A review of computational modeling and deep brain stimulation: applications to Parkinson's disease

doi:10.1007/s10483-020-2689-9

摘要/Abstract

摘要： Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson's disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress. Modeling mainly focuses on the cortex-thalamus-basal ganglia (CTBG) circuit and its sub-circuits, which helps to explore the dynamic behavior of the parkinsonian network, such as synchronization. Deep brain stimulation (DBS) is an effective strategy for the treatment of PD. At present, many studies are based on the side effects of the DBS. However, the translation from modeling results to clinical disease mitigation therapy still faces huge challenges. Here, we introduce the progress of DBS improvement. Its specific purpose is to develop novel DBS treatment methods, optimize the treatment effect of DBS for each patient, and focus on the study in closed-loop DBS. Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

关键词: computational model, deep brain stimulation (DBS), Parkinson's disease (PD), basal ganglia (BG)

Abstract: Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson's disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress. Modeling mainly focuses on the cortex-thalamus-basal ganglia (CTBG) circuit and its sub-circuits, which helps to explore the dynamic behavior of the parkinsonian network, such as synchronization. Deep brain stimulation (DBS) is an effective strategy for the treatment of PD. At present, many studies are based on the side effects of the DBS. However, the translation from modeling results to clinical disease mitigation therapy still faces huge challenges. Here, we introduce the progress of DBS improvement. Its specific purpose is to develop novel DBS treatment methods, optimize the treatment effect of DBS for each patient, and focus on the study in closed-loop DBS. Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

Key words: computational model, deep brain stimulation (DBS), Parkinson's disease (PD), basal ganglia (BG)

中图分类号:

37N25

Ying YU, Xiaomin WANG, Qishao WANG, Qingyun WANG. A review of computational modeling and deep brain stimulation: applications to Parkinson's disease[J]. Applied Mathematics and Mechanics (English Edition), 2020, 41(12): 1747-1768.

