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

Biomarkers of Motor Outcomes After Stroke

Predicting motor outcomes after stroke based on clinical judgment alone is often inaccurate and can lead to inefficient and inequitable allocation of rehabilitation resources. Prediction tools are being developed so that clinicians can make evidence-based, accurate, and reproducible prognoses for individual patients. Biomarkers of corticospinal tract structure and function can improve prediction tool performance, particularly for patients with initially moderate to severe motor impairment. Being able to make accurate predictions for individual patients supports rehabilitation planning and communication with patients and families.

Do lower limb motor-evoked potentials predict walking outcomes post-stroke?

BACKGROUND

This observational study examined whether lower limb (LL) motor-evoked potentials (MEPs) 1 week post-stroke predict recovery of independent walking, use of ankle-foot orthosis (AFO) or walking aid, at 3 and 6 months post-stroke.

METHODS

Non-ambulatory participants were recruited 5 days post-stroke. Transcranial magnetic stimulation was used to determine tibialis anterior MEP status and clinical assessments (age, National Institutes of Health Stroke Scale (NIHSS), ankle dorsiflexion strength, LL motricity index, Berg Balance Test) were completed 1 week post-stroke. Functional Ambulation Category (FAC), use of AFO and walking aid were assessed 3 months and 6 months post-stroke. MEP status, alone and combined with clinical measures, and walking outcomes at 3 and 6 months were analysed with Pearson χ2 and multivariate binary logistic regression.

RESULTS

Ninety participants were included (median age 72 years (38-97 years)). Most participants (81%) walked independently (FAC ≥ 4), 17% used an AFO, and 49% used a walking aid 3 months post-stroke with similar findings at 6 months. Independent walking was better predicted by age, LL strength and Berg Balance Test (accuracy 92%, 95% CI 85% to 97%) than MEP status (accuracy 73%, 95% CI 63% to 83%). AFO use was better predicted by NIHSS and MEP status (accuracy 88%, 95% CI 79% to 94%) than MEP status alone (accuracy 76%, 95% CI 65% to 84%). No variables predicted use of walking aids.

CONCLUSIONS

The presence of LL MEPs 1-week post-stroke predicts independent walking at 3 and 6 months post-stroke. However, the absence of MEPs does not preclude independent walking. Clinical factors, particularly age, balance and stroke severity, more strongly predict independent walking than MEP status. LL MEP status adds little value as a biomarker for walking outcomes.

Measuring Neuroplasticity in Response to Cardiovascular Exercise in People With Stroke: A Critical Perspective

BACKGROUND

Rehabilitative treatments that promote neuroplasticity are believed to improve recovery after stroke. Animal studies have shown that cardiovascular exercise (CE) promotes neuroplasticity but the effects of this intervention on the human brain and its implications for the functional recovery of patients remain unclear. The use of biomarkers has enabled the assessment of cellular and molecular events that occur in the central nervous system after brain injury. Some of these biomarkers have proven to be particularly valuable for the diagnosis of severity, prognosis of recovery, as well as for measuring the neuroplastic response to different treatments after stroke.

OBJECTIVES

To provide a critical analysis on the current evidence supporting the use of neurophysiological, neuroimaging, and blood biomarkers to assess the neuroplastic response to CE in individuals poststroke.

RESULTS

Most biomarkers used are responsive to the effects of acute and chronic CE interventions, but the response appears to be variable and is not consistently associated with functional improvements. Small sample sizes, methodological variability, incomplete information regarding patient's characteristics, inadequate standardization of training parameters, and lack of reporting of associations with functional outcomes preclude the quantification of the neuroplastic effects of CE poststroke using biomarkers.

CONCLUSION

Consensus on the optimal biomarkers to monitor the neuroplastic response to CE is currently lacking. By addressing critical methodological issues, future studies could advance our understanding of the use of biomarkers to measure the impact of CE on neuroplasticity and functional recovery in patients with stroke.

