Cognitive Demands Influence Upper Extremity Motor Performance During Recovery From Acute Stroke

EEG Provides Insights Into Motor Control and Neuroplasticity During Stroke Recovery

In many branches of medicine, treatment is guided by measuring its effects on underlying physiology. In this regard, the efficacy of rehabilitation/recovery therapies could be enhanced if their administration was guided by measurements that directly capture treatment effects on neural function. Measures of brain function via EEG may be useful toward this goal and have advantages such as ease of bedside acquisition, safety, and low cost. This review synthetizes EEG studies during the subacute phase poststroke, when spontaneous recovery is maximal, and focuses on movement. Event-related measures reflect cortical activation and inhibition, while connectivity measures capture the function of cortical networks. Several EEG-based measures are related to motor outcomes poststroke and warrant further evaluation. Ultimately, they may be useful for clinical decision-making and clinical trial design in stroke neurorehabilitation.

Distal Versus Proximal Arm Improvement After Paired Vagus Nerve Stimulation Therapy After Chronic Stroke

OBJECTIVE

To evaluate differences in upper-extremity (UE) segment-specific (proximal or distal segment) recovery after vagus nerve stimulation (VNS) paired with UE rehabilitation (Paired-VNS) compared with rehabilitation with sham-VNS (Control). We also assessed whether gains in specific UE segments predicted clinically meaningful improvement.

DESIGN

This study reports on a secondary analysis of Vagus nerve stimulation paired with rehabilitation for UE motor function after chronic ischemic stroke (VNS-REHAB), a randomized, triple-blinded, sham-controlled pivotal trial. A Rasch latent regression was used to determine differences between Paired-VNS and Controls for distal and proximal UE changes after in-clinic therapy and 3 months later. Subsequently, we ran a random forest model to assess candidate predictors of meaningful improvement. Each item of the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and Wolf Motor Function Test (WMFT) was evaluated as a predictor of response to treatment.

SETTING

Nineteen stroke rehabilitation centers in the USA and UK.

PARTICIPANTS

Dataset included 108 participants (N=108) with chronic ischemic stroke and moderate-to-severe UE impairments.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

FMA-UE and WMFT.

RESULTS

Distal UE improvement was significantly greater in the Paired-VNS group than in Controls immediately after therapy (95% confidence interval, 0.27-0.73; P≤.001) and after 3 months (95% confidence interval, 0.16-0.75; P=.003). Both groups showed similar improvement in proximal UE at both time points. A subset of both distal and proximal items from the FMA-UE and WMFT were predictors of meaningful improvement.

CONCLUSIONS

Paired-VNS improved distal UE impairment in chronic stroke to a greater degree than intensive rehabilitation alone. Proximal improvements were equally responsive to either treatment. Given that meaningful UE recovery is predicted by improvements across both proximal and distal segments, Paired-VNS may facilitate improvement that is otherwise elusive.

Effects of Hand Motor Interventions on Cognitive Outcomes Post-stroke: A Systematic Review and Bayesian Network Meta-analysis

OBJECTIVE

To synthetize the evidence on the effects of hand rehabilitation (RHB) interventions on cognition post-stroke and compare their efficacy.

DATA SOURCES

PubMed, Embase, Cochrane, Scopus, Web of Science, and CINAHL were searched from inception to November 2022.

DATA SELECTION

Randomized controlled trials conducted in adults with stroke where the effects of hand motor interventions on any cognitive domains were assessed.

DATA EXTRACTION

Data were extracted by 2 independent reviewers. A Bayesian Network Meta-analysis (NMA) was applied for measures with enough studies and comparisons. Risk of bias was assessed with the Cochrane Risk of Bias tool.

DATA SYNTHESIS

Fifteen studies were included in qualitative synthesis, and 11 in NMA. Virtual reality (VR) (n=7), robot-assisted (n=5), or handgrip strength (n=3) training were the experimental interventions and conventional RHB (n=14) control intervention. Two separate NMA were performed with MoCA (n=480 participants) and MMSE (n=350 participants) as outcome measures. Both coincided that the most probable best interventions were robot-assisted and strength training, according to SUCRA and rankogram, followed by conventional RHB and VR training. No significant differences between any of the treatments were found in the MoCA network, but in the MMSE, robot-assisted and strength training were significantly better than conventional RHB and VR. No significant differences between robot-assisted and strength training were found nor between conventional RHB and VR.

