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Mirfathollahi A, Ghodrati MT, Shalchyan V, Daliri MR. Decoding locomotion speed and slope from local field potentials of rat motor cortex. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106961. [PMID: 35759821 DOI: 10.1016/j.cmpb.2022.106961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/02/2022] [Accepted: 06/14/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Local Field Potentials (LFPs) recorded from the primary motor cortex (M1) have been shown to be very informative for decoding movement parameters, and these signals can be used to decode forelimb kinematic and kinetic parameters accurately. Although locomotion is one of the most basic and important motor abilities of humans and animals, the potential of LFPs in decoding abstract hindlimb locomotor parameters has not been investigated. This study investigates the feasibility of decoding speed and slope of locomotion, as two important abstract parameters of walking, using the LFP signals. METHODS Rats were trained to walk smoothly on a treadmill with different speeds and slopes. The brain signals were recorded using the microwire arrays chronically implanted in the hindlimb area of M1 while rats walked on the treadmill. LFP channels were spatially filtered using optimal common spatial patterns to increase the discriminability of speeds and slopes of locomotion. Logarithmic wavelet band powers were extracted as basic features, and the best features were selected using the statistical dependency criterion before classification. RESULTS Using 5 s LFP trials, the average classification accuracies of four different speeds and seven different slopes reached 90.8% and 86.82%, respectively. The high-frequency LFP band (250-500 Hz) was the most informative band about these parameters and contributed more than other frequency bands in the final decoder model. CONCLUSIONS Our results show that the LFP signals in M1 accurately decode locomotion speed and slope, which can be considered as abstract walking parameters needed for designing long-term brain-computer interfaces for hindlimb locomotion control.
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Affiliation(s)
- Alavie Mirfathollahi
- Neuroscience and Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran; Institute for Cognitive Science Studies (ICSS), Tehran, Pardis 16583-44575, Iran
| | - Mohammad Taghi Ghodrati
- Neuroscience and Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran
| | - Vahid Shalchyan
- Neuroscience and Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran.
| | - Mohammad Reza Daliri
- Neuroscience and Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran
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Fathi Y, Erfanian A. Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network. Front Neurosci 2022; 16:801818. [PMID: 35401098 PMCID: PMC8990134 DOI: 10.3389/fnins.2022.801818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/02/2022] [Indexed: 11/13/2022] Open
Abstract
To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.
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Affiliation(s)
- Yaser Fathi
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
| | - Abbas Erfanian
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Abbas Erfanian,
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Hu D, Wang S, Li B, Liu H, He J. Spinal Cord Injury-Induced Changes in Encoding and Decoding of Bipedal Walking by Motor Cortical Ensembles. Brain Sci 2021; 11:brainsci11091193. [PMID: 34573213 PMCID: PMC8469283 DOI: 10.3390/brainsci11091193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 11/24/2022] Open
Abstract
Recent studies have shown that motor recovery following spinal cord injury (SCI) is task-specific. However, most consequential conclusions about locomotor functional recovery from SCI have been derived from quadrupedal locomotion paradigms. In this study, two monkeys were trained to perform a bipedal walking task, mimicking human walking, before and after T8 spinal cord hemisection. Importantly, there is no pharmacological therapy with nerve growth factor for monkeys after SCI; thus, in this study, the changes that occurred in the brain were spontaneous. The impairment of locomotion on the ipsilateral side was more severe than that on the contralateral side. We used information theory to analyze single-cell activity from the left primary motor cortex (M1), and results show that neuronal populations in the unilateral primary motor cortex gradually conveyed more information about the bilateral hindlimb muscle activities during the training of bipedal walking after SCI. We further demonstrated that, after SCI, progressively expanded information from the neuronal population reconstructed more accurate control of muscle activity. These results suggest that, after SCI, the unilateral primary motor cortex could gradually regain control of bilateral coordination and motor recovery and in turn enhance the performance of brain–machine interfaces.
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Affiliation(s)
- Dingyin Hu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100081, China;
- Correspondence:
| | - Shirong Wang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100081, China;
| | - Bo Li
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
| | - Honghao Liu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
| | - Jiping He
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.L.); (H.L.); (J.H.)
