Transforming modeling in neurorehabilitation: clinical insights for personalized rehabilitation.

Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization.

Muscle forces and joint moments estimated by electromyography (EMG)-driven musculoskeletal models are sensitive to the wrapping surface geometry defining muscle-tendon lengths and moment arms. Despite this sensitivity, wrapping surface properties are typically not personalized to subject movement data. This study developed a novel method for personalizing OpenSim cylindrical wrapping surfaces during EMG-driven model calibration. To avoid the high computational cost of repeated OpenSim muscle analyses, the method uses two-level polynomial surrogate models. Outer-level models specify time-varying muscle-tendon lengths and moment arms as functions of joint angles, while inner-level models specify time-invariant outer-level polynomial coefficients as functions of wrapping surface parameters. To evaluate the method, we used walking data collected from two individuals post-stroke and performed four variations of EMG-driven lower extremity model calibration: 1) no calibration of scaled generic wrapping surfaces (NGA), 2) calibration of outer-level polynomial coefficients for all muscles (SGA), 3) calibration of outer-level polynomial coefficients only for muscles with wrapping surfaces (LSGA), and 4) calibration of cylindrical wrapping surface parameters for muscles with wrapping surfaces (PGA). On average compared to NGA, SGA reduced lower extremity joint moment matching errors by 31%, LSGA by 24%, and PGA by 12%, with the largest reductions occurring at the hip. Furthermore, PGA reduced peak hip joint contact force by 47% bodyweight, which was the most consistent with published in vivo measurements. The proposed method for EMG-driven model calibration with wrapping surface personalization produces physically realistic OpenSim models that reduce joint moment matching errors while improving prediction of hip joint contact force.

Development of an assessment of bilateral locomotor efficacy for individuals post-stroke.

BACKGROUND: A common framework is needed to assess walking impairments in older adults and individuals with stroke. This study develops an Assessment of Bilateral Locomotor Efficacy (ABLE) that is a straightforward indicator of walking function. RESEARCH QUESTION: Can we develop a clinically accessible index of walking function that summarizes gait dysfunction secondary to stroke? METHODS: The ABLE index was developed using a retrospective sample of 14 community-dwelling older adults. Data from 33 additional older adults and 105 individuals with chronic post-stroke hemiparesis were used to validate the index by factor analysis of the score components and correlation with multiple common assessments of lower extremity impairment and function. RESULTS: The ABLE consists of four components summed for a maximum possible score of 12. The components include self-selected walking speed (SSWS), speed change from SSWS to fastest speed, non-paretic leg step length change from SSWS to fastest speed, and peak paretic leg ankle power. The ABLE revealed good concurrent validity with all recorded functional assessments. Factor analysis suggested that the ABLE measures two factors: one for forward progression and another for speed adaptability. SIGNIFICANCE: The ABLE offers a straightforward, objective measure of walking function in adults, including individuals with chronic stroke. The index may also prove useful as a screening tool for subclinical pathology in community-dwelling older adults, but further testing is required. We encourage utilization of this index and reproduction of findings to adapt and refine the instrument for wider use and eventual clinical application.

Variation of body weight supported treadmill training parameters during a single session can modulate muscle activity patterns in post-stroke gait.

