Cortical Modulations Increase in Early Sessions with Brain-Machine Interface

Background During planning and execution of reaching movements, the activity of cortical motor neurons is modulated by a diversity of motor, sensory, and cognitive signals. Brain-machine interfaces (BMIs) extract part of these modulations to directly control artificial actuators. However, cortical modulations that emerge in the novel context of operating the BMI are poorly understood. Methodology/Principal Findings Here we analyzed the changes in neuronal modulations that occurred in different cortical motor areas as monkeys learned to use a BMI to control reaching movements. Using spike-train analysis methods we demonstrate that the modulations of the firing-rates of cortical neurons increased abruptly after the monkeys started operating the BMI. Regression analysis revealed that these enhanced modulations were not correlated with the kinematics of the movement. The initial enhancement in firing rate modulations declined gradually with subsequent training in parallel with the improvement in behavioral performance. Conclusions/Significance We conclude that the enhanced modulations are related to computational tasks that are significant especially in novel motor contexts. Although the function and neuronal mechanism of the enhanced cortical modulations are open for further inquiries, we discuss their potential role in processing execution errors and representing corrective or explorative activity. These representations are expected to contribute to the formation of internal models of the external actuator and their decoding may facilitate BMI improvement.

[1]  U. Fano.  Ionization Yield of Radiations. II. The Fluctuations of the Number of Ions , 1947 .

[2]  N. L. Johnson,et al.  Multivariate Analysis , 1958, Nature.

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

[4]  J. T. Massey,et al.  Mental rotation of the neuronal population vector. , 1989, Science.

[5]  Michael I. Jordan,et al.  Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..

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

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

[8]  Tracy L. Faber,et al.  Role of posterior parietal cortex in the recalibration of visually guided reaching , 1996, Nature.

[9]  Per Christian Hansen,et al.  Rank-Deficient and Discrete Ill-Posed Problems , 1996 .

[10]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[11]  E. Bizzi,et al.  Neural basis of motor control and its cognitive implications , 1998, Trends in Cognitive Sciences.

[12]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[13]  S. Wise,et al.  Changes in motor cortical activity during visuomotor adaptation , 1998, Experimental Brain Research.

[14]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[15]  J R Flanagan,et al.  Composition and Decomposition of Internal Models in Motor Learning under Altered Kinematic and Dynamic Environments , 1999, The Journal of Neuroscience.

[16]  Alexander Borst,et al.  Information theory and neural coding , 1999, Nature Neuroscience.

[17]  H. Eng,et al.  Synthesis of β-Tubulin, Actin, and Other Proteins in Axons of Sympathetic Neurons in Compartmented Cultures , 1999, The Journal of Neuroscience.

[18]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

[19]  John W. Krakauer,et al.  Independent learning of internal models for kinematic and dynamic control of reaching , 1999, Nature Neuroscience.

[20]  A B Schwartz,et al.  Motor cortical representation of speed and direction during reaching. , 1999, Journal of neurophysiology.

[21]  A. Dickinson,et al.  Neuronal coding of prediction errors. , 2000, Annual review of neuroscience.

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

[23]  T. Ebner,et al.  Representation of accuracy in the dorsal premotor cortex , 2000, The European journal of neuroscience.

[24]  Joerg Blasius,et al.  Geometric Data Analysis , 2000 .

[25]  M. Laubach,et al.  Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task , 2022 .

[26]  Scott T. Grafton,et al.  Forward modeling allows feedback control for fast reaching movements , 2000, Trends in Cognitive Sciences.

[27]  Apostolos P Georgopoulos,et al.  Neural aspects of cognitive motor control , 2000, Current Opinion in Neurobiology.

[28]  Miguel A. L. Nicolelis,et al.  Actions from thoughts , 2001, Nature.

[29]  Carolyn R. Mason,et al.  Central processes for the multiparametric control of arm movements in primates , 2001, Current Opinion in Neurobiology.

[30]  E. Bizzi,et al.  Neuronal Correlates of Motor Performance and Motor Learning in the Primary Motor Cortex of Monkeys Adapting to an External Force Field , 2001, Neuron.

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

[32]  Rieko Osu,et al.  Composition and decomposition learning of reaching movements under altered environments: An examination of the multiplicity of internal models , 2002, Systems and Computers in Japan.

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

[34]  E. Vaadia,et al.  Preparatory activity in motor cortex reflects learning of local visuomotor skills , 2003, Nature Neuroscience.

[35]  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.

[36]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.

[37]  W. Hines,et al.  A multivariate solution for cyclic data, applied in modelling locomotor forces , 1987, Biological Cybernetics.

[38]  J. Tanji,et al.  Functional specialization in dorsal and ventral premotor areas. , 2004, Progress in brain research.

[39]  Christopher L Passaglia,et al.  Impact of noise on retinal coding of visual signals. , 2004, Journal of neurophysiology.

[40]  Don H. Johnson,et al.  Point process models of single-neuron discharges , 1996, Journal of Computational Neuroscience.

[41]  E. Bizzi,et al.  Neuronal activity in the supplementary motor area of monkeys adapting to a new dynamic environment. , 2004, Journal of neurophysiology.

[42]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[43]  Yasmin L. Hashambhoy,et al.  Neural Correlates of Reach Errors , 2005, The Journal of Neuroscience.

[44]  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.

[45]  Brigitte Le Roux,et al.  Geometric Data Analysis , 2005 .

[46]  P. van Donkelaar,et al.  The Human Dorsal Premotor Cortex Generates On-Line Error Corrections during Sensorimotor Adaptation , 2006, The Journal of Neuroscience.