Predicting recovery of voluntary upper extremity movement in subacute stroke patients with severe upper extremity paresis

The minimal important difference for the Postural Assessment Scale for Stroke Patients in the subacute stage

BACKGROUND

The minimal important difference (MID) of the Postural Assessment Scale for Stroke Patients (PASS) remains unknown, limiting the interpretation of change scores.

OBJECTIVES

To estimate the MID of the PASS in patients with subacute stroke.

METHODS

Data at admission and discharge for 240 participants were retrieved from a longitudinal study. The "mobility" item of the Barthel Index was used as the anchor for indicating the improvement of posture control. Receiver operating characteristic (ROC) method was used to estimate the anchor-based MID of the PASS.

RESULTS

The ROC method identified a MID of 3.0 points, with a sensitivity of 81.0 % and a specificity of 75.6 %.

CONCLUSION

The MID of the PASS was 3.0 points, indicating that if a patient achieves an improvement of 3.0 or more points on the PASS, they have a clinically important improvement in posture control. Our results can help in interpreting change scores and aid in understanding the clinical values of treatment outcomes.

Neuroimaging prognostic factors for treatment response to motor imagery training after stroke

The efficacy of motor imagery training for motor recovery is well acknowledged, but with substantial inter-individual variability in stroke patients. To help optimize motor imagery training therapy plans and screen suitable patients, this study aimed to explore neuroimaging biomarkers explaining variability in treatment response. Thirty-nine stroke patients were randomized to a motor imagery training group (n = 22, received a combination of conventional rehabilitation therapy and motor imagery training) and a control group (n = 17, received conventional rehabilitation therapy and health education) for 4 weeks of interventions. Their demography and clinical information, brain lesion from structural MRI, spontaneous brain activity and connectivity from rest fMRI, and sensorimotor brain activation from passive motor task fMRI were acquired to identify prognostic factors. We found that the variability of outcomes from sole conventional rehabilitation therapy could be explained by the reserved sensorimotor neural function, whereas the variability of outcomes from motor imagery training + conventional rehabilitation therapy was related to the spontaneous activity in the ipsilesional inferior parietal lobule and the local connectivity in the contralesional supplementary motor area. The results suggest that additional motor imagery training treatment is also efficient for severe patients with damaged sensorimotor neural function, but might be more effective for patients with impaired motor planning and reserved motor imagery.

May Dual Transcranial Direct Current Stimulation Enhance the Efficacy of Robot-Assisted Therapy for Promoting Upper Limb Recovery in Chronic Stroke?

OBJECTIVE

To assess whether dual transcranial direct current stimulation (tDCS) may enhance the efficacy of exoskeleton robotic training on upper limb motor functions in patients with chronic stroke.

METHODS

A prospective, bi-center, double-blind, randomized clinical trial study was performed. Patients with moderate-to-severe stroke (according to The National Institute of Health Stroke Scale) were randomly assigned to receive dual or sham tDCS immediately before robotic therapy (10 sessions, 2 weeks). The primary outcome was the Fugl-Meyer for Upper Extremity, assessed before, after, and at the 12-week follow-up. Neurophysiological evaluation of corticospinal projections to upper limb muscles was performed by recording motor evoked potentials (MEPs). ClinicalTrials.gov-NCT03026712.

RESULTS

Two hundred and sixty individuals were tested for eligibility, of which 80 were enrolled and agreed to participate. Excluding 14 dropouts, 66 patients were randomly assigned into the 2 groups. Results showed that chronic patients were stable before treatment and significantly improved after that. The records within subject improvements were not significantly different between the 2 groups. However, a post-hoc analysis subdividing patients in 2 subgroups based on the presence or absence of MEPs at the baseline showed a significantly higher effect of real tDCS in patients without MEPs when compared to patients with MEPs (F = 4.6, P = .007).

CONCLUSION

The adjunction of dual tDCS to robotic arm training did not further enhance recovery in the treated sample of patients with chronic stroke. However, a significant improvement in the subgroup of patients with a severe corticospinal dysfunction (as suggested by the absence of MEPs) suggests that they could benefit from such a treatment combination.

