Decoding bipedal locomotion from the rat sensorimotor cortex.
OBJECTIVE
Decoding forelimb movements from the firing activity of cortical neurons has been interfaced with robotic and prosthetic systems to replace lost upper limb functions in humans. Despite the potential of this approach to improve locomotion and facilitate gait rehabilitation, decoding lower limb movement from the motor cortex has received comparatively little attention. Here, we performed experiments to identify the type and amount of information that can be decoded from neuronal ensemble activity in the hindlimb area of the rat motor cortex during bipedal locomotor tasks.


METHODS
Rats were trained to stand, step on a treadmill, walk overground and climb staircases in a bipedal posture. To impose this gait, the rats were secured in a robotic interface that provided support against the direction of gravity and in the mediolateral direction, but behaved transparently in the forward direction. After completion of training, rats were chronically implanted with a micro-wire array spanning the left hindlimb motor cortex to record single and multi-unit activity, and bipolar electrodes into 10 muscles of the right hindlimb to monitor electromyographic signals. Whole-body kinematics, muscle activity, and neural signals were simultaneously recorded during execution of the trained tasks over multiple days of testing. Hindlimb kinematics, muscle activity, gait phases, and locomotor tasks were decoded using offline classification algorithms.


RESULTS
We found that the stance and swing phases of gait and the locomotor tasks were detected with accuracies as robust as 90% in all rats. Decoded hindlimb kinematics and muscle activity exhibited a larger variability across rats and tasks.


CONCLUSIONS
Our study shows that the rodent motor cortex contains useful information for lower limb neuroprosthetic development. However, brain-machine interfaces estimating gait phases or locomotor behaviors, instead of continuous variables such as limb joint positions or speeds, are likely to provide more robust control strategies for the design of such neuroprostheses.
The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. Bertarelli Foundation Chair in Translational Neuralengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
J
J
Rigosa
A
A
Panarese
N
N
Dominici
L
L
Friedli
R
R
van den Brand
J
J
Carpaneto
J
J
DiGiovanna
G
G
Courtine
S
S
Micera
D000465:Algorithms; D000818:Animals; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology; D005260:Female; D005684:Gait / Q000502:physiology*; D006614:Hindlimb / Q000502:physiology*; D008124:Locomotion / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D011917:Rats, Inbred Lew; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D066191:Sensorimotor Cortex / Q000502:physiology*