CONCLUSIONS

Motor interventions can improve MoCA/MMSE scores post-stroke. Most probable best interventions were robot-assisted and strength training. Limited literature assessing domain-specific cognitive effects was found.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Motor Learning in Robot-Based Haptic Dyads: A Review

Rehabilitation robots have the potential to alleviate the global burden of neurorehabilitation. Robot-based multiplayer gaming with virtual and haptic interaction may improve motivation, engagement, and implicit learning in robotic therapy. Over the past few years, there has been growing interest in robot mediated haptic dyads, or human-robot-robot-human interaction. The effect of such a paradigm on motor learning in general and specifically for individuals with motor and/or cognitive impairments is an open area of research. We reviewed the literature to investigate the effect of a robot-based haptic dyad on motor learning. Thirty-eight articles met the inclusion criteria for this review. We summarize study characteristics including device, haptic rendering, and experimental task. Our main findings indicate that dyadic training's impact on motor learning is inconsistent in that some studies show significant improvement of motor training while others show no influence. We also find that the relative skill level of the partner and interaction characteristics such as stiffness of connection and availability of visual information influence motor learning outcomes. We discuss implications for neurorehabilitation and conclude that additional research is needed to determine optimal interaction characteristics for motor learning and to extend this research to individuals with cognitive and motor impairments.

Predictors of Cognitive Functions After Stroke Assessed Using the Wechsler Adult Intelligence Scale: A Retrospective Study

Background

The mechanism(s) of cognitive impairment remains complex, making it difficult to confirm the factors influencing poststroke cognitive impairment (PSCI).

Objective

This study quantitatively investigated the degree of influence and interactions of clinical indicators of PSCI.

Methods

Information from 270 patients with PSCI and their Wechsler Adult Intelligence Scale (WAIS-RC) scores, totaling 18 indicators, were retrospectively collected. Correlations between the indicators and WAIS scores were calculated. Multiple linear regression model(MLR), genetic algorithm modified Back-Propagation neural network(GA-BP), logistic regression model (LR), XGBoost model (XGB), and structural equation model were used to analyze the degree of influence of factors on the WAIS and their mediating effects.

Results

Seven indicators were significantly correlated with the WAIS scores: education, lesion side, aphasia, frontal lobe, temporal lobe, diffuse lesions, and disease course. The MLR showed significant effect of education, lesion side, aphasia, diffuse lesions, and frontal lobe on the WAIS. The GA-BP included five factors: education, aphasia, frontal lobe, temporal lobe, and diffuse lesions. LR predicted that the lesion side contributed more to mild cognitive impairment, while education, lesion side, aphasia, and course of the disease contributed more to severe cognitive impairment. XGB showed that education, side of the lesion, aphasia, and diffuse lesions contributed the most to PSCI. Aphasia plays a significant mediating role in patients with severe PSCI.

Conclusions

Education, lesion side, aphasia, frontal lobe, and diffuse lesions significantly affected PSCI. Aphasia is a mediating variable between clinical information and the WAIS in patients with severe PSCI.

The "Little Circles Test" (LCT): a dusted-off tool for assessing fine visuomotor function

BACKGROUND

The fine visuomotor function is commonly impaired in several neurological conditions. However, there is a scarcity of reliable neuropsychological tools to assess such a critical domain.

AIMS

The aim of this study is to explore the psychometric properties and provide normative data for the Visual-Motor Speed and Precision Test (VMSPT).

RESULTS

Our normative sample included 220 participants (130 females) aged 18-86 years (mean education = 15.24 years, SD = 3.98). Results showed that raw VMSPT scores were affected by higher age and lower education. No effect of sex or handedness was shown. Age- and education-based norms were provided. VMSPT exhibited weak-to-strong correlations with well-known neuropsychological tests, encompassing a wide range of cognitive domains of clinical relevance. By gradually intensifying the cognitive demands, the test becomes an indirect, performance-oriented measure of executive functioning. Finally, VMSPT seems proficient in capturing the speed-accuracy trade-off typically observed in the aging population.

CONCLUSIONS

This study proposes the initial standardization of a versatile, time-efficient, and cost-effective neuropsychological tool for assessing fine visuomotor coordination. We propose renaming the VMSPT as the more approachable "Little Circles Test" (LCT).

Psychometric properties of the Chinese version of the Arm Activity Measure in people with chronic stroke

Introduction

The Arm Activity Measure was developed to assess active and passive functions of the upper limb in people with unilateral paresis, but a Chinese version is not available and its psychometric properties have not been specifically tested in people with stroke. This study aimed to translate and culturally adapt the Chinese version of the Arm Activity Measure (ArmA-C) and establish its psychometric properties in people with chronic stroke.

Methods

The psychometric properties of ArmA-C were determined in 100 people with chronic stroke.

Results

The ArmA-C had good test-retest reliability (intraclass correlation coefficients [ICC] = 0.87-0.93; quadratic weighted Kappa coefficients = 0.53-1.00). A floor effect was identified in section A of the ArmA-C. The content validity and internal consistency (Cronbach's alpha coefficients = 0.75-0.95) were good. The construct validity of the ArmA-C was supported by acceptable fit to the two-factor structure model and significant correlations with the Fugl-Meyer Assessment for Upper Extremity score, grip strength, the Wolf Motor Function Test score, the Trail Walking Test completion time, and the Oxford Participation and Activities Questionnaire scores.

