Neural Substrates of Muscle Co-contraction during Dynamic Motor Adaptation

Synaptic inputs to motor neurons underlying muscle coactivation for functionally different tasks have different spectral characteristics

The central nervous system (CNS) may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of cocontraction, which does not contribute to effective torque generation but allows to modulate joints' mechanical stiffness. Although it has been suggested that the generation of force and the modulation of stiffness rely on separate pathways, a characterization of the differences between the synaptic inputs to motor neurons (MNs) underlying these tasks is still missing. In this study, participants coactivated the same pair of upper-limb muscles, i.e., the biceps brachii and the triceps brachii, to perform two functionally different tasks: limb stiffness modulation or endpoint force generation. Spike trains of MNs were identified through decomposition of high-density electromyograms (EMGs) collected from the two muscles. Cross-correlogram showed a higher synchronization between MNs recruited to modulate stiffness, whereas cross-muscle coherence analysis revealed peaks in the β-band, which is commonly ascribed to a cortical origin. These peaks did not appear during the coactivation for force generation, thus suggesting separate cortical inputs for stiffness modulation. Moreover, a within-muscle coherence analysis identified two subsets of MNs that were selectively recruited to generate force or regulate stiffness. This study is the first to highlight different characteristics, and probable different neural origins, of the synaptic inputs driving a pair of muscles under different functional conditions. We suggest that stiffness modulation is driven by cortical inputs that project to a separate set of MNs, supporting the existence of a separate pathway underlying the control of stiffness.NEW & NOTEWORTHY The characterization of the pathways underlying force generation or stiffness modulation are still unknown. In this study, we demonstrated that the common input to motor neurons of antagonist muscles shows a high-frequency component when muscles are coactivated to modulate stiffness but not to generate force. Our results provide novel insights on the neural strategies for the recruitment of multiple muscles by identifying specific spectral characteristics of the synaptic inputs underlying functionally different tasks.

Changes in co-contraction magnitude during functional tasks following anterior cruciate ligament reconstruction: A systematic review

BACKGROUND

Anterior cruciate ligament reconstruction (ACLR) is a common orthopedic surgery procedure whose incidence has increased over the past few decades. Nevertheless, it is believed that neuromuscular control remains altered from the early stages after ACLR to later years. Therefore, the aim of this study was to systematically evaluate the magnitude of co-contraction during functional tasks in subjects with unilateral ACLR.

METHODS

A systematic review design was followed. The search strategy was conducted in PubMed, Scopus, EBSCO, PEDro, Cochrane Library, and Web of Science databases from inception to March 2024. The inclusion criteria involved studies using electromyography (EMG) data to calculate muscle pair activation via the co-contraction index (CCI) in ACLR individuals during functional tasks. The Preferred Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and study quality was evaluated using National Institutes of Health (NIH) Study Quality Assessment Tools.

RESULTS

The search strategy found a total of 792 studies, of which 15 were included in this systematic review after reviewing the eligibility criteria. The magnitude of co-contraction was assessed in a total of 433 ACLR individuals and 206 controls during functional tasks such as hop, drop-land, step-up/step-down, and gait. Overall, approximately 79.6% of individuals who had undergone ACLR exhibited increased levels of co-contraction magnitude in the ACLR limb, while 8.5% showed low co-contraction levels.

CONCLUSIONS

The findings of the review suggest that, during functional tasks, most individuals who have undergone ACLR exhibit changes of co-contraction magnitude in the involved limb.