Evidence supporting the benefits of locomotor training (LT) to improve walking ability following stroke are inconclusive and could likely be improved with a better understanding of the effects of individual parameters i.e., body weight support (BWS), speed, and therapist assistance and their interactions with walking ability and specific impairments. We evaluated changes in muscle activity of thirty-seven individuals with chronic stroke (> 6 months), in response to a single session of LT at their self-selected or fastest-comfortable speed (FS) with three levels of BWS (0%, 15%, and 30%), and at FS with 30% BWS and seven different combinations of therapist assistance at the paretic foot, non-paretic foot, and trunk. Altered Muscle Activation Pattern (AMAP), a previously developed tool in our lab was used to evaluate the effects of LT parameter variation on eight lower-extremity muscle patterns in individuals with stroke. Repeated-measures mixed-model ANOVA was used to determine the effects of speed, BWS, and their interaction on AMAP scores. The Wilcoxon-signed rank test was used to determine the effects of therapist-assisted conditions on AMAP scores. Increased BWS mostly improved lower-extremity muscle activity patterns, but increased speed resulted in worse plantar flexor activity. Abnormal early plantar flexor activity during stance decreased with assistance at trunk and both feet, exaggerated plantar flexor activity during late swing decreased with assistance to the non-paretic foot or trunk, and diminished gluteus medius activity during stance increased with assistance to paretic foot and/or trunk. Therefore, different sets of training parameters have different immediate effects on activation patterns of each muscle and gait subphases.

Computational evaluation of psoas muscle influence on walking function following internal hemipelvectomy with reconstruction.

An emerging option for internal hemipelvectomy surgery is custom prosthesis reconstruction. This option typically recapitulates the resected pelvic bony anatomy with the goal of maximizing post-surgery walking function while minimizing recovery time. However, the current custom prosthesis design process does not account for the patient's post-surgery prosthesis and bone loading patterns, nor can it predict how different surgical or rehabilitation decisions (e.g., retention or removal of the psoas muscle, strengthening the psoas) will affect prosthesis durability and post-surgery walking function. These factors may contribute to the high observed failure rate for custom pelvic prostheses, discouraging orthopedic oncologists from pursuing this valuable treatment option. One possibility for addressing this problem is to simulate the complex interaction between surgical and rehabilitation decisions, post-surgery walking function, and custom pelvic prosthesis design using patient-specific neuromusculoskeletal models. As a first step toward developing this capability, this study used a personalized neuromusculoskeletal model and direct collocation optimal control to predict the impact of ipsilateral psoas muscle strength on walking function following internal hemipelvectomy with custom prosthesis reconstruction. The influence of the psoas muscle was targeted since retention of this important muscle can be surgically demanding for certain tumors, requiring additional time in the operating room. The post-surgery walking predictions emulated the most common surgical scenario encountered at MD Anderson Cancer Center in Houston. Simulated post-surgery psoas strengths included 0% (removed), 50% (weakened), 100% (maintained), and 150% (strengthened) of the pre-surgery value. However, only the 100% and 150% cases successfully converged to a complete gait cycle. When post-surgery psoas strength was maintained, clinical gait features were predicted, including increased stance width, decreased stride length, and increased lumbar bending towards the operated side. Furthermore, when post-surgery psoas strength was increased, stance width and stride length returned to pre-surgery values. These results suggest that retention and strengthening of the psoas muscle on the operated side may be important for maximizing post-surgery walking function. If future studies can validate this computational approach using post-surgery experimental walking data, the approach may eventually influence surgical, rehabilitation, and custom prosthesis design decisions to meet the unique clinical needs of pelvic sarcoma patients.

EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation.

