用户名: 密码: 验证码:
大鼠压杆实验系统设计及运动皮层神经解码研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
脑-机接口是在脑和外部设备之间建立不依赖常规外周神经和肌肉的直接的信息交流通路。实现该技术关键在于建立切实可行的实验系统、精确检测实验动物肢体运动和神经信息以及准确地从记录到的神经元锋电位中解析出运动信号。目前以运动控制为目标的脑-机接口研究主要是以初级运动皮层为主,对次级运动皮层的研究还不够深入,而次级运动皮层的神经元集群发放也与肢体运动间也存在一定相关性。因此,本论文以大鼠为实验对象,建立了大鼠压杆行为实验系统,设计大鼠神经元集群-压杆信号的同步采集系统,并成功采集到大鼠初级和次级运动皮层神经信号,深入探讨了大鼠的初级、次级运动皮层的神经元集群发放与肢体运动间的相关性及运动皮层神经解码。本论文的主要研究内容和特色为:
     1、本论文设计了大鼠压杆行为实验系统,实现了神经-压杆信号的同步采集,提高后续数据分析处理效率。其中针对大鼠压杆实验中大鼠前肢运动参数的记录,设计了压杆检测系统,包括传感器和检测系统,小量程大行程压力传感器能够满足大鼠前肢压杆动作持续时间短、压力微小的需要;检测系统实现了实时在线记录大鼠前肢运动压力及在线调整压杆运动参数功能,为实验中大鼠压杆行为的规范及前肢压力的信号采集提供了可靠稳定的实验平台。
     2、本论文对原有的大鼠脑-机接口系统进行了改进,将电极植入脑区扩展至两个运动皮层,突破了当前对大鼠脑-机接口中单一运动皮层神经信号的记录,且成功记录到大鼠初级和次级运动皮层神经元集群活动信息。
     3、针对大鼠次级运动皮层的神经元集群发放在肢体运动中表现出一定的活动,本论文建立了大鼠初级及次级运动皮层神经解码模型,从神经元发放信息中解析出运动信号,在采用偏最小二乘回归算法对次级运动皮层神经解码时,压杆预测值与真实值的相关系数在0.5以上,表明次级运动皮层神经元发放也可以用于对大鼠前肢压杆运动的预测。
     本论文研究建立了大鼠压杆行为实验系统,实现了大鼠肢体运动-神经信号的同步采集及大鼠前肢压杆运动参数在线记录,将有助于提高脑-机接口实验中动物运动信息的检测能力;同时建立了次级运动皮层的神经解码,分析了大鼠次级运动皮层神经元发放与压杆运动间的相关性,结果表明次级运动皮层神经元发放同样能够用于对压杆信号进行预测,可做为脑-机接口重要的信息输入源,提供额外的信息补充。
Brain-machine interface (BMI) is a novel information interaction approach between brain and external devices not depending on peripheral nervous system. The key to realize this technology lies in establishing feasible experiment system, detecting body movement and neural information accurately of experimental animals and decoding the motion signal accurately from the recorded neurons spike potential. At present, BMI aims at motion control is mainly concentrated in the analysis of neurons in primary motor cortex, while the research on secondary motor cortex is not insufficiently thorough, although it is also show some of the correlation between neural ensemble firing in secondary motor cortex and physical movement. Therefore, this thesis choses rats as experimental subject, established the rats pressure lever behavior experiment system, designed the synchronous data acquisition system of rats neural ensemble-pressure lever signals, acquired the rats primary and secondary motor cortex neural signals successfully, probed into the correlation between the rats neural ensemble firing in primary and secondary motor cortex and body movement, studied neuron decoding of the motor cortex. The following are innovation and main parts of the thesis.
     1、This thesis designed the rats pressure lever behavior experiment system, realized the neurons-pressure lever signal synchronization acquisition, improved the follow-up data processing efficiency. According to the experimental rats'forelimb movement parameter records, this thesis designed the pressure lever detection system, including the sensor and detecting system, small range big trip pressure sensor can satisfy the needs of rats'forelimb pressure lever short duration and tiny pressure; Detection system realized the real-time online record rats'forelimb movement pressure and online adjustment of pressure lever motion parameter, which provides reliable and stable experimental platform for experimental rats' pressure lever behavior standard and forelimb pressure signal acquisition.
     2、This thesis improved the original rats brain machine interface system, expanded the electrodes implanted brain area to two motor cortex, break through the current single motor cortex neural signal records of rats brain machine interface, and recorded successfully the rats primary and secondary motor cortex neural ensemble activity information.