Biomechanical effects of muscle loading on early healing of femoral stem fractures: a combined musculoskeletal dynamics and finite element approach

Femoral stem fractures (FST) are often accompanied by muscle injuries, however, what muscle injuries affect fracture healing and to what extent is unknown. The purpose of this study was to analyze the extent to which different muscles affect FST healing through a combined musculoskeletal dynamics and finite element approach. Modeling the lower extremity musculoskeletal system for 12 different muscle comprehensives. Muscle and joint reaction forces on the femur were calculated and these data were used as boundary conditions input to the FSTs model to predict the degree of muscle influence on fracture healing. Finally, we will investigate the extent to which muscle influences FST healing during knee flexion. Muscle and joint forces are highly dependent on joint motion and have a significant biomechanical influence on interfragmentary strain (IFS) healing. The psoas major (PM), gastrocnemius lateralis (GL) and gastrocnemius medialis (GM) muscles play a major role in standing, with GM > PM > GL, whereas the gluteus medius posterior (GMP), vastus intermedius (VI), vastus medialis (VM), vastus lateralis superior (VLS), and adductor magnus distalis (AMD) muscles play a major role in knee flexion, with VLS > VM > VI > AMD > GMP. Mechanical stimulus-controlled healing can be facilitated when the knee joint is flexed less than 20°. Different muscles exert varying degrees of influence on the healing of fractures. Therefore, comprehending the impact of particular muscles on fracture site tissue FST healing can aid orthopedic surgeons in formulating improved surgical and rehabilitation strategies.

Upper Limbs Muscle Co-Contraction Changes Correlate With The Physical Motor Impairments in CMT

Background

Subjects with Charcot-Marie-Tooth (CMT) disease show hands impairment which is a relevant problem affecting the quality of life. This symptom is related to muscle weakness and reduced motor coordination of the upper limb. However, most studies focus on lower limb impairment, therefore the investigation of upper limb disability is necessary to identify biomarkers able to monitor disease-specific features and to tailor rehabilitation.

Objective

This study aimed at characterizing upper limb muscle co-contraction using the co-contraction index (CCI) in CMT population.

Methods

Upper limb kinematic and electromyography (EMG) data were collected from fourteen CMT subjects (6-CMT1A and 8-CMT1X) during motor tasks typical of daily living activities. Rudolph's CCI was used to quantify muscle co-contraction of four muscle pairs acting on shoulder, elbow and wrist. All CMT subjects underwent clinical examination. Thirteen healthy subjects served as the normative reference (HC).

Results

CMT1X and CMT1A showed a significant reduction in CCI for distal and proximal muscle pairs compared to HC. Furthermore, CMT1A showed greater values of CCI compared to CMT1X mainly for the axial and axial-to-proximal muscle pairs. Movement speed and smoothness were not altered compared to HC. In addition, EMG metrics showed moderate-to-strong significant correlations with clinical outcomes.

Conclusions

CCI was able to quantify disease-specific deficits with respect to the normative reference, highlighting motor control alterations even before motor output impairment. CCI was also sensitive in detecting CMT subtypes-based differences and adopted compensatory strategies. Our findings suggest that CCI can be an outcome measure for CMT disease monitoring and interventional studies.

Dorsomedial frontal cortex damage impairs error-based, but not reinforcement-based motor learning in humans

We adapt our movements to new and changing environments through multiple processes. Sensory error-based learning counteracts environmental perturbations that affect the sensory consequences of movements. Sensory errors also cause the upregulation of reflexes and muscle co-contraction. Reinforcement-based learning enhances the selection of movements that produce rewarding outcomes. Although some findings have identified dissociable neural substrates of sensory error- and reinforcement-based learning, correlative methods have implicated dorsomedial frontal cortex in both. Here, we tested the causal contributions of dorsomedial frontal to adaptive motor control, studying people with chronic damage to this region. Seven human participants with focal brain lesions affecting the dorsomedial frontal and 20 controls performed a battery of arm movement tasks. Three experiments tested: (i) the upregulation of visuomotor reflexes and muscle co-contraction in response to unpredictable mechanical perturbations, (ii) sensory error-based learning in which participants learned to compensate predictively for mechanical force-field perturbations, and (iii) reinforcement-based motor learning based on binary feedback in the absence of sensory error feedback. Participants with dorsomedial frontal damage were impaired in the early stages of force field adaptation, but performed similarly to controls in all other measures. These results provide evidence for a specific and selective causal role for the dorsomedial frontal in sensory error-based learning.

