Modeling Precision Grip Force in Controls and Parkinson’s Disease Patients

Abstract

Precision grip (PG) is the ability to hold an object between forefinger and thumb. Lifting objects in PG require delicate finger grip force (GF) control. Healthy controls modulate GF depending on size, weight, surface curvature, and friction. The difference between the actual GF generated and the minimum GF required to prevent the object from slipping is known as safety margin (SM). Published results suggest that OFF-medicated Parkinson’s disease (PD) patients generated average SM identical to that of controls with increased SM variance. PD patients on medication demonstrated higher average SM with SM variance identical to that of controls. Previously known computational models provide an insight on how the GF is generated and controlled but are unsuitable for modeling the GF in PD patients. In this chapter, we present a Go/Explore/NoGo (GEN) algorithm in a utility-based decision-making framework to explain the SM generated by healthy controls and PD patients both during ON and OFF medication. The study suggests that PD GF is a result of dopamine-level-dependent suboptimal decision-making-based force selection and the suitability of the GEN algorithm to model decision-making tasks.