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Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes

Nature Neuroscience volume 13, pages 105–111 (2010)Cite this article

Abstract

Coordinated spiking activity in neuronal ensembles, in local networks and across multiple cortical areas, is thought to provide the neural basis for cognition and adaptive behavior. Examining such collective dynamics at the level of single neuron spikes has remained, however, a considerable challenge. We found that the spiking history of small and randomly sampled ensembles (∼20−200 neurons) could predict subsequent single neuron spiking with substantial accuracy in the sensorimotor cortex of humans and nonhuman behaving primates. Furthermore, spiking was better predicted by the ensemble's history than by the ensemble's instantaneous state (Ising models), emphasizing the role of temporal dynamics leading to spiking. Notably, spiking could be predicted not only by local ensemble spiking histories, but also by spiking histories in different cortical areas. These strong collective dynamics may provide a basis for understanding cognition and adaptive behavior at the level of coordinated spiking in cortical networks.

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Figure 1: History point process models, intrinsic and ensemble history effects, and conditional spiking probabilities.
Figure 2: Prediction of single-neuron spiking and weak pair-wise correlations.
Figure 3: Predictive power of intra-areal (M1) ensemble histories.
Figure 4: Predictive power of instantaneous collective states (Ising models).
Figure 5: Predictive power of intra- and inter-areal neuronal ensemble histories.
Figure 6: Predictive power, mean spiking rates, spike train irregularity and information rates.

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References

  1. Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity (Springer-Verlag, New York, 1998).

  2. Elston, G.N. & Rockland, K.S. The pyramidal cell of the sensorimotor cortex of the macaque monkey: phenotypic variation. Cereb. Cortex 12, 1071–1078 (2002).

    Article  PubMed  Google Scholar 

  3. Dayan, P. & Abbott, L.F. Theoretical Neuroscience (MIT Press, Cambridge, Massachusetts, 2001).

  4. Destexhe, A., Rudolph, M. & Paré, D. The high-conductance state of neocortical neurons in vivo. Nat. Rev. Neurosci. 4, 739–751 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Arieli, A., Sterkin, A., Grinvald, A. & Aertsen, A. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871 (1996).

    Article  CAS  PubMed  Google Scholar 

  6. Harris, K.D., Csicsvari, J., Hirase, H., Dragoi, G. & Buzsaki, G. Organization of cell assemblies in the hippocampus. Nature 424, 552–556 (2003).

    Article  CAS  PubMed  Google Scholar 

  7. Schneidman, E., Berry, M., Segev, R. & Bialek, W. Weak pair-wise correlations imply strongly correlated network states in a neural population. Nature 440, 1007–1012 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shlens, J. et al. The structure of multi-neuron firing patterns in primate retina. J. Neurosci. 26, 8254–8266 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Pillow, J.W. et al. Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature 454, 995–999 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Yu, S., Huang, D., Singer, W. & Nikolic, D. A small world of neuronal synchrony. Cereb. Cortex 18, 2891–2901 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tang, A. et al. A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. J. Neurosci. 28, 505–518 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jones, E.G., Coulter, J.D. & Hendry, S.H. Intracortical connectivity of architectonic fields in the somatic sensory, motor and parietal cortex of monkeys. J. Comp. Neurol. 181, 291–347 (1978).

    Article  CAS  PubMed  Google Scholar 

  13. Shimazu, H., Maier, M.A., Cerri, G., Kirkwood, P.A. & Lemon, R.N. Macaque ventral premotor cortex exerts powerful facilitation of motor cortex outputs to upper limb motoneurons. J. Neurosci. 24, 1200–1211 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kalaska, J.F., Scott, S.H., Cisek, P. & Sergio, L.E. Cortical control of reaching movements. Curr. Opin. Neurobiol. 7, 849–859 (1997).

    Article  CAS  PubMed  Google Scholar 

  15. Shadmehr, R. & Krakauer, J.W. A computational neuroanatomy for motor control. Exp. Brain Res. 185, 359–381 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Truccolo, W., Eden, U.T., Fellows, M.R., Donoghue, J.P. & Brown, E.N. A point process framework for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects. J. Neurophysiol. 93, 1074–1089 (2005).

    Article  PubMed  Google Scholar 

  17. Paninski, L., Pillow, J. & Lewi, J. Statistical models for neural encoding, decoding, and optimal stimulus design. Prog. Brain Res. 165, 493–507 (2007).

    Article  PubMed  Google Scholar 

  18. Truccolo, W. Stochastic point process models for multivariate neuronal spike train data: collective dynamics and neural decoding. in Analysis of Parallel Spike Train Data (eds. Grün, S. & Rotter, S.) (Springer, New York, in the press).

  19. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006).

    Article  Google Scholar 

  20. Bamber, D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J. Math. Psychol. 12, 387–415 (1975).

    Article  Google Scholar 

  21. Hatsopoulos, N.G., Xu, Q. & Amit, Y. Encoding of movement fragments in the motor cortex. J. Neurosci. 27, 5105–5114 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Jaynes, E.T. On the rationale of maximum entropy methods. Proc. IEEE 70, 939–952 (1982).