Adaptation of the Concept of Brain Reserve for the Prediction of Stroke Outcome: Proxies, Neural Mechanisms, and Significance for Research

The prediction of stroke outcome is challenging due to the high inter-individual variability in stroke patients. We recently suggested the adaptation of the concept of brain reserve (BR) to improve the prediction of stroke outcome. This concept was initially developed alongside the one for the cognitive reserve for neurodegeneration and forms a valuable theoretical framework to capture high inter-individual variability in stroke patients. In the present work, we suggest and discuss (i) BR-proxies-quantitative brain characteristics at the time stroke occurs (e.g., brain volume, hippocampus volume), and (ii) proxies of brain pathology reducing BR (e.g., brain atrophy, severity of white matter hyperintensities), parameters easily available from a routine MRI examination that might improve the prediction of stroke outcome. Though the influence of these parameters on stroke outcome has been partly reported individually, their independent and combined impact is yet to be determined. Conceptually, BR is a continuous measure determining the amount of brain structure available to mitigate and compensate for stroke damage, thus reflecting individual differences in neural resources and a capacity to maintain performance and recover after stroke. We suggest that stroke outcome might be defined as an interaction between BR at the time stroke occurs and lesion load. BR in stroke can potentially be influenced, e.g., by modifying cardiovascular risk factors. In addition to the potential power of the BR concept in a mechanistic understanding of inter-individual variability in stroke outcome and establishing individualized therapeutic approaches, it might help to strengthen the synergy of preventive measures in stroke, neurodegeneration, and healthy aging.

Outcome Prediction by Combining Corticospinal Tract Lesion Load with Diffusion-tensor Fractional Anisotropy in Patients after Hemorrhagic Stroke

Objectives

The objective of this study was to evaluate the predictive precision of combining the corticospinal tract lesion load (CST-LL) with the diffusion-tensor fractional anisotropy of the corticospinal tract (CST-FA) in the lesioned hemispheres regarding motor outcomes.

Methods

Patients with putaminal and/or thalamic hemorrhage who had undergone computed tomography (CT) soon after onset in our hospital were retrospectively enrolled. The CST-LL was calculated after registration of the CT images to a standard brain. Diffusion-tensor imaging was performed during the second week after onset. Standardized automated tractography was employed to calculate the CST-FA. Outcomes were assessed at discharge from our affiliated rehabilitation facility using total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor total; null to full, 0 to 25). Multivariate regression analysis was performed with CST-LL and CST-FA as explanatory variables and SIAS-motor total as a target value.

Results

Twenty-five patients participated in this study. SIAS-motor total ranged from 0 to 25 (median, 17). CST-LL ranged from 0.298 to 7.595 (median, 2.522) mL, and the lesion-side CST-FA ranged from 0.211 to 0.530 (median, 0.409). Analysis revealed that both explanatory variables were detected as statistically significant contributory factors. The estimated t values indicated that the contributions of these two variables were almost equal. The obtained regression model accounted for 63.9% of the variability of the target value.

Conclusions

Incorporation of the CST-LL with the lesion-side CST-FA enhances the precision of the stroke outcome prediction model.

Brain oscillations in reflecting motor status and recovery induced by action observation-driven robotic hand intervention in chronic stroke

Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain-computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl-Meyer Assessment (FMA), and significant improvements (p < 0.05) on both scales were found in patients after the intervention. Averaged EEG band power in the affected hemisphere presented negative correlations with scales pre-training; however, no significant correlations (p > 0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, p < 0.01). Importantly, motor improvements in ARAT correlate significantly with task EEG power changes (low-beta, c.c = 0.71, p = 0.005; high-beta, c.c = 0.71, p = 0.004) and task/rest EEG power ratio changes (delta, c.c = -0.738, p = 0.003; low-beta, c.c = 0.67, p = 0.009; high-beta, c.c = 0.839, p = 0.000). These results suggest that, in chronic stroke, EEG band power may not be a good indicator of motor status. However, ipsilesional oscillation changes in the delta and beta bands provide potential biomarkers related to the therapeutic-induced improvement of motor function in effective BCI intervention, which may be useful in understanding the brain plasticity changes and contribute to evaluating therapy and recovery in chronic-stage motor rehabilitation.