     3、According to the rats secondary motor cortex neural ensemble firing show some of the activities in the body movement, this thesis established the rats primary and secondary motor cortex neuron decoding model, to resolve the motion signal from the neurons firing information, correlation coefficients between the predicted value of pressure lever and the real value is more than0.5when used the partial least-squares regression algorithm decoding the secondary motor cortex neurons firing, which shows that secondary motor cortex neurons firing can also be used to predict rats forelimb pressure lever motion.
     This thesis established rats pressure lever behavior experiment system, realized the rats forelimb movements-neural signal synchronization acquisition and rats forelimb pressure lever motion parameters online records, which will help improve the brain-machine interface experiment animal movement information detection ability; and this thesis established the secondary motor cortex neuron decoding, analyzed the correlation between the rats secondary motor cortex neuron firing and pressure lever movement, and the results show that secondary motor cortex neurons firing can also be used to predict pressure lever signal and can be used as brain machine interface important information input source, provide additional information.
引文
Abe K, Pan L H, Watanabe M. Kato T, Itoyama Y. Induction of nitrotyrosine-like immunoreactivity in the lower motor neuron of amyotrophic lateral sclerosis[J]. Neuroscience Letters,1995 (2):152-154.
    Bai-kun W, Yang G. Li Z. Brain-computer interface:A new channel for information communicate between human brain and environments [J]. Biomedical Engineering Foreign Medical Sciences,2005 (1):4-9.
    Berger H. Uber das elektrenkephalogramm des menschen[J]. European Archives of Psychiatry and Clinical Neuroscience,1929 (1):527-570.
    Birbaumer N. Breaking the silence:brain-computer interfaces (BCI) for communication and motor control[J]. Psychophysiology,2006 (6):517-32.
    Bo H, Qingyu T, Fusheng Y, Tianxiang C. ApEn and cross-ApEn:property, fast algorithm and preliminary application to the study of EEG and cognition[J]. Signal Processing,1999 (2):100-108.
    Brockwell A, Rojas A, Kass R. Recursive Bayesian decoding of motor cortical signals by particle filtering[J]. Journal of neurophysiology,2004 (4):1899-1907.
    Brown E V. Remote Control Training. A bicoastal senior living enterprise reduces training and help desk costs and time with old and new software[J]. Health Manag Technol,2007 (3):18-19.
    Carmena J M, Lebedev M A, Crist R E. O'Doherty J E. Santucci D M, Dimitrov D F, Patil P G, Henriquez C S, Nicolelis M A. Learning to control a brain-machine interface for reaching and grasping by primates[J]. PLoS Biol,2003 (2):E42.
    Chapin J K, Moxon K A, Markowitz R S, Nicolelis M A. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex[J]. Nature Neuroscience.1999 (7):664-670.
    Colebatch J, Gandevia S. The distribution of muscular weakness in upper motor neuron lesions affecting the arm[J]. Brain,1989 (3):749-763.
    Colebatch J G, Deiber M P, Passingham R E, Friston K J, Frackowiak R S. Regional cerebral blood flow during voluntary arm and hand movements in human subjects[J]. J Neurophysiol,1991 (6):1392-1401.
    Compano R. Future ICTs for Active Ageing:Brain-machine and Brain-computer Interfaces[M].23,2009:235.
    Dobelle W H, Turkel J, Henderson D C, Evans J R. Mapping the representation of the visual field by electrical stimulation of human visual cortex[J]. Am J Ophthalmol.1979 (4):727-735.
    Farwell L A. Donchin E. Talking off the top of your head:toward a mental prosthesis utilizing event-related brain potentials[J]. Electroencephalogr Clin Neurophysiol,1988 (6):510-523.
    Ferrari P F, Gallese V, Rizzolatti G, Fogassi L. Mirror neurons responding to the observation of ingestive and communicative mouth actions in the monkey ventral premotor cortex[J]. European Journal of Neuroscience,2003 (8): 1703-1714.
    Friehs G M. Zerris V A, Ojakangas C L. Fellows M R, Donoghue J P. Brain-machine and brain-computer interfaces[J]. Stroke,2004 (11 suppl 1):2702-2705.
    Gao X R, Xu D F, Cheng M, Gao S K. A BCI-based environmental controller for the motion-disabled[J]. Ieee Transactions on Neural Systems and Rehabilitation Engineering,2003a (2):137-140.
    Gao Y, Black M J, Bienenstock E. Wu W, Donoghue J P. A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions. IEEE,2003b:189-192.
