Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands; Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands.
Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands.
Parkinsonism Relat Disord. 2022 Dec;105:32-38. doi: 10.1016/j.parkreldis.2022.10.027. Epub 2022 Oct 28.
Parkinson's disease (PD) is a heterogeneous disorder with great variability in motor and non-motor manifestations. It is hypothesized that different motor subtypes are characterized by different neuropsychiatric and cognitive symptoms, but the underlying correlates in cerebral connectivity remain unknown. Our aim is to compare brain network connectivity between the postural instability and gait disorder (PIGD) and tremor-dominant (TD) subtypes, using both a within- and between-network analysis.
This cross-sectional resting-state fMRI study includes 81 PD patients, 54 belonging to the PIGD and 27 to the TD subgroup. Group-level spatial maps were created using independent component analysis. Differences in functional connectivity were investigated using dual regression analysis and inter-network connectivity analysis. An additional voxel-based morphometry analysis was performed to examine if results were influenced by grey matter atrophy.
The PIGD subgroup scored worse than the TD subgroup on all cognitive domains. Resting-state fMRI network analyses suggested that the connection between the visual and sensorimotor network is a potential differentiator between PIGD and TD subgroups. However, after correcting for dopaminergic medication use these results were not significant anymore. There was no between-group difference in grey matter volume.
Despite clear motor and cognitive differences between the PIGD and TD subtypes, no significant differences were found in network connectivity. Methodological challenges, substantial symptom heterogeneity and many involved variables make analyses and hypothesis building around PD subtypes highly complex. More sensitive visualisation methods combined with machine learning approaches may be required in the search for characteristic underpinnings of PD subtypes.
帕金森病（PD）是一种异质性疾病，运动和非运动表现存在很大差异。有人假设，不同的运动亚型具有不同的神经精神和认知症状，但大脑连接中的潜在相关性尚不清楚。我们的目的是使用基于网络内和网络间的分析，比较姿势不稳和步态障碍（PIGD）与震颤为主（TD）亚型之间的脑网络连接。
本横断面静息态 fMRI 研究纳入 81 例 PD 患者，其中 54 例属于 PIGD 组，27 例属于 TD 组。使用独立成分分析创建组水平的空间图谱。使用双回归分析和网络间连接分析研究功能连接的差异。还进行了额外的基于体素的形态测量学分析，以检查结果是否受灰质萎缩的影响。
PIGD 亚组在所有认知领域的评分均低于 TD 亚组。静息态 fMRI 网络分析表明，视觉和感觉运动网络之间的连接可能是 PIGD 和 TD 亚组之间的潜在区分因素。然而，在纠正多巴胺能药物使用后，这些结果不再显著。两组之间的灰质体积没有差异。
尽管 PIGD 和 TD 亚型之间存在明显的运动和认知差异，但在网络连接方面没有发现显著差异。方法学挑战、显著的症状异质性和许多涉及的变量使得围绕 PD 亚型的分析和假设构建变得非常复杂。可能需要更敏感的可视化方法结合机器学习方法来寻找 PD 亚型的特征基础。