Conclusions

The ArmA-C is reliable and valid for assessing active and passive functions in people with chronic stroke.

Improved processing speed and decreased functional connectivity in individuals with chronic stroke after paired exercise and motor training

After stroke, impaired motor performance is linked to an increased demand for cognitive resources. Aerobic exercise improves cognitive function in neurologically intact populations and may be effective in altering cognitive function post-stroke. We sought to determine if high-intensity aerobic exercise paired with motor training in individuals with chronic stroke alters cognitive-motor function and functional connectivity between the dorsolateral prefrontal cortex (DLPFC), a key region for cognitive-motor processes, and the sensorimotor network. Twenty-five participants with chronic stroke were randomly assigned to exercise (n = 14; 66 ± 11 years; 4 females), or control (n = 11; 68 ± 8 years; 2 females) groups. Both groups performed 5-days of paretic upper limb motor training after either high-intensity aerobic exercise (3 intervals of 3 min each, total exercise duration of 23-min) or watching a documentary (control). Resting-state fMRI, and trail making test part A (TMT-A) and B were recorded pre- and post-intervention. Both groups showed implicit motor sequence learning (p < 0.001); there was no added benefit of exercise for implicit motor sequence learning (p = 0.738). The exercise group experienced greater overall cognitive-motor improvements measured with the TMT-A. Regardless of group, the changes in task score, and dwell time during TMT-A were correlated with a decrease in DLPFC-sensorimotor network functional connectivity (task score: p = 0.025; dwell time: p = 0.043), which is thought to reflect a reduction in the cognitive demand and increased automaticity. Aerobic exercise may improve cognitive-motor processing speed post-stroke.

Distinguishing Distinct Neural Systems for Proximal vs Distal Upper Extremity Motor Control After Acute Stroke

BACKGROUND AND OBJECTIVES

The classic and singular pattern of distal greater than proximal upper extremity motor deficits after acute stroke does not account for the distinct structural and functional organization of circuits for proximal and distal motor control in the healthy CNS. We hypothesized that separate proximal and distal upper extremity clinical syndromes after acute stroke could be distinguished and that patterns of neuroanatomical injury leading to these 2 syndromes would reflect their distinct organization in the intact CNS.

METHODS

Proximal and distal components of motor impairment (upper extremity Fugl-Meyer score) and strength (Shoulder Abduction Finger Extension score) were assessed in consecutively recruited patients within 7 days of acute stroke. Partial correlation analysis was used to assess the relationship between proximal and distal motor scores. Functional outcomes including the Box and Blocks Test (BBT), Barthel Index (BI), and modified Rankin scale (mRS) were examined in relation to proximal vs distal motor patterns of deficit. Voxel-based lesion-symptom mapping was used to identify regions of injury associated with proximal vs distal upper extremity motor deficits.

RESULTS

A total of 141 consecutive patients (49% female) were assessed 4.0 ± 1.6 (mean ± SD) days after stroke onset. Separate proximal and distal upper extremity motor components were distinguishable after acute stroke (p = 0.002). A pattern of proximal more than distal injury (i.e., relatively preserved distal motor control) was not rare, observed in 23% of acute stroke patients. Patients with relatively preserved distal motor control, even after controlling for total extent of deficit, had better outcomes in the first week and at 90 days poststroke (BBT, ρ = 0.51, p < 0.001; BI, ρ = 0.41, p < 0.001; mRS, ρ = 0.38, p < 0.001). Deficits in proximal motor control were associated with widespread injury to subcortical white and gray matter, while deficits in distal motor control were associated with injury restricted to the posterior aspect of the precentral gyrus, consistent with the organization of proximal vs distal neural circuits in the healthy CNS.

DISCUSSION

These results highlight that proximal and distal upper extremity motor systems can be selectively injured by acute stroke, with dissociable deficits and functional consequences. Our findings emphasize how disruption of distinct motor systems can contribute to separable components of poststroke upper extremity hemiparesis.

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Domain-Specific Outcome Measures in Clinical Trials of Therapies Promoting Stroke Recovery: A Suggested Blueprint

Different deficits recover to different degrees and with different time courses after stroke, indicating that plasticity differs across the brain's neural systems after stroke. To capture these differences, domain-specific outcome measures have received increased attention. Such measures have potential advantages over global outcome scales, which combine recovery across many domains into a single score and so blur the ability to capture individual measures of stroke recovery. Use of a global end point to rate disability can overlook substantial recovery in specific domains, such as motor or language, and may not differentiate between good and poor recovery for specific neurological domains. In light of these points, a blueprint is proposed for using domain-specific outcome measures in stroke recovery trials. Key steps include selecting a domain in the context of preclinical data, picking a domain-specific clinical trial end point, anchoring inclusion criteria to this end point, scoring this end point both before and after treatment, and then pursuing regulatory approval on the basis of the domain-specific results. This blueprint is intended to foster clinical trials that, by using domain-specific end points, are able to demonstrate favorable results in clinical trials of therapies that promote stroke recovery.

