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Neural decoding in motor cortex using state space models with hidden states

from Part I - State space methods for neural data

Published online by Cambridge University Press:  05 October 2015

By
W. Wu  and
S. Liu
Edited by
Zhe Chen
W. Wu
Affiliation:
Florida State University
S. Liu
Affiliation:
Florida State University
Zhe Chen
Affiliation:
New York University
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Summary

Introduction

Loss of functions is a common problem in this real world. Human beings often suffer from various diseases and traumas which include visual, auditory and motor impairments, for example: blindness, deafness, brainstem stroke, ALS (amyotrophic lateral sclerosis) and spinal cord injury. Current investigations search for an interface between the brain and external devices to restore these functions. New technical terms such as “neural prostheses,” “brain–machine interface (BMI),” “brain–computer interface (BCI)” are broadly appearing in up-to-date research articles as well as other media.

Fast developments in biotechnology have given us the ability to measure and record population neuronal activity with more precision and accuracy than ever before, allowing researchers to study and perform detailed analyses which may have been impossible just a few years ago. In particular, with this advancement in technology, it is now possible to construct an interface to bridge the gap between neuronal spiking activity and external devices that help control real-world applications (Lebedev & Nicolelis 2006; Schwartz et al. 2006; Homer et al. 2013). The primary goal of this research is to be able to restore motor function to physically disabled patients (Hochberg et al. 2006): spike recordings would be “decoded” to provide an external prosthetic device with a neurally controlled signal in the hope that the movement can be restored in its original form.

Pioneer work was conducted over the past decade in several research groups which demonstrated that hand movement can be represented by the neural activity of a population of cells in monkey's motor cortex (Schwartz & Moran 1999; Wessberg et al. 2000; Serruya et al. 2002). They also developed various mathematical algorithms to infer the hand motion with real-time performance. The results were noteworthy: the inference was fast and the estimation was accurate enough to perform a neural control task. Their work also demonstrated that the direct neural tasks can be successfully accomplished using the proposed algorithms (Wessberg et al. 2000; Serruya et al. 2002; Taylor et al. 2002). However, there are still various issues that need to be addressed, such as longterm stability of the micro electrode array implants, efficacy and safety, low power consumption, and mechanical reliability (Donoghue 2002; Chestek et al. 2007; Homer et al. 2013).

Type
Chapter
Information
Advanced State Space Methods for Neural and Clinical Data , pp. 207 - 230
Publisher: Cambridge University Press
Print publication year: 2015