参考文献

[1] DORSEY, E., SHERER, T., OKUN, M., and BLOEM, B. The rise of Parkinson's disease. American Scientist, 108, 176(2020)
[2] DEXTER, D. T. and JENNER, P. Parkinson disease:from pathology to molecular disease mechanisms. Free Radical Biology and Medicine, 62, 132-144(2013)
[3] GALVAN, A. and WICHMANN, T. Pathophysiology of parkinsonism. Clinical Neurophysiology, 119, 1459-1474(2008)
[4] RUBIN, J. E., MCINTYRE, C. C., TURNER, R. S., and WICHMANN, T. Basal ganglia activity patterns in parkinsonism and computational modeling of their downstream effects:basal ganglia activity patterns in parkinsonism. European Journal of Neuroscience, 36, 2213-2228(2012)
[5] SANTANIELLO, S., GALE, J. T., and SARMA, S. V. Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease. WIREs Systems Biology and Medicine, 10, e1421(2018)
[6] JAKOBS, M., LEE, D. J., and LOZANO, A. M. Modifying the progression of Alzheimer's and Parkinson's disease with deep brain stimulation. Neuropharmacology, 171, 107860(2020)
[7] PLOTKIN, J. L. and GOLDBERG, J. A. Thinking outside the box (and arrow):current themes in striatal dysfunction in movement disorders. Neuroscientist, 25, 359-379(2019)
[8] BAR-GAD, I., MORRIS, G., and BERGMAN, H. Information processing, dimensionality reduction and reinforcement learning in the basal ganglia. Progress in Neurobiology, 71, 439-473(2003)
[9] MALLET, N., DELGADO, L., CHAZALON, M., MIGUELEZ, C., and BAUFRETON, J. Cellular and synaptic dysfunctions in Parkinson's disease:stepping out of the striatum. Cells, 8, 1005(2019)
[10] DAMODARAN, S., CRESSMAN, J. R., JEDRZEJEWSKI-SZMEK, Z., and BLACKWELL, K. T. Desynchronization of fast-spiking interneurons reduces-band oscillations and imbalance in firing in the dopamine-depleted striatum. Journal of Neuroscience, 35, 1149-1159(2015)
[11] ALBIN, R. L., YOUNG, A. B., and PENNEY, J. B. The functional anatomy of basal ganglia disorders. Trends in Neurosciences, 12, 366-375(1989)
[12] HUMPHRIES, M. D., WOOD, R., and GURNEY, K. Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit. Neural Networks, 22, 1174-1188(2009)
[13] MANDALI, A., RENGASWAMY, M., CHAKRAVARTHY, V. S., and MOUSTAFA, A. A. A spiking basal ganglia model of synchrony, exploration and decision making. Frontiers in Neuroscience, 9, 191(2015)
[14] BAHUGUNA, J., AERTSEN, A., KUMAR, A., and BLACKWELL, K. T. Existence and control of go/no-go decision transition threshold in the striatum. PLoS Computational Biology, 11, e1004233(2015)
[15] MUDDAPU, V. R., MANDALI, A., CHAKRAVARTHY, V. S., and RAMASWAMY, S. A computational model of loss of dopaminergic cells in Parkinson's disease due to glutamate-induced excitotoxicity. Frontiers in Neural Circuits, 13, 11(2019)
[16] HODGKIN, A. L. and HUXLEY, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Bulletin of Mathematical Biology, 52, 25-71(1990)
[17] MA, J. and TANG, J. A review for dynamics in neuron and neuronal network. Nonlinear Dynamics, 89, 1569-1578(2017)
[18] MA, J., YANG, Z., YANG, L., and TANG, J. A physical view of computational neurodynamics. Journal of Zhejiang University-Science A, 20, 639-659(2019)
[19] WANG, W. and WANG, R. Control strategy of central pattern generator gait movement under condition of attention selection. Applied Mathematics and Mechanics (English Edition), 37(7), 957-966(2016) https://doi.org/10.1007/s10483-016-2096-9
[20] IZHIKEVICH, E. M. Simple model of spiking neurons. IEEE Transactions on Neural Network, 14, 1569-1572(2003)
[21] TERMAN, D., RUBIN, J. E., YEW, A. C., and WILSON, C. J. Activity patterns in a model for the subthalamopallidal network of the basal ganglia. Journal of Neuroscience, 22, 2963-2976(2002)
[22] RUBIN, J. E. and TERMAN, D. High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model. Journal of Computational Neuroscience, 16, 211-235(2004)
[23] SO, R. Q., KENT, A. R., and GRILL, W. M. Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning:a computational modeling study. Journal of Computational Neuroscience, 32, 499-519(2012)
[24] BROWN, P. and WILLIAMS, D. Basal ganglia local field potential activity:character and functional significance in the human. Clinical Neurophysiology, 116, 2510-2519(2005)
[25] KREITZER, A. C. Physiology and pharmacology of striatal neurons. Annual Review of Neuroscience, 32, 127-147(2009)