Trajectories of arm recovery early after stroke: an exploratory study using latent class growth analysis

AIM

To investigate trajectories of recovery of motor arm function after stroke during inpatient rehabilitation.

MATERIALS AND METHODS

Data were available from 74 consecutively-admitted stroke survivors receiving inpatient rehabilitation from an inception cohort study. Heterogeneity of arm recovery in the first 4-weeks was investigated using latent class analysis and weekly Box and Block Test (BBT) scores. Optimal number of clusters were determined; characterised and cluster associated factors explored.

RESULTS

A 4-cluster model was identified, including 19 participants with low baseline arm function and minimal recovery ('LOWstart/LOWprogress', 26%), 15 with moderate function and low recovery ('MODstart/LOWprogress', 20%), 15 with low function and high recovery ('LOWstart/HIGHprogress', 20%), and 25 with moderate function and recovery ('MODstart/MODprogress', 34%). Compared to LOWstart/LOWprogress: LOWstart/HIGHprogress presented earlier post-stroke (β, 95%CI) (-4.81 days, -8.94 to -0.69); MODstart/MODprogress had lower modified Rankin Scale scores (-0.74, -1.15 to -0.32); and MODstart/LOWprogress, LOWstart/HIGHprogress and MODstart/MODprogress had higher admission BBT (23.58, 18.82 to 28.34; 4.85, 0.85 to 9.61; 28.02, 23.82 to 32.21), Upper Limb-Motor Assessment Scale (9.60, 7.24 to 11.97; 3.34, 0.97 to 5.70; 10.86, 8.77 to 12.94), Action Research Arm Test (31.09, 22.86 to 39.33; 12.69, 4.46 to 20.93; 38.01, 30.76 to 45.27), and Manual Muscle Test scores (10.64, 7.07 to 14.21; 6.24, 2.67 to 9.81; 11.87, 8.72 to 15.01).

CONCLUSIONS

We found unique patterns of arm recovery with distinct characteristics for each cluster. Better understanding of patterns of arm recovery can guide future models and intervention development.KEY MESSAGESArm recovery early after stroke follows four distinct trajectories that relate to time post stroke, initial stroke severity and baseline level of motor arm function.Identification of recovery patterns gives insight into the uniqueness of individual's recovery.This study offers a novel approach on which to build and develop future models of arm recovery.

Applicability of fractional anisotropy from standardized automated tractography for outcome prediction of patients after stroke

[Purpose] Diffusion-tensor fractional anisotropy has been used for outcome prediction in stroke patients. We assessed the clinical applicability of the two major fractional anisotropy methodologies-fractional anisotropy derived from segmentation maps in the standard brain (region of interest) and fractional anisotropy derived from standardized automated tractography-in relation to outcomes. [Participants and Methods] The study design was a retrospective survey of medical records collected from October 2021 to September 2022. Diffusion-tensor imaging was conducted in the second week after stroke onset. Outcomes were assessed using the total score of the motor component of the Stroke Impairment Assessment Set (null to full, 0 to 25). Correlations between fractional anisotropy and the outcomes were then assessed. [Results] Fourteen patients with hemorrhagic stroke were sampled. The fractional anisotropy from standardized automated tractography of the corticospinal tract on the lesion side (mean ± standard deviation, 0.403 ± 0.070) was significantly and tightly correlated (r=0.813) with the outcomes (13.4 ± 9.2), whereas the fractional anisotropy from a region of interest set in the cerebral peduncle on the lesion side (0.548 ± 0.064) was not significantly correlated with the outcomes (r=0.507). [Conclusion] The findings suggest that fractional anisotropy derived from standardized automated tractography can be more applicable to outcome prediction than that derived from a region of interest defined in the standard brain.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9±18.0 years; time since stroke 12.2±0.2 months; normalised motor score 0.7±0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