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References

Brillinger, D. (1988). Maximum likelihood analysis of spike trains of interacting nerve cells. Biological Cybernetics 59, 189–200.CrossRefGoogle Scholar
Brillinger, D. (1992). Nerve cell spike train data analysis: a progression of technique. Journal of the American Statistical Association 87, 260–271.CrossRefGoogle Scholar
Brockwell, A. E., Rojas, A. L. & Kass, R. E. (2004). Recursive Bayesian decoding of motor cortical signals by particle filtering. Journal of Neurophysiology 91, 1899–1907.Google Scholar
Brown, E. N., Frank, L., Tang, D., Quirk, M. & Wilson, M. A. (1998). A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. Journal of Neuroscience 18, 7411–7425.CrossRefGoogle Scholar
Buesing, L., Macke, J. & Sahani, M. (2013). Spectral learning of linear dynamics from generalized-linear observations with application to neural population data. In Advances in Neural Information Processing Systems 25, Cambridge, MA: MIT Press, pp. 1691-1699.
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. L. (2003). Learning to control a brain-machine interface for reaching and grasping by primates. PLoS, Biology 1(2), 001–016.CrossRefGoogle Scholar
Carmena, J. M., Lebedev, M. A., Henriquez, C. S. & Nicolelis, M. A. L. (2005). Stable ensemble performance with single-neuron variability during reaching movements in primates. Journal of Neuroscience 25, 10712–10716.CrossRefGoogle Scholar
Chestek, C. A., Batista, A. P., Santhanam, G., Yu, B. M., Afshar, A., Cunningham, J. P., Gilja, V., Ryu, S. I., Churchland, M. M. & Shenoy, K. V. (2007). Single-neuron stability during repeated reaching in macaque premotor cortex. Journal of Neuroscience 27, 10742–10750.Google Scholar
Cunningham, J., Yu, B., Gilja, V., Ryu, S. & Shenoy, K. (2008). Toward optimal target placement for neural prosthetic devices. Journal of Neurophysiology 100, 3445–3457.Google Scholar
Dempster, A., Laird, N. & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1–38.Google Scholar
Donoghue, J. P. (2002). Connecting cortex to machines: recent advances in brain interfaces. Nature Neuroscience 5, 1085–1088.CrossRefGoogle Scholar
Eden, U., Frank, L., Barbieri, R., Solo, V. & Brown, E. (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation 16, 971–988.CrossRefGoogle Scholar
Georgopoulos, A., Kalaska, J., Caminiti, R. & Massey, J. (1982). On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. Journal of Neuroscience 8(11), 1527–1537.CrossRefGoogle Scholar
Georgopoulos, A., Schwartz, A. & Kettner, R. (1986). Neural population coding of movement direction. Science 233, 1416–1419.Google Scholar
Haykin, S. (2001). Kalman Filtering and Neural Networks, John Wiley & Sons, Inc.
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D.Penn, R. D. & Donoghue, J. P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171.CrossRefGoogle Scholar
Homer, M. L., Nurmikko, A. V., Donoghue, J. P. & Hochberg, L. R. (2013). Sensors and decoding for intracortical brain computer interfaces. Annual Review of Biomedical Engineering 15, 383–405.CrossRefGoogle Scholar
Humphrey, D. R., Schmidt, E. M. & Thompson, W. D. (1970). Predicting measures of motor performance from multiple cortical spike trains. Science 170, 758–762.CrossRefGoogle Scholar
Kollmorgen, S. & Hahnloser, R. (2014). Dynamic alignment models for neural coding. PLoS Computational Biology 10, e1003508.CrossRefGoogle Scholar
Koyama, S., Pacuteerez-Bolde, L. C., Shalizi, C. & Kass, R. (2008). Laplace's method in neural decoding. In COSYNE Meeting, 2008 Abstract.Google Scholar
Kulkarni, J. E. & Paninski, L. (2007a). Common-input models for multiple neural spike-train data. Network: Computation in Neural Systems 18, 375–407.Google Scholar
Kulkarni, J. E. & Paninski, L. (2007b). State-space decoding of goal directed movement. IEEE Signal Processing Magazine 25(1), 78–86.
Lawhern, V., Wu, W., Hatsopoulos, N. G. & Paninski, L. (2010). Population decoding of motor cortical activity using a generalized linear model with hidden states. Journal of Neuroscience Methods 189, 267–280.CrossRefGoogle Scholar
Lebedev, M. A. & Nicolelis, M. A. (2006). Brain-machine interfaces: past, present and future. Trends in Neurosciences 29(9), 536–546.CrossRefGoogle Scholar
Macke, J., Buesing, L., Cunningham, J., Yu, B., Shenoy, K. & Sahani, M. (2011). Empirical models of spiking in neural populations. In Advances in Neural Information Processing Systems 24, Cambridge, MA: MIT Press.
Moeller, J., Syversveen, A. & Waagepetersen, R. (1998). Log-Gaussian Cox processes. Scandinavian Journal of Statistics 25, 451–482.CrossRefGoogle Scholar
Moran, D. & Schwartz, A. (1999). Motor cortical representation of speed and direction during reaching. Journal of Neurophysiology 82(5), 2676–2692.CrossRefGoogle Scholar
Nykamp, D. (2007). A mathematical framework for inferring connectivity in probabilistic neuronal networks. Mathematical Biosciences 205, 204–251.CrossRefGoogle Scholar
Paninski, L. (2004). Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems 15, 243–262.Google Scholar
Paninski, L., Ahmadian, Y., Ferreira, D., Koyama, S., Rahnama Rad, K., Vidne, M., Vogelstein, J. & Wu, W. (2010). A new look at state-space models for neural data. Journal of Computational Neuroscience 29, 107–126.Google Scholar
Paninski, L., Fellows, M., Hatsopoulos, N. & Donoghue, J. P. (2004). Spatiotemporal tuning of motor cortical neurons for hand position and velocity. Journal of Neurophysiology 91, 515–532.CrossRefGoogle Scholar
