scholarly journals Methods for Lowering the Power Consumption of OS-Based Adaptive Deep Brain Stimulation Controllers

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2349
Author(s):  
Roberto Rodriguez-Zurrunero ◽  
Alvaro Araujo ◽  
Madeleine M. Lowery
Keyword(s):  
Deep Brain Stimulation ◽  
Power Consumption ◽  
Brain Stimulation ◽  
Energy Efficient ◽  
Sampling Rate ◽  
Implantable Devices ◽  
Duty Cycling ◽  
Wide Range ◽  
Dynamic Sampling ◽  
Deep Brain

The identification of a new generation of adaptive strategies for deep brain stimulation (DBS) will require the development of mixed hardware–software systems for testing and implementing such controllers clinically. Towards this aim, introducing an operating system (OS) that provides high-level features (multitasking, hardware abstraction, and dynamic operation) as the core element of adaptive deep brain stimulation (aDBS) controllers could expand the capabilities and development speed of new control strategies. However, such software frameworks also introduce substantial power consumption overhead that could render this solution unfeasible for implantable devices. To address this, in this work four techniques to reduce this overhead are proposed and evaluated: a tick-less idle operation mode, reduced and dynamic sampling, buffered read mode, and duty cycling. A dual threshold adaptive deep brain stimulation algorithm for suppressing pathological oscillatory neural activity was implemented along with the proposed energy saving techniques on an energy-efficient OS, YetiOS, running on a STM32L476RE microcontroller. The system was then tested using an emulation environment coupled to a mean field model of the parkinsonian basal ganglia to simulate local field potential (LFPs) which acted as a biomarker for the controller. The OS-based controller alone introduced a power consumption overhead of 10.03 mW for a sampling rate of 1 kHz. This was reduced to 12 μW by applying the proposed tick-less idle mode, dynamic sampling, buffered read and duty cycling techniques. The OS-based controller using the proposed methods can facilitate rapid and flexible testing and implementation of new control methods. Furthermore, the approach has the potential to become a central element in future implantable devices to enable energy-efficient implementation of a wide range of control algorithms across different neurological conditions and hardware platforms.

scholarly journals Psychiatric neurosurgery in the 21st century: overview and the growth of deep brain stimulation

2017 ◽  
Vol 41 (5) ◽  
pp. 281-286 ◽  
Author(s):  
Kenneth Barrett
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Obsessive Compulsive ◽  
Major Depressive ◽  
Future Directions ◽  
Compulsive Disorder ◽  
Wide Range ◽  
Psychiatric Conditions ◽  
Ablative Procedures ◽  
Deep Brain

SummaryAmbulatory deep brain stimulation (DBS) became possible in the late 1980s and was initially used to treat people with movement disorders. Trials of DBS in people with treatment-resistant psychiatric disorder began in the late 1990s, initially focusing on obsessive-compulsive disorder, major depressive disorder and Tourette syndrome. Despite methodological issues, including small participant numbers and lack of consensus over brain targets, DBS is now being trialled in a wide range of psychiatric conditions. There has also been more modest increase in ablative procedures. This paper reviews these developments in the light of contemporary brain science, considers future directions and discusses why the approach has not been adopted more widely within psychiatry.

Deep brain stimulation for prolonged disorders of consciousness

2016 ◽  
Vol 11 (4) ◽  
pp. 105-111
Author(s):  
Gilberto KK Leung
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Functional Neuroimaging ◽  
Vegetative State ◽  
Persistent Vegetative State ◽  
Conscious State ◽  
Disorders Of Consciousness ◽  
Minimal Conscious State ◽  
Wide Range ◽  
Deep Brain

Deep brain stimulation has emerged as a “last resort” therapy for patients with prolonged disorders of consciousness. The latter encompasses a range of conditions including minimal conscious state and persistent vegetative state. Functional neuroimaging studies have shown that minimal conscious state and persistent vegetative state have different patterns of residual brain function and may therefore respond differently to deep brain stimulation. The failure to distinguish between the two conditions in this context can give rise to false expectation, misunderstanding and ill-guided treatment. As a halfway technology for prolonged disorders of consciousness, deep brain stimulation could also produce improvement in awareness that is in fact harm, and its impact may involve a wide range of public interests. This paper will discuss related ethical and legal issues with an emphasis on the distinction between minimal conscious state and persistent vegetative state in the application of deep brain stimulation.

