Decoding neural activity to predict rat locomotion using intracortical and epidural arrays

OBJECTIVE Recovery of voluntary gait after spinal cord injury (SCI) requires the restoration of effective motor cortical commands, either by means of a mechanical connection to the limbs, or by restored functional connections to muscles. The latter approach might use functional electrical stimulation (FES), driven by cortical activity, to restore voluntary movements. Moreover, there is evidence that this peripheral stimulation, synchronized with patients' voluntary effort, can strengthen descending projections and recovery. As a step towards establishing such a cortically-controlled FES system for restoring function after SCI, we evaluate here the type and quantity of neural information needed to drive such a brain machine interface (BMI) in rats. We compared the accuracy of the predictions of hindlimb electromyograms (EMG) and kinematics using neural data from an intracortical array and a less-invasive epidural array. APPROACH Seven rats were trained to walk on a treadmill with a stable pattern. One group of rats (n  =  4) was implanted with intracortical arrays spanning the hindlimb sensorimotor cortex and EMG electrodes in the contralateral hindlimb. Another group (n  =  3) was implanted with epidural arrays implanted on the dura overlying hindlimb sensorimotor cortex. EMG, kinematics and neural data were simultaneously recorded during locomotion. EMGs and kinematics were decoded using linear and nonlinear methods from multiunit activity and field potentials. MAIN RESULTS Predictions of both kinematics and EMGs were effective when using either multiunit spiking or local field potentials (LFPs) recorded from intracortical arrays. Surprisingly, the signals from epidural arrays were essentially uninformative. Results from somatosensory evoked potentials (SSEPs) confirmed that these arrays recorded neural activity, corroborating our finding that this type of array is unlikely to provide useful information to guide an FES-BMI for rat walking. SIGNIFICANCE We believe that the accuracy of our decoders in predicting EMGs from multiunit spiking activity is sufficient to drive an FES-BMI. Our future goal is to use this rat model to evaluate the potential for cortically-controlled FES to be used to restore locomotion after SCI, as well as its further potential as a rehabilitative technology for improving general motor function.

[1]  M. Popovic,et al.  Functional electrical therapy: retraining grasping in spinal cord injury , 2006, Spinal Cord.

[2]  K. Ragnarsson.  Functional electrical stimulation after spinal cord injury: current use, therapeutic effects and future directions , 2008, Spinal Cord.

[3]  A. Schwartz,et al.  High-performance neuroprosthetic control by an individual with tetraplegia , 2013, The Lancet.

[4]  Christie K. Ferreira,et al.  Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study , 2011, The Lancet.

[5]  Hiroshi Yokoi,et al.  Motion classification using epidural electrodes for low-invasive brain-machine interface , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Steve I Perlmutter,et al.  Targeted, activity-dependent spinal stimulation produces long-lasting motor recovery in chronic cervical spinal cord injury , 2015, Proceedings of the National Academy of Sciences.

[7]  M. Popovic,et al.  Gait training regimen for incomplete spinal cord injury using functional electrical stimulation , 2006, Spinal Cord.

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

[9]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[10]  Francis R. Willett,et al.  Restoration of reaching and grasping in a person with tetraplegia through brain-controlled muscle stimulation: a proof-of-concept demonstration , 2017, The Lancet.

[11]  N. Birbaumer,et al.  Brain–computer interfaces for communication and rehabilitation , 2016, Nature Reviews Neurology.

[12]  Jessica P McCabe,et al.  Recovery of Coordinated Gait , 2011, Neurorehabilitation and neural repair.

[13]  Jay M. Bauman,et al.  Rules to limp by: joint compensation conserves limb function after peripheral nerve injury , 2013, Biology Letters.

[14]  M. Tresch,et al.  Design and evaluation of a chronic EMG multichannel detection system for long-term recordings of hindlimb muscles in behaving mice. , 2013, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[15]  Dustin J Tyler,et al.  Neural interfaces for somatosensory feedback: bringing life to a prosthesis. , 2015, Current opinion in neurology.

[16]  Arun Ramakrishnan,et al.  Multiple Types of Movement-Related Information Encoded in Hindlimb/Trunk Cortex in Rats and Potentially Available for Brain–Machine Interface Controls , 2009, IEEE Transactions on Biomedical Engineering.

[17]  T. Sinkjaer,et al.  Clinical evaluation of Functional Electrical Therapy in acute hemiplegic subjects. , 2003, Journal of rehabilitation research and development.

[18]  Susan J. Harkema,et al.  Motor recovery after activity-based training with spinal cord epidural stimulation in a chronic motor complete paraplegic , 2017, Scientific Reports.

[19]  Matthew C Tresch,et al.  Chronic electromyograms in treadmill running SOD1 mice reveal early changes in muscle activation , 2017, The Journal of physiology.

[20]  L. Miller,et al.  Restoration of grasp following paralysis through brain-controlled stimulation of muscles , 2012, Nature.

[21]  James W. Stinear,et al.  Contralesional paired associative stimulation increases paretic lower limb motor excitability post-stroke , 2008, Experimental Brain Research.

[22]  Monica A. Perez,et al.  Motor Recovery after Spinal Cord Injury Enhanced by Strengthening Corticospinal Synaptic Transmission , 2012, Current Biology.

[23]  Kim Lajoie,et al.  Cortical mechanisms involved in visuomotor coordination during precision walking , 2008, Brain Research Reviews.

[24]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[25]  Naotaka Fujii,et al.  Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques , 2012, Journal of neural engineering.

[26]  Jack DiGiovanna,et al.  Corticospinal neuroprostheses to restore locomotion after spinal cord injury , 2014, Neuroscience Research.

