和舌回以及小脑中发现了相似的淋巴结缺陷；此外，在 PD-M 淋巴结缺陷中，涉及多个额叶和顶叶区域，与认知评分相关。NBS 发现，两个 PD 组的默认模式和小脑网络都涉及类似的连接（在 PD-M 中更大程度），而 PD-M 而不是 PD-N，显示涉及额顶网络的连接改变。使用 NBS 识别的连接，SVM 允许在 PD-N 和 HC (90%)、PD-M 和 HC (85%) 以及两个 PD 组 (65%) 之间进行高精度区分。这些结果表明默认模式和小脑破坏是 PD 的特征，在 PD-M 中更是如此，而额顶叶破坏具有诊断潜力。