Design of a Multi-Sensor System for Exploring the Relation between Finger Spasticity and Voluntary Movement in Patients with Stroke

A novel wearable multi-sensor data glove system is developed to explore the relation between finger spasticity and voluntary movement in patients with stroke. Many stroke patients suffer from finger spasticity, which is detrimental to their manual dexterity. Diagnosing and assessing the degrees of spasticity require neurological testing performed by trained professionals to estimate finger spasticity scores via the modified Ashworth scale (MAS). The proposed system offers an objective, quantitative solution to assess the finger spasticity of patients with stroke and complements the manual neurological test. In this work, the hardware and software components of this system are described. By requiring patients to perform five designated tasks, biomechanical measurements including linear and angular speed, acceleration, and pressure at every finger joint and upper limb are recorded, making up more than 1000 features for each task. We conducted a preliminary clinical test with 14 subjects using this system. Statistical analysis is performed on the acquired measurements to identify a small subset of features that are most likely to discriminate a healthy patient from patients suffering from finger spasticity. This encouraging result validates the feasibility of this proposed system to quantitatively and objectively assess finger spasticity.

Brain’s Energy After Stroke: From a Cellular Perspective Toward Behavior

Stroke is a neurological condition that impacts activity performance and quality of life for survivors. While neurological impairments after the event explain the performance of patients in specific activities, the origin of such impairments has traditionally been explained as a consequence of structural and functional damage to the nervous system. However, there are important mechanisms related to energy efficiency (trade-off between biological functions and energy consumption) at different levels that can be related to these impairments and restrictions: first, at the neuronal level, where the availability of energy resources is the initial cause of the event, as well as determines the possibilities of spontaneous recovery. Second, at the level of neural networks, where the “small world” operation of the network is compromised after the stroke, implicating a high energetic cost and inefficiency in the information transfer, which is related to the neurological recovery and clinical status. Finally, at the behavioral level, the performance limitations are related to the highest cost of energy or augmented energy expenditure during the tasks to maintain the stability of the segment, system, body, and finally, the behavior of the patients. In other words, the postural homeostasis. In this way, we intend to provide a synthetic vision of the energy impact of stroke, from the particularities of the operation of the nervous system, its implications, as one of the determinant factors in the possibilities of neurological, functional, and behavioral recovery of our patients.

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.

Using Machine Learning to develop a short-form measure assessing 5 functions in patients with stroke

OBJECTIVE

This study aimed to develop and validate a machine learning based short measure (the ML-5F) to assess 5 functions (activities of daily living (ADL), balance, upper extremity (UE) and lower extremity (LE) motor function, and mobility) in patients with stroke.

DESIGN

Secondary data from a previous study. A follow-up study assessed patients with stroke using the Barthel Index (BI), Postural Assessment Scale for Stroke (PASS), and Stroke Rehabilitation Assessment of Movement (STREAM) at hospital admission and discharge.

SETTING

A rehabilitation unit in a medical center.

PARTICIPANTS

A total of 307 patients.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The BI, PASS, and STREAM.

RESULTS

A machine learning algorithm, Extreme Gradient Boosting, was used to select 15 items from the BI, PASS, and STREAM, and transformed the raw scores of the selected items into the scores of the ML-5F. The ML-5F demonstrated good concurrent validity (Pearson's r = 0.88-0.98) and responsiveness (standardized response mean = 0.28-1.01).

CONCLUSIONS

The ML-5F comprises only 15 items but demonstrates sufficient concurrent validity and responsiveness to assess ADL, balance, UE and LE functions, and mobility in patients with stroke. The ML-5F shows great potential as an efficient outcome measure in clinical settings.

Easily Conducted Tests During the First Week Post-stroke Can Aid the Prediction of Arm Functioning at 6 Months

Background: Prognostic models can estimate the recovery of arm functioning after stroke, guide the selection of individual training strategies, and inform patient selection in clinical trials. Several models for early prediction of arm recovery have been proposed, but their implementation has been hindered by insufficient external validation, limited evidence of their impact on patient outcomes, and reliance on predictors that are not feasible in regular clinical practice.

Objectives: To determine the predictive value of new and previously reported tests that can be easily conducted in regular clinical settings for early prognosis of two levels of favorable arm recovery at 6 months post-stroke.

Methods: We performed a secondary analysis of merged data (n = 223) from two Scandinavian prospective longitudinal cohorts. The candidate predictors were seven individual tests of motor function and the sensory function measured by the Fugl-Meyer Assessment of Upper Extremity within 7 days post-stroke, and the whole motor section of this assessment. For each candidate predictor, we calculated the adjusted odds ratio (OR) of two levels of residual motor impairment in the affected arm at 6 months post-stroke: moderate-to-mild (≥32 points on the motor section of the Fugl-Meyer Assessment of Upper Extremity, FMA-UE) and mild (FMA-UE ≥ 58 points).