Subject-specific electromyography (EMG)-driven musculoskeletal models that predict muscle forces have the potential to enhance our knowledge of internal biomechanics and neural control of normal and pathological movements. However, technical gaps in experimental EMG measurement, such as inaccessibility of deep muscles using surface electrodes or an insufficient number of EMG channels, can cause difficulties in collecting EMG data from muscles that contribute substantially to joint moments, thereby hindering the ability of EMG-driven models to predict muscle forces and joint moments reliably. This study presents a novel computational approach to address the problem of a small number of missing EMG signals during EMG-driven model calibration. The approach (henceforth called "synergy extrapolation" or SynX) linearly combines time-varying synergy excitations extracted from measured muscle excitations to estimate 1) unmeasured muscle excitations and 2) residual muscle excitations added to measured muscle excitations. Time-invariant synergy vector weights defining the contribution of each measured synergy excitation to all unmeasured and residual muscle excitations were calibrated simultaneously with EMG-driven model parameters through a multi-objective optimization. The cost function was formulated as a trade-off between minimizing joint moment tracking errors and minimizing unmeasured and residual muscle activation magnitudes. We developed and evaluated the approach by treating a measured fine wire EMG signal (iliopsoas) as though it were "unmeasured" for walking datasets collected from two individuals post-stroke-one high functioning and one low functioning. How well unmeasured muscle excitations and activations could be predicted with SynX was assessed quantitatively for different combinations of SynX methodological choices, including the number of synergies and categories of variability in unmeasured and residual synergy vector weights across trials. The two best methodological combinations were identified, one for analyzing experimental walking trials used for calibration and another for analyzing experimental walking trials not used for calibration or for predicting new walking motions computationally. Both methodological combinations consistently provided reliable and efficient estimates of unmeasured muscle excitations and activations, muscle forces, and joint moments across both subjects. This approach broadens the possibilities for EMG-driven calibration of muscle-tendon properties in personalized neuromusculoskeletal models and may eventually contribute to the design of personalized treatments for mobility impairments.

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains.

Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings. Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%. Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%. Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.

Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings.

Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings. In this study, we assessed the feasibility of implementing 3D MLMC technology outside the traditional laboratory environment to evaluate its potential as a tool for outcomes assessment in neurorehabilitation. A sample population of 166 individuals aged 9-87 years (mean 43.7, S.D. 20.4) of varied health history were evaluated at six different locations in the community over a 3-month period. Participants walked overground at self-selected (SS) and fastest comfortable (FC) speeds. Feasibility measures considered the expansion, implementation, and practicality of this MLMC system. A subset of the sample population (46 individuals) walked over a pressure-sensitive walkway (PSW) concurrently with MLMC to assess agreement of the spatiotemporal gait parameters measured between the two systems. Twelve spatiotemporal parameters were compared using mean differences, Bland-Altman analysis, and intraclass correlation coefficients for agreement (ICC2,1) and consistency (ICC3,1). All measures showed good to excellent agreement between MLMC and the PSW system with cadence, speed, step length, step time, stride length, and stride time showing strong similarity. Furthermore, this information can inform the development of rehabilitation strategies targeting gait dysfunction. These first experiments provide evidence for feasibility of using MLMC in community and clinical practice environments to acquire robust 3D kinematic data from a diverse population. This foundational work enables future investigation with MLMC especially its use as a digital biomarker of disease progression and rehabilitation outcome.

Evidence for shared neural information between muscle synergies and corticospinal efficacy.

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.

Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies.

Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called "synergy extrapolation" or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.

Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking.

Assessment of metabolic cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed walking data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized electromyographic (EMG)-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subject's CoT at different gait speeds using three metabolic cost models (Umberger et al., 2003; Bhargava et al., 2004; Umberger, 2010). The calculated CoT values were compared with published CoT data as a function of walking speed, step length asymmetry, stance time asymmetry, double support time asymmetry, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, only SOCal and EMGCal with the Bhargava metabolic cost model were able to reproduce accurately published experimental trends between CoT and various clinical measures of walking asymmetry post-stroke. Tuning of the parameters in the different metabolic cost models could potentially resolve the observed CoT magnitude differences between model predictions and experimental measurements. Realistic CoT predictions may allow researchers to predict human performance, surgical outcomes, and rehabilitation outcomes reliably using computational simulations.

The effect of time since stroke, gender, age, and lesion size on thalamus volume in chronic stroke: a pilot study.

