Sensorimotor Integration: A Review of Neural and Computational Models and the Impact of Parkinson’s Disease

Abstract

Sensorimotor integration (SMI) is a complex process that allows humans to perceive and interact with their environment. Any impairment in SMI may impact the day-to-day functioning of humans, particularly evident in Parkinson’s Disease (PD). SMI is critical to accurate perception and modulation of motor outputs. Therefore, understanding the associated neural pathways and mathematical underpinnings is crucial. In this article, a systematic review of the proposed neural and computational models associated with SMI is performed. While the neural models discuss the neural architecture and regions, the computational models explore the mathematical or computational mechanisms involved in SMI. The article then explores how PD may impair SMI, reviewing studies that discuss deficits in the perception of various modalities, pointing to an SMI impairment. This helps in understanding the nature of SMI deficits in PD. Overall, the review offers comprehensive insights into the basis of SMI and the effect of PD on SMI, enabling clinicians to better understand the SMI mechanisms and facilitate the development of targeted therapies to mitigate SMI deficits in PD.