Response of primate joint afferent neurons to mechanical stimulation of knee joint

1977 ◽  
Vol 40 (1) ◽  
pp. 1-8 ◽  
Author(s):  
P. Grigg ◽  
B. J. Greenspan
Keyword(s):  
Knee Joint ◽  
Dorsal Root ◽  
Joint Angle ◽  
Afferent Neurons ◽  
Time Constants ◽  
Joint Afferents ◽  
Stretch Receptors ◽  
Angular Displacements ◽  
Gastrocnemius Muscles ◽  
Stimulation Of

1. One hundred thirty-eight knee joint afferents from posterior articular nerve (PAN), in primates, were recorded in dorsal root filaments. Responses of afferents were studied in relation to both passive manipulations of the knee and active contractions of quadriceps, semimembranosus, and gastrocnemius muscles. 2. When the knee was passively rotated, most neurons discharged only when extreme angular displacements were achieved. Response of neurons responding to passive extensions was linearly related to the torque applied to the knee. With maintained extensions, discharge in extension neurons adapted slowly. Some of the time constants of adaptation were similar to those for simultaneously recorded torque relaxation. 3. Contractions of quadriceps, semimembranosus, or gastrocnemius muscles could activate many neurons in the absence of changes in joint angle. For quadriceps-activated neurons, rather high torques (mean = 2,450 g with cm) were required. 4. The results support the hypothesis that joint afferents function as capsullar stretch receptors, responding to those mechanical events which result in loading of the capsule.

Mechanical sensitivity of group III and IV afferents from posterior articular nerve in normal and inflamed cat knee

1986 ◽  
Vol 55 (4) ◽  
pp. 635-643 ◽  
Author(s):  
P. Grigg ◽  
H. G. Schaible ◽  
R. F. Schmidt
Keyword(s):  
Sciatic Nerve ◽  
Knee Joint ◽  
Conduction Velocity ◽  
Mechanical Stimulation ◽  
Dorsal Root ◽  
Receptive Fields ◽  
Group Iv ◽  
Mechanical Sensitivity ◽  
Group Iii ◽  
Stimulation Of

Recordings were performed from sciatic nerve or dorsal root filaments in 28 cats to study single group III (conduction velocity 2.5-20 m/s) and group IV (conduction velocity less than 2.5 m/s) units supplying the knee joint via the posterior articular nerve (PAN). In seven of these cats the knee joint had been inflamed artificially. Recordings from sciatic nerve filaments revealed responses to local mechanical stimulation of the joint in only 3 of 41 group IV units and in 12 of 18 group III units from the normal joint. In the inflamed joint 14 of 36 group IV units and 24 of 36 group III units were excited with local mechanical stimulation. In recordings from dorsal root filaments (normal joint) 4 of 11 group IV units and 7 of 13 group III units were activated by stimulating the joint locally. In the normal joint four group IV units (recorded from dorsal root filaments) responded only to rotations against the resistance of the tissue, whereas the majority of the fibers did not respond even to forceful movements. Group III units with local mechanosensitivity in the normal joint reacted strongly or weakly to movements in the working range of the joint or only to movements against resistance of the tissue. In the inflamed joint, group IV fibers (recorded in sciatic nerve filaments) with detectable receptive fields responded strongly to gentle movements or only to movements against resistance of tissue. Some did not react to movements. Group III units reacted strongly or weakly to gentle movements or only to movements against resistance of the tissue.(ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals OP0083 DORSAL ROOT GANGLIA INFILTRATING MACROPHAGES MAINTAIN OSTEOARTHRITIS PAIN

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 55.2-56
Author(s):  
R. Raoof ◽  
C. Martin ◽  
H. De Visser ◽  
J. Prado ◽  
S. Versteeg ◽  
...  
Keyword(s):  
Knee Joint ◽  
Dorsal Root Ganglia ◽  
Immune Cells ◽  
Dorsal Root ◽  
Weight Bearing ◽  
Joint Damage ◽  
Osteoarthritis Pain ◽  
Knee Pathology ◽  
Femoral Condyles ◽  
Over Time

