Research on Effect Index of Closed-loop Deep Brain Stimulation in Parkinson's Disease Based on Model
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摘要: 随着人口老龄化的加重，多发于中老年人的帕金森病(PD)将给社会带来沉重的压力。然而基于模型的闭环深部脑刺激(DBS)治疗PD的研究中刺激效果评估指标单一且不直观，因此该文提出统一帕金森病评分量表(UPDRS)类估计(SUE)。丘脑底核β频带(13～35 Hz)的异常振荡已被证明与PD运动障碍密切相关，该文首先验证了β频带在刺激与未刺激状态下的功率变化对计算模型与闭环深部脑刺激(DBS)算法的可行性。然后统计分析了β爆发在时域的分布情况，并根据时间长度将β爆发二分类为长爆发与短爆发。由于临床上采用统一帕金森病评分量表(UPDRS)对PD的治疗效果进行评价，所以将β爆发的时域特性与UPDRS评分进行对比，并结合β爆发的二分类结果提出了估计刺激疗效的指标SUE。实验结果表明SUE与β爆发的持续时间有较强的相关性，能很好地模拟UPDRS在刺激状态与未刺激状态下的变化，为今后基于模型的闭环DBS研究奠定了基础。
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关键词:
- 计算模型 /
- 帕金森病 /
- 闭环深部脑刺激 /
- β爆发 /
- 统一帕金森病评分量表
Abstract: With the continuous improvement of the aging population, Parkinson’s Disease (PD) that is more prevalent in middle-aged and elderly people will put heavy burden on society. However, the stimulation effect evaluation index of model-based Deep Brain Stimulation (DBS) for PD is single and not intuitive. Therefore, a new index the Similar to Unified Parkinson Disease Rating Scale (UPDRS) Estimates (SUE) is proposed. The feasibility of the computational model and the closed-loop DBS algorithm is firstly verified according to the power changes of the β band (13～35 Hz). The distribution of β bursts in time domain is statistically analyzed, and is dichotomized into long and short bursts, then SUE is proposed. The experimental results show that SUE has a strong correlation with the duration of β bursts, the change of UPDRS under stimulated state and unstimulated state is well simulated, and a foundation for the future model-based closed-loop DBS research is laid. -
[1] MCGREGOR M M and NELSON A B. Circuit mechanisms of Parkinson's disease[J]. Neuron, 2019, 101(6): 1042–1056. doi: 10.1016/j.neuron.2019.03.004 [2] KRAUSS J K, LIPSMAN N, AZIZ T, et al. Technology of deep brain stimulation: Current status and future directions[J]. Nature Reviews Neurology, 2021, 17(2): 75–87. doi: 10.1038/s41582-020-00426-z [3] HABETS J G V, HEIJMANS M, KUIJF M L, et al. An update on adaptive deep brain stimulation in Parkinson's disease[J]. Movement Disorders, 2018, 33(12): 1834–1843. doi: 10.1002/mds.115 [4] SU Fei, CHEN Min, ZU Linlu, et al. Model-based closed-loop suppression of Parkinsonian beta band oscillations through origin analysis[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 450–457. doi: 10.1109/TNSRE.2021.3056544 [5] TINKHAUSER G, POGOSYAN A, TAN Huiling, et al. Beta burst dynamics in Parkinson's disease OFF and ON dopaminergic medication[J]. Brain, 2017, 140(11): 2968–2981. doi: 10.1093/brain/awx252 [6] TINKHAUSER G, POGOSYAN A, LITTLE S, et al. The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson's disease[J]. Brain, 2017, 140(4): 1053–1067. doi: 10.1093/brain/awx010 [7] STARR P A and OSTREM J L. Commentary on “Adaptive deep brain stimulation in advanced Parkinson disease”[J]. Annals of Neurology, 2013, 74(3): 447–448. doi: 10.1002/ana.23966 [8] LITTLE S, BEUDEL M, ZRINZO L, et al. Bilateral adaptive deep brain stimulation is effective in Parkinson's disease[J]. Journal of Neurology, Neurosurgery & Psychiatry, 2016, 87(7): 717–721. doi: 10.1136/jnnp-2015-310972 [9] LITTLE S, TRIPOLITI E, BEUDEL M, et al. Adaptive deep brain stimulation for Parkinson's disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting[J]. Journal of Neurology, Neurosurgery & Psychiatry, 2016, 87(12): 1388–1389. doi: 10.1136/jnnp-2016-313518 [10] TERMAN D, RUBIN J E, YEW A C, et al. Activity patterns in a model for the subthalamopallidal network of the Basal Ganglia[J]. Journal of Neuroscience, 2002, 22(7): 2963–2976. doi: 10.1523/JNEUROSCI.22-07-02963.2002 [11] RUBIN J E and TERMAN D. High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model[J]. Journal of