Deep Brain Stimulation for Neurological Diseases

Author(s):  
Casey H Halpern ◽  
Howard I Hurtig ◽  
Gordon H Baltuch
2019 ◽  
Vol 121 (1) ◽  
pp. 1-3 ◽  
Author(s):  
Bassam Al-Fatly

Deep brain stimulation is a powerful neurostimulation technique that proved its efficacy in treating a group of neurological diseases. Several scientific works tried to understand the mechanism of action of deep brain stimulation. Wang et al. ( J Neurosci 38: 4556–4568, 2018) demonstrated new evidence on the role of interregional neuro-oscillatory coherence as a promising model to explain mechanism the of deep brain stimulation.


2013 ◽  
Vol 17 (4) ◽  
pp. 22-30 ◽  
Author(s):  
Clare M. Davidson ◽  
Annraoi M. dePaor ◽  
Madeleine M. Lowery

2019 ◽  
Vol 5 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Yongxin Wen ◽  
Haibo Yang ◽  
Xinhua Bao

Deep brain stimulation (DBS) is considered as a treatment option for many neurological diseases. Many patients with movement disorders exhibit remarkable improvement after DBS. Owing to its minimally invasive nature, reversibility, and adjustability, DBS has been increasingly used over the past several decades. Dystonia is one of the most common movement disorders among children, and there is no effective treatment. Recently, some surgeon groups have performed DBS surgery for children. However, the outcomes of DBS in children are not well characterized. Here we mainly discuss the efficacy of DBS against childhood-onset dystonia and introduce the main procedure of pediatric DBS based on our own experience.


2021 ◽  
pp. 1-18
Author(s):  
Yu-si Chen ◽  
Kai Shu ◽  
Hui-cong Kang

Alzheimer’s disease (AD) is becoming a prevalent disease in the elderly population. Past decades have witnessed the development of drug therapies with varying targets. However, all drugs with a single molecular target fail to reverse or ameliorate AD progression, which ultimately results in cortical and subcortical network dysregulation. Deep brain stimulation (DBS) has been proven effective for the treatment of Parkinson’s disease, essential tremor, and other neurological diseases. As such, DBS has also been gradually acknowledged as a potential therapy for AD. The current review focuses on DBS of the nucleus basalis of Meynert (NBM). As a critical component of the cerebral cholinergic system and the Papez circuit in the basal ganglia, the NBM plays an indispensable role in the subcortical regulation of memory, attention, and arousal state, which makes the NBM a promising target for modulation of neural network dysfunction and AD treatment. We summarized the intricate projection relations and functionality of the NBM, current approaches for stereotactic localization and evaluation of the NBM, and the therapeutic effects of NBM-DBS both in patients and animal models. Furthermore, the current shortcomings of NBM-DBS, such as variations in cortical blood flow, increased temperature in the target area, and stimulation-related neural damage, were presented.


2006 ◽  
Vol 16 (07) ◽  
pp. 1977-1987 ◽  
Author(s):  
OLEKSANDR V. POPOVYCH ◽  
CHRISTIAN HAUPTMANN ◽  
PETER A. TASS

A novel control method for desynchronization of strongly synchronized populations of interacting oscillators is described. We show that an ensemble's mean field, nonlinearly processed and fed back into the ensemble, causes an effective desynchronization. The method is mild, demand controlled, and robust against system and stimulation parameter variations. The desynchronization and decoupling effects of the method are illustrated by examples of one and two interacting populations of limit-cycle oscillators. We suggest our method for mild and effective deep brain stimulation in neurological diseases characterized by pathological cerebral synchronization.


2009 ◽  
Vol 89 (1) ◽  
pp. 79-123 ◽  
Author(s):  
Paolo Gubellini ◽  
Pascal Salin ◽  
Lydia Kerkerian-Le Goff ◽  
Christelle Baunez

Author(s):  
Dmitrii Krylov ◽  
Remi Tachet des Combes ◽  
Romain Laroche ◽  
Michael Rosenblum ◽  
Dmitry V. Dylov

Malfunctioning neurons in the brain sometimes operate synchronously, reportedly causing many neurological diseases, e.g. Parkinson’s. Suppression and control of this collective synchronous activity are therefore of great importance for neuroscience, and can only rely on limited engineering trials due to the need to experiment with live human brains. We present the first Reinforcement Learning (RL) gym framework that emulates this collective behavior of neurons and allows us to find suppression parameters for the environment of synthetic degenerate models of neurons. We successfully suppress synchrony via RL for three pathological signaling regimes, characterize the framework’s stability to noise, and further remove the unwanted oscillations by engaging multiple PPO agents.


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