Automated hippocampal segmentation algorithms evaluated in stroke patients

Deep learning segmentation algorithms can produce reproducible results in a matter of seconds. However, their application to more complex datasets is uncertain and may fail in the presence of severe structural abnormalities-such as those commonly seen in stroke patients. In this investigation, six recent, deep learning-based hippocampal segmentation algorithms were tested on 641 stroke patients of a multicentric, open-source dataset ATLAS 2.0. The comparisons of the volumes showed that the methods are not interchangeable with concordance correlation coefficients from 0.266 to 0.816. While the segmentation algorithms demonstrated an overall good performance (volumetric similarity [VS] 0.816 to 0.972, DICE score 0.786 to 0.921, and Hausdorff distance [HD] 2.69 to 6.34), no single out-performing algorithm was identified: FastSurfer performed best in VS, QuickNat in DICE and average HD, and Hippodeep in HD. Segmentation performance was significantly lower for ipsilesional segmentation, with a decrease in performance as a function of lesion size due to the pathology-based domain shift. Only QuickNat showed a more robust performance in volumetric similarity. Even though there are many pre-trained segmentation methods, it is important to be aware of the possible decrease in performance for the segmentation results on the lesion side due to the pathology-based domain shift. The segmentation algorithm should be selected based on the research question and the evaluation parameter needed. More research is needed to improve current hippocampal segmentation methods.

Predicting Motor Outcomes Using Atlas-Based Voxel Features of Post-Stroke Neuroimaging: A Scoping Review

BACKGROUND

Atlas-based voxel features have the potential to aid motor outcome prognostication after stroke, but are seldom used in clinically feasible prediction models. This could be because neuroimaging feature development is a non-standardized, complex, multistep process. This is a barrier to entry for researchers and poses issues for reproducibility and validation in a field of research where sample sizes are typically small.

OBJECTIVES

The primary aim of this review is to describe the methodologies currently used in motor outcome prediction studies using atlas-based voxel neuroimaging features. Another aim is to identify neuroanatomical regions commonly used for motor outcome prediction.

METHODS

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol was constructed and OVID Medline and Scopus databases were searched for relevant studies. The studies were then screened and details about imaging modality, image acquisition, image normalization, lesion segmentation, region of interest determination, and imaging measures were extracted.

RESULTS

Seventeen studies were included and examined. Common limitations were a lack of detailed reporting on image acquisition and the specific brain templates used for normalization and a lack of clear reasoning behind the atlas or imaging measure selection. A wide variety of sensorimotor regions relate to motor outcomes and there is no consensus use of one single sensorimotor atlas for motor outcome prediction.

CONCLUSION

There is an ongoing need to validate imaging predictors and further improve methodological techniques and reporting standards in neuroimaging feature development for motor outcome prediction post-stroke.

Exploring the Utility of Autonomic Nervous System Evaluation for Stroke Prognosis

Stroke is a major cause of functional disability and is increasing in frequency. Therefore, stroke prognosis must be both accurate and timely. Among other biomarkers, heart rate variability (HRV) is investigated in terms of prognostic accuracy within stroke patients. The literature research of two databases (MEDLINE and Scopus) is performed to trace all relevant studies published within the last decade addressing the potential utility of HRV for stroke prognosis. Only the full-text articles published in English are included. In total, forty-five articles have been traced and are included in the present review. The prognostic value of biomarkers of autonomic dysfunction (AD) in terms of mortality, neurological deterioration, and functional outcome appears to be within the range of known clinical variables, highlighting their utility as prognostic tools. Moreover, they may provide additional information regarding poststroke infections, depression, and cardiac adverse events. AD biomarkers have demonstrated their utility not only in the setting of acute ischemic stroke but also in transient ischemic attack, intracerebral hemorrhage, and traumatic brain injury, thus representing a promising prognostic tool whose clinical application may greatly facilitate individualized stroke care.

