TY - JOUR
T1 - Mathematical modeling for description of oscillation suppression induced by deep brain stimulation
AU - Liu, Chen
AU - ZHOU, Changsong
AU - Wang, Jiang
AU - Loparo, Kenneth A.
N1 - Funding Information:
Manuscript received July 12, 2017; revised November 26, 2017 and June 22, 2018; accepted July 2, 2018. Date of publication July 5, 2018; date of current version September 6, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 61701336, in part by the Natural Science Foundation of Tianjin, China, under Grant 17JCQNJC00800, in part by the funding of Hong Kong Scholars Programs under Grant XJ2016006, in part by the Foundation of Tianjin University under Grant 2017XZC-0053, in part by the Hong Kong Baptist University (HKBU) Strategic Development Fund through the HKBU Faculty Research Grant under Grant FRG2/15-16/025, and in part by RGC, University Grant Committee of the HKSAR and HKBU. (Corresponding authors: Changsong Zhou; Jiang Wang.) C. Liu is with the School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China, and also with the Centre for Nonlinear Studies, Department of Physics, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong (e-mail: [email protected]).
PY - 2018/9
Y1 - 2018/9
N2 - A mathematical modeling for description of oscillation suppression by deep brain stimulation (DBS) is explored in this paper. High-frequency DBS introduced to the basal ganglia network can suppress pathological neural oscillations that occur in the Parkinsonian state. However, selecting appropriate stimulation parameters remains a challenging issue due to the limited understanding of the underlying mechanisms of the Parkinsonian state and its control. In this paper, we use a describing function analysis to provide an intuitive way to select the optimal stimulation parameters based on a biologically plausible computational model of the Parkinsonian neural network. By the stability analysis using the describing function method, effective DBS parameter regions for inhibiting the pathological oscillations can be predicted. Additionally, it is also found that a novel sinusoidal-shaped DBS may become an alternative stimulation pattern and expends less energy, but with a different mechanism. This paper provides new insight into the possible mechanisms underlying DBS and a prediction of optimal DBS parameter settings, and even suggests how to select novel DBS wave patterns for the treatment of movement disorders, such as Parkinson's disease.
AB - A mathematical modeling for description of oscillation suppression by deep brain stimulation (DBS) is explored in this paper. High-frequency DBS introduced to the basal ganglia network can suppress pathological neural oscillations that occur in the Parkinsonian state. However, selecting appropriate stimulation parameters remains a challenging issue due to the limited understanding of the underlying mechanisms of the Parkinsonian state and its control. In this paper, we use a describing function analysis to provide an intuitive way to select the optimal stimulation parameters based on a biologically plausible computational model of the Parkinsonian neural network. By the stability analysis using the describing function method, effective DBS parameter regions for inhibiting the pathological oscillations can be predicted. Additionally, it is also found that a novel sinusoidal-shaped DBS may become an alternative stimulation pattern and expends less energy, but with a different mechanism. This paper provides new insight into the possible mechanisms underlying DBS and a prediction of optimal DBS parameter settings, and even suggests how to select novel DBS wave patterns for the treatment of movement disorders, such as Parkinson's disease.
KW - basal ganglia
KW - deep brain stimulation
KW - describing function
KW - Oscillations suppression
KW - Parkinsonian state
UR - http://www.scopus.com/inward/record.url?scp=85049424524&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2018.2853118
DO - 10.1109/TNSRE.2018.2853118
M3 - Journal article
C2 - 29994400
AN - SCOPUS:85049424524
SN - 1534-4320
VL - 26
SP - 1649
EP - 1658
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 9
M1 - 8404106
ER -