Human reaching control in dynamic environments

Closed-loop models of movement control have attracted growing interest in how the nervous system transforms sensory information into motor commands, and several brain structures have been identified as neural substrates for these computational operations. Recently, several studies have focused on how these models need to be updated when environmental parameters change. Current evidence suggests that when the task changes, rapid control updates enable flexible modifications of current actions and online decisions. At the same time, when movement dynamics change, humans use different strategies based on a combination of adaptation and modulation of controller sensitivity to exogenous perturbations (robust control). This review proposes a unified framework to capture these results based on online estimation of model parameters with dynamic updates in control. The reviewed studies also identify the time scales of associated behavioral mechanisms to guide future research on the neural basis of movement control.

Ankle Strategies for Step-Aside Movement during Straight Walking

The step-aside movement, also known as the dodging step, is a common maneuver for avoiding obstacles while walking. However, differences in neural control mechanisms and ankle strategies compared to straight walking can pose a risk of falling. This study aimed to examine the differences in tibialis anterior (TA), peroneus longus (PL), and soleus (SOL) muscle contractions, foot center of pressure (CoP) displacement, and ground reaction force (GRF) generation between step-aside movement and straight walking to understand the mechanism behind step-aside movement during walking. Twenty healthy young male participants performed straight walking and step-aside movements at comfortable walking speeds. The participants' muscle contractions, CoP displacement, and GRF were measured. The results show significant greater bilateral ankle muscle contractions during the push and loading phases of step-aside movement than during straight walking. Moreover, the CoP displacement, GRF generation mechanism, and timing differed from those observed during straight walking. These findings provide valuable insights for rehabilitation professionals in the development of clinical decisions for populations at a risk of falls and lacking gait stability.

The 'Postural Rhythm' of the Ground Reaction Force during Upright Stance and Its Conversion to Body Sway-The Effect of Vision, Support Surface and Adaptation to Repeated Trials

The ground reaction force (GRF) recorded by a platform when a person stands upright lies at the interface between the neural networks controlling stance and the body sway deduced from centre of pressure (CoP) displacement. It can be decomposed into vertical (VGRF) and horizontal (HGRF) vectors. Few studies have addressed the modulation of the GRFs by the sensory conditions and their relationship with body sway. We reconsidered the features of the GRFs oscillations in healthy young subjects (n = 24) standing for 90 s, with the aim of characterising the possible effects of vision, support surface and adaptation to repeated trials, and the correspondence between HGRF and CoP time-series. We compared the frequency spectra of these variables with eyes open or closed on solid support surface (EOS, ECS) and on foam (EOF, ECF). All stance trials were repeated in a sequence of eight. Conditions were randomised across different days. The oscillations of the VGRF, HGRF and CoP differed between each other, as per the dominant frequency of their spectra (around 4 Hz, 0.8 Hz and <0.4 Hz, respectively) featuring a low-pass filter effect from VGRF to HGRF to CoP. GRF frequencies hardly changed as a function of the experimental conditions, including adaptation. CoP frequencies diminished to <0.2 Hz when vision was available on hard support surface. Amplitudes of both GRFs and CoP oscillations decreased in the order ECF > EOF > ECS ≈ EOS. Adaptation had no effect except in ECF condition. Specific rhythms of the GRFs do not transfer to the CoP frequency, whereas the magnitude of the forces acting on the ground ultimately determines body sway. The discrepancies in the time-series of the HGRF and CoP oscillations confirm that the body's oscillation mode cannot be dictated by the inverted pendulum model in any experimental conditions. The findings emphasise the robustness of the VGRF "postural rhythm" and its correspondence with the cortical theta rhythm, shed new insight on current principles of balance control and on understanding of upright stance in healthy and elderly people as well as on injury prevention and rehabilitation.

