Machine Learning Approach Shows Strong Ability to Predict Gait Dysfunction in Parkinson Disease

Reading Time: < 1 minute

Freezing of gait (FOG) presents a challenging and paroxysmal gait disorder, primarily manifesting during the advanced stages of Parkinson’s disease (PD). FOG is characterized by sudden and unexpected interruptions in walking, coupled with significant difficulties in moving, all of which significantly heighten the risk of falls. While the precise pathophysiological underpinnings of FOG in PD remain elusive, recent advancements in MRI technology and nuclear medicine have shed valuable light on the intricacies of this condition.

Cutting-edge neuroimaging studies have uncovered both structural and functional anomalies within various cortical and subcortical brain regions among PD patients grappling with FOG. In this comprehensive exploration, we embark on a systematic review of the extensive neuroimaging literature, meticulously adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Our investigation encompasses a thorough analysis of diverse MRI techniques, enabling the precise estimation of grey matter loss and white matter degeneration. Furthermore, we delve into the realm of functional brain changes, examining the intricate insights offered by functional MRI and nuclear medicine imaging studies.

By consolidating these findings, our current review not only furnishes an up-to-date understanding of this field but also serves as a compendium of the potential mechanisms that may underlie the enigmatic phenomenon of FOG in PD.

Source NCBI

Neurologica
Author: Neurologica