Recent stroke studies have shown that the ipsi-lesional thalamus longitudinally and significantly decreases after stroke in the acute and subacute stages. However, additional considerations in the chronic stages of stroke require exploration including time since stroke, gender, intracortical volume, aging, and lesion volume to better characterize thalamic differences after cortical infarct. This cross-sectional retrospective study quantified the ipsilesional and contralesional thalamus volume from 69 chronic stroke subjects' anatomical MRI data (age 35-92) and related the thalamus volume to time since stroke, gender, intracortical volume, age, and lesion volume. The ipsi-lesional thalamus volume was significantly smaller than the contra-lesional thalamus volume (t(68) = 13.89, p < 0.0001). In the ipsilesional thalamus, significant effect for intracortical volume (t(68) = 2.76, p = 0.008), age (t(68) = 2.47, p = 0.02), lesion volume (t(68) = - 3.54, p = 0.0008), and age*time since stroke (t(68) = 2.46, p = 0.02) were identified. In the contralesional thalamus, significant effect for intracortical volume (t(68) = 3.2, p = 0.002) and age (t = - 3.17, p = 0.002) were identified. Clinical factors age and intracortical volume influence both ipsi- and contralesional thalamus volume and lesion volume influences the ipsilesional thalamus. Due to the cross-sectional nature of this study, additional research is warranted to understand differences in the neural circuitry and subsequent influence on volumetrics after stroke.

Gait asymmetry pattern following stroke determines acute response to locomotor task.

BACKGROUND: Given the prevalence of gait dysfunction following stroke, walking recovery is a primary goal of rehabilitation. However, current gait rehabilitation approaches fail to demonstrate consistent benefits. Gait asymmetry, prevalent among stroke survivors who regain the ability to walk, is associated with an increased energy cost of walking and is a significant predictor of falls post-stroke. Furthermore, differential patterns of gait asymmetry may respond differently to gait training parameters. RESEARCH QUESTION: The purpose of this study was to determine whether differential responses to locomotor task condition occur on the basis of step length asymmetry pattern (Symmetrical, NPshort, Pshort) observed during overground walking. METHODS: Participants first walked overground at their self-selected walking speed. Overground data were compared against three task conditions all tested during treadmill walking: self-selected speed with 0% body weight support (TM); self-selected speed with 30 % body weight support (BWS); and fastest comfortable speed with 30 % body weight support and nonparetic leg guidance (GuidanceNP). Our primary metrics were: symmetry indices of step length, stride length, and single limb support duration. RESULTS: We identified differences in the response to locomotor task conditions for each step length asymmetry subgroup. GuidanceNP induced an acute spatial symmetry only in the NPshort group and temporal symmetry in the Symmetrical and Pshort groups. Importantly, we found the TM and BWS conditions were insufficient to impact either spatial or temporal gait symmetry. SIGNIFICANCE: Task conditions consistent with locomotor training do not produce uniform effects across subpatterns of gait asymmetry. We identified differential responses to locomotor task conditions between groups with distinct asymmetry patterns, suggesting these subgroups may require unique intervention strategies. Despite group differences in asymmetry characteristics, improvements in symmetry noted in each group were driven by changes in both the paretic and nonparetic limbs.

How Well Do Commonly Used Co-contraction Indices Approximate Lower Limb Joint Stiffness Trends During Gait for Individuals Post-stroke?

Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCIs to approximate lower limb joint stiffness trends during gait. Calibrated EMG-driven lower extremity musculoskeletal models constructed for two individuals post-stroke were used to generate the quantities required for CCI calculations and model-based estimation of joint stiffness. CCIs were calculated for various combinations of antagonist muscle pairs based on two common CCI formulations: Rudolph et al. (2000) (CCI 1) and Falconer and Winter (1985) (CCI 2). CCI 1 measures antagonist muscle activation relative to not only total activation of agonist plus antagonist muscles but also agonist muscle activation, while CCI 2 measures antagonist muscle activation relative to only total muscle activation. We computed the correlation between these two CCIs and model-based estimates of sagittal plane joint stiffness for the hip, knee, and ankle of both legs. Although we observed moderate to strong correlations between some CCI formulations and corresponding joint stiffness, these associations were highly dependent on the methodological choices made for CCI computation. Specifically, we found that: (1) CCI 1 was generally more correlated with joint stiffness than was CCI 2, (2) CCI calculation using EMG signals with calibrated electromechanical delay generally yielded the best correlations with joint stiffness, and (3) choice of antagonist muscle pairs significantly influenced CCI correlation with joint stiffness. By providing guidance on how methodological choices influence CCI correlation with joint stiffness trends, this study may facilitate a simpler alternate approach for studying joint stiffness during human movement.