    Gazianiga M S, Mangun G R. Cognitive neuroscience[M].1998.
    Georgopoulos A P, Schwartz A B, Kettner R E. Neuronal population coding of movement direction[J]. Science,1986(4771):1416-1419.
    Ginter F, Suominen H, Pyysalo S, Salakoski T. Combining hidden Markov models and latent semantic analysis for topic segmentation and labeling:method and clinical application[J]. Int J Med Inform,2009 (12):el-6.
    Halsband U, Freund H J. Premotor cortex and conditional motor learning in man[J]. Brain,1990 (1):207-222.
    Henderson D C, Evans J R, Dobelle W H. The relationship between stimulus parameters and phosphene threshold/brightness, during stimulation of human visual cortex[J]. Trans Am Soc Artif Intern Organs,1979:367-371.
    Hochberg L R, Bacher D, Jarosiewicz B, Masse N Y, Simeral J D, Vogel J, Haddadin S, Liu J. Cash S S. van der Smagt P. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm[J]. Nature,2012 (7398):372-375.
    Ikeda A, Luders H O, Burgess R C, Shibasaki H. Movement-related potentials recorded from supplementary motor area and primary motor area. Role of supplementary motor area in voluntary movements[J]. Brain,1992:1017-1043.
    Kennedy P R, Bakay R A. Restoration of neural output from a paralyzed patient by a direct brain connection[J]. Neuroreport,1998 (8):1707-1711.
    Krepki R, Blankertz B, Curio G, Muller K R. The Berlin Brain-Computer Interface (BBCI):towards a new communication channel for on-line control in gaming applications[J]. Multimedia Tools and Applications (Special Issue on Distributed Adaption. Representation and Processing of Multimedia Information),2007 (-):460-477.
    Lecuyer A, Lotte F, Reilly R B, Leeb R, Hirose M, Slater M. Brain-computer interfaces, virtual reality, and videogames[J]. Computer,2008 (10):66-72.
    Laubach M, Wessberg J, Nicolelis M A L. Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task[J]. Nature,2000 (6786):567-571.
    Lauer R T, Kilgore K L, Peckham P H, Bhadra N, Keith M W. The function of the finger intrinsic muscles in response to electrical stimulation[J]. IEEE Trans Rehabil Eng,1999(1):19-26.
    Lebedev M A, Nicolelis M A. Brain-machine interfaces:past, present and future[J]. Trends Neurosci,2006 (9):536-546.
    Leeb R, Lee F, Keinrath C, Scherer R, Bischof H, Pfurtscheller G. Brain-computer communication:motivation, aim, and impact of exploring a virtual apartment[J]. IEEE Trans Neural Syst Rehabil Eng,2007 (4):473-482.
    Lotte F, Fujisawa J, Touyama H, Ito R, Hirose M, Lecuyer A. Towards ambulatory brain-computer interfaces:a pilot study with P300 signals. ACM,2009: 336-339.
    Luppino G, Matelli M, Camarda R, Rizzolatti G. Corticocortical connections of area F3 (SMA-proper) and area F6 (pre-SMA) in the macaque monkey [J]. The Journal of comparative neurology,1993 (1):114-140.
    Matsuzaka Y, Aizawa H, Tanji J. A motor area rostral to the supplementary motor area (presupplementary motor area) in the monkey:neuronal activity during a learned motor task[J]. J Neurophysiol,1992 (3):653-662.
    Minati L, Nigri A, Rosazza C, Bruzzone M G. Thoughts turned into high-level commands:Proof-of-concept study of a vision-guided robot arm driven by functional MRI (fMRI) signals[J]. Med Eng Phys,2012 (5):650-658.
    Mita A, Mushiake H, Shima K, Matsuzaka Y, Tanji J. Interval time coding by neurons in the presupplementary and supplementary motor areas[J]. Nature Neuroscience,2009 (4):502-507.
    Mitsumoto H, Ulug A, Pullman S, Gooch C, Chan S, Tang M X, Mao X, Hays A, Floyd A, Battista V. Quantitative objective markers for upper and lower motor neuron dysfunction in ALS[J]. Neurology,2007 (17):1402-1410.
    Moran D W, Schwartz A B. Motor cortical activity during drawing movements: population representation during spiral tracing[J]. J Neurophysiol,1999 (5): 2693-2704.
    Muller-Putz G R, Scherer R, Pfurtscheller G, Rupp R. EEG-based neuroprosthesis control:a step towards clinical practice[J]. Neuroscience Letters,2005 (1-2): 169-174.