Computational Neuroscience, 2004, 16(3): 211–235. doi: 10.1023/B:JCNS.0000025686.47117.67 [12] LU Meili, WEI Xile, and LOPARO K A. Investigating synchronous oscillation and deep brain stimulation treatment in a model of cortico-basal ganglia network[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(11): 1950–1958. doi: 10.1109/TNSRE.2017.2707100 [13] KUMARAVELU K, BROCKER D T, and GRILL W M. A biophysical model of the cortex-basal ganglia-thalamus network in the 6-OHDA lesioned rat model of Parkinson's disease[J]. Journal of Computational Neuroscience, 2016, 40(2): 207–229. doi: 10.1007/s10827-016-0593-9 [14] GORZELIC P, SCHIFF S J, and SINHA A. Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease[J]. Journal of Neural Engineering, 2013, 10(2): 026016. doi: 10.1088/1741-2560/10/2/026016 [15] GAO Qitong, NAUMANN M, JOVANOV I, et al. Model-based design of closed loop deep brain stimulation controller using reinforcement learning[C]. The ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), Sydney, Australia, 2020: 108–118. [16] LIU Chen, WANG Jiang, DENG Bin, et al. Closed-loop control of tremor-predominant Parkinsonian state based on parameter estimation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(10): 1109–1121. doi: 10.1109/TNSRE.2016.2535358 [17] LIU Chen, ZHAO Ge, WANG Jiang, et al. Neural network-based closed-loop deep brain stimulation for modulation of pathological oscillation in Parkinson's disease[J]. IEEE Access, 2020, 8: 161067–161079. doi: 10.1109/ACCESS.2020.3020429 [18] LIU Chen, ZHAO Ge, WANG Jiang, et al. Delayed feedback-based suppression of pathological oscillations in a neural mass model[J]. IEEE Transactions on Cybernetics, 2019, 51(10): 5046–5056. doi: 10.1109/TCYB.2019.2923317 [19] ZAVALA B, BRITTAIN J S, JENKINSON N, et al. Subthalamic nucleus local field potential activity during the Eriksen flanker task reveals a novel role for theta phase during conflict monitoring[J]. Journal of Neuroscience, 2013, 33(37): 14758–14766. doi: 10.1523/jneurosci.1036-13.2013 [20] CAGNAN H, DUFF E P, and BROWN P. The relative phases of basal ganglia activities dynamically shape effective connectivity in Parkinson's disease[J]. Brain, 2015, 138(6): 1667–1678. doi: 10.1093/brain/awv093 [21] JENKINSON N and BROWN P. New insights into the relationship between dopamine, beta oscillations and motor function[J]. Trends in Neurosciences, 2011, 34(12): 611–618. doi: 10.1016/j.tins.2011.09.003 [22] BOUTHOUR W, MÉGEVAND P, DONOGHUE J, et al. Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond[J]. Nature Reviews Neurology, 2019, 15(6): 363. doi: 10.1038/s41582-019-0189-x [23] SUN Qifeng, ZHAO Dechun, CHENG Shanshan, et al. A feature extraction method for adaptive DBS using an improved EMD[J]. International Journal of Neuroscience, 2018, 128(10): 975–986. doi: 10.1080/00207454.2018.1450253 [24] DANESHZAND M, FAEZIPOUR M, and BARKANA B D. Robust desynchronization of Parkinson's disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation[J]. PLoS One, 2018, 13(11): e0207761. doi: 10.1371/journal.pone.0207761 [25] LATTERI A, ARENA P, and MAZZONE P. Characterizing Deep Brain Stimulation effects in computationally efficient neural network models[J]. Nonlinear Biomedical Physics, 2011, 5(1): 2. doi: 10.1186/1753-4631-5-2 [26] FAN Denggui, WANG Zhihui, and WANG Qingyun. Optimal control of directional deep brain stimulation in the parkinsonian neuronal network[J]. Communications in Nonlinear Science and Numerical Simulation, 2016, 36: 219–237. doi: 10.1016/j.cnsns.2015.12.005 [27] PRIORI A, FOFFANI G, ROSSI L, et al. Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations[J]. Experimental Neurology, 2013, 245: 77–86. doi: 10.1016/j.expneurol.2012.09.013 -
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