[26] DAMODARAN, S., EVANS, R. C., and BLACKWELL, K. T. Synchronized firing of fast-spiking interneurons is critical to maintain balanced firing between direct and indirect pathway neurons of the striatum. Journal of Neurophysiology, 111, 836-848(2014)
[27] MCCARTHY, M. M., MOORE-KOCHLACS, C., GU, X., BOYDEN, E. S., HAN, X., and KOPELL, N. Striatal origin of the pathologic beta oscillations in Parkinson's disease. Proceedings of the National Academy of Sciences, 108, 11620-11625(2011)
[28] WOLF, J. A. NMDA/AMPA ratio impacts state transitions and entrainment to oscillations in a computational model of the nucleus accumbens medium spiny projection neuron. Journal of Neuroscience, 25, 9080-9095(2005)
[29] KUMARAVELU, K., BROCKER, D. T., and GRILL, W. M. A biophysical model of the cortexbasal ganglia-thalamus network in the 6-OHDA lesioned rat model of Parkinson's disease. Journal of Computational Neuroscience, 40, 207-229(2016)
[30] YU, Y. and WANG, Q. Oscillation dynamics in an extended model of thalamic-basal ganglia. Nonlinear Dynamics, 98, 1065-1080(2019)
[31] CAIOLA, M. and HOLMES, M. H. Model and analysis for the onset of parkinsonian firing patterns in a simplified basal ganglia. International Journal of Neural, Systems, 29, 1850021(2019)
[32] GILLIES, A., WILLSHAW, D., and LI, Z. Subthalamic-pallidal interactions are critical in determining normal and abnormal functioning of the basal ganglia. Proceedings of the Royal Society B:Biological Science, 269, 545-551(2002)
[33] HOLGADO, A. J. N., TERRY, J. R., and BOGACZ, R. Conditions for the generation of beta oscillations in the subthalamic nucleus-globus pallidus network. Journal of Neuroscience, 30, 12340-12352(2010)
[34] PLENZ, D. and KITAL, S. T. A basal ganglia pacemaker formed by the subthalamic nucleus and external globus pallidus. nature, 400, 677-682(1999)
[35] ROBINSON, P. A., RENNIE, C. J., and ROWE, D. L. Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. Physical Review E, 65, 041924(2002)
[36] VAN ALBADA, S. J., GRAY, R. T., DRYSDALE, P. M., and ROBINSON, P. A. Mean-field modeling of the basal ganglia-thalamocortical system II. Journal of Theoretical Biology, 257, 664-688(2009)
[37] VAN ALBADA, S. J. and ROBINSON, P. A. Mean-field modeling of the basal gangliathalamocortical system I. Journal of Theoretical Biology, 257, 642-663(2009)
[38] CHEN, M., GUO, D., WANG, T., JING, W., XIA, Y., XU, P., LUO, C., VALDES-SOSA, P. A., and YAO, D. Bidirectional control of absence seizures by the basal ganglia:a computational evidence. PLoS Computational Biology, 10, e1003495(2014)
[39] FAN, D., ZHENG, Y., YANG, Z., and WANG, Q. Improving control effects of absence seizures using single-pulse alternately resetting stimulation (SARS) of corticothalamic circuit. Applied Mathematics and Mechanics (English Edition), 41(9), 1287-1302(2020) https://doi.org/10.1007/s10483-020-2644-8
[40] YU, Y., ZHANG, H., ZHANG, L., and WANG, Q. Dynamical role of pedunculopntine nucleus stimulation on controlling Parkinson's disease. Physica A:Statistical Mechanics and Its Applications, 525, 834-848(2019)
[41] KERR, C. C., VAN ALBADA, S. J., NEYMOTIN, S. A., CHADDERDON, G. L., ROBINSON, P. A., and LYTTON, W. W. Cortical information flow in Parkinson's disease:a composite network/field model. Frontiers in Computational Neuroscience, 7, 39(2013)
[42] FASANO, A., APPEL-CRESSWELL, S., JOG, M., ZUROWKSKI, M., DUFF-CANNING, S., COHN, M., PICILLO, M., HONEY, C. R., PANISSET, M., and MUNHOZ, R. P. Medical management of Parkinson's disease after initiation of deep brain stimulation. Canadian Journal of Neurological Sciences, 43, 626-634(2016)
[43] HICKEY, P. and STACY, M. Deep brain stimulation:a paradigm shifting approach to treat Parkinson's disease. Frontiers in Neuroscience, 10, 173(2016)
[44] BENABID, A. L., CHABARDES, S., MITROFANIS, J., and POLLAK, P. Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease. The Lancet Neurology, 8, 67-81(2009)
[45] PICILLO, M., LOZANO, A. M., KOU, N., PUPPI MUNHOZ, R., and FASANO, A. Programming deep brain stimulation for Parkinson's disease:the Toronto western hospital algorithms. Brain Stimulation, 9, 425-437(2016)
[46] FAN, D., WANG, Z., and WANG, Q. Optimal control of directional deep brain stimulation in the parkinsonian neuronal network. Communications in Nonlinear Science and Numerical Simulation, 36, 219-237(2016)