Brain regions associated with Brunnstrom and functional independence measure scores in patients after a stroke: a tract-based spatial statistics study

[Purpose] We aimed to assess diffusion tensor fractional anisotropy to outline the brain regions associated with the long-term motor and cognitive functional outcomes of patients with stroke. [Participants and Methods] Eighty patients from our previous study were enrolled. Fractional anisotropy maps were acquired on days 14-21 after stroke onset, and tract-based spatial statistics were applied. Outcomes were scored using the Brunnstrom recovery stage and Functional Independence Measure motor and cognition components. Fractional anisotropy images were assessed in relation to outcome scores using the general linear model. [Results] For both the right (n=37) and left (n=43) hemisphere lesion groups, the corticospinal tract and the anterior thalamic radiation were most strongly associated with the Brunnstrom recovery stage. In contrast, the cognition component involved large regions encompassing the anterior thalamic radiation, superior longitudinal fasciculus, inferior longitudinal fasciculus, uncinate fasciculus, cingulum bundle, forceps major, and forceps minor. The results for the motor component were intermediate between those for the Brunnstrom recovery stage and those for the cognition component. [Conclusion] Motor-related outcomes were associated with fractional anisotropy decreases in the corticospinal tract, whereas cognitive outcomes were related to broad regions of association and commissural fibers. This knowledge will help scheduling appropriate rehabilitative treatments.

Applicability of automated tractography during acute care stroke rehabilitation

[Purpose] To assess the clinical applicability of a novel automated tractography tool named XTRACT during acute stroke rehabilitation. [Participants and Methods] Three patients with left hemisphere stroke were sampled. Diffusion tensor images were acquired on the second week, and automated tractography was then applied. Tractography images and fractional anisotropy (FA) values in the corticospinal tract (CST) and arcuate fasciculus (AF) were assessed in relation to hemiparesis and aphasia. [Results] Patient 1 was nearly asymptomatic; FA in the left CST was 0.610 and that in the AF was 0.509. Patient 2 had severe hemiparesis and mild motor aphasia. Tractography images of the CST and AF were blurred; FA in the left CST was 0.295 and that in the AF was 0.304. Patient 3 showed no hemiparesis or aphasia at initial assessment. Tractography image of the CST was intact but that of the AF was less clear; FA in the left CST was 0.586 and that in the AF was 0.338. Considering the less clear images of the AF and lower FA value in Patients 2 and 3, further examinations for aphasia were performed, which revealed agraphia. [Conclusion] Visualization and quantification of neural fibers using automated tractography promoted planning acute care rehabilitative treatment in patients with stroke.

Exploring the Impact of Cerebral Microbleeds on Stroke Management

Stroke constitutes a major cause of functional disability and mortality, with increasing prevalence. Thus, the timely and accurate prognosis of stroke outcomes based on clinical or radiological markers is vital for both physicians and stroke survivors. Among radiological markers, cerebral microbleeds (CMBs) constitute markers of blood leakage from pathologically fragile small vessels. In the present review, we evaluated whether CMBs affect ischemic and hemorrhagic stroke outcomes and explored the fundamental question of whether CMBs may shift the risk-benefit balance away from reperfusion therapy or antithrombotic use in acute ischemic stroke patients. A literature review of two databases (MEDLINE and Scopus) was conducted to identify all the relevant studies published between 1 January 2012 and 9 November 2022. Only full-text articles published in the English language were included. Forty-one articles were traced and included in the present review. Our findings highlight the utility of CMB assessments, not only in the prognostication of hemorrhagic complications of reperfusion therapy, but also in forecasting hemorrhagic and ischemic stroke patients' functional outcomes, thus indicating that a biomarker-based approach may aid in the provision of counseling for patients and families, improve the selection of more appropriate medical therapies, and contribute to a more accurate choice of patients for reperfusion therapy.