Slower than normal walking speeds involve a pattern shift in joint and temporal coordination contributions.

Kinematic and spatiotemporal gait parameters are known to scale with gait speed, though inter-joint coordination during swing remains consistent, at least across comfortable speeds. The purpose of this study was to determine whether coordination patterns serving limb clearance and shortening change across a range of gait speeds. We assessed 17 healthy adults walking overground at their self-selected speed and multiple, progressively slower speeds. We collected lower extremity kinematics with 3D motion analysis and quantified joint influence, or relative joint contributions, to limb clearance and shortening. We investigated changes in coordination using linear mixed models to determine magnitude and timing differences of joint influence across walking speeds. Joint influences serving limb clearance (hip, knee, and ankle) reduced considerably with slower walking speeds. Similarly, knee and ankle influences on limb shortening reduced with slower walking speeds. Temporally, joint influences on limb clearance varied across walking speeds. Notably, the temporal order of peak hip and knee influences reversed below typical self-selected walking speeds. For limb shortening, the timing of knee and ankle influences occurred later in the gait cycle as walking speed decreased. While relative joint contributions serve limb clearance and shortening scale with walking speeds, our results demonstrate that temporal coordination of limb clearance is altered in healthy individuals as walking speed falls below the range of typical self-selected walking speeds.

Lower extremity long-latency reflexes differentiate walking function after stroke.

The neural mechanisms of walking impairment after stroke are not well characterized. Specifically, there is a need for understanding the mechanisms of impaired plantarflexor power generation in late stance. Here, we investigated the association between two neurophysiologic markers, the long-latency reflex (LLR) response and dynamic facilitation of antagonist motor-evoked responses, and walking function. Fourteen individuals with chronic post-stroke hemiparesis and thirteen healthy controls performed both isometric and dynamic plantarflexion. Transcranial magnetic stimulation (TMS) assessed supraspinal drive to the tibialis anterior. LLR activity was assessed during dynamic voluntary plantarflexion and individuals post-stroke were classified as either LLR present (LLR+) or absent (LLR-). All healthy controls and nine individuals post-stroke exhibited LLRs, while five did not. LLR+ individuals revealed higher clinical scores, walking speeds, and greater ankle plantarflexor power during walking compared to LLR- individuals. LLR- individuals exhibited exaggerated responses to TMS during dynamic plantarflexion relative to healthy controls. The LLR- subset revealed dysfunctional modulation of stretch responses and antagonist supraspinal drive relative to healthy controls and the higher functioning LLR+ individuals post-stroke. These abnormal physiologic responses allow for characterization of individuals post-stroke along a dimension that is clinically relevant and provides additional information beyond standard behavioral assessments. These findings provide an opportunity to distinguish among the heterogeneity of lower extremity motor impairments present following stroke by associating them with responses at the nervous system level.

Altered muscle activation patterns (AMAP): an analytical tool to compare muscle activity patterns of hemiparetic gait with a normative profile.

