Parkinson's disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (tremor, bradykinesia/akinesia, and rigidity), PD patients also show other motor deficits, including gait disturbance, speech deficits, and impaired handwriting. However, along with these key motor symptoms, PD patients also experience cognitive deficits in attention, executive function, working memory, and learning. Recent evidence suggests that these motor and cognitive deficits of PD are not completely dissociable, as aspects of cognitive dysfunction can impact motor performance in PD. In this article, we provide a review of behavioral and neural studies on the associations between motor symptoms and cognitive deficits in PD, specifically akinesia/bradykinesia, tremor, gait, handwriting, precision grip, and speech production. This review paves the way for providing a framework for understanding how treatment of cognitive dysfunction, for example cognitive rehabilitation programs, may in turn influence the motor symptoms of PD. © 2016 by De Gruyter.

Srinivasa Chakravarthy

Department Of Biotechnology

AKINESIA

Attention

BRADYKINESIA

cognitive defect

COGNITIVE REHABILITATION

DISEASE ASSOCIATION

EXECUTIVE FUNCTION

GAIT DISORDER

HANDWRITING

Human

Learning

MOTOR DYSFUNCTION

Parkinson disease

PRECISION GRIP

priority journal

review

Speech

SPEECH DISORDER

tremor

working memory

Animal

behavior

pathophysiology

physiology

SHORT TERM MEMORY

Animals

COGNITIVE DYSFUNCTION

Humans

MEMORY, SHORT-TERM

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Reviews in the Neurosciences

Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions. © 2013 Helie, Chakravarthy and Moustafa.

Frontiers in Computational Neuroscience

Exploring the cognitive and motor functions of the basal ganglia: An integrative review of computational cognitive neuroscience models

We propose a computational model of Precision Grip (PG) performance in normal subjects and Parkinson's Disease (PD) patients. Prior studies on grip force generation in PD patients show an increase in grip force during ON medication and an increase in the variability of the grip force during OFF medication (Ingvarsson et al., 1997; Fellows et al., 1998). Changes in grip force generation in dopamine-deficient PD conditions strongly suggest contribution of the Basal Ganglia, a deep brain system having a crucial role in translating dopamine signals to decision making. The present approach is to treat the problem of modeling grip force generation as a problem of action selection, which is one of the key functions of the Basal Ganglia. The model consists of two components: (1) the sensory-motor loop component, and (2) the Basal Ganglia component. The sensory-motor loop component converts a reference position and a reference grip force, into lift force and grip force profiles, respectively. These two forces cooperate in grip-lifting a load. The sensory-motor loop component also includes a plant model that represents the interaction between two fingers involved in PG, and the object to be lifted. The Basal Ganglia component is modeled using Reinforcement Learning with the significant difference that the action selection is performed using utility distribution instead of using purely Value-based distribution, thereby incorporating risk-based decision making. The proposed model is able to account for the PG results from normal and PD patients accurately (Ingvarsson et al., 1997; Fellows et al., 1998). To our knowledge the model is the first model of PG in PD conditions. © 2013 Gupta, Balasubramani and Chakravarthy.

Computational model of precision grip in parkinson's disease: A utility based approach

Handwriting in Parkinson's disease (PD) is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen tip velocity. Although PD handwriting features have been used for diagnostics, they are not based on a signaling model of basal ganglia (BG). In this letter, we present a computational model of handwriting generation that highlights the role of BG. When PD conditions like reduced dopamine and altered dynamics of the subthalamic nucleus and globus pallidus externa subsystems are simulated, the handwriting produced by the model manifested characteristic PD handwriting distortions like micrographia and velocity fluctuations. Our approach to PD modeling is in tune with the perspective that PD is a dynamic disease. © 2008 Massachusetts Institute of Technology.

Neural Computation

Understanding parkinsonian handwriting through a computational model of basal ganglia

Journal of Neurology, Neurosurgery & Psychiatry

Cholinesterase inhibitors for Parkinson's disease: a systematic review and meta-analysis

Brain

Parkinson’s disease dementia: a neural networks perspective

Interrelations between cognitive dysfunction and motor symptoms of Parkinson's disease: Behavioral and neural studies

Journal	Data powered by TypesetReviews in the Neurosciences
Publisher	Data powered by TypesetWalter de Gruyter GmbH
ISSN	03341763
Open Access	No