Parkinson's disease (PD) is characterized by overlapping motor, neuropsychiatric, and cognitive symptoms. Worse performance in one domain is associated with worse performance in the other domains. Commonality analysis (CA) is a method of variance partitioning in multiple regression, used to separate the specific and common influence of collinear predictors. We apply, for the first time, CA to the functional connectome to investigate the unique and common neural connectivity underlying the interface of the symptom domains in 74 non‐demented PD subjects. Edges were modeled as a function of global motor, cognitive, and neuropsychiatric scores. CA was performed, yielding measures of the unique and common contribution of the symptom domains. Bootstrap confidence intervals were used to determine the precision of the estimates and to directly compare each commonality coefficient. The overall model identified a network with the caudate nucleus as a hub. Neuropsychiatric impairment accounted for connectivity in the caudate‐dorsal anterior cingulate and caudate‐right dorsolateral prefrontal‐right inferior parietal circuits, while caudate‐medial prefrontal connectivity reflected a unique effect of both neuropsychiatric and cognitive impairment. Caudate‐precuneus connectivity was explained by both unique and shared influence of neuropsychiatric and cognitive symptoms. Lastly, posterior cortical connectivity reflected an interplay of the unique and common effects of each symptom domain. We show that CA can determine the amount of variance in the connectome that is unique and shared amongst motor, neuropsychiatric, and cognitive symptoms in PD, thereby improving our ability to interpret the data while gaining novel insight into networks at the interface of these symptom domains.

中文翻译：

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帕金森病运动、神经精神和认知症状界面上的共同和独特的连接：共性分析。

帕金森病 (PD) 的特点是运动、神经精神和认知症状重叠。一个域中较差的性能与其他域中较差的性能相关联。共性分析 (CA) 是多元回归中的一种方差划分方法，用于分离共线预测变量的特定影响和共同影响。我们首次将 CA 应用于功能连接组，以研究 74 名非痴呆 PD 受试者症状域界面背后的独特和常见的神经连接。边缘被建模为整体运动、认知和神经精神评分的函数。执行 CA，产生症状域的独特和共同贡献的措施。Bootstrap 置信区间用于确定估计的精度并直接比较每个共性系数。整个模型确定了一个以尾状核为中心的网络。神经精神障碍导致尾状背前扣带回和尾状右侧背外侧前额叶右下顶叶回路的连通性，而尾状内侧前额叶连通性反映了神经精神和认知障碍的独特影响。尾状核前叶连接可以通过神经精神和认知症状的独特和共同影响来解释。最后，后皮质连接反映了每个症状域的独特和共同影响的相互作用。