Results: Patients with partial shoulder abduction (OR 14.6), elbow extension (OR 15.9), and finger extension (OR 9.5) were more likely to reach FMA-UE ≥ 32. Patients with full function on all individual motor tests (OR 5.5–35.3) or partial elbow extension, pronation/supination, wrist dorsiflexion and grasping ability (OR 2.1–18.3) were more likely to achieve FMA-UE ≥ 58 compared with those with absent function. Intact sensory function (OR 2.0–2.2) and moderate motor impairment on the FMA-UE (OR 7.5) were also associated with favorable outcome.

Conclusions: Easily conducted motor tests can be useful for early prediction of arm recovery. The added value of this study is the prediction of two levels of a favorable functional outcome from simple motor tests. This knowledge can be used in the development of prognostic models feasible in regular clinical settings, inform patient selection and stratification in future trials, and guide clinicians in the selection of individualized training strategies for improving arm functioning after stroke.

Clinical Trial Registration:ClinicalTrials.gov: NCT02250365, NCT01115348.

Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke

Background and Purpose- Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods- We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ≤2) and those with poor outcome (modified Rankin Scale score >2). Results- Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions- These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.

Can robot-based measurements improve prediction of motor performance after robot-assisted upper-limb rehabilitation in patients with moderate-to-severe sub-acute stroke?

PURPOSE

Patients with moderate-to-severe stroke-related upper limb impairment can benefit from repetitive robot-assisted training. However, predicting motor performance in these patients from baseline measurements, including robot-based parameters would help clinicians to provide optimal treatments for each individual.

METHODS

Forty-six patients with sub-acute stroke underwent a 16-session upper limb rehabilitation combining usual care and robotic therapy. Motor outcomes (Fugl-Meyer Assessment Upper Extremity (FMA) score) were retrospectively analysed and potential predictors of motor outcome (including baseline FMA scores, kinematics and number of repetitions performed in the first session etc.) were determined.

RESULTS

The 16-sessions upper limb combined training program led to significantly improved clinical outcomes (gains of 13.8±11.2 for total FMA score and 7.3±6.7 for FMA Shoulder/Elbow score). For the prediction model, time since stroke poorly explained the FMA total score (R2 < 35%). The model however found that time since stroke and initial value of FMA Shoulder/Elbow score were predictors of the FMA Shoulder/Elbow score: (R2 = 59.6%).

CONCLUSION

This study found that clinical prediction of motor outcomes after moderate-to-severe upper-limb paresis is limited. However, initial proximal motor impairment severity predicted proximal motor performance. The value of baselines kinematics and of the number of repeated movements at initiation in the prediction would need further studies.

Preliminary research of a novel center-driven robot for upper extremity rehabilitation

BACKGROUND

Loss of upper limb function often appears after stroke. Robot-assisted systems are becoming increasingly common in upper extremity rehabilitation. Rehabilitation robot provides intensive motor therapy, which can be performed in a repetitive, accurate and controllable manner.

OBJECTIVE

This study aims to propose a novel center-driven robot for upper extremity rehabilitation.

METHODS

A new power transmission mechanism is designed to transfer the power to elbow and shoulder joints from three motors located on the base. The forward and inverse kinematics equations of the center-driven robot (CENTROBOT) are deduced separately. The theoretical values of the scope of joint movements are obtained with the Denavit-Hartenberg parameters method. A prototype of the CENTROBOT is developed and tested.

RESULTS

The elbow flexion/extension, shoulder flexion/extension and shoulder adduction/abduction can be realized of the center-driven robot. The angles value of joints are in conformity with the theoretical value.

CONCLUSIONS

The CENTROBOT reduces the overall size of the robot arm, the influence of motor noise, radiation and other adverse factors by setting all motors on the base. It can satisfy the requirements of power and movement transmission of the robot arm.

Therapeutic effects of sensory input training on motor function rehabilitation after stroke

Motor dysfunction is a common and severe complication of stroke that affects the quality of life of these patients. Currently, motor function rehabilitation predominantly focuses on active movement training; nevertheless, the role of sensory input is usually overlooked. Sensory input is very important to motor function. Voluntary functional movement necessitates preparation, execution, and monitoring functions of the central nervous system, while the monitoring needs the participation of the sensory system. Sensory signals affect motor functions by inputting external environment information and intrinsic physiological status as well as by guiding initiation of the motor system. Recent studies focusing on sensory input-based rehabilitation training for post-stroke dyskinesia have demonstrated that sensory function has significant effects on voluntary functional movements. In conclusion, sensory input plays a crucial role in motor function rehabilitation, and the combined sensorimotor training modality is more effective than conventional motor-oriented approaches.