[47] WEI, X. F. and GRILL, W. M. Impedance characteristics of deep brain stimulation electrodes in vitro and in vivo. Journal of Neural Engineering, 6, 046008(2009)
[48] BUTSON, C. R. and MCINTYRE, C. C. Current steering to control the volume of tissue activated during deep brain stimulation. Brain Stimulation, 1, 7-15(2008)
[49] MORO, E., ESSELINK, R. J. A., XIE, J., HOMMEL, M., BENABID, A. L., and POLLAK, P. The impact on Parkinson's disease of electrical parameter settings in STN stimulation. Neurology, 59, 706-713(2002)
[50] PIRINI, M., ROCCHI, L., SENSI, M., and CHIARI, L. A computational modelling approach to investigate different targets in deep brain stimulation for Parkinson's disease. Journal of Computational Neuroscience, 26, 91-107(2009)
[51] WONGSARNPIGOON, A. and GRILL, W. M. Energy-efficient waveform shapes for neural stimulation revealed with a genetic algorithm. Journal of Neural Engineering, 7, 046009(2010)
[52] FOUTZ, T. J. and MCINTYRE, C. C. Evaluation of novel stimulus waveforms for deep brain stimulation. Journal of Neural Engineering, 7, 066008(2010)
[53] DANESHZAND, M., FAEZIPOUR, M., and BARKANA, B. D. Computational stimulation of the basal ganglia neurons with cost effective delayed Gaussian waveforms. Frontiers in Computational Neuroscience, 11, 73(2017)
[54] LIU, C., WANG, J., DENG, B., LI, H., FIETKIEWICZ, C., and LOPARO, K. A. Noise-induced improvement of the parkinsonian state:a computational study. IEEE Transactions on Cybernetics, 49, 3655-3664(2019)
[55] HASHIMOTO, T., ELDER, C. M., OKUN, M. S., PATRICK, S. K., and VITEK, J. L. Stimulation of the subthalamic nucleus changes the firing pattern of pallidal neurons. Journal of Neuroscience, 23, 1916-1923(2003)
[56] REESE, R., LEBLOIS, A., STEIGERWALD, F., PÖTTER-NERGER, M., HERZOG, J., MEHDORN, H. M., DEUSCHL, G., MEISSNER, W. G., and VOLKMANN, J. Subthalamic deep brain stimulation increases pallidal firing rate and regularity. Experimental Neurology, 229, 517-521(2011)
[57] FOUTZ, T. J. and MCINTYRE, C. C. Evaluation of novel stimulus waveforms for deep brain stimulation. Journal of Neural Engineering, 7, 066008(2010)
[58] CONTARINO, M. F., BOUR, L. J., VERHAGEN, R., LOURENS, M. A. J., DE BIE, R. M. A., VAN DEN MUNCKHOF, P., and SCHUURMAN, P. R. Directional steering:a novel approach to deep brain stimulation. Neurology, 83, 1163-1169(2014)
[59] CHIKEN, S. and NAMBU, A. Mechanism of deep brain stimulation:inhibition, excitation, or disruption? Neuroscientist, 22, 313-322(2016)
[60] TASS, P. A. Stochastic phase resetting of two coupled phase oscillators stimulated at different times. Physical Review E, 67, 051902(2003)
[61] TASS, P. A. A model of desynchronizing deep brain stimulation with a demand-controlled coordinated reset of neural subpopulations. Biological Cybernetics, 89, 81-88(2003)
[62] HAUPTMANN, C., POPOVYCH, O., and TASS, P. A. Effectively desynchronizing deep brain stimulation based on a coordinated delayed feedback stimulation via several sites:a computational study. Biological Cybernetics, 93, 463-470(2005)
[63] POPOVYCH, O. V. and TASS, P. A. Desynchronizing electrical and sensory coordinated reset neuromodulation. Frontiers in Human Neuroscience, 6, 58(2012)
[64] LYSYANSKY, B., POPOVYCH, O. V., and TASS, P. A. Optimal number of stimulation contacts for coordinated reset neuromodulation. Frontiers in Neuroengineering, 6, 5(2013)
[65] FAN, D. and WANG, Q. Improving desynchronization of parkinsonian neuronal network via triplet-structure coordinated reset stimulation. Journal of Theoretical Biology, 370, 157-170(2015)
[66] YU, Y., HAO, Y., and WANG, Q. Model-based optimized phase-deviation deep brain stimulation for Parkinson's disease. Neural Networks, 122, 308-319(2020)
[67] CAPOZZO, A., FLORIO, T., CONFALONE, G., MINCHELLA, D., MAZZONE, P., and SCARNATI, E. Low frequency stimulation of the pedunculopontine nucleus modulates electrical activity of subthalamic neurons in the rat. Journal of Neural Transmission, 116, 51-56(2009)
[68] GARCIA-RILL, E., LUSTER, B., D'ONOFRIO, S., MAHAFFEY, S., BISAGNO, V., and URBANO, F. J. Pedunculopontine arousal system physiology-deep brain stimulation (DBS). Sleep Science, 8, 153-161(2015)
[69] WANG, J. W., ZHANG, Y. Q., ZHANG, X. H., WANG, Y. P., LI, J. P., and LI, Y. J. Deep brain stimulation of pedunculopontine nucleus for postural instability and gait disorder after Parkinson disease:a meta-analysis of individual patient data. World Neurosurgery, 102, 72-78(2017)
