A neurocomputational model of the basal ganglia for the analysis of motor deficits after dopaminergic loss in Parkinson's disease

Abstract

The basal ganglia (BG) is part of a basic feedback circuit, regulating cortical function, such as voluntary movement control, via their influence on thalamocortical projections. BG disorders, namely Parkinsons 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 evidence suggests 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 disease. Therefore, a key challenge in PD research is to identify and validate markers for the preclinical and prodromal stage of the illness. We propose a mechanistic neurocomputational model of the BG at 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 networks predict that there may exist abnormalities of motor learning process for physiological alterations in the BG which do not yet involve the presence of symptoms typical of the clinical diagnosis. Our model may account for the discovery of markers for an early diagnosis of the disease and give directions for developing novel noninvasive support systems.