Modulation of neural co-firing to enhance network transmission and improve motor function after stroke

Stroke is a leading cause of disability. While neurotechnology has shown promise for improving upper limb recovery after stroke, efficacy in clinical trials has been variable. Our central thesis is that to improve clinical translation, we need to develop a common neurophysiological framework for understanding how neurotechnology alters network activity. Our perspective discusses principles for how motor networks, both healthy and those recovering from stroke, subserve reach-to-grasp movements. We focus on neural processing at the resolution of single movements, the timescale at which neurotechnologies are applied, and discuss how this activity might drive long-term plasticity. We propose that future studies should focus on cross-area communication and bridging our understanding of timescales ranging from single trials within a session to across multiple sessions. We hope that this perspective establishes a combined path forward for preclinical and clinical research with the goal of more robust clinical translation of neurotechnology.

Functional connectivity drives stroke recovery: shifting the paradigm from correlation to causation

Stroke is a leading cause of disability, with deficits encompassing multiple functional domains. The heterogeneity underlying stroke poses significant challenges in the prediction of post-stroke recovery, prompting the development of neuroimaging-based biomarkers. Structural neuroimaging measurements, particularly those reflecting corticospinal tract injury, are well-documented in the literature as potential biomarker candidates of post-stroke motor recovery. Consistent with the view of stroke as a 'circuitopathy', functional neuroimaging measures probing functional connectivity may also prove informative in post-stroke recovery. An important step in the development of biomarkers based on functional neural network connectivity is the establishment of causality between connectivity and post-stroke recovery. Current evidence predominantly involves statistical correlations between connectivity measures and post-stroke behavioural status, either cross-sectionally or serially over time. However, the advancement of functional connectivity application in stroke depends on devising experiments that infer causality. In 1965, Sir Austin Bradford Hill introduced nine viewpoints to consider when determining the causality of an association: (i) strength; (ii) consistency; (iii) specificity; (iv) temporality; (v) biological gradient; (vi) plausibility; (vii) coherence; (viii) experiment; and (ix) analogy. Collectively referred to as the Bradford Hill Criteria, these points have been widely adopted in epidemiology. In this review, we assert the value of implementing Bradford Hill's framework to stroke rehabilitation and neuroimaging. We focus on the role of neural network connectivity measurements acquired from task-oriented and resting-state functional MRI, EEG, magnetoencephalography and functional near-infrared spectroscopy in describing and predicting post-stroke behavioural status and recovery. We also identify research opportunities within each Bradford Hill tenet to shift the experimental paradigm from correlation to causation.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

Recovery after stroke: the severely impaired are a distinct group

INTRODUCTION

Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.

METHODS

We designed a Bayesian hierarchical model to estimate 3-6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5-30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.

RESULTS

Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3-6 months outcomes could be done with an R²=63.5% (95% CI=51.4% to 75.5%).

CONCLUSIONS

Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

Coherent neural oscillations inform early stroke motor recovery

Neural oscillations may contain important information pertaining to stroke rehabilitation. This study examined the predictive performance of electroencephalography‐derived neural oscillations following stroke using a data‐driven approach. Individuals with stroke admitted to an inpatient rehabilitation facility completed a resting‐state electroencephalography recording and structural neuroimaging around the time of admission and motor testing at admission and discharge. Using a lasso regression model with cross‐validation, we determined the extent of motor recovery (admission to discharge change in Functional Independence Measurement motor subscale score) prediction from electroencephalography, baseline motor status, and corticospinal tract injury. In 27 participants, coherence in a 1–30 Hz band between leads overlying ipsilesional primary motor cortex and 16 leads over bilateral hemispheres predicted 61.8% of the variance in motor recovery. High beta (20–30 Hz) and alpha (8–12 Hz) frequencies contributed most to the model demonstrating both positive and negative associations with motor recovery, including high beta leads in supplementary motor areas and ipsilesional ventral premotor and parietal regions and alpha leads overlying contralesional temporal–parietal and ipsilesional parietal regions. Electroencephalography power, baseline motor status, and corticospinal tract injury did not significantly predict motor recovery during hospitalization (R² = 0–6.2%). Findings underscore the relevance of oscillatory synchronization in early stroke rehabilitation while highlighting contributions from beta and alpha frequency bands and frontal, parietal, and temporal–parietal regions overlooked by traditional hypothesis‐driven prediction models.