Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches

Frontiers in Neurology, Journal Year: 2022, Volume and Issue: 13

Published: March 9, 2022

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology DBS continues to progress, efficacy will continue improve disease indications expand. Hardware advances such as longer-lasting batteries reduce frequency battery replacement segmented leads facilitate improvements in effectiveness have potential minimize side effects. Targeting specialized imaging sequences “connectomics” improved accuracy lead positioning trajectory planning. Software closed-loop remote programming enable be more personalized accessible technology. The future promising holds further quality life. In this review we address past, present DBS.

Language: Английский

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Virtual brain twins: from basic neuroscience to clinical use

National Science Review, Journal Year: 2024, Volume and Issue: 11(5)

Published: Feb. 27, 2024

ABSTRACT Virtual brain twins are personalized, generative and adaptive models based on data from an individual’s for scientific clinical use. After a description of the key elements virtual twins, we present standard model personalized whole-brain network models. The personalization is accomplished using subject’s imaging by three means: (1) assemble cortical subcortical areas in subject-specific space; (2) directly map connectivity into models, which can be generalized to other parameters; (3) estimate relevant parameters through inversion, typically probabilistic machine learning. We use healthy ageing five diseases: epilepsy, Alzheimer’s disease, multiple sclerosis, Parkinson’s disease psychiatric disorders. Specifically, introduce spatial masks demonstrate their physiological pathophysiological hypotheses. Finally, pinpoint challenges future directions.

Language: Английский

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N2GNet tracks gait performance from subthalamic neural signals in Parkinson’s disease

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 4, 2025

Abstract Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson’s disease (PWP) by adjusting the in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected features which may result a less robust biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), novel learning-based regression model capable of tracking gait performance from subthalamic nucleus local field potentials (STN LFPs). The LFP data were acquired when eighteen PWP performed stepping place, ground reaction forces measured to track weight shifts representing performance. By exhibiting stronger correlation compared higher-correlation beta power two leads outperforming other evaluated designs, N2GNet effectively leverages comprehensive frequency band, not limited range, solely STN LFPs.

Language: Английский

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The pathophysiology of Parkinson's disease tremor

Journal of the Neurological Sciences, Journal Year: 2022, Volume and Issue: 435, P. 120196 - 120196

Published: Feb. 19, 2022

Tremor is one of the primary motor symptoms Parkinson's disease (PD), and it characterized by a highly phenomenological heterogeneity. Clinical experimental observations suggest that tremor in PD cannot be interpreted merely as an expression dopaminergic denervation basal ganglia. Accordingly, other neurotransmitter systems brain areas are involved. We here review neurochemical, neurophysiological, neuroimaging data basis presence dysfunctional network underlying PD. will discuss role altered oscillations synchronization two partially overlapping central circuitries, e.g., cerebello-thalamo-cortical ganglia-cortical loops. also emphasize pathophysiological consequences abnormal interplay between systems. While there many currently unknown controversial aspects field, we highlight possible translational practical implications research advances understanding pathophysiology A better this issue likely facilitating future therapeutic approaches to patients based on medications invasive non-invasive stimulation techniques. This article part Special Issue "Tremor" edited Daniel D. Truong, Mark Hallett, Aasef Shaikh.

Language: Английский

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Clinical neurophysiology of Parkinson’s disease and parkinsonism

Clinical Neurophysiology Practice, Journal Year: 2022, Volume and Issue: 7, P. 201 - 227

Published: Jan. 1, 2022

This review is part of the series on clinical neurophysiology movement disorders. It focuses Parkinson’s disease and parkinsonism. The topics covered include pathophysiology tremor, rigidity bradykinesia, balance gait disturbance myoclonus in disease. use electroencephalography, electromyography, long latency reflexes, cutaneous silent period, studies cortical excitability with single paired transcranial magnetic stimulation, plasticity, intraoperative microelectrode recordings recording local field potentials from deep brain electrocorticography are also reviewed. In addition to advancing knowledge pathophysiology, neurophysiological can be useful refining diagnosis, localization surgical targets, help develop novel therapies for

Language: Английский

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Cortical phase-amplitude coupling is key to the occurrence and treatment of freezing of gait

