Deep neurobehavioral phenotyping uncovers neural fingerprints of locomotor deficits in Parkinson's disease

Abstract

Gait deficits present an unresolved therapeutic challenge in Parkinsons Disease. At the behavioral level, symptoms exhibit heterogeneity, including bradykinesia and hypokinesia during cyclical limb movement, as well as sudden interruptions in the gait sequence, also known as freezing of gait. The neural activities that drive these various deficits remain largely unknown. Here, we investigated the neural correlates of gait sequence interruptions with deep neurobehavioral phenotyping. For this, we transformed kinematic trajectories and cortical oscillations into continuous time series of multimodal feature vectors. Next, we applied machine learning, combining low-dimensional embedding with supervised classification, to identify cortical oscillation features that drive gait deficits. In a rodent Parkinsons disease model, our approach revealed that gait, akinesia, and stationary movements occupy prominently different regions in the low-dimensional embedding space. Among the predominant features separating the states, we found Hjorth complexity and mobility to modulate with the onset of akinetic episodes. Additionally, we validated our analysis approach in two Parkinson patients with freezing of gait, where neural features in STN recordings partially reflected the findings from ECoG measurements in rodents. The presented neurobehavioral phenotyping approach is translational and can easily generalize to the analysis of other complex movement disorders. Together, our results highlight specific features of neural oscillations as potential biomarkers that may support the development of adaptive closed-loop algorithms for gait therapy in PD.