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Abstract Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson’s disease, Essential Tremor, Dystonia, Essential Tremor, Huntington’s disease, and Tourette’s syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder – BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.

Background: Sleep dysfunction is disabling in people with Parkinson’s disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep. Objective: Develop an adaptive Deep Brain Stimulation algorithm that modulates stimulation parameters in response to intracranially classified sleep stages. Methods: We performed at-home, multi-night intracranial electrocorticography and polysomnogram recordings to train personalized linear classifiers for discriminating the N3 NREM sleep stage. Classifiers were embedded into investigational Deep Brain Stimulators for N3 specific adaptive DBS. Results: We report high specificity of embedded, autonomous, intracranial electrocorticography N3 sleep stage classification across two participants and provide proof-of-principle of successful sleep stage specific adaptive Deep Brain Stimulation. Conclusion: Multi-night cortico-basal recordings and sleep specific adaptive Deep Brain Stimulation provide an experimental framework to investigate sleep pathophysiology and mechanistic interactions with stimulation, towards the development of therapeutic neurostimulation paradigms directly targeting sleep dysfunction.

The anterior cingulate cortex (ACC) has been extensively implicated in the functional brain network underlying chronic pain. Electrical stimulation of the ACC has been proposed as a therapy for refractory chronic pain, although, mechanisms of therapeutic action are still unclear. As stimulation of the ACC has been reported to produce many different behavioral and perceptual responses, this region likely plays a varied role in sensory and emotional integration as well as modulating internally generated perceptual states. In this case series, we report the emergence of subjective musical hallucinations (MH) after electrical stimulation of the ACC in two patients with refractory chronic pain. In an N-of-1 analysis from one patient, we identified neural activity (local field potentials) that distinguish MH from both the non-MH condition and during a task involving music listening. Music hallucinations were associated with reduced alpha band activity and increased gamma band activity in the ACC. Listening to similar music was associated with different changes in ACC alpha and gamma power, extending prior results that internally generated perceptual phenomena are supported by circuits in the ACC. We discuss these findings in the context of phantom perceptual phenomena and posit a framework whereby chronic pain may be interpreted as a persistent internally generated percept.

We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.

Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson’s disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson’s disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician—for example, during remote home assessments. Videos (n=729) were collected as part of routine MDS-UPDRS gait assessments of Parkinson’s patients, and a deep learning library was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician. Six features were calculated from time-series signals of the extracted key-points. These features characterized key aspects of the movement including speed (step frequency, estimated using a novel Gamma-Poisson Bayesian model), arm swing, postural control and smoothness (or roughness) of movement. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation. Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 606 video clips showing patients walking towards or away from the camera (Pearson’s r=0.80, p<0.001). Our classifier achieved a balanced accuracy of 50% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians’ ratings in 95% of cases. There was a significant correlation between clinician labels and model estimates (Spearman’s ρ=0.52, p<0.001). We show how the interpretability of the feature values could be used by clinicians to support their decision-making and provide insight into the model’s objective UPDRS rating estimation. The severity of gait impairment in Parkinson’s disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e., videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective rating (or second opinion), and has the potential to be used for remote home assessments, which would allow for more frequent monitoring.

The observation that fetal neurons are able to survive and function when transplanted into the adult brain fostered the development of cellular therapy as a promising approach to achieve neuronal replacement for treatment of diseases of the adult central nervous system. This approach has been demonstrated to be efficacious in patients with Parkinson's disease after transplantation of human fetal neurons. The use of human fetal tissue is limited by ethical, infectious, regulatory, and practical concerns. Other mammalian fetal neural tissue could serve as an alternative cell source. Pigs are a reasonable source of fetal neuronal tissue because of their brain size, large litters, and the extensive experience in rearing them in captivity under controlled conditions. In Phase I studies porcine fetal neural cells grafted unilaterally into Parkinson's disease (PD) and Huntington's disease (HD) patients are being evaluated for safety and efficacy. Clinical improvement of 19% has been observed in the Unified Parkinson's Disease Rating Scale “off” state scores in 10 PD patients assessed 12 months after unilateral striatal transplantation of 12 million fetal porcine ventral mesencephalic (VM) cells. Several patients have improved more than 30%. In a single autopsied PD patient some porcine fetal VM cells were observed to survive 7 months after transplantation. Twelve HD patients have shown a favorable safety profile and no change in total functional capacity score 1 year after unilateral striatal placement of up to 24 million fetal porcine striatal cells. Xenotransplantation of fetal porcine neurons is a promising approach to delivery of healthy neurons to the CNS. The major challenges to the successful use of xenogeneic fetal neuronal cells in neurodegenerative diseases appear to be minimizing immune-mediated rejection, management of the risk of xenotic (cross-species) infections, and the accurate assessment of clinical outcome of diseases that are slowly progressive.