[70] MARTENS, H. C. F., TOADER, E., DECRÉ, M. M. J., ANDERSON, D. J., VETTER, R., KIPKE, D. R., BAKER, K. B., JOHNSON, M. D., and VITEK, J. L. Spatial steering of deep brain stimulation volumes using a novel lead design. Clinical Neurophysiology, 122, 558-566(2011)
[71] BEUTER, A., LEFAUCHEUR, J. P., and MODOLO, J. Closed-loop cortical neuromodulation in Parkinson's disease:an alternative to deep brain stimulation? Clinical Neurophysiology, 125, 874-885(2014)
[72] MIRZA, K. B., GOLDEN, C. T., NIKOLIC, K., and TOUMAZOU, C. Closed-loop implantable therapeutic neuromodulation systems based on neurochemical monitoring. Frontiers in Neuroscience, 13, 808(2019)
[73] GRANT, P. F. and LOWERY, M. M. Simulation of cortico-basal ganglia oscillations and their suppression by closed loop deep brain stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21, 584-594(2013)
[74] LITTLE, S. and BROWN, P. What brain signals are suitable for feedback control of deep brain stimulation in Parkinson's disease? Annals of the New York Academy of Sciences, 1265, 9-24(2012)
[75] POPOVYCH, O. V. and TASS, P. A. Multisite delayed feedback for electrical brain stimulation. Frontiers in Physiology, 9, 46(2018)
[76] GUO, Y. and RUBIN, J. E. Multi-site stimulation of subthalamic nucleus diminishes thalamocortical relay errors in a biophysical network model. Neural Networks, 24, 602-616(2011)
[77] 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. PLoS One, 13, e0207761(2018)
[78] SU, F., WANG, J., NIU, S., LI, H., DENG, B., LIU, C., and WEI, X. Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia-thalamic network. Neural Networks, 98, 283-295(2018)
[79] MEIDAHL, A. C., TINKHAUSER, G., HERZ, D. M., CAGNAN, H., DEBARROS, J., and BROWN, P. Adaptive deep brain stimulation for movement disorders:the long road to clinical therapy:adaptive DBS review. Movement Disorders, 32, 810-819(2017)
[80] LITTLE, S., BEUDEL, M., ZRINZO, L., FOLTYNIE, T., LIMOUSIN, P., HARIZ, M., NEAL, S., CHEERAN, B., CAGNAN, H., GRATWICKE, J., AZIZ, T. Z., POGOSYAN, A., and BROWN, P. Bilateral adaptive deep brain stimulation is effective in Parkinson's disease. Journal of Neurology, Neurosurgery, Psychiatry, 87, 717-721(2016)
[81] LITTLE, S., POGOSYAN, A., NEAL, S., ZAVALA, B., ZRINZO, L., HARIZ, M., FOLTYNIE, T., LIMOUSIN, P., ASHKAN, K., FITZGERALD, J., GREEN, A. L., AZIZ, T. Z., and BROWN, P. Adaptive deep brain stimulation in advanced Parkinson disease. Annals of Neurology, 74, 449-457(2013)
[82] VELISAR, A., SYRKIN-NIKOLAU, J., BLUMENFELD, Z., TRAGER, M. H., AFZAL, M. F., PRABHAKAR, V., and BRONTE-STEWART, H. Dual threshold neural closed loop deep brain stimulation in Parkinson disease patients. Brain Stimulation, 12, 868-876(2019)
[83] GORZELIC, P., SCHIFF, S. J., and SINHA, A. Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease. Journal of Neural Engineering, 10, 026016(2013)
[84] MASTRO, K. J., ZITELLI, K. T., WILLARD, A. M., LEBLANC, K. H., KRAVITZ, A. V., and GITTIS, A. H. Cell-specific pallidal intervention induces long-lasting motor recovery in dopaminedepleted mice. Nature Neuroscience, 20, 815-823(2017)
[85] MASTRO, K. J., BOUCHARD, R. S., HOLT, H. A. K., and GITTIS, A. H. Transgenic mouse lines subdivide external segment of the globus pallidus (GPe) neurons and reveal distinct GPe output pathways. Journal of Neuroscience, 34, 2087-2099(2014)
[86] GITTIS, A. H., BERKE, J. D., BEVAN, M. D., CHAN, C. S., MALLET, N., MORROW, M. M., and SCHMIDT, R. New roles for the external globus pallidus in basal ganglia circuits and behavior. Journal of Neuroscience, 34, 15178-15183(2014)
[87] GITTIS, A. H. and YTTRI, E. A. Translating insights from optogenetics into therapies for Parkinson's disease. Current Opinion in Biomedical Engineering, 8, 14-19(2018)
[88] LIANG, S. and WANG, Z. Controlling a neuron by stimulating a coupled neuron. Applied Mathematics and Mechanics (English Edition), 40(1), 13-24(2019) https://doi.org/10.1007/s10483-019-2407-8
[89] ZHANG, X., LIU, S., ZHAN, F., WANG, J., and JIANG, X. The effects of medium spiny neuron morphologcial changes on basal ganglia network under external electric field:a computational modeling study. Frontiers in Computational Neuroscience, 11, 91(2017)