title: Understanding Parkinsonian handwriting through a computational model of basal ganglia.
abstract: 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.
authors.affiliation: Machine Learning Group, IDIAP Research Institute, CH-1920 Martigny, Switzerland. Gangadhar.Garipelli@idiap.ch
authors.forename: Garipelli
authors.initials: G
authors.lastname: Gangadhar
authors.forename: Denny
authors.initials: D
authors.lastname: Joseph
authors.forename: V Srinivasa
authors.initials: VS
authors.lastname: Chakravarthy
mesh_terms: D001479:Basal Ganglia / Q000503:physiopathology*; D006236:Handwriting; D006801:Humans; D008959:Models, Neurological; D010300:Parkinson Disease / Q000503:physiopathology*