Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion. (1st March 2021)

• Record Type: Journal Article
• Authors: Fathi, Yaser; Erfanian, Abbas
• Is Part Of: Journal of neural engineering. Volume 18:Number 2(2021)
• Publication Date: 2021-03-01
• Subjects: neural decoding -- hindlimb kinematics -- ascending tracts -- descending tracts -- spinal cord -- dorsal column -- lateral column; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/abd82a
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord to decode the hindlimb kinematics during walking on a treadmill. Approach . Two different experimental paradigms (i.e. active and passive) were used in the current study. During active experiments, five cats were trained to walk bipedally while their hands were kept on the front frame of the treadmill for balance or to walk quadrupedally. During passive experiments, the limb was passively moved by the experimenter. Local field potential (LFP) activity was recorded using a microwire array implanted in the dorsal column (DC) and lateral column (LC) of the L3–L4 spinal segments. The amplitude and frequency components of the LFP formed the feature set, and the elastic net regularization was used to decode the hindlimb joint angles. Main results. The results show that there is no significant difference between the information content of the signals recorded from the DC and LC regions during walking on the treadmill, but the information content of the DC is significantly higher than that of the LC during passively applied movement of the hindlimb in the anesthetized cats. Moreover, the decoding performance obtained using the recorded signals from the DC is comparable with that from the LC during locomotion. However, the decoding performance obtained using the recording channels in the DC is significantly better than that obtained using the signals recorded from the LC. The long-term analysis shows that robust decoding performance can be achieved over 2–3 months without a significant decrease in performance. Significance . This work presents a promising approach to developing a natural and robust motor neuroprosthesis device using descending neural signals to execute the movement and ascending neural signals as the feedback information to control the movement.