BACKGROUND: Stroke survivors often have lower extremity sensorimotor impairments, resulting in an inability to sufficiently recruit muscle activity at appropriate times in a gait cycle. Currently there is a lack of a standardized method that allows comparison of muscle activation in hemiparetic gait post-stroke to a normative profile. METHODS: We developed a new tool to quantify altered muscle activation patterns (AMAP). AMAP accounts for spatiotemporal asymmetries in stroke gait by evaluating the deviations of muscle activation specific to each gait sub-phase. It also recognizes the characteristic variability within the healthy population. The inter-individual variability of normal electromyography (EMG) patterns within some sub-phases of the gait cycle is larger compared to others, therefore AMAP penalizes more for deviations in a gait sub-phase with a constant profile (absolute active or inactive) vs variable profile. EMG data were collected during treadmill walking, from eight leg muscles of 34 stroke survivors at self-selected speeds and 20 healthy controls at four different speeds. Stroke survivors' AMAP scores, for timing and amplitude variations, were computed in comparison to healthy controls walking at speeds matched to the stroke survivors' self-selected speeds. RESULTS: Altered EMG patterns in the stroke population quantified using AMAP agree with the previously reported EMG alterations in stroke gait that were identified using qualitative methods. We defined scores ranging between ±2.57 as "normal". Only 9% of healthy controls were outside "normal" window for timing and amplitude. Percentages of stroke subjects outside the "normal" window for each muscle were, Soleus = 79%; 73%, Medial Gastrocnemius = 62%; 79%, Tibialis Anterior = 62%; 59%, and Gluteus Medius = 48%; 51% for amplitude and timing component respectively, alterations were relatively smaller for the other four muscles. Paretic-propulsion was negatively correlated to AMAP scores for the timing component of Soleus. Stroke survivors' self-selected walking speed was negatively correlated with AMAP scores for amplitude and timing of Soleus but only amplitude of Medial gastrocnemius (p < 0.05). CONCLUSIONS: Our results validate the ability of AMAP to identify alterations in the EMG patterns within the stroke population and its potential to be used to identify the gait phases that may require more attention when developing an optimal gait training paradigm. TRIAL REGISTRATION: ClinicalTrials.gov NCT00712179 , Registered July 3rd 2008.

External biomechanical constraints impair maximal voluntary grip force stability post-stroke.

BACKGROUND: Grip strength is frequently measured as a global indicator of motor function. In clinical populations, such as hemiparesis post-stroke, grip strength is associated with upper-extremity motor impairment, function, and ability to execute activities of daily living. However, biomechanical configuration of the distal arm and hand may influence the magnitude and stability of maximal voluntary grip force and varies across studies. The influence of distal arm/hand biomechanical configuration on grip force remains unclear. Here we investigated how biomechanical configuration of the distal arm/hand influence the magnitude and trial-to-trial variability of maximal grip force performed in similar positions with variations in external constraint. METHODS: We studied three groups of 20 individuals: healthy young, healthy older, and individuals post-stroke. We tested maximal voluntary grip force in 4 conditions: 1: self-determined/"free"; 2: standard; 3: fixed arm-rest; 4: gripper fixed to arm-rest, using an instrumented grip dynamometer in both dominant/non-dominant and non-paretic/paretic hands. FINDINGS: Regardless of hand or group, maximal voluntary grip force was highest when the distal limb was most constrained (i.e., Condition 4), followed by the least constrained (i.e., Condition 1) (Cohen's f = 0.52, P's < 0.001). Coefficient of variation among three trials was greater in the paretic hand compared with healthy individuals, particularly in more (Conditions 3 and 4) compared to less (Conditions 1 and 2) constrained conditions (Cohen's f = 0.29, P's < 0.05). INTERPRETATION: These findings have important implications for design of rehabilitation interventions and devices. Particularly in individuals post-stroke, external biomechanical constraints increase maximal voluntary grip force variability while fewer biomechanical constraints yield more stable performance.

Pelvic excursion during walking post-stroke: A novel classification system.

