Lower extremity long-latency reflexes differentiate walking function after stroke

Banks, Caitlin L.; Little, Virginia L.; Walker, Eric R.; Patten, Carolynn

doi:10.1007/s00221-019-05614-y

Cited by 5 publications

(6 citation statements)

References 49 publications

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“…In the absence of bilateral motor cortical reactivity and the ability to modulate GABAergic network connectivity that is important for normal motor activity, 17,18,55 severely impaired stroke survivors could have potentially engaged subcortical structures to a higher degree during volitional lower limb muscle activation. 30 In older adult populations, who demonstrate heightened reliance on the cerebral cortex for balance and walking compared to young adults, 49,51,52 the compromised contribution of the motor cortex to lower limb motor control after stroke may be particularly detrimental to balance and walking function. Levels of intracortical inhibition in older adults has been previously associated with interlimb coordination performance during walking.…”

Section: Discussionmentioning

confidence: 99%

“…Still, lower limb motor network flexibility may not have a cortical origin, as subcortical networks substantially contribute to mobility function and, in some cases, recovery of mobility after stroke. 30 Direct measures of TMS-evoked cortical connectivity in lower limb motor regions could help elucidate the neural origins and mechanisms underpinning lower limb motor network flexibility.…”

Section: Introductionmentioning

confidence: 99%

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Cortical motor network flexibility during lower limb motor activity and deficiencies after stroke

Palmer

Kesar

Wolf

et al. 2020

Preprint

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Background and Purpose: Motor network flexibility, the ability to switch between different neural network connectivity configurations, is a key feature of normal motor behavior and motor learning. Limited flexibility of the motor system to adapt to environmental stimuli is a pervasive feature of mobility impairment in chronic stroke survivors. The reduced capacity to modulate cortical motor network connectivity during motor behavior has been implicated as a key neural mechanism for constrained flexibility of the motor system. However, whether the theory of reduced cortical motor network flexibility prevails in the lower limb corticomotor system and in the context of functional lower limb motor behaviors remains unknown. In this study, we tested the capacity of the lower limb motor cortex to react to external stimuli using transcranial magnetic stimulation (TMS) and the ability of the cortex to modulate motor network connectivity during lower limb motor activity in a group of chronic stroke survivors and age-matched older adult controls. We evaluated the relationship between activity-dependent modulation of TMS-evoked motor cortical connectivity and post-stroke clinical and biomechanical walking function as well as corticospinal excitability of the paretic leg. Methods: Chronic stroke survivors (n=14) and older adults (n=9) completed concurrent TMS-electroencephalography (EEG) testing, clinical evaluation, and biomechanical walking assessment. EEG was recorded during TMS delivered over the contralesional (c)M1 and ipsilesional (i)M1 during rest and active ipsilateral plantarflexion. Interhemispheric connectivity was calculated as the post-TMS (0-300ms) imaginary part of coherence value between electrodes overlying cM1 and iM1 within the beta frequency range (15-30Hz). We compared cM1 and iM1 TMS-evoked beta coherence between groups during rest and active conditions and tested associations with walking impairment and paretic leg corticospinal excitability in stroke survivors. Results: In older adult controls, TMS-evoked beta coherence was greater compared to stroke survivors and showed a reduction from rest during active motor conditions regardless of hemisphere. Stroke survivors showed lower TMS-evoked beta coherence during cM1 TMS and a lack of modulation between rest and active conditions. Higher cM1 TMS-evoked beta coherence at rest and reduction of beta coherence during paretic plantarflexion was associated with greater paretic ankle moment during walking and lower levels of clinical lower limb motor impairment. Increased beta coherence during iM1 TMS at rest was associated with the presence of a MEP in the paretic lower limb. Conclusions: We found that, like findings in animal models and the upper limb, the lower limb corticomotor system showed reduced cortical network reactivity to external stimuli (TMS), and impaired modulation of cortical network connectivity during lower limb motor activity after stroke. Impaired cortical reactivity and modulation were associated with post-stroke clinical and biomechanical walking function and corticomotor excitability of the paretic leg, supporting the link between cortical network connectivity, motor network flexibility and lower limb motor behavior. These findings have important implications for the development of targeted and individualized treatments to improve lower limb disability in stroke survivors.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cortical motor network flexibility during lower limb motor activity and deficiencies after stroke

Palmer

Kesar

Wolf

et al. 2020

Preprint

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“…Procedures. Preampli ed EMG electrodes (Motion Lab Systems, MA-420, Baton Rouge, LA, USA) were placed on 8 lower-limb muscles of each leg using SENIAM guidelines (Table 1) 43 alongside 14mm re ective markers for motion capture placed to con gure a modi ed Helen Hayes marker set 44 . Next, selfselected walking speed (SSWS) and baseline walking characteristics were identi ed while participants walked on an instrumented treadmill (Bertec, Columbus, OH, USA) wearing a fall arrest harness (Therastride, St. Louis MO, USA; Robertson Harness Inc, Ft Collins, CO, USA) for safety.…”

Section: Methodsmentioning

confidence: 99%

Evidence for Shared Neural Information Between Muscle Synergies and Corticospinal Efficacy