    Nachev P, Kennard C, Husain M. Functional role of the supplementary and pre-supplementary motor areas[J]. Nature Reviews Neuroscience,2008 (11): 856-869.
    Nelson W T, Hettinger L J, Cunningham J A, Roe M M, Haas M W, Dennis L B. Navigating through virtual flight environments using brain-body-actuated control. IEEE,1997:30-37.
    Paninski L, Fellows M R, Hatsopoulos N G, Donoghue J P. Spatiotemporal tuning of motor cortical neurons for hand position and velocity [J]. J Neurophysiol.2004 (1):515-532.
    Parnian N, Golnaraghi F. Integration of a multi-camera vision system and strapdown inertial navigation system (SDINS) with a modified Kalman filter[J]. Sensors (Basel),2010 (6):5378-5394.
    Passingham R. Premotor cortex and preparation for movement[J]. Experimental Brain Research,1988 (3):590-596.
    Pei X M. Zheng C X, Xu J, Bin G Y, Wang H W. Multi-channel linear descriptors for event-related EEG collected in brain computer interface[J]. J Neural Eng.2006 (1):52-58.
    Perez-Marcos D, Buitrago J A, Velasquez F D. Writing through a robot:a proof of concept for a brain-machine interface[J]. Med Eng Phys,2011 (10):1314-7.
    Pfurtscheller G, Lopes da Silva F. Event-related EEG/MEG synchronization and desynchronization:basic principles[J]. Clinical Neurophysiology,1999 (11): 1842-1857.
    Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication[J]. Proceedings of the IEEE,2001 (7):1123-1134.
    Plass-Oude Bos D, Reuderink B, Laar B, Gurkok H, Muhl C, Poel M, Nijholt A, Heylen D. Brain-computer interfacing and games[J]. Brain-Computer Interfaces,2010:149-178.
    Rao S M, Binder J R, Bandettini P A, Hammeke T A, Yetkin F Z, Jesmanowicz A, Lisk L M, Morris G L, Mueller W M, Estkowski L D, et al. Functional magnetic resonance imaging of complex human movements[J]. Neurology,1993 (11): 2311-2318.
    Russo G S, Backus D A, Ye S P, Crutcher M D. Neural activity in monkey dorsal and ventral cingulate motor areas:Comparison with the supplementary motor area[J]. Journal of neurophysiology,2002 (5):2612-2629.
    Sach M, Winkler G, Glauche V, Liepert J, Heimbach B, Koch M A, Buchel C. Weiller C. Diffusion tensor MRI of early upper motor neuron involvement in amyotrophic lateral sclerosis[J]. Brain,2004 (2):340-350.
    Sakurai Y. Population coding by cell assemblies--what it really is in the brain [J]. Neuroscience Research,1996 (1):1-16.
    Sayadi O, Shamsollahi M B. ECG denoising and compression using a modified extended Kalman filter structure[J]. IEEE Trans Biomed Eng,2008 (9):2240-8.
    Scherer R, Lee F, Schlogl A, Leeb R, Bischof H, Pfurtscheller G. Toward self-paced brain-computer communication:navigation through virtual worlds[J]. IEEE Trans Biomed Eng,2008 (2 Pt 1):675-682.
    Shima K, Tanji J. Both supplementary and presupplementary motor areas are crucial for the temporal organization of multiple movements[J]. Journal of neurophysiology,1998 (6):3247-3260.
    Shima K, Tanji J. Neuronal activity in the supplementary and presupplementary motor areas for temporal organization of multiple movements [J]. Journal of neurophysiology,2000 (4):2148-2160.
    Thobbi A, Kadam R, Sheng W. Achieving Remote Presence using a Humanoid Robot Controlled by a Non-Invasive BCI Device[J]. anandthobbiyolasitecom.2010 (1):41-45.
    Thulasidas M, Guan C, Wu J. Robust classification of EEG signal for brain-computer interface[J]. IEEE Trans Neural Syst Rehabil Eng,2006 (1):24-29.
    Wang Y, Gao X, Hong B. Jia C. Gao S. Brain-computer interfaces based on visual evoked potentials[J]. Engineering in Medicine and Biology Magazine, IEEE, 2008 (5):64-71.
    Wang Y, Wang R, Gao X. Hong B, Gao S. A practical VEP-based brain-computer interface[J]. IEEE Trans Neural Syst Rehabil Eng,2006 (2):234-239.
    Wen X, Zhao X, Yao L. Synchrony of basic neuronal network based on event related EEG[J]. Advances in Neural Networks-ISNN 2005,2005:977-977.
