Neuroscience Research Australia Sydney, NSW, Australia ; Faculty of Medicine, School of Medical Sciences, University of New South Wales Sydney, NSW, Australia.
School of Social Sciences and Psychology and the Marcs Institute for Brain and Behaviour, University of Western Sydney Sydney, NSW, Australia.
Front Comput Neurosci. 2013 Dec 12;7:180. doi: 10.3389/fncom.2013.00180. eCollection 2013.
Discrimination learning deficits in Parkinson's disease (PD) have been well-established. Using both behavioral patient studies and computational approaches, these deficits have typically been attributed to dopamine imbalance across the basal ganglia. However, this explanation of impaired learning in PD does not account for the possible contribution of other pathological changes that occur in the disease process, importantly including gray matter loss. To address this gap in the literature, the current study explored the relationship between fronto-striatal gray matter atrophy and learning in PD. We employed a discrimination learning task and computational modeling in order to assess learning rates in non-demented PD patients. Behaviorally, we confirmed that learning rates were reduced in patients relative to controls. Furthermore, voxel-based morphometry imaging analysis demonstrated that this learning impairment was directly related to gray matter loss in discrete fronto-striatal regions (specifically, the ventromedial prefrontal cortex, inferior frontal gyrus and nucleus accumbens). These findings suggest that dopaminergic imbalance may not be the sole determinant of discrimination learning deficits in PD, and highlight the importance of factoring in the broader pathological changes when constructing models of learning in PD.
帕金森病（PD）患者的辨别学习缺陷已得到充分证实。通过行为患者研究和计算方法，这些缺陷通常归因于基底神经节中多巴胺的失衡。然而，这种对 PD 学习障碍的解释并不能说明疾病过程中发生的其他病理变化的可能贡献，重要的是包括灰质损失。为了解决文献中的这一差距，本研究探讨了 PD 患者额纹状体灰质萎缩与学习之间的关系。我们采用了辨别学习任务和计算模型来评估非痴呆 PD 患者的学习率。行为上，我们证实与对照组相比，患者的学习率降低。此外，基于体素的形态计量成像分析表明，这种学习障碍与离散的额纹状体区域（特别是腹内侧前额叶皮层、下额回和伏隔核）的灰质损失直接相关。这些发现表明，多巴胺能失衡可能不是 PD 患者辨别学习缺陷的唯一决定因素，并强调在构建 PD 学习模型时考虑更广泛的病理变化的重要性。