Brain, Journal Year: 2022, Volume and Issue: 145(7), P. 2407 - 2421

Published: March 29, 2022

Abstract Freezing of gait is a debilitating symptom in advanced Parkinson’s disease and responds heterogeneously to treatments such as deep brain stimulation. Recent studies indicated that cortical dysfunction involved the development freezing, while evidence depicting specific role primary motor cortex multi-circuit pathology freezing lacking. Since abnormal beta-gamma phase-amplitude coupling recorded from patients with indicates parkinsonian state responses therapeutic stimulation, we hypothesized this metric might reveal unique information on understanding improving therapy for gait. Here, directly potentials using subdural electrocorticography synchronously captured optoelectronic motion-tracking systems 16 freely-walking who received subthalamic nucleus stimulation surgery. Overall, 451 timed up-and-go walking trials quantified 7073 s stable 3384 conditions on/off-stimulation with/without dual-tasking. We found (i) high was detected (i.e. contained freezing), but not non-freezing trials, caused by dual-tasking or lack movement; (ii) episodes within also demonstrated abnormally couplings, which predicted severity; (iii) reduced these couplings simultaneously improved freezing; (iv) were at similar levels, still lower severity than no-stimulation trials. These findings suggest elevated higher probabilities freezing. Therapeutic alleviates both decoupling oscillations enhancing resistance coupling. formalized novel ‘bandwidth model,’ specifies dysfunction, cognitive burden emergence By targeting key elements model, may develop next-generation approaches

Language: Английский

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Machine learning for adaptive deep brain stimulation in Parkinson’s disease: closing the loop

Journal of Neurology, Journal Year: 2023, Volume and Issue: 270(11), P. 5313 - 5326

Published: Aug. 2, 2023

Abstract Parkinson’s disease (PD) is the second most common neurodegenerative bearing a severe social and economic impact. So far, there no known modifying therapy current available treatments are symptom oriented. Deep Brain Stimulation (DBS) established as an effective treatment for PD, however systems lag behind today’s technological potential. Adaptive DBS, where stimulation parameters depend on patient’s physiological state, emerges important step towards “smart” strategy that enables adaptive personalized therapy. This new facilitated by currently neurotechnologies allowing simultaneous monitoring of multiple signals, providing relevant information. Advanced computational models analytical methods tool to explore richness data identify signal properties close loop in DBS. To tackle this challenge, machine learning (ML) applied DBS have gained popularity due their ability make good predictions presence variables subtle patterns. ML based approaches being explored at different fronts such identification electrophysiological biomarkers development control systems, leading relief. In review, we how can help overcome challenges closed-loop particularly its role search electrophysiology biomarkers. Promising results demonstrate potential supporting generation with better management delivery, resulting more efficient patient-tailored treatments.

Language: Английский

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Interfacing Biology and Electronics with Memristive Materials

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(32)

Published: Feb. 25, 2023

Abstract Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, evolution memristive materials alongside technological progress is opening application perspectives also biomedical field, for implantable lab‐on‐a‐chip devices where advanced sensing generate amount data. are emerging as bioelectronic links merging biosensing with computation, acting physical processors analog signals or framework digital architectures. Recent developments processing electrical neural signals, well transduction chemical biomarkers endocrine functions, reviewed. It concluded critical perspective future applicability pivotal building blocks bio‐AI fusion concepts bionic schemes.

Language: Английский

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Sleep-Aware Adaptive Deep Brain Stimulation Control: Chronic Use at Home With Dual Independent Linear Discriminate Detectors

Frontiers in Neuroscience, Journal Year: 2021, Volume and Issue: 15

Published: Oct. 18, 2021

Adaptive deep brain stimulation (aDBS) is a promising new technology with increasing use in experimental trials to treat diverse array of indications such as movement disorders (Parkinson's disease, essential tremor), psychiatric (depression, OCD), chronic pain and epilepsy. In many aDBS trials, neural biomarker interest compared predefined threshold amplitude adjusted accordingly. Across implant locations, potential biomarkers are greatly influenced by sleep. Successful embedded adaptive detectors must incorporate strategy account for sleep, avoid unwanted or unexpected algorithm behavior. Here, we show dual design two independent detectors, one used track sleep state (wake/sleep) the other parkinsonian motor (medication-induced fluctuations). six hemispheres (four patients) 47 days, our detector successfully transitioned mode while patients were sleeping, resumed tracking when awake. Designing "sleep aware" algorithms may prove crucial deployment clinically effective fully algorithms.

Language: Английский

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Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials

Neurobiology of Disease, Journal Year: 2022, Volume and Issue: 168, P. 105692 - 105692

Published: March 16, 2022

Electrophysiological biomarkers reflecting the pathological activities in basal ganglia are essential to gain an etiological understanding of Parkinson's disease (PD) and develop a method diagnosing treating disease. Previous studies that explored electrophysiological PD have focused mainly on oscillatory or periodic such as beta gamma oscillations. Emerging evidence has suggested nonoscillatory, aperiodic component reflects firing rate synaptic current changes corresponding cognitive states. Nevertheless, it never been thoroughly examined whether can be used biomarker PD. In this study, we parameters hemiparkinsonian rats tested its practicality activity. We found set parameters, offset exponent, were significantly decreased by nigrostriatal lesion. To further prove usefulness biomarkers, acute levodopa treatment reverted offset. then compared with previously established PD, frequency oscillation. low negative correlation power. showed performance machine learning-based prediction improved using both power component, which each other. suggest will provide more sensitive measurement early diagnosis potential use feedback parameter for adaptive deep brain stimulation.

Language: Английский

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