Speech Rate Mediated Vowel and Stop Voicing Distinctiveness in Parkinson's Disease

2021 ◽  
pp. 1-28
Author(s):  
Thea Knowles ◽  
Scott G. Adams ◽  
Mandar Jog
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Voice Onset Time ◽  
Speech Rate ◽  
Onset Time ◽  
Speaking Rate ◽  
Wide Range ◽  
Deep Brain

Purpose The purpose of this study was to quantify changes in acoustic distinctiveness in two groups of talkers with Parkinson's disease as they modify across a wide range of speaking rates. Method People with Parkinson's disease with and without deep brain stimulation and older healthy controls read 24 carrier phrases at different speech rates. Target nonsense words in the carrier phrases were designed to elicit stop consonants and corner vowels. Participants spoke at seven self-selected speech rates from very slow to very fast, elicited via magnitude production. Speech rate was measured in absolute words per minute and as a proportion of each talker's habitual rate. Measures of segmental distinctiveness included a temporal consonant measure, namely, voice onset time, and a spectral vowel measure, namely, vowel articulation index. Results All talkers successfully modified their rate of speech from slow to fast. Talkers with Parkinson's disease and deep brain stimulation demonstrated greater baseline speech impairment and produced smaller proportional changes at the fast end of the continuum. Increasingly slower speaking rates were associated with increased temporal contrasts (voice onset time) but not spectral contrasts (vowel articulation). Faster speech was associated with decreased contrasts in both domains. Talkers with deep brain stimulation demonstrated more aberrant productions across all speaking rates. Conclusions Findings suggest that temporal and spectral segmental distinctiveness are asymmetrically affected by speaking rate modifications in Parkinson's disease. Talkers with deep brain stimulation warrant further investigation with regard to speech changes they make as they adjust their speaking rate.

Deep Brain Stimulation

2021 ◽  
Author(s):  
Tipu Aziz ◽  
Holly Roy
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Brain Regions ◽  
Clinical Aspects ◽  
Wide Range ◽  
Deep Brain

Deep brain stimulation (DBS) is a neurosurgical technology that allows the manipulation of activity within specific brain regions through delivery of electrical stimulation via implanted electrodes. The growth of DBS has led to research around the development of novel interventions for a wide range of neurological and neuropsychiatric conditions, including Parkinson’s disease, dystonia, chronic pain, Tourette’s syndrome, treatment-resistant depression, anorexia nervosa, and Alzheimer’s disease. Some of these treatment approaches have a high level of efficacy as well as an established place in the clinical armamentarium for the diseases in question, such as DBS for movement disorders, including Parkinson’s disease. Other interventions are at a more developmental stage, such as DBS for depression and Alzheimer’s disease. Success both in clinical aspects of DBS and new innovations depends on a close-knit multidisciplinary team incorporating experts in the underlying condition (often neurologists and psychiatrists); neurosurgeons; nurse specialists, who may be involved in device programming and other aspects of patient care; and researchers including neuroscientists, imaging specialists, engineers, and signal analysts. Directly linked to the growth of DBS as a specialty is allied research around neural signals analysis and device development, which feed directly back into further clinical progress. The close links between clinical DBS and basic and translational research make it an exciting and fast-moving area of neuroscience.

Deep Brain Stimulation in Gilles de la Tourette Syndrome

Neurosurgery ◽  
2015 ◽  
Vol 78 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Domenico Servello ◽  
Edvin Zekaj ◽  
Christian Saleh ◽  
Nicholas Lange ◽  
Mauro Porta
Keyword(s):  
Deep Brain Stimulation ◽  
Tourette Syndrome ◽  
Scale Score ◽  
Brain Stimulation ◽  
Rating Scale ◽  
Severity Rating ◽  
Wide Range ◽  
Tic Severity ◽  
Deep Brain