[27]  G M Davis,et al.  Benefits of FES gait in a spinal cord injured population , 2007, Spinal Cord.

[28]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[29]  Lee E Miller,et al.  Prediction of upper limb muscle activity from motor cortical discharge during reaching , 2007, Journal of neural engineering.

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

[31]  Nicholas P. Szrama,et al.  Decoding three-dimensional reaching movements using electrocorticographic signals in humans , 2016, Journal of neural engineering.

[32]  Arthur Prochazka,et al.  Control of urinary bladder function with devices: successes and failures. , 2006, Progress in brain research.

[33]  J. Wolpaw.  Brain-computer interfaces. , 2013, Handbook of clinical neurology.

[34]  Silvestro Micera,et al.  Brain-controlled modulation of spinal circuits improves recovery from spinal cord injury , 2018, Nature Communications.

[35]  Karen A. Moxon,et al.  Decoding Hindlimb Movement for a Brain Machine Interface after a Complete Spinal Transection , 2012, PloS one.

[36]  M. Sirota,et al.  The role of the motor cortex in the control of vigour of locomotor movements in the cat. , 1993, The Journal of physiology.

[37]  Eran Stark,et al.  Predicting Movement from Multiunit Activity , 2007, The Journal of Neuroscience.

[38]  Miguel A. L. Nicolelis,et al.  Extracting Kinematic Parameters for Monkey Bipedal Walking from Cortical Neuronal Ensemble Activity , 2009, Front. Integr. Neurosci..

[39]  Robert D Flint,et al.  Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control , 2016, The Journal of Neuroscience.

[40]  K. Kilgore,et al.  Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: a multicenter study. , 2001, Archives of physical medicine and rehabilitation.

[41]  François Chollet,et al.  Induction of Cortical Plastic Changes in Wrist Muscles by Paired Associative Stimulation in the Recovery Phase of Stroke Patients , 2009, Neurorehabilitation and neural repair.

[42]  Marc W. Slutzky,et al.  Continuous decoding of human grasp kinematics using epidural and subdural signals , 2017, Journal of neural engineering.

[43]  S. Micera,et al.  Decoding bipedal locomotion from the rat sensorimotor cortex , 2015, Journal of neural engineering.

[44]  Brendan Z. Allison,et al.  Brain-Computer Interfaces , 2010 .

[45]  S Grillner,et al.  Central pattern generators for locomotion, with special reference to vertebrates. , 1985, Annual review of neuroscience.

[46]  R. Hall,et al.  Organization of motor and somatosensory neocortex in the albino rat , 1974 .

[47]  Karim G. Oweiss.  Statistical signal processing for neuroscience and neurotechnology , 2010 .

[48]  B. Andrews,et al.  Improving limb flexion in FES gait using the flexion withdrawal response for the spinal cord injured person. , 1993, Journal of biomedical engineering.

[49]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[50]  L. Miller,et al.  Decoding the rat forelimb movement direction from epidural and intracortical field potentials , 2011, Journal of neural engineering.

[51]  J. Wolpaw,et al.  Spinal cord plasticity in acquisition and maintenance of motor skills , 2007, Acta physiologica.

[52]  Jamie L. Reed,et al.  Cortical Neuron Response Properties Are Related to Lesion Extent and Behavioral Recovery after Sensory Loss from Spinal Cord Injury in Monkeys , 2014, The Journal of Neuroscience.

[53]  E. Marsolais,et al.  Restoration of key grip and release in the C6 tetraplegic patient through functional electrical stimulation. , 1980 .

[54]  Karen A. Moxon,et al.  Restoration of Hindlimb Movements after Complete Spinal Cord Injury Using Brain-Controlled Functional Electrical Stimulation , 2017, Front. Neurosci..

[55]  F. Lacquaniti,et al.  Coordinate transformations in the control of cat posture. , 1994, Journal of neurophysiology.

[56]  S. Gandevia,et al.  Proprioceptive signals contribute to the sense of body ownership , 2011, The Journal of physiology.

[57]  Silvestro Micera,et al.  Engagement of the Rat Hindlimb Motor Cortex across Natural Locomotor Behaviors , 2016, The Journal of Neuroscience.

[58]  P. Peckham,et al.  Functional electrical stimulation for neuromuscular applications. , 2005, Annual review of biomedical engineering.

[59]  Dejan B Popovic,et al.  Restitution of reaching and grasping promoted by functional electrical therapy. , 2002, Artificial organs.

[60]  S. Solla,et al.  Toward the Restoration of Hand Use to a Paralyzed Monkey: Brain-Controlled Functional Electrical Stimulation of Forearm Muscles , 2009, PloS one.

[61]  S. McMahon,et al.  Neuromodulation in the restoration of function after spinal cord injury , 2018, The Lancet Neurology.

[62]  Nicholas V. Annetta,et al.  Restoring cortical control of functional movement in a human with quadriplegia , 2016, Nature.

[63]  G. Alon,et al.  Persons with C5 or C6 tetraplegia achieve selected functional gains using a neuroprosthesis. , 2003, Archives of physical medicine and rehabilitation.

[64]  S. Giszter,et al.  Adaptation to a Cortex-Controlled Robot Attached at the Pelvis and Engaged during Locomotion in Rats , 2011, The Journal of Neuroscience.

[65]  Barry D. Van Veen,et al.  Estimating cortical column sensory networks in rodents from micro-electrocorticograph (μECoG) recordings , 2017, NeuroImage.

[66]  A. Pavlovic,et al.  Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. , 2016, Journal of neurophysiology.