Centre for Mind/Brain Sciences, University of Trento, Trento, 38068 Rovereto, Italy.
Cereb Cortex. 2023 Aug 23;33(17):9896-9907. doi: 10.1093/cercor/bhad252.
Functional alterations in brain connectivity have previously been described in Parkinson's disease, but it is not clear whether individual differences in connectivity profiles might be also linked to severity of motor-symptom manifestation. Here we investigated the relevance of individual functional connectivity patterns measured with resting-state fMRI with respect to motor-symptom severity in Parkinson's disease, through a whole-brain, data-driven approach (connectome-based predictive modeling). Neuroimaging and clinical data of Parkinson's disease patients from the Parkinson's Progression Markers Initiative were derived at baseline (session 1, n = 81) and at follow-up (session 2, n = 53). Connectome-based predictive modeling protocol was implemented to predict levels of motor impairment from individual connectivity profiles. The resulting predictive model comprised a network mainly involving functional connections between regions located in the cerebellum, and in the motor and frontoparietal networks. The predictive power of the model was stable along disease progression, as the connectivity within the same network could predict levels of motor impairment, even at a later stage of the disease. Finally, connectivity profiles within this network could be identified at the individual level, suggesting the presence of individual fingerprints within resting-state fMRI connectivity associated with motor manifestations in Parkinson's disease.
先前已经描述了帕金森病患者大脑连接功能的改变，但尚不清楚连接谱的个体差异是否也与运动症状表现的严重程度有关。在这里，我们通过全脑、数据驱动的方法（连接组预测建模）研究了静息态 fMRI 测量的个体功能连接模式与帕金森病运动症状严重程度的相关性。帕金森进展标志物倡议的帕金森病患者的神经影像学和临床数据是在基线（第 1 期，n=81）和随访（第 2 期，n=53）时获得的。实施了基于连接组的预测建模方案，以根据个体连接谱预测运动障碍的程度。由此产生的预测模型包括一个网络，主要涉及位于小脑以及运动和额顶网络内的区域之间的功能连接。该模型的预测能力在疾病进展过程中是稳定的，因为同一网络内的连接可以预测运动障碍的程度，即使在疾病的后期阶段也是如此。最后，可以在个体水平上识别出该网络内的连接谱，这表明在与帕金森病运动表现相关的静息态 fMRI 连接中存在个体指纹。
