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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 (Parkinsons disease, Essential Tremor, Dystonia, Essential Tremor, Huntingtons disease, and Tourettes 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.

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13–35 Hz) to prokinetic theta (4–10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders. [ABSTRACT FROM AUTHOR]

Aims: Deep brain stimulation (DBS) in the ventral intermediate nucleus (Vim‐DBS) is the preferred surgical therapy for essential tremor (ET). Tolerance and disease progression are considered to be the two main reasons underlying the loss of long‐term efficacy of Vim‐DBS. This study aimed to explore whether Vim‐DBS shows long‐term loss of efficacy and to evaluate the reasons for this diminished efficacy from different aspects. Methods: In a repeated‐measures meta‐analysis of 533 patients from 18 studies, Vim‐DBS efficacy was evaluated at ≤6 months, 7–12 months, 1–3 years, and ≥4 years. The primary outcomes were the score changes in different components of the Fahn‐Tolosa‐Marin Tremor Rating Scale (TRS; total score, motor score, hand‐function score, and activities of daily living [ADL] score). Secondary outcomes were the long‐term predictive factors. Results: The TRS total, motor, and ADL scores showed significant deterioration with disease progression (p = 0.002, p = 0.047, and p < 0.001, respectively), while the TRS total (p < 0.001), hand‐function (p = 0.036), and ADL (p = 0.004) scores indicated a significant long‐term reduction in DBS efficacy, although the motor subscore indicated no loss of efficacy. Hand‐function (p < 0.001) and ADL (p = 0.028) scores indicated DBS tolerance, while the TRS total and motor scores did not. Stimulation frequency and preoperative score were predictive factors for long‐term results. Conclusion: This study provides level 3a evidence that long‐term Vim‐DBS is effective in controlling motor symptoms without waning benefits. The efficacy reduction for hand function was caused by DBS tolerance, while that for ADL was caused by DBS tolerance and disease progression. More attention should be given to actual functional recovery rather than changes in motor scores in patients with ET. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate outcomes of deep brain stimulation (DBS) for Meige syndrome, compare the efficacy of globus pallidus internus (GPi) and subthalamic nucleus (STN) as targets, and identify potential outcome predictors., Methods: The PubMed, Embase, and Web of Science databases were systematically searched to collect individual data from patients with Meige syndrome receiving DBS. Outcomes were assessed using the Burke-Fahn-Marsden Dystonia Rating Scale motor (BFMDRS-M) and disability (BFMDRS-D) scores. Data were analyzed using pooled meta-analysis. The study is registered in the PROSPERO database., Results: The analysis included 233 patients from 26 studies, with significant publication bias (p = 0.008, Egger's test), but showed significant improvements in BFMDRS-M (65.09% ± 26.65%) and BFMDRS-D (53.48% ± 42.44%) scores at the final follow-up (mean duration 27.10 ± 33.64 months). No significant differences were observed in BFMDRS-M score improvement (mean difference -2.58%, 95% CI -15.84% to 10.69%; p = 0.430) or risk difference for response (-0.97%, 95% CI -10.08% to 8.15%; p = 0.835) between the GPi and STN target groups at the final follow-up across all follow-up periods (0 to ≤ 6, > 6 to ≤ 12, > 12 to ≤ 24, > 24 to ≤ 36, and > 36 months). Multiple regression analysis revealed a negative correlation between disease duration and treatment efficacy and a positive correlation between preoperative BFMDRS score and treatment outcome., Conclusions: DBS significantly improves motor symptoms and disability in patients with Meige syndrome, with GPi and STN targets providing comparable efficacy. The efficacy of DBS diminishes with longer disease duration, underscoring the importance of early intervention.

Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.