Pallidal activities during sleep and sleep decoding in dystonia, Huntington's, and Parkinson's disease

Pathological pallidal beta activity in Parkinson’s disease is sustained during sleep and associated with sleep disturbance

“…In the awake state, both of these strategies have demonstrated a clear association between beta activity and PD motor signs, where basal ganglia beta activity is elevated when compared to patients with dystonia 20 , 21 and robustly associated with motor sign severity, most recently reproduced in a cohort of 106 patients 24 . We have now extended both approaches to the sleep state, for which previous studies 11 – 15 have demonstrated that beta is influenced by sleep-stage transitions, promising potential utility for sleep-stage decoding 25 , but the relative pathological significance remained unaddressed. Our study extends these insights by providing direct evidence that compared to dystonia subjects, pallidal beta activity in PD is continuously higher across sleep stages and correlated with sleep disturbance.…”

“…While generally very promising, it is important to note, that pathophysiological phenomena may interact with such machine learning algorithms, as recently shown for beta bursts that can detriment grip-force decoding in PD 39 . In the future, sleep may be one of many decoding targets 25 , through which machine learning will extend the clinical utility of adaptive DBS adjusting to the individual challenges of our patients in their everyday lives 40 – 42 . Such intelligent adaptive DBS systems may not only improve the nocturnal motor symptoms of PD but also address sleep and sleep-related dysfunctions in PD as a whole.…”

Generalized sleep decoding with basal ganglia signals in multiple movement disorders

“…Demographics, disease information, and sleep parameters of each disease-target group are shown in Table 1. Previous sleep decoding studies employing different classifiers have obtained varied accuracies [16][17][18][19] . We evaluated the performance of eight commonly used machine learning classifiers in our PD-STN dataset.…”

“…Prior research, including our own [16][17][18][19] , has attempted to decode sleep stages based on basal ganglia local field potentials (LFPs) recorded from DBS electrodes. This approach has proven to be feasible and potentially reduces the use case for additional wearable devices.…”

Structural, Functional, and Genetic Changes Surrounding Electrodes Implanted in the Brain

“…Communication in the central nervous system occurs via the widely recognized modalities of electrical and chemical transmission between neurons, and these dynamic interactions generate signals providing information on brain function and behavior . In recent years, implantable electrodes have enabled the detection and stimulation of neural signals to study and treat a variety of neurological diseases including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Tourette’s syndrome, obsessive compulsive disorder, epilepsy, chronic pain, paralysis, and depression. − As clinical use expands and interest in nonclinical use of brain implants enters public consciousness, questions related to the safe and ethical use of implanted electrodes have received added attention. , Observations of unexplained suboptimal or variable recording performance over chronic implantation periods, shifting stimulation thresholds, and off-target effects of neuromodulation indicate that the understanding of device–tissue interactions remains incomplete. − Further characterization of the biological response to implants, as well as definitively ascribing those processes to the design choices (surface chemistry, topography, mechanical characteristics, and feature sizes) that initiate reactivity, is needed in order to achieve a more seamless interface with predictable long-term performance …”