A standardized protocol for manually segmenting stroke lesions on high-resolution T1-weighted MR images

Although automated methods for stroke lesion segmentation exist, many researchers still rely on manual segmentation as the gold standard. Our detailed, standardized protocol for stroke lesion tracing on high-resolution 3D T1-weighted (T1w) magnetic resonance imaging (MRI) has been used to trace over 1,300 stroke MRI. In the current study, we describe the protocol, including a step-by-step method utilized for training multiple individuals to trace lesions ("tracers") in a consistent manner and suggestions for distinguishing between lesioned and non-lesioned areas in stroke brains. Inter-rater and intra-rater reliability were calculated across six tracers trained using our protocol, resulting in an average intraclass correlation of 0.98 and 0.99, respectively, as well as a Dice similarity coefficient of 0.727 and 0.839, respectively. This protocol provides a standardized guideline for researchers performing manual lesion segmentation in stroke T1-weighted MRI, with detailed methods to promote reproducibility in stroke research.

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

Early prediction of upper limb functioning after stroke using clinical bedside assessments: a prospective longitudinal study

Early and accurate prediction of recovery is needed to assist treatment planning and inform patient selection in clinical trials. This study aimed to develop a prediction algorithm using a set of simple early clinical bedside measures to predict upper limb capacity at 3-months post-stroke. A secondary analysis of Stroke Arm Longitudinal Study at Gothenburg University (SALGOT) included 94 adults (mean age 68 years) with upper limb impairment admitted to stroke unit). Cluster analysis was used to define the endpoint outcome strata according to the 3-months Action Research Arm Test (ARAT) scores. Modelling was carried out in a training (70%) and testing set (30%) using traditional logistic regression, random forest models. The final algorithm included 3 simple bedside tests performed 3-days post stroke: ability to grasp, to produce any measurable grip strength and abduct/elevate shoulder. An 86-94% model sensitivity, specificity and accuracy was reached for differentiation between poor, limited and good outcome. Additional measurement of grip strength at 4 weeks post-stroke and haemorrhagic stroke explained the underestimated classifications. External validation of the model is recommended. Simple bedside assessments have advantages over more lengthy and complex assessments and could thereby be integrated into routine clinical practice to aid therapy decisions, guide patient selection in clinical trials and used in data registries.

Randomized Controlled Trial of Robot-Assisted Gait Training versus Therapist-Assisted Treadmill Gait Training as Add-on Therapy in Early Subacute Stroke Patients: The GAITFAST Study Protocol

The GAITFAST study (gait recovery in patients after acute ischemic stroke) aims to compare the effects of treadmill-based robot-assisted gait training (RTGT) and therapist-assisted treadmill gait training (TTGT) added to conventional physical therapy in first-ever ischemic stroke patients. GAITFAST (Clinicaltrials.gov identifier: NCT04824482) was designed as a single-blind single-center prospective randomized clinical trial with two parallel groups and a primary endpoint of gait speed recovery up to 6 months after ischemic stroke. A total of 120 eligible and enrolled participants will be randomly allocated (1:1) in TTGT or RTGT. All enrolled patients will undergo a 2-week intensive inpatient rehabilitation including TTGT or RTGT followed by four clinical assessments (at the beginning of inpatient rehabilitation 8-15 days after stroke onset, after 2 weeks, and 3 and 6 months after the first assessment). Every clinical assessment will include the assessment of gait speed and walking dependency, fMRI activation measures, neurological and sensorimotor impairments, and gait biomechanics. In a random selection (1:2) of the 120 enrolled patients, multimodal magnetic resonance imaging (MRI) data will be acquired and analyzed. This study will provide insight into the mechanisms behind poststroke gait behavioral changes resulting from intensive rehabilitation including assisted gait training (RTGT or TTGT) in early subacute IS patients.