Background:Pain is a major debilitating symptom of knee osteoarthritis (OA). However, the extent of joint damage in OA does not correlate well with the severity of pain. The mechanisms that govern OA pain are poorly understood. Immune cells infiltrating nervous tissue may contribute to pain maintenance.Objectives:Here we investigated the role of macrophages in the initiation and maintenance of OA pain.Methods:Knee joint damage was induced by an unilateral injection of mono-iodoacetate (MIA) or after application of a groove at the femoral condyles of rats fed on high fat diet. Pain-like behaviors were followed over time using von Frey test and dynamic weight bearing. Joint damage was assessed by histology. Dorsal root ganglia (DRG) infiltrating immune cells were assessed over time using flow cytometry. To deplete monocytes and macrophages, Lysmcrex Csfr1-Stop-DTR were injected intrathecal or systemically with diptheria toxin (DT).Results:Intraarticular monoiodoacetate injection induced OA and signs of persistent pain, such as mechanical hyperalgesia and deficits in weight bearing. The persisting pain-like behaviors were associated with accumulation of F4/80+macrophages with an M1-like phenotype in the lumbar DRG appearing from 1 week after MIA injection, and that persisted till at least 4 weeks after MIA injection. Macrophages infiltrated DRG were also observed in the rat groove model of OA, 12 weeks after application of a groove at the femoral condyles. Systemic or local depletion of DRG macrophages during established MIA-induced OA completely ablated signs of pain, without affecting MIA-induced knee pathology. Intriguingly when monocytes/macrophages were depleted prior to induction of osteoarthritis, pain-like behaviors still developed, however these pain-like behaviors did not persist over time.In vitro,sensory neurons innervating the affected OA joint programmed macrophages into a M1 phenotype. Local repolarization of M1-like DRG macrophages towards M2 by intrathecal injection of M2 macrophages or anti-inflammatory cytokines resolved persistent OA-induced pain.Conclusion:Overall we show that macrophages infiltrate the DRG after knee damage and acquire a M1-like phenotype and maintain pain independent of the lesions in the knee joint. DRG-infiltrating macrophages are not required for induction of OA pain. Reprogramming M1-like DRG-infiltrating macrophages may represent a potential strategy to treat OA pain.Acknowledgments:This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreements No 814244 and No 642720. Dutch Arthritis SocietyDisclosure of Interests:Ramin Raoof: None declared, Christian Martin: None declared, Huub de Visser: None declared, Judith Prado: None declared, Sabine Versteeg: None declared, Anne Heinemans: None declared, Simon Mastbergen: None declared, Floris Lafeber Shareholder of: Co-founder and shareholder of ArthroSave BV, Niels Eijkelkamp: None declared

scholarly journals The Effects of External Loads and Muscle Forces on the Knee Joint Ligaments during Walking: A Musculoskeletal Model Study

2021 ◽  
Vol 11 (5) ◽  
pp. 2356
Author(s):  
Carlo Albino Frigo ◽  
Lucia Donno
Keyword(s):  
Knee Joint ◽  
Cruciate Ligament ◽  
Musculoskeletal Model ◽  
Optimization Approach ◽  
Muscle Forces ◽  
Collateral Ligaments ◽  
Flexion Extension ◽  
Lateral Collateral Ligaments ◽  
Gastrocnemius Muscles ◽  
External Loads