BACKGROUND: Researchers and clinicians often use gait speed to classify hemiparetic gait dysfunction because it offers clinical predictive capacity. However, gait speed fails to distinguish unique biomechanical characteristics that differentiate aspects of gait dysfunction. RESEARCH QUESTION: Here we describe a novel classification of hemiparetic gait dysfunction based on biomechanical traits of pelvic excursion. We hypothesize that individuals with greater deviation of pelvic excursion, relative to controls, demonstrate greater impairment in key gait characteristics. METHODS: We compared 41 participants (61.0 ±â€¯11.2yrs) with chronic post-stroke hemiparesis to 21 non-disabled controls (55.8 ±â€¯9.0yrs). Participants walked on an instrumented split-belt treadmill at self-selected walking speed. Pelvic excursion was quantified as the peak-to-peak magnitude of pelvic motion in three orthogonal planes (i.e., tilt, rotation, and obliquity). Raw values of pelvic excursion were compared against the distribution of control data to establish deviation scores which were assigned bilaterally for the three planes producing six values per individual. Deviation scores were then summed to produce a composite pelvic deviation score. Based on composite scores, participants were allocated to one of three categories of hemiparetic gait dysfunction with progressively increasing pelvic excursion deviation relative to controls: Type I (n = 15) - minimal pelvic excursion deviation; Type II (n = 20) - moderate pelvic excursion deviation; and Type III (n = 6) - marked pelvic excursion deviation. We assessed resulting groups for asymmetry in key gait parameters including: kinematics, joint powers temporally linked to the stance-to-swing transition, and timing of lower extremity muscle activity. RESULTS: All groups post-stroke walked at similar self-selected speeds; however, classification based on pelvic excursion deviation revealed progressive asymmetry in gait kinematics, kinetics and temporal patterns of muscle activity. SIGNIFICANCE: The progressive asymmetry revealed in multiple gait characteristics suggests exaggerated pelvic motion contributes to gait dysfunction post-stroke.

Short Intracortical Inhibition During Voluntary Movement Reveals Persistent Impairment Post-stroke.

Objective: Short intracortical inhibition (SICI) is a GABAA-mediated phenomenon, argued to mediate selective muscle activation during coordinated motor activity. Markedly reduced SICI has been observed in the acute period following stroke and, based on findings in animal models, it has been posited this disinhibitory phenomenon may facilitate neural plasticity and contribute to early motor recovery. However, it remains unresolved whether SICI normalizes over time, as part of the natural course of stroke recovery. Whether intracortical inhibition contributes to motor recovery in chronic stroke also remains unclear. Notably, SICI is typically measured at rest, which may not fully reveal its role in motor control. Here we investigated SICI at rest and during voluntary motor activity to determine: (1) whether GABAA-mediated inhibition recovers, and (2) how GABAA-mediated inhibition is related to motor function, in the chronic phase post-stroke. Methods: We studied 16 chronic stroke survivors (age: 64.6 ± 9.3 years; chronicity: 74.3 ± 52.9 months) and 12 age-matched healthy controls. We used paired-pulse transcranial magnetic stimulation (TMS) to induce SICI during three conditions: rest, submaximal grip, and performance of box-and-blocks. Upper-extremity Fugl-Meyer Assessment and Box-and-Blocks tests were used to evaluate motor impairment in stroke survivors and manual dexterity in all participants, respectively. Results: At rest, SICI revealed no differences between ipsilesional and contralesional hemispheres of either cortical or subcortical stroke survivors, or healthy controls (P's > 0.05). During box-and-blocks, however, ipsilesional hemisphere SICI was significantly reduced (P = 0.025), especially following cortical stroke (P < 0.001). SICI in the ipsilesional hemisphere during box-and-blocks task was significantly related to paretic hand dexterity (r = 0.56, P = 0.039) and motor impairment (r = 0.56, P = 0.037). Conclusions: SICI during motor activity, but not rest, reveals persistent impairment in chronic stroke survivors indicating that inhibitory brain circuits responsible for motor coordination do not fully normalize as part of the natural history of stroke recovery. Observation that reduced SICI (i.e., disinhibition) is associated with greater motor impairment and worse dexterity in chronic hemiparetic individuals suggests the response considered to promote neuroplasticity and recovery in the acute phase could be maladaptive in the chronic phase post-stroke.