Alternative prognosis of recovery assessment for the hemiparetic limb (APRAHL): a biomarker-free algorithm that predicts recovery potential for stroke patients

Objective

Stroke is a significant cause of disability, rendering patients with inability to perform activities of daily living due to lack of functional recovery. Precise prognosis in the early stage after stroke could enable realistic goal-setting and efficient resource allocation. Prediction algorithms have been tested and validated in the past, but they were using neurological biomarkers; thus, they were time-consuming, difficult to apply, expensive, and potentially harmful. The aim of this study was to create a new prediction algorithm that would not utilize any biomarkers.

Methods

A total of 127 stroke patients prospectively enrolled at day 3 after their stroke (mean age: 71, males n: 84, females n: 43). First, a sum of shoulder abduction and finger extension (SAFE) Medical Research Council (MRC) score was graded at day 3. Secondly, a binarized response was marked by the Mobilization and Simulation of Neuromuscular Tissue (MaSoNT) concept’s basic application on the upper limb. Third, the National Institutes of Health Stroke Scale (NIHSS) score was assessed. All data from the patients were included in a Classification and Regression Tree analysis to predict upper limb function 3 months post-stroke according to the Action Research Arm Test score at week 12.

Results

The Classification And Regression Tree (CART) analysis was performed that combines three different scores in order to predict upper-limb recovery: the SAFE score, MaSoNT’s application response, and the NIHSS. The overall correct prediction of the new algorithm is 69% which is lower than previous algorithms, though not significantly.

Conclusion

This study offers basic data to support the validity of the APRAHL algorithm. The new algorithm is faster and easier, but less accurate. Future studies are needed to create new algorithms that do not involve neurological biomarkers so that they will cost less and be easily applicable by health professionals.

Longitudinal prediction of motor dysfunction after stroke: a disconnectome study

Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure-function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke.

The Clinical Utility of Leukoaraiosis as a Prognostic Indicator in Ischemic Stroke Patients

Stroke constitutes a major cause of functional disability with increasing prevalence among adult individuals. Thus, it is of great importance for both clinicians and stroke survivors to be provided with a timely and accurate prognostication of functional outcome. A great number of biomarkers capable of yielding useful information regarding stroke patients' recovery propensity have been evaluated so far with leukoaraiosis being among them. Literature research of two databases (MEDLINE and Scopus) was conducted to identify all relevant studies published between 1 January 2012 and 25 June 2022 that dealt with the clinical utility of a current leukoaraiosis as a prognostic indicator following stroke. Only full-text articles published in English language were included. Forty-nine articles have been traced and are included in the present review. Our findings highlight the prognostic value of leukoaraiosis in an acute stroke setting. The assessment of leukoaraiosis with visual rating scales in CT/MRI imaging appears to be able to reliably provide important insight into the recovery potential of stroke survivors, thus significantly enhancing stroke management. Yielding additional information regarding both short- and long-term functional outcome, motor recovery capacity, hemorrhagic transformation, as well as early neurological deterioration following stroke, leukoaraiosis may serve as a valuable prognostic marker poststroke. Thus, leukoaraiosis represents a powerful prognostic tool, the clinical implementation of which is expected to significantly facilitate the individualized management of stroke patients.

Regional cerebral blood perfusion changes in chronic stroke survivors as potential brain correlates of the functional outcome following gamified home-based rehabilitation (IntelliRehab)-a pilot study

BACKGROUND

Hospital-based stroke rehabilitation for stroke survivors in developing countries may be limited by staffing ratios and length of stay that could hamper recovery potential. Thus, a home-based, gamified rehabilitation system (i.e., IntelliRehab) was tested for its ability to increase cerebral blood flow (CBF), and the secondary impact of changes on the upper limb motor function and functional outcomes.

OBJECTIVE

To explore the effect of IntelliRehab on CBF in chronic stroke patients and its correlation with the upper limb motor function.