Development of a Computerized Adaptive Testing System for Assessing 5 Functions in Patients with Stroke: A Simulation and Validation Study

OBJECTIVE

The authors aimed to develop and validate the Computerized Adaptive Testing System for Assessing 5 Functions in Patients with Stroke (CAT-5F) based on the Barthel Index (BI), Postural Assessment Scale for Stroke patients (PASS), and Stroke Rehabilitation Assessment of Movement (STREAM) to improve the efficiency of assessment. The purposes of the CAT-5F assessment are to describe patients' levels of impairments or disabilities in the 5 functions and to serve as an outcome measure in patients with stroke.

DESIGN

This is a data-mining study based on data from a previous study using simulation analysis to develop and validate the CAT-5F.

SETTING

One rehabilitation unit in a medical center in Taiwan served as the setting for this study.

PARTICIPANTS

Data were retrieved from totals of 540 (initial assessment) and 309 (discharge assessment) participants with stroke assessed in a previous study. The assessment data (N=540) were from the BI, PASS, and STREAM.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The outcome measures for this study were from BI, PASS, and STREAM.

RESULTS

The CAT-5F using the optimal stopping rule (limited reliability increased <0.010) had good Rasch reliability across the 5 functions (0.86-0.96) and needed 12.7 items, on average, for the whole administration. The concurrent validity (Pearson product-moment correlation coefficient, r=0.91-0.96) and responsiveness (standardized response mean=0.33-0.91) of the CAT-5F were sufficient in the patients.

CONCLUSION

The CAT-5F has sufficient administrative efficiency, reliability, concurrent validity, and responsiveness to simultaneously assess basic activities of daily living, postural control, upper extremity/lower extremity motor functions, and mobility in patients with stroke.

Music meets robotics: a prospective randomized study on motivation during robot aided therapy

BACKGROUND

Robots have been successfully applied in motor training during neurorehabilitation. As music is known to improve motor function and motivation in neurorehabilitation training, we aimed at integrating music creation into robotic-assisted motor therapy. We developed a virtual game-like environment with music for the arm therapy robot ARMin, containing four different motion training conditions: a condition promoting creativity (C+) and one not promoting creativity (C-), each in a condition with (V+) and without (V-) a visual display (i.e., a monitor). The visual display was presenting the game workspace but not contributing to the creative process itself. In all four conditions the therapy robot haptically displayed the game workspace. Our aim was to asses the effects of creativity and visual display on motivation.

METHODS

In a prospective randomized single-center study, healthy participants were randomly assigned to play two of the four training conditions, either with (V+) or without visual display (V-). In the third round, the participants played a repetition of the preferred condition of the two first rounds, this time with a new V condition (i.e., with or without visual display). For each of the three rounds, motivation was measured with the Intrinsic Motivation Inventory (IMI) in the subscales interest/enjoyment, perceived choice, value/usefulness, and man-machine-relation. We recorded the actual training time, the time of free movement, and the velocity profile and administered a questionnaire to measure perceived training time and perceived effort. All measures were analysed using linear mixed models. Furthermore, we asked if the participants would like to receive the created music piece.

RESULTS

Sixteen healthy subjects (ten males, six females, mean age: 27.2 years, standard deviation: 4.1 years) with no known motor or cognitive deficit participated. Promotion of creativity (i.e., C+ instead of C-) significantly increased the IMI-item interest/enjoyment (p=0.001) and the IMI-item perceived choice (p=0.010). We found no significant effects in the IMI-items man-machine relation and value/usefulness. Conditions promoting creativity (with or without visual display) were preferred compared to the ones not promoting creativity. An interaction effect of promotion of creativity and omission of visual display was present for training time (p=0.013) and training intensity (p<0.001). No differences in relative perceived training time, perceived effort, and perceived value among the four training conditions were found.

CONCLUSIONS

Promoting creativity in a visuo-audio-haptic or audio-haptic environment increases motivation in robot-assisted therapy. We demonstrated the feasibility of performing an audio-haptic music creation task and recommend to try the system on patients with neuromuscular disorders.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02720341. Registered 25 March 2016, https://clinicaltrials.gov/ct2/show/NCT02720341.

Interhemispheric Pathways Are Important for Motor Outcome in Individuals with Chronic and Severe Upper Limb Impairment Post Stroke

Background

Severity of arm impairment alone does not explain motor outcomes in people with severe impairment post stroke.