Young

Banks

McGuirk

et al. 2022

Preprint

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Stroke survivors often exhibit gait dysfunction which compromises self-efficacy and quality of life. Muscle Synergy Analysis (MSA), derived from electromyography (EMG), has been argued as a method to quantify the complexity of descending motor commands and serve as a direct correlate of neural function. However, controversy remains regarding this interpretation, specifically attribution of MSA as a neuromarker. Here we sought to determine the relationship between MSA and accepted neurophysiological parameters of motor efficacy in healthy controls, high (HFH) and low (LFH) functioning stroke survivors. Surface EMG was collected from twenty-four participants while walking at their self-selected speed. Concurrently, transcranial magnetic stimulation (TMS) was administered, during walking, to elicit motor evoked potentials (MEPs) in the plantarflexor muscles during the pre-swing phase of gait. MSA was able to differentiate control and LFH individuals. Conversely, motor neurophysiological parameters including soleus MEP area, revealed that MEP latency differentiated control and HFH individuals. Significant correlations were revealed between MSA and motor neurophysiological parameters adding evidence to our understanding of MSA as a correlate of neural function and highlighting the utility of combining MSA with other relevant outcomes to aid interpretation of this analysis technique.

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“…There is a body of evidence supporting the practical importance of muscle stretch responses -specifically the LLR component -in the neurological research. Previous studies showed that LLR can be considered as the primary outcome measure in various rehabilitation and robot-aided training protocols for several neurological diseases including stroke, Parkinson's disease (PD), spinal cord injury, and cerebellar ataxia (Sinkjaer and Hayashi, 1989;Hayashi et al, 2001;Trumbower et al, 2013;Mirbagheri et al, 2015;Banks et al, 2019;Deneri et al, 2020). One recent study in 2019 (Banks et al, 2019), distinguished LLR as a promising physiological marker of walking dysfunction in chronic stroke.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies showed that LLR can be considered as the primary outcome measure in various rehabilitation and robot-aided training protocols for several neurological diseases including stroke, Parkinson's disease (PD), spinal cord injury, and cerebellar ataxia (Sinkjaer and Hayashi, 1989;Hayashi et al, 2001;Trumbower et al, 2013;Mirbagheri et al, 2015;Banks et al, 2019;Deneri et al, 2020). One recent study in 2019 (Banks et al, 2019), distinguished LLR as a promising physiological marker of walking dysfunction in chronic stroke. Trumbower et al, also demonstrated that there is a bilateral impaired regulation of the LLR during tasks which require increased stability in both the paretic and nonparetic upper limbs of stroke survivors (Trumbower et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Effects of Perturbation Velocity, Direction, Background Muscle Activation, and Task Instruction on Long-Latency Responses Measured From Forearm Muscles

Weinman

Arfa-Fatollahkhani

Zonnino

et al. 2021

Front. Hum. Neurosci.

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The central nervous system uses feedback processes that occur at multiple time scales to control interactions with the environment. The long-latency response (LLR) is the fastest process that directly involves cortical areas, with a motoneuron response measurable 50 ms following an imposed limb displacement. Several behavioral factors concerning perturbation mechanics and the active role of muscles prior or during the perturbation can modulate the long-latency response amplitude (LLRa) in the upper limbs, but the interactions among many of these factors had not been systematically studied before. We conducted a behavioral study on thirteen healthy individuals to determine the effect and interaction of four behavioral factors – background muscle torque, perturbation direction, perturbation velocity, and task instruction – on the LLRa evoked from the flexor carpi radialis (FCR) and extensor carpi ulnaris (ECU) muscles after velocity-controlled wrist displacements. The effects of the four factors were quantified using both a 0D statistical analysis on the average perturbation-evoked EMG signal in the period corresponding to an LLR, and using a timeseries analysis of EMG signals. All factors significantly modulated LLRa, and their combination nonlinearly contributed to modulating the LLRa. Specifically, all the three-way interaction terms that could be computed without including the interaction between instruction and velocity significantly modulated the LLR. Analysis of the three-way interaction terms of the 0D model indicated that for the ECU muscle, the LLRa evoked when subjects are asked to maintain their muscle activation in response to the perturbations was greater than the one observed when subjects yielded to the perturbations (p < 0.001), but this effect was not measured for muscles undergoing shortening or in absence of background muscle activation. Moreover, higher perturbation velocity increased the LLRa evoked from the stretched muscle in presence of a background torque (p < 0.001), but no effects of velocity were measured in absence of background torque. Also, our analysis identified significant modulations of LLRa in muscles shortened by the perturbation, including an interaction between torque and velocity, and an effect of both torque and velocity. The time-series analysis indicated the significance of additional transient effects in the LLR region for muscles undergoing shortening.

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Lower extremity long-latency reflexes differentiate walking function after stroke

Cited by 5 publications

References 49 publications

Cortical motor network flexibility during lower limb motor activity and deficiencies after stroke

Cortical motor network flexibility during lower limb motor activity and deficiencies after stroke

Evidence for Shared Neural Information Between Muscle Synergies and Corticospinal Efficacy

Effects of Perturbation Velocity, Direction, Background Muscle Activation, and Task Instruction on Long-Latency Responses Measured From Forearm Muscles

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