A musculoskeletal model was developed to analyze the tensions of the knee joint ligaments during walking and to understand how they change with changes in the muscle forces. The model included the femur, tibia, patella and all components of cruciate and collateral ligaments, quadriceps, hamstrings and gastrocnemius muscles. Inputs to the model were the muscle forces, estimated by a static optimization approach, the external loads (ground reaction forces and moments) and the knee flexion/extension movement corresponding to natural walking. The remaining rotational and translational movements were obtained as a result of the dynamic equilibrium of forces. The validation of the model was done by comparing our results with literature data. Several simulations were carried out by sequentially removing the forces of the different muscle groups. Deactivation of the quadriceps produced a decrease of tension in the anterior cruciate ligament (ACL) and an increase in the posterior cruciate ligament (PCL). By removing the hamstrings, the tension of ACL increased at the late swing phase, while the PCL force dropped to zero. Specific effects were observed also at the medial and lateral collateral ligaments. The removal of gastrocnemius muscles produced an increase of tension only on PCL and lateral collateral ligaments. These results demonstrate how musculoskeletal models can contribute to knowledge about complex biomechanical systems as the knee joint.

scholarly journals Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4966
Author(s):  
Xunju Ma ◽  
Yali Liu ◽  
Qiuzhi Song ◽  
Can Wang
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Surface Electromyography ◽  
Joint Angle ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Knee Joint Angle ◽  
Long Short Term Memory

Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.

scholarly journals Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

2019 ◽  
Vol 6 ◽  
pp. 205566831986854 ◽  
Author(s):  
Rob Argent ◽  
Sean Drummond ◽  
Alexandria Remus ◽  
Martin O’Reilly ◽  
Brian Caulfield
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Joint Angle ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Reference Standard ◽  
Inertial Measurement ◽  
Knee Joint Angle ◽  
Single Sensor

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

Stimulation of Dorsal Root Ganglia for the Management of Complex Regional Pain Syndrome: A Prospective Case Series

Pain Practice ◽  
2014 ◽  
Vol 15 (3) ◽  
pp. 208-216 ◽  
Author(s):  
Jean-Pierre Van Buyten ◽  
Iris Smet ◽  
Liong Liem ◽  
Marc Russo ◽  
Frank Huygen
Keyword(s):  
Complex Regional Pain Syndrome ◽  
Dorsal Root Ganglia ◽  
Dorsal Root ◽  
Case Series ◽  
Pain Syndrome ◽  
Prospective Case Series ◽  
Stimulation Of

BIOMECHANICAL ANALYSIS OF THE STANDING LONG JUMP

2003 ◽  
Vol 15 (05) ◽  
pp. 186-192 ◽  
Author(s):  
WEN-LAN WU ◽  
JIA-HROUNG WU ◽  
HWAI-TING LIN ◽  
GWO-JAW WANG
Keyword(s):  
Knee Joint ◽  
Motion Analysis ◽  
Joint Angle ◽  
Three Dimensional ◽  
The Body ◽  
Initial Angle ◽  
Knee Joint Angle ◽  
Standing Long Jump ◽  
Long Jump ◽  
Arm Motion

The purposes of the present study were to (1) investigate the effects of the arm movement and initial knee joint angle employed in standing long jump by the ground reaction force analysis and three-dimensional motion analysis; and (2) investigate how the jump performance of the female gender related to the body configuration. Thirty-four healthy adult females performed standing long jump on a force platform with full effort. Body segment and joint angles were analyzed by three-dimensional motion analysis system. Using kinetic and kinematic data, the trajectories on mass center of body, knee joint angle, magnitude of peak takeoff force, and impulse generation in preparing phase were calculated. Average standing long jump performances with free arm motion were +1.5 times above performance with restricted arm motion in both knee initial angles. The performances with knee 90° initial flexion were +1.2 times above performance with knee 45° initial flexion in free and restricted arm motions. Judging by trajectories of the center mass of body (COM), free arm motion improves jump distance by anterior displacement of the COM in starting position. The takeoff velocity with 90° knee initial angle was as much as 11% higher than in with 45° knee initial angle. However, the takeoff angles on the COM trajectory showed no significant differences between each other. It was found that starting jump from 90° bend knee relatively extended the time that the force is applied by the leg muscles. To compare the body configurations and the jumping scores, there were no significant correlations between jump scores and anthropometry data. The greater muscle mass or longer leg did not correlated well with the superior jumping performance.

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