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing 100081, China;
- Center for Neural Interface Design, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 86287, USA
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Botzheim L, Laczko J, Torricelli D, Mravcsik M, Pons JL, Oliveira Barroso F. Effects of gravity and kinematic constraints on muscle synergies in arm cycling. J Neurophysiol 2021; 125:1367-1381. [PMID: 33534650 DOI: 10.1152/jn.00415.2020] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Arm cycling is a bimanual motor task used in medical rehabilitation and in sports training. Understanding how muscle coordination changes across different biomechanical constraints in arm cycling is a step toward improved rehabilitation approaches. This exploratory study aims to get new insights on motor control during arm cycling. To achieve our main goal, we used the muscle synergies analysis to test three hypotheses: 1) body position with respect to gravity (sitting and supine) has an effect on muscle synergies; 2) the movement size (crank length) has an effect on the synergistic behavior; 3) the bimanual cranking mode (asynchronous and synchronous) requires different synergistic control. Thirteen able-bodied volunteers performed arm cranking on a custom-made device with unconnected cranks, which allowed testing three different conditions: body position (sitting vs. supine), crank length (10 cm vs. 15 cm), and cranking mode (synchronous vs. asynchronous). For each of the eight possible combinations, subjects cycled for 30 s while electromyography of eight muscles (four from each arm) were recorded: biceps brachii, triceps brachii, anterior deltoid, and posterior deltoid. Muscle synergies in this eight-dimensional muscle space were extracted by nonnegative matrix factorization. Four synergies accounted for over 90% of muscle activation variances in all conditions. Results showed that synergies were affected by body position and cranking mode but practically unaffected by movement size. These results suggest that the central nervous system may employ different motor control strategies in response to external constraints such as cranking mode and body position during arm cycling.NEW & NOTEWORTHY Recent studies analyzed muscle synergies in lower limb cycling. Here, we examine upper limb cycling and specifically the effect of body position with respect to gravity, movement size, and cranking mode on muscle coordination during arm cranking tasks. We show that altered body position and cranking mode affects modular organization of muscle activities. To our knowledge, this is the first study assessing motor control through muscle synergies framework during upper limb cycling with different constraints.
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Affiliation(s)
- Lilla Botzheim
- Department of Information Technology and Biorobotics, Institute of Mathematics and Informatics, Faculty of Sciences, University of Pecs, Pecs, Hungary.,Neurorehabilitation and Motor Control Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Jozsef Laczko
- Department of Information Technology and Biorobotics, Institute of Mathematics and Informatics, Faculty of Sciences, University of Pecs, Pecs, Hungary.,Neurorehabilitation and Motor Control Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Diego Torricelli
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain
| | - Mariann Mravcsik
- Department of Information Technology and Biorobotics, Institute of Mathematics and Informatics, Faculty of Sciences, University of Pecs, Pecs, Hungary.,Neurorehabilitation and Motor Control Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Jose L Pons
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain.,Legs & Walking AbilityLab, Shirley Ryan AbilityLab, Chicago, Illinois.,Department of Biomedical Engineering and Mechanical Engineering, McCormick School of Engineering, Northwestern University, Chicago, Illinois.,Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Filipe Oliveira Barroso
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain
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Fathi Y, Erfanian A. Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion. J Neural Eng 2021; 18. [PMID: 33395669 DOI: 10.1088/1741-2552/abd82a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 01/04/2021] [Indexed: 11/11/2022]
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 for decoding the hindlimb kinematics during walking on the 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 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. But, 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 for control of the movement.
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Affiliation(s)
- Yaser Fathi
- Biomedical Engineering, Iran University of Science and Technology, Narmak, Resalat Square, Hengam Street, Iran University of Science and Technology, Tehran, Tehran, 16844, Iran (the Islamic Republic of)
| | - Abbas Erfanian
- Biomedical Engineering, Iran University of Science & Technology, Hengam Street, Narmak, Tehran 16844, Iran, Tehran, 16844, IRAN, ISLAMIC REPUBLIC OF
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