    Wen X T, Zhao X J, Yao L. Wu X. Applications of Granger causality model to connectivity network based on fMRI time series[J]. Advances in Natural Computation,2006:205-213.
    Wessberg J, Stambaugh C R, Kralik J D, Beck P D, Laubach M, Chapin J K, Kim J. Biggs S J, Srinivasan M A, Nicolelis M A. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates[J]. Nature,2000 (6810): 361-365.
    Williams R W, Herrup K. The control of neuron number[J]. Annual Review of Neuroscience,1988:423-453.
    Wolpaw J R, Birbaumer N, Heetderks W J, McFarland D J. Peckham P H, Schalk G, Donchin E, Quatrano L A, Robinson C J, Vaughan T M. Brain-computer interface technology:a review of the first international meeting[J]. IEEE Trans Rehabil Eng,2000 (2):164-173.
    Wolpaw J R, McFarland D J, Vaughan T M, Schalk G. The Wadsworth Center brain-computer interface (BCI) research and development program[J]. IEEE Trans Neural Syst Rehabil Eng,2003 (2):204-207.
    Wu B, Su Y, Zhang J H, Li X, Zhang J C, Cheng W D, Zheng X X. A virtual Chinese keyboard BCI system based on P 300 potentials[J]. Dianzi Xuebao(Acta Electronica Sinica),1745 (8):1733-1738.
    Wu W, Black M J, Mumford D, Gao Y, Bienenstock E, Donoghue J P. Modeling and decoding motor cortical activity using a switching Kalman filter[J]. IEEE Trans Biomed Eng,2004 (6):933-942.
    Wu W, Gao X R, Hong B, Gao S K. Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL)[J]. Ieee Transactions on Biomedical Engineering,2008a (6):1733-1743.
    Wu W, Gao Y, Bienenstock E, Donoghue J P, Black M J. Bayesian population decoding of motor cortical activity using a Kalman filter[J]. Neural Comput,2006 (1): 80-118.
    Wu Z, Yao D. Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs[J]. J Neural Eng,2008b (1):36-43.
    Wu Z H, Lai Y X, Xia Y, Wu D, Yao D Z. Stimulator selection in SSVEP-based BCI[J]. Medical Engineering & Physics,2008c (8):1079-1088.
    Xu S, Talwar S K, Hawley E S, Li L, Chapin J K. A multi-channel telemetry system for brain microstimulation in freely roaming animals[J]. J Neurosci Methods,2004 (1-2):57-63.
    Ye X, Wang P, Liu J, Zhang S, Jiang J, Wang Q, Chen W, Zheng X. A portable telemetry system for brain stimulation and neuronal activity recording in freely behaving small animals[J]. J Neurosci Methods,2008 (2):186-193.
    Yifeng B, Jian X. Long Y. Kernel partial least-squares regression. IEEE.2006: 1231-1238.
    Yin G, Zhang J, Tian Y Yao D. A multi-component decomposition algorithm for event-related potentials[J]. J Neurosci Methods,2009 (1):219-227.
    Yu Y, Zhang S M, Zhang H J. Liu X C, Zhang Q S, Zheng X X, Dai J H. Neural decoding based on probabilistic neural network[J]. Journal of Zhejiang University-Science B,2010 (4):298-306.
    Zhang J W. Zheng C X, Xie A. Bispectrum analysis of focal ischemic cerebral EEG signal using third-order recursion method[J]. Biomedical Engineering, IEEE Transactions on,2000 (3):352-359.
    Zhao Q B. Zhang L Q, Cichocki A. EEG-based asynchronous BCI control of a car in 3D virtual reality environments[J]. Chinese Science Bulletin,2009 (1):78-87.
    王惠文.偏最小二乘回归方法及其应用[M].国防工业出版社,1999.
    代建华,章怀坚,张韶岷,李茜,刘晓春,郝耀耀,于毅,蒋凯,刘俊.大鼠运动皮层神经元集群锋电位时空模式解析[J].中国科学:C辑,2009(008):736-745.
    沈红斌,王士同,吴小俊.离群模糊核聚类算法[J].软件学报,2004(7).
    蒋红卫,夏结来.偏最小二乘回归及其应用[J].第四军医大学学报,2003(3):280-283.
    郑旭媛.神经元集群的时空编码研究现状[J].国际生物医学工程杂志,2007(3):186-188.
    陈小默,洪波,高上凯.神经元群体解码方法及其在脑-机接口中的应用[J].北京生物医学工程,2007(3):330-333.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700