Multijoint movement control in Parkinson's disease

2001 ◽  
Vol 140 (3) ◽  
pp. 335-344 ◽  
Author(s):  
Rachael Seidler ◽  
Jay Alberts ◽  
George Stelmach
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Movement Control ◽  
Multijoint Movement

Delayed visual feedback and movement control in Parkinson's disease

1990 ◽  
Vol 110 (2) ◽  
pp. 228-235 ◽  
Author(s):  
A. Beuter ◽  
J.G. Milton ◽  
C. Labrie ◽  
L. Glass ◽  
S. Gauthier
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Visual Feedback ◽  
Movement Control ◽  
Delayed Visual Feedback

Deficits in saccadic eye-movement control in Parkinson's disease

2005 ◽  
Vol 43 (5) ◽  
pp. 784-796 ◽  
Author(s):  
Florence Chan ◽  
Irene T. Armstrong ◽  
Giovanna Pari ◽  
Richard J. Riopelle ◽  
Douglas P. Munoz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Eye Movement ◽  
Movement Control ◽  
Saccadic Eye Movement ◽  
Eye Movement Control

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

2021 ◽  
Author(s):  
Ilaria Gigi ◽  
Rosa Senatore ◽  
Angelo Marcelli
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Motor Learning ◽  
Early Stage ◽  
Motor Task ◽  
Movement Control ◽  
Neural System ◽  
Motor Deficits ◽  
Cortical Function

The basal ganglia (BG) are part of a basic feedback circuit, regulating cortical function, such as voluntary movement control, via their influence on thalamocortical projections. BG disorders, namely Parkinson's disease (PD), characterized by the loss of neurons in the substantia nigra (SN), involve the progressive loss of motor functions. The process that leads to these neural alterations is still unknown. At the present, PD cannot be cured, but an early diagnosis (ED) could allow to better manage its symptoms and evolution. A branch of neuroscience research is currently investigating the possibility of using motor alterations, e.g. handwriting, caused by the disease as diagnostic signs in the early stage of the disease, expression of small entity of SN lesion. In the present work, we propose a neurocomputational model to investigate the behaviour of the simulated neural system after several degrees of lesion, with the aim of evaluating, if possible, which is the smallest lesion compromising motor learning. The performance of the network in learning a novel motor task has been analyzed, in physiological and pathological conditions. The proposed neural network proves that there may exist abnormalities of motor learning process, due to alterations in the BG, which do not yet involve the presence of symptoms typical of the confirmed diagnosis, since the network shows having some difficulties in motor learning already with 20% DA depletion.

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