    Article  Google Scholar 

  23. Cover, T.M. & Thomas, J.A. Elements of Information Theory (Wiley & Sons, New York, 1991).

  24. Landau, L.D. & Lifshitz, E.M. Statistical Physics 3rd edn (Butterworth & Heinemann, Oxford, UK, 1980).

  25. Amit, D.J. Modeling Brain Function (Cambridge University Press, Cambridge, UK, 1989).

  26. Geman, S. & Geman, D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984).

    Article  CAS  PubMed  Google Scholar 

  27. Marre, O., El Boustani, S., Fregnac, Y. & Destexhe, A. Prediction of spatiotemporal patterns of neural activity from pair-wise correlations. Phys. Rev. Lett. 102, 138101 (2009).

    Article  CAS  PubMed  Google Scholar 

  28. Paninski, L., Fellows, M.R., Hatsopoulos, N.G. & Donoghue, J.P. Spatiotemporal tuning of motor neurons for hand position and velocity. J. Neurophysiol. 91, 515–532 (2004).

    Article  PubMed  Google Scholar 

  29. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).

    Article  CAS  PubMed  Google Scholar 

  30. Serruya, M.D., Hatsopoulos, N.G., Paninski, L., Fellows, M.R. & Donoghue, J.P. Instant neural control of a movement signal. Nature 416, 141–142 (2002).

    Article  CAS  PubMed  Google Scholar 

  31. Hochberg, L.R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Truccolo, W., Friehs, G.M., Donoghue, J.P. & Hochberg, L.R. Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. J. Neurosci. 28, 1163–1178 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yousry, T.A. et al. Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. Brain 120, 141–157 (1997).

    Article  PubMed  Google Scholar 

  34. Daley, D.J. & Vere-Jones, D. An Introduction to Theory of Point Processes (Springer, New York, 2003).

  35. Geweke, J. Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc. 77, 304–314 (1982).

    Article  Google Scholar 

  36. Besag, J. Statistical analysis of non-lattice data. Statistician 24, 179–195 (1975).

    Article  Google Scholar 

  37. Geman, S. & Graffigne, C. Markov random field image models and their application to computer vision. Proc. Int. Congr. Math. 1496–1517 (1986).

  38. Gidas, B. Consistency of maximum likelihood and pseudo-likelihood estimators for Gibbsian distributions. in Stochastic Differential Systems, Stochastic Control Theory and Applications (eds. Fleming, W. & Lions, P.-L.) (Springer, New York, 1988).

  39. Hyvärinen, A. Consistency of pseudolikelihood estimation of fully visible Boltzmann machines. Neural Comput. 18, 2283–2292 (2006).

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank M.R. Fellows, C. Vargas-Irwin and B. Philip for collecting the nonhuman primate data. We thank our clinical trial participants for their dedication to this research, G. Friehs for his role as surgical investigator for the pilot clinical trial, J. Mukand for his role as clinical investigator for the pilot clinical trial, and A. Caplan, M. Serruya, M. Saleh and other employees of Cyberkinetics Neurotechnology Systems for data collection, manufacturing and clinical trial management. This study was based on work supported in part by the Office of Research and Development, Rehabilitation R&D Service, Department of Veterans Affairs (L.R.H. and J.P.D.). This work was supported by the National Institute of Neurological Disorders and Stroke (5K01NS057389-02 to W.T. and NS-25074 (Javits Award) to J.P.D.), the National Institute of Child Health and Human Development/National Center for Medical Rehabilitation Research (N01-HD-53403, subcontract to L.R.H.), the Massachusetts General Hospital Deane Institute (L.R.H.), the Doris Duke Charitable Foundation (L.R.H) and the National Institute on Deafness and Other Communication Disorders (R01-DC-009899 to L.R.H.).

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Authors and Affiliations

  1. Department of Neuroscience, Brown University, Providence, Rhode Island, USA

    Wilson Truccolo & John P Donoghue

  2. Brown Institute for Brain Science, Brown University, Providence, Rhode Island, USA

    Wilson Truccolo, Leigh R Hochberg & John P Donoghue

  3. Division of Engineering, Brown University, Providence, Rhode Island, USA

    Leigh R Hochberg

  4. Department of Veterans Affairs, Rehabilitation Research and Development Service, Providence, Rhode Island, USA

    Leigh R Hochberg & John P Donoghue

  5. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Wilson Truccolo & Leigh R Hochberg

  6. Brigham and Women's Hospital, and Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Leigh R Hochberg

Authors
  1. Wilson Truccolo
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Contributions

W.T. conceived the study's central ideas and conducted the data analyses. W.T. wrote the paper with contributions from L.R.H. and J.P.D. L.R.H. and J.P.D. contributed to the clinical research design. L.R.H. was the principal investigator for the pilot clinical trial. J.P.D. supervised the nonhuman primate experiments.

Corresponding author

Correspondence to Wilson Truccolo.

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Competing interests

J.P.D. was Chief Scientific Officer, director, and received stock holdings and compensation from Cyberkinetics Neurotechnology Systems. L.R.H. received research support from Massachusetts General and Spaulding Rehabilitation Hospitals, which in turn received clinical trial support from Cyberkinetics Neurotechnology Systems, which ceased operation in 2009. The BrainGate pilot clinical trials are now directed by Massachusetts General Hospital.

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Truccolo, W., Hochberg, L. & Donoghue, J. Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes. Nat Neurosci 13, 105–111 (2010). https://doi.org/10.1038/nn.2455

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