Selection and parameterization of cortical neurons for neuroprosthetic control

When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.

[1]  Michael J. Black,et al.  Development of neuromotor prostheses for humans. , 2004, Supplements to Clinical neurophysiology.

[2]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[3]  Teresa H. Y. Meng,et al.  Model-based neural decoding of reaching movements: a maximum likelihood approach , 2004, IEEE Transactions on Biomedical Engineering.

[4]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[5]  Apostolos P. Georgopoulos,et al.  Three-dimensional drawings in isometric conditions: relation between geometry and kinematics , 2004, Experimental Brain Research.

[6]  E. Fetz,et al.  Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. , 1973, Journal of neurophysiology.

[7]  Dawn M. Taylor,et al.  Extraction algorithms for cortical control of arm prosthetics , 2001, Current Opinion in Neurobiology.

[8]  T. Ebner,et al.  Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys. , 1993, Journal of neurophysiology.

[9]  Matthew Fellows,et al.  Robustness of neuroprosthetic decoding algorithms , 2003, Biological Cybernetics.

[10]  D R Humphrey,et al.  Predicting Measures of Motor Performance from Multiple Cortical Spike Trains , 1970, Science.

[11]  S. Scott,et al.  Changes in motor cortex activity during reaching movements with similar hand paths but different arm postures. , 1995, Journal of neurophysiology.

[12]  Eberhard E. Fetz,et al.  Cortical mechanisms controlling limb movement , 1993, Current Opinion in Neurobiology.

[13]  R. Andersen,et al.  Neural prosthetic control signals from plan activity , 2003, Neuroreport.

[14]  Robert E. Kass,et al.  A Spike-Train Probability Model , 2001, Neural Computation.

[15]  T. Ebner,et al.  Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons. , 1995, Journal of neurophysiology.

[16]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[17]  Jiping He,et al.  Disassociation between primary motor cortical activity and movement kinematics during adaptation to reach perturbations , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  E E Fetz,et al.  Effects of operantly conditioning epileptic unit activity on seizure frequencies and electrophysiology of neocortical experimental foci. , 1974, Experimental neurology.

[19]  A. P. Georgopoulos,et al.  Primate motor cortex and free arm movements to visual targets in three- dimensional space. I. Relations between single cell discharge and direction of movement , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[20]  Nicholas G. Hatsopoulos,et al.  Brain-machine interface: Instant neural control of a movement signal , 2002, Nature.

[21]  Jiping He,et al.  Reorganization of Neural Activity in Cerebral Cortex during Adaptation to External Force Perturbations of Reaching Movement , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[22]  E E Fetz,et al.  Operantly conditioned firing patterns of epileptic neurons in the monkey motor cortex. , 1973, Experimental neurology.

[23]  J P Donoghue,et al.  Representations based on neuronal interactions in motor cortex. , 2001, Progress in brain research.

[24]  S. Scott.  The role of primary motor cortex in goal-directed movements: insights from neurophysiological studies on non-human primates , 2003, Current Opinion in Neurobiology.

[25]  José Carlos Príncipe,et al.  Ascertaining the importance of neurons to develop better brain-machine interfaces , 2004, IEEE Transactions on Biomedical Engineering.

[26]  A. Georgopoulos,et al.  The motor cortex and the coding of force. , 1992, Science.

[27]  K. Horch,et al.  Residual function in peripheral nerve stumps of amputees: implications for neural control of artificial limbs. , 2004, The Journal of hand surgery.

[28]  E. Fetz,et al.  Correlations between activity of motor cortex cells and arm muscles during operantly conditioned response patterns , 1975, Experimental Brain Research.

[29]  W. Timberlake,et al.  Stimulus and response contingencies in the misbehavior of rats. , 1982, Journal of experimental psychology. Animal behavior processes.

[30]  John F. Kalaska,et al.  Spatial coding of movement: A hypothesis concerning the coding of movement direction by motor cortical populations , 1983 .

[31]  K. Horch,et al.  Effects of short-term training on sensory and motor function in severed nerves of long-term human amputees. , 2005, Journal of neurophysiology.

[32]  A. P. Georgopoulos,et al.  Movement parameters and neural activity in motor cortex and area 5. , 1994, Cerebral cortex.

[33]  Nicholas Hatsopoulos,et al.  Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. , 2004, Journal of neurophysiology.

[34]  J. S. Thomas,et al.  Operant conditioning of firing patterns in monkey cortical neurons , 1977, Experimental Neurology.

[35]  E. Fetz,et al.  Operant conditioning of isolated activity in specific muscles and precentral cells. , 1972, Brain research.

[36]  E. Fetz.  Operant Conditioning of Cortical Unit Activity , 1969, Science.

[37]  E. Fetz,et al.  Operant Conditioning of Specific Patterns of Neural and Muscular Activity , 1971, Science.

[38]  E E Fetz,et al.  Behavioral control of firing patterns of normal and abnormal neurons in chronic epileptic cortex. , 1974, Experimental neurology.

[39]  A B Schwartz,et al.  Arm trajectory and representation of movement processing in motor cortical activity , 2000, The European journal of neuroscience.

[40]  Miriam Zacksenhouse,et al.  Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface , 2005, The Journal of Neuroscience.

[41]  E. Schmidt,et al.  Fine control of operantly conditioned firing patterns of cortical neurons , 1978, Experimental Neurology.

[42]  D.M. Taylor,et al.  Information conveyed through brain-control: cursor versus robot , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[43]  Dawn M. Taylor,et al.  Signal acquisition and analysis for cortical control of neuroprosthetics , 2004, Current Opinion in Neurobiology.

[44]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[45]  L. Paninski,et al.  Spatiotemporal tuning of motor cortical neurons for hand position and velocity. , 2004, Journal of neurophysiology.

[46]  S I Helms Tillery,et al.  Training in Cortical Control of Neuroprosthetic Devices Improves Signal Extraction from Small Neuronal Ensembles , 2003, Reviews in the neurosciences.

[47]  Jiping He,et al.  Adaptive behavior of cortical neurons during a perturbed arm-reaching movement in a nonhuman primate. , 2004, Progress in brain research.

[48]  L. Paninski,et al.  Sequential movement representations based on correlated neuronal activity , 2003, Experimental Brain Research.

[49]  A. Schwartz,et al.  On the relationship between joint angular velocity and motor cortical discharge during reaching. , 2001, Journal of neurophysiology.