The basal ganglia (BG) is part of a basic feedback circuit regulating cortical function, such as voluntary movements control, via their influence on thalamocortical projections. BG disorders, namely Parkinson’s disease (PD), characterized by the loss of neurons in the substantia nigra, involve the progressive loss of motor functions. At the present, PD is incurable. Converging evidences suggest the onset of PD-specific pathology prior to the appearance of classical motor signs. This latent phase of neurodegeneration in PD is of particular relevance in developing more effective therapies by intervening at the earliest stages of the disease. Therefore, a key challenge in PD research is to identify and validate markers for the preclinical and prodromal stages of the illness. We propose a mechanistic neurocomputational model of the BG at a mesoscopic scale to investigate the behavior of the simulated neural system after several degrees of lesion of the substantia nigra, with the aim of possibly evaluating which is the smallest lesion compromising motor learning. In other words, we developed a working framework for the analysis of theoretical early-stage PD. While simulations in healthy conditions confirm the key role of dopamine in learning, in pathological conditions the network predicts that there may exist abnormalities of the motor learning process, for physiological alterations in the BG, that do not yet involve the presence of symptoms typical of the clinical diagnosis.

中文翻译：

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帕金森病运动学习障碍的发生：一项计算研究

基底神经节 (BG) 是调节皮质功能的基本反馈回路的一部分，例如通过影响丘脑皮质投射来调节随意运动控制。BG 疾病，即帕金森病 (PD)，其特征是黑质神经元丧失，涉及运动功能的进行性丧失。目前，PD是无法治愈的。综合证据表明，PD 特异性病理学先于经典运动体征出现。PD 神经变性的潜伏期对于通过在疾病的最早阶段进行干预来开发更有效的疗法特别重要。因此，PD 研究的一个关键挑战是识别和验证疾病临床前和前驱阶段的标志物。我们提出了一种介观尺度的 BG 机械神经计算模型，以研究黑质几度病变后模拟神经系统的行为，目的是可能评估哪个是损害运动学习的最小病变。换句话说，我们开发了一个用于分析理论早期 PD 的工作框架。虽然健康条件下的模拟证实了多巴胺在学习中的关键作用，但在病理条件下，网络预测运动学习过程可能存在异常，即 BG 的生理变化，但尚未涉及典型症状的存在。临床诊断。