Pfau, D., Pnevmatikakis, E. & Paninski, L. (2013). Robust learning of low-dimensional dynamics from large neural ensembles. In Advances in Neural Information Processing Systems 26, pp. 2391–2399.Google Scholar
Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A., Chichilnisky, E. & Simoncelli, E. (2008). Spatiotemporal correlations and visual signaling in a complete neuronal population. Nature 454, 995–999.Google Scholar
Pistohl, T., Ball, T., Schulze-Bonhage, A., Aertsen, A. & Mehring, C. (2008). Prediction of arm movement trajectories from ecog-recordings in humans. Journal of Neuroscience Methods 167, 105–114.CrossRefGoogle Scholar
Rissanen, J. (1989). Stochastic Complexity in Statistical Inquiry, Boston: MA: World Scientific.
Sanchez, J. C., Erdogmus, D., Principe, J. C., Wessberg, J. & Nicolelis, M. A. L. (2005). Interpreting spatial and temporal neural activity through a recurrent neural network brain machine interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13, 213–219.CrossRefGoogle Scholar
Santhanam, G., Yu, B. M., Gilja, V., Ryu, S. I., Afshar, A., Sahani, M. & Shenoy, K. V. (2009). Factor-analysis methods for higher-performance neural prostheses. Journal of Neurophysiology 102, 1315–1330.Google Scholar
Schwartz, A. &Moran, D. (1999). Motor cortical activity during drawing movements: Population representation during lemniscate tracing. Journal of Neurophysiology 82(5), 2705–2718.Google Scholar
Schwartz, A., Tracy Cui, X., Weber, D. & Moran, D. (2006). Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 56, 205–220.Google Scholar
Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. (2002). Brainmachine interface: instant neural control of a movement signal. Nature 416, 141–142.CrossRefGoogle Scholar
Shoham, S. (2001), Advances towards an implantable motor cortical interface, PhD thesis, University of Utah.
Shumway, R. H. & Stoffer, D. S. (1982). An approach to time series smoothing and forecasting using the em algorithm. Journal of Time Series Analysis 3, 253–264.CrossRefGoogle Scholar
Shumway, R. H. & Stoffer, D. S. (2006). Time Series Analysis and Its Applications, New York: Springer.
Smith, A. C. & Brown, E. N. (2003). Estimating a state-space model from point process observations. Neural Computation 15, 965–991.Google Scholar
Snyder, D. L. & Miller, M. I. (1991). Random Point Process in Time and Space, New York: Springer.
Srinivasan, L. & Brown, E. N. (2007). A state-space framework for movement control to dynamic goals through brain-driven interfaces. IEEE Transactions on Biomedical Engineering 54(3), 526–535.CrossRefGoogle Scholar
Srinivasan, L., Eden, U. T., Mitter, S. K. & Brown, E. N. (2007). General-purpose filter design for neural prosthetic devices. Journal of Neurophysiology 98, 2456–2475.CrossRefGoogle Scholar
Srinivasan, L., Eden, U. T., Willsky, A. S. & Brown, E. N. (2006). A state-space analysis for reconstruction of goal-directed movements using neural signals. Neural Computation 18, 2465–2494.Google Scholar
Stevenson, I. H., Rebesco, J. M., Hatsopoulos, N. G., Haga, Z., Miller, L. E. & Kording, K. P. (2009). Bayesian inference of functional connectivity and network structure from spikes. IEEE Transactions on Neural Systems and Rehabilitation Engineering 17, 203–213.CrossRefGoogle Scholar
Taylor, D., Helms Tillery, S. & Schwartz, A. (2002). Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832.CrossRefGoogle Scholar
Truccolo, W. & Donoghue, J. (2007). Nonparametric modeling of neural point processes via stochastic gradient boosting regression. Neural Computation 19, 672–705.Google Scholar
Truccolo, W., Eden, U., Fellows, M., Donoghue, J. & Brown, E. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects. Journal of Neurophysiology 93, 1074–1089.Google Scholar
Truccolo, W., Hochberg, L. R. & Donoghue, J. (2009). Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes. Nature Neuroscience 13, 105–111.Google Scholar
Wessberg, J., Stambaugh, C., Kralik, J., Beck, P. L. M., Chapin, J., Kim, J., Biggs, S., Srinivasan, M. & Nicolelis, M. (2000). Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365.Google Scholar
Wu, W., Gao, Y., Bienenstock, E., Donoghue, J. P. & Black, M. J. (2006). Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Computation 18(1), 80–118.CrossRefGoogle Scholar
Wu, W. & Hatsopoulos, N. (2006). Evidence against a single coordinate system representation in the motor cortex. Experimental Brain Research 175(2), 197–210.CrossRefGoogle Scholar
Wu, W., Kulkarni, J. E., Hatsopoulos, N. G. & Paninski, L. (2009). Neural decoding of hand motion using a linear state-space model with hidden states. IEEE Transactions on Neural Systems and Rehabilitation Engineering 17, 370–378.Google Scholar
Wu, W., Shaikhouni, A., Donoghue, J. P. & Black, M. J. (2004). Closed-loop neural control of cursor motion using a Kalman filter. In Proceedings of IEEE Conference on Engineering in Medicine and Biology, pp. 4126–4129.Google Scholar
Yu, B. M., Afshar, A., Santhanam, G., Ryu, S. I., Shenoy, K.V. & Sahani, M. (2006). Extracting dynamical structure embedded in neural activity. In Advances in Neural Information Processing Systems 18, Cambridge, MA: MIT Press.
Yu, B. M., Kemere, C., Santhanam, G., Ryu, S. I., Meng, T. H., Sahani, M. & Shenoy, K. V. (2007). Mixture of trajectory models for neural decoding of goal-directed movements. Journal of Neurophysiology 97, 3763–3780.CrossRefGoogle Scholar

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