Abstract BACKGROUND: Gilles de la Tourette syndrome (GTS) is a severe neuropsychiatric disorder with childhood onset, characterized by disabling motor and vocal tics lasting for more than 1 year and associated with a wide range of psychiatric comorbidities. Pharmacological treatment is indicated for moderate to severe GTS patients. However, when GTS is refractory to conventional medical and behavioral treatments, deep brain stimulation (DBS) can be considered as a last resort therapeutic avenue. OBJECTIVE: To evaluate the efficacy of DBS and its comorbidities in the largest pool of GTS patients to date. METHODS: Our cohort study was based on 48 patients' refractory to conventional treatment who underwent DBS for GTS at Galeazzi Institute, Milan, Italy. An exhaustive preoperative and a follow-up battery of tests was performed including the Yale Global Tic Severity Rating Scale, the Yale-Brown Obsessive Compulsive Scale, the Beck Depression Inventory, the State Trait Anxiety Inventory, and the Subjective Social Impairment on a 10-point Visual Analogue Scale tests. RESULTS: Eleven patients in whom the device was removed for inflammatory complications or for poor compliance were excluded from final analysis. Twenty-seven of the remaining 37 patients had a Yale Global Tic Severity Rating Scale score at the last follow-up that was less than 35. Of the 37 patients, in 29 cases (78%) a reduction of more than 50% of the Yale Global Tic Severity Rating Scale score was observed. CONCLUSION: The clinical efficacy of DBS in GTS is promising. Although DBS is associated with risks, as is any surgical intervention, DBS should be considered as a last resort therapeutic option in carefully selected GTS patients.

Calculating deep brain stimulation amplitudes and power consumption by constrained optimization

2019 ◽  
Vol 16 (1) ◽  
pp. 016020 ◽  
Author(s):  
Ruben Cubo ◽  
Markus Fahlström ◽  
Elena Jiltsova ◽  
Helena Andersson ◽  
Alexander Medvedev
Keyword(s):  
Deep Brain Stimulation ◽  
Power Consumption ◽  
Constrained Optimization ◽  
Brain Stimulation ◽  
Deep Brain

scholarly journals Anticonvulsant vs. Proconvulsant Effect of in situ Deep Brain Stimulation at the Epileptogenic Focus

2021 ◽  
Vol 15 ◽  
Author(s):  
Ping Chou ◽  
Chung-Chin Kuo
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Rational Design ◽  
The Other ◽  
Anticonvulsant Effect ◽  
Epileptogenic Focus ◽  
Kindling Effect ◽  
Wide Range ◽  
Deep Brain

Since deep brain stimulation (DBS) at the epileptogenic focus (in situ) denotes long-term repetitive stimulation of the potentially epileptogenic structures, such as the amygdala, the hippocampus, and the cerebral cortex, a kindling effect and aggravation of seizures may happen and complicate the clinical condition. It is, thus, highly desirable to work out a protocol with an evident quenching (anticonvulsant) effect but free of concomitant proconvulsant side effects. We found that in the basolateral amygdala (BLA), an extremely wide range of pulsatile stimulation protocols eventually leads to the kindling effect. Only protocols with a pulse frequency of ≤1 Hz or a direct current (DC), with all of the other parameters unchanged, could never kindle the animal. On the other hand, the aforementioned DC stimulation (DCS), even a pulse as short as 10 s given 5 min before the kindling stimuli or a pulse given even to the contralateral BLA, is very effective against epileptogenicity and ictogenicity. Behavioral, electrophysiological, and histological findings consistently demonstrate success in seizure quenching or suppression as well as in the safety of the specific DBS protocol (e.g., no apparent brain damage by repeated sessions of stimulation applied to the BLA for 1 month). We conclude that in situ DCS, with a novel and rational design of the stimulation protocol composed of a very low (∼3% or 10 s/5 min) duty cycle and assuredly devoid of the potential of kindling, may make a successful antiepileptic therapy with adequate safety in terms of little epileptogenic adverse events and tissue damage.

scholarly journals Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

2020 ◽  
Vol 10 (11) ◽  
pp. 809
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Oleg Shylo ◽  
Ritesh A. Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Technological Evolution ◽  
Advanced Parkinson’S Disease ◽  
Cochrane Database ◽  
Wide Range ◽  
Clinical Phenotyping ◽  
Deep Brain

Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson’s disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information’s Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers’ (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

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