Objective

Define the contribution of brain biomarkers to upper limb motor outcomes in people with severe arm impairment post stroke.

Methods

Paretic arm impairment (Fugl-Meyer upper limb, FM-UL) and function (Wolf Motor Function Test rate, WMFT-rate) were measured in 15 individuals with severe (FM-UL ≤ 30/66) and 14 with mild–moderate (FM-UL > 40/66) impairment. Transcranial magnetic stimulation and diffusion weight imaging indexed structure and function of the corticospinal tract and corpus callosum. Separate models of the relationship between possible biomarkers and motor outcomes at a single chronic (≥6 months) time point post stroke were performed.

Results

Age (ΔR²0.365, p = 0.017) and ipsilesional-transcallosal inhibition (ΔR²0.182, p = 0.048) explained a 54.7% (p = 0.009) variance in paretic WMFT-rate. Prefrontal corpus callous fractional anisotropy (PF-CC FA) alone explained 49.3% (p = 0.007) variance in FM-UL outcome. The same models did not explain significant variance in mild–moderate stroke. In the severe group, k-means cluster analysis of PF-CC FA distinguished two subgroups, separated by a clinically meaningful and significant difference in motor impairment (p = 0.049) and function (p = 0.006) outcomes.

Conclusion

Corpus callosum function and structure were identified as possible biomarkers of motor outcome in people with chronic and severe arm impairment.

Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke

PURPOSE

Despite improved acute treatment and new tools to facilitate recovery, most patients have motor deficits after stroke, often causing disability. However, motor impairment varies considerably among patients, and recovery in the acute/subacute phase is difficult to predict using clinical measures alone, particularly in severely impaired patients. Accurate early prediction of recovery would help rationalize rehabilitation goals and improve the design of trials testing strategies to facilitate recovery.

METHODS

We review the role of diffusion tensor imaging (DTI) in predicting motor recovery after stroke, in monitoring treatment response, and in evaluating white matter remodeling. We critically appraise DTI studies and discuss their limitations, and we explore directions for future study.

RESULTS

Growing evidence suggests that combining clinical scores with information about corticospinal tract (CST) integrity can improve predictions about motor outcome. The extent of CST damage on DTI and/or the overlap between the CST and a lesion are key prognostic factor that determines motor performance and outcome. Three main strategies to quantify stroke-related CST damage have been proposed: (i) measuring FA distal to the stroke area, (ii) measuring the number of fibers that go through the stroke with tractography, and (iii) measuring the overlap between the stroke and a CST map derived from healthy age- and gender-matched controls.

CONCLUSION

Recovery of motor function probably involves remodeling of the CST proper and/or a greater reliance on alternative motor tracts through spontaneous and treatment-induced plasticity. DTI-metrics represent promising clinical biomarkers to predict motor recovery and to monitor and predict the response to neurorehabilitative interventions.

Prediction of Walking and Arm Recovery after Stroke: A Critical Review

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery. In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review

Background There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked "high" in overall evidence quality. Conclusions There were 3 prevalent methodological limitations: (a) lack of cross-validation, (b) lack of minimal clinically important difference (MCID) for motor outcomes, and (c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.

Ten problems and solutions when predicting individual outcome from lesion site after stroke

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients.

Heart Rate Variability Is Associated with Motor Outcome 3-Months after Stroke

OBJECTIVES

The primary objective of this paper was to determine whether heart rate variability (HRV) acquired upon admission to inpatient rehabilitation is associated with motor outcome 3 months after stroke. The secondary objective of this paper was to determine whether HRV shows a strong association with the motor outcome 3 months after stroke in individuals with severe initial motor impairments.

METHODS

We recruited 13 patients with acute stroke from an acute inpatient rehabilitation hospital. A Holter monitor was placed upon admission and Fugl-Meyer Upper Extremity and Lower Extremity Subscales were used to assess the movement of the affected upper and lower extremities 3 months after admission. The standard deviation of R-R intervals was used to quantify HRV.

RESULTS

A Spearman rank correlation revealed a strong positive and significant correlation between HRV upon admission and movement of the affected upper extremity (r = .70, P = .01) and affected lower extremity (r = .60, P = .03) at 3 months. For patients with severe initial motor impairments, HRV showed a strong positive association with the movement of the affected upper (r = .61, P = .04) and lower (r = .70, P = .04) extremities at 3 months.

CONCLUSION

HRV is strongly associated with motor outcome after stroke and provides a promising marker to explore the mechanisms associated with motor recovery after stroke.