Previous computational model‐based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease‐related activity changes or specified dopamine‐dependent activation or learning rules. Inspired by recent model‐based analysis of resting‐state fMRI, we have taken a data‐driven approach. We fit the free parameters of a spiking neuro‐computational model to match correlations of blood oxygen level‐dependent signals between different basal ganglia nuclei and obtain subject‐specific neuro‐computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular “rate model” of the basal ganglia. Our study suggests that a model‐based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson‐related changes in the basal ganglia and corresponding treatments.

中文翻译：

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基于计算模型的静息功能磁共振成像推断帕金森氏病基底节神经节通路变化的分析

先前基于计算模型的理解帕金森氏病相关动态变化的方法对帕金森氏病相关活动变化或特定的多巴胺依赖性激活或学习规则做出了特殊假设。受近期基于模型的静息功能磁共振成像分析的启发，我们采用了数据驱动的方法。我们拟合了尖峰神经计算模型的自由参数，以匹配不同基底神经节核之间的血氧水平依赖性信号的相关性，并获得了两个受试者组的特定于受试者的神经计算模型：帕金森病患者和匹配的对照组。在比较对照组和帕金森氏模型组之间的平均静止放电率和连接强度时，发现与先前的实验观察结果一致的几个显着差异。我们讨论了这种方法的含义，并将其结果与流行的基底神经节“速率模型”进行了比较。我们的研究表明，对健康人和帕金森病患者的影像数据进行基于模型的分析，对于将来更好地了解基底节和相关治疗中与帕金森有关的变化是一种很有前途的方法。