حساب محاسباتی عصبی اختلالات شناختی در بیماری پارکینسون

Parkinson's disease (PD) is caused by the accelerated death of dopamine (DA) producing neurons. Numerous studies documenting cognitive deficits of PD patients have revealed impairments in a variety of tasks related to memory, learning, visuospatial skills, and attention. While there have been several studies documenting cognitive deficits of PD patients, very few computational models have been proposed. In this article, we use the COVIS model of category learning to simulate DA depletion and show that the model suffers from cognitive symptoms similar to those of human participants affected by PD. Specifically, DA depletion in COVIS produced deficits in rule-based categorization, non-linear information-integration categorization, probabilistic classification, rule maintenance, and rule switching. These were observed by simulating results from younger controls, older controls, PD patients, and severe PD patients in five well-known tasks. Differential performance among the different age groups and clinical populations was modeled simply by changing the amount of DA available in the model. This suggests that COVIS may not only be an adequate model of the simulated tasks and phenomena but also more generally of the role of DA in these tasks and phenomena.

Parkinson's disease (PD) is caused by the accelerated death of dopamine (DA) producing neurons. The pattern of cell loss is opposite from and more severe than in normal aging. Within the substantia nigra pars compacta (SNpc), cell loss is predominately found in the ventral tier with less (but still extensive) damage in the dorsal tier (Fearnley and Lees, 1991 and Gibb and Lees, 1991). In contrast, normal aging yields substantially less cell loss and in a dorsal-to-ventral pattern. Parkinsonian motor symptoms appear after a loss of 60–70% of SNpc cells and 70–80% of DA levels in striatal nuclei (Bernheimer et al., 1973 and Gibb and Lees, 1991). Motor symptoms include tremor, rigidity, bradykinesia, and akinesia. In addition to motor deficits, non-demented PD patients present cognitive symptoms that resemble those observed in patients with frontal damage. Numerous studies documenting cognitive deficits of PD patients have revealed impairment in a variety of tasks related to memory, learning, visuospatial skills, and attention (e.g., ignoring irrelevant and maintaining relevant information: Gotham, Brown & Marsden, 1988). While there are a plethora of studies documenting cognitive deficits of PD patients (for a review, see Price, Filoteo, & Maddox, 2009), very few computational models have been proposed to investigate the variegated landscape of deficits observed in those studies. In this article, we use the COVIS model of category learning (Ashby et al., 1998 and Ashby et al., 2011) to simulate DA depletion and we show that the depleted model suffers from cognitive symptoms similar to those of human participants affected by PD.