Actigraphy Enables Home Screening of Rapid Eye Movement Behavior Disorder in Parkinson's Disease.

OBJECTIVES: Rapid eye movement sleep behavior disorder (RBD) is a potentially harmful, often overlooked sleep disorder affecting up to 70% of Parkinson's disease patients. Current diagnosis relies on nocturnal video-polysomnography, which is an expensive and cumbersome examination requiring specific clinical expertise. Here, we explored the use of wrist actigraphy to enable automatic RBD diagnoses in home settings. METHODS: A total of 26 Parkinson's disease patients underwent 2-week home wrist actigraphy, followed by two in-laboratory evaluations. Patients were classified as RBD versus non-RBD based on dream enactment history and video-polysomnography. We comprehensively characterized patients' movement patterns during sleep using actigraphic signals. We then trained machine learning classification algorithms to discriminate patients with or without RBD using the most relevant features. Classification performance was quantified with respect to clinical diagnosis, separately for in-laboratory and at-home recordings. Performance was further validated in a control group of non-Parkinson's disease patients with other sleep conditions. RESULTS: To characterize RBD, actigraphic features extracted from both (1) individual movement episodes and (2) global nocturnal activity were critical. RBD patients were more active overall, and showed movements that were shorter, of higher magnitude, and more scattered in time. Using these features, our classification algorithms reached an accuracy of 92.9 ± 8.16% during in-clinic tests. When validated on home recordings in Parkinson's disease patients, accuracy reached 100% over a 2-week window, and was 94.4% in non-Parkinson's disease control patients. Features showed robustness across tests and conditions. INTERPRETATION: These results open new perspectives for faster, cheaper, and more regular screening of sleep disorders, both for routine clinical practice and clinical trials. ANN NEUROL 2023;93:317-329.

Adaptive, personalized closed-loop therapy for Parkinson's disease: biochemical, neurophysiological, and wearable sensing systems.

INTRODUCTION: Motor complication management is one of the main unmet needs in Parkinson's disease patients. AREAS COVERED: Among the most promising emerging approaches for handling motor complications in Parkinson's disease, adaptive deep brain stimulation strategies operating in closed-loop have emerged as pivotal to deliver sustained, near-to-physiological inputs to dysfunctional basal ganglia-cortical circuits over time. Existing sensing systems that can provide feedback signals to close the loop include biochemical-, neurophysiological- or wearable-sensors. Biochemical sensing allows to directly monitor the pharmacokinetic and pharmacodynamic of antiparkinsonian drugs and metabolites. Neurophysiological sensing relies on neurotechnologies to sense cortical or subcortical brain activity and extract real-time correlates of symptom intensity or symptom control during DBS. A more direct representation of the symptom state, particularly the phenomenological differentiation and quantification of motor symptoms, can be realized via wearable sensor technology. EXPERT OPINION: Biochemical, neurophysiologic, and wearable-based biomarkers are promising technological tools that either individually or in combination could guide adaptive therapy for Parkinson's disease motor symptoms in the future.

Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection.

The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes' equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called 'prototypic' and 'arkypallidal' neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions.

Neuroprosthetic technologies to augment the impact of neurorehabilitation after spinal cord injury.

Spinal cord injury leads to a range of disabilities, including limitations in locomotor activity, that seriously diminish the patients' autonomy and quality of life. Electrochemical neuromodulation therapies, robot-assisted rehabilitation and willpower-based training paradigms restored supraspinal control of locomotion in rodent models of severe spinal cord injury. This treatment promoted extensive and ubiquitous remodeling of spared circuits and residual neural pathways. In four chronic paraplegic individuals, electrical neuromodulation of the spinal cord resulted in the immediate recovery of voluntary leg movements, suggesting that the therapeutic concepts developed in rodent models may also apply to humans. Here, we briefly review previous work, summarize current developments, and highlight impediments to translate these interventions into medical practice to improve functional recovery of spinal-cord-injured individuals.