Changes in Resting State Functional Connectivity Associated with Dynamic Adaptation of Wrist Movements

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure. Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measured via changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on this timescale, nor has its relationship to adaptative behavior been established. We used a functional magnetic resonance imaging (fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wrist movements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during a motor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networks in three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioral retention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance, and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFC increased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network. Increases within the cortico-cerebellar network were specific to dynamic adaptation, as they were associated with behavioral measures of adaptation and retention, indicating that this network has a functional role in consolidation. Instead, decreases in rsFC within the cortical sensorimotor network were associated with motor control processes independent from adaptation and retention.SIGNIFICANCE STATEMENT Motor memory consolidation processes have been studied via functional magnetic resonance imaging (fMRI) by analyzing changes in resting state functional connectivity (rsFC) occurring more than 30 min after adaptation. However, it is unknown whether consolidation processes are detectable immediately (<15 min) following dynamic adaptation. We used an fMRI-compatible wrist robot to localize brain regions involved in dynamic adaptation in the cortico-thalamic-cerebellar (CTC) and cortical sensorimotor networks and quantified changes in rsFC within each network immediately after adaptation. Different patterns of change in rsFC were observed compared with studies conducted at longer latencies. Increases in rsFC in the cortico-cerebellar network were specific to adaptation and retention, while interhemispheric decreases in the cortical sensorimotor network were associated with alternate motor control processes but not with memory formation.

Treatment of spasticity

Spasticity is characterized by an enhanced size and reduced threshold for activation of stretch reflexes and is associated with "positive signs" such as clonus and spasms, as well as "negative features" such as paresis and a loss of automatic postural responses. Spasticity develops over time after a lesion and can be associated with reduced speed of movement, cocontraction, abnormal synergies, and pain. Spasticity is caused by a combination of damage to descending tracts, reductions in inhibitory activity within spinal cord circuits, and adaptive changes within motoneurons. Increased tone, hypertonia, can also be caused by changes in passive stiffness due to, for example, increase in connective tissue and reduction in muscle fascicle length. Understanding the cause of hypertonia is important for determining the management strategy as nonneural, passive causes of stiffness will be more amenable to physical rather than pharmacological interventions. The management of spasticity is determined by the views and goals of the patient, family, and carers, which should be integral to the multidisciplinary assessment. An assessment, and treatment, of trigger factors such as infection and skin breakdown should be made especially in people with a recent change in tone. The choice of management strategies for an individual will vary depending on the severity of spasticity, the distribution of spasticity (i.e., whether it affects multiple muscle groups or is more prominent in one or two groups), the type of lesion, and the potential for recovery. Management options include physical therapy, oral agents; focal therapies such as botulinum injections; and peripheral nerve blocks. Intrathecal baclofen can lead to a reduction in required oral antispasticity medications. When spasticity is severe intrathecal phenol may be an option. Surgical interventions, largely used in the pediatric population, include muscle transfers and lengthening and selective dorsal root rhizotomy.

Muscle Co-Contraction Detection in the Time-Frequency Domain

Background: Muscle co-contraction plays a significant role in motion control. Available detection methods typically only provide information in the time domain. The current investigation proposed a novel approach for muscle co-contraction detection in the time–frequency domain, based on continuous wavelet transform (CWT). Methods: In the current study, the CWT-based cross-energy localization of two surface electromyographic (sEMG) signals in the time–frequency domain, i.e., the CWT coscalogram, was adopted for the first time to characterize muscular co-contraction activity. A CWT-based denoising procedure was applied for removing noise from the sEMG signals. Algorithm performances were checked on synthetic and real sEMG signals, stratified for signal-to-noise ratio (SNR), and then validated against an approach based on the acknowledged double-threshold statistical algorithm (DT). Results: The CWT approach provided an accurate prediction of co-contraction timing in simulated and real datasets, minimally affected by SNR variability. The novel contribution consisted of providing the frequency values of each muscle co-contraction detected in the time domain, allowing us to reveal a wide variability in the frequency content between subjects and within stride. Conclusions: The CWT approach represents a relevant improvement over state-of-the-art approaches that provide only a numerical co-contraction index or, at best, dynamic information in the time domain. The robustness of the methodology and the physiological reliability of the experimental results support the suitability of this approach for clinical applications.