METHODS

Two-dimensional pulsed Arterial Spin Labelling (2D-pASL) was used to obtain CBF images of stable, chronic stroke subjects (n = 8) over 3-months intervention period. CBF alterations were mapped, and the detected differences were marked as regions of interest. Motor functions represented by Fugl-Meyer Upper Extremity Assessment (FMA) and Stroke Impact Scale (SIS) were used to assess the primary and secondary outcomes, respectively.

RESULTS

Regional CBF were significantly increased in right inferior temporal gyrus and left superior temporal white matter after 1-month (p = 0.044) and 3-months (p = 0.01) of rehabilitation, respectively. However, regional CBF in left middle fronto-orbital gyrus significantly declined after 1-month of rehabilitation (p = 0.012). Moreover, SIS-Q7 and FMA scores significantly increased after 1-month and 3-months of rehabilitation. There were no significant correlations, however, between CBF changes and upper limb motor function.

CONCLUSIONS

Participants demonstrated improved motor functions, supporting the benefit of using IntelliRehab as a tool for home-based rehabilitation. However, within-participant improvements may have limited potential that suggests the need for a timely administration of IntelliRehab to get the maximum capacity of improvement.

Research and application advances in rehabilitation assessment of stroke

Stroke has a high incidence and disability rate, and rehabilitation is an effective means to reduce the disability rate of patients. To systematize rehabilitation assessment, which is the foundation for rehabilitation therapy, we summarize the assessment methods commonly used in research and clinical applications, including the various types of stroke rehabilitation scales and their applicability, and related biomedical detection technologies, including surface electromyography (sEMG), motion analysis systems, transcranial magnetic stimulation (TMS), magnetic resonance imaging (MRI), and combinations of different techniques. We also introduce some assessment techniques that are still in the experimental phase, such as the prospective application of artificial intelligence (AI) with optical correlation tomography (OCT) in stroke rehabilitation. This review provides a useful bibliography for the assessment of not only the severity of stroke injury, but also the therapeutic effects of stroke rehabilitation, and establishes a solid base for the future development of stroke rehabilitation skills.

Automating provision of feedback to stroke patients with and without information on compensatory movements: A pilot study

Providing effective feedback to patients in a rehabilitation training program is essential. As technologies are being developed to support patient training, they need to be able to provide the users with feedback on their performance. As there are various aspects on which feedback can be given (e.g., task success and presence of compensatory movements), it is important to ensure that users are not overwhelmed by too much information given too frequently by the assistive technology. We created a rule-based set of guidelines for the desired hierarchy, timing, and content of feedback to be used when stroke patients train with an upper-limb exercise platform which we developed. The feedback applies to both success on task completion and to the execution of compensatory movements, and is based on input collected from clinicians in a previous study. We recruited 11 stroke patients 1–72 months from injury onset. Ten participants completed the training; each trained with the rehabilitation platform in two configurations: with motor feedback (MF) and with no motor feedback (control condition) (CT). The two conditions were identical, except for the feedback content provided: in both conditions they received feedback on task success; in the MF condition they also received feedback on making undesired compensatory movements during the task. Participants preferred the configuration that provided feedback on both task success and quality of movement (MF). This pilot experiment demonstrates the feasibility of a system providing both task-success and movement-quality feedback to patients based on a decision tree which we developed.

Observational Study of Neuroimaging Biomarkers of Severe Upper Limb Impairment After Stroke

BACKGROUND AND OBJECTIVES

It is difficult to predict poststroke outcome for individuals with severe motor impairment because both clinical tests and corticospinal tract (CST) microstructure may not reliably indicate severe motor impairment. Here, we test whether imaging biomarkers beyond the CST relate to severe upper limb (UL) impairment poststroke by evaluating white matter microstructure in the corpus callosum (CC). In an international, multisite hypothesis-generating observational study, we determined if (1) CST asymmetry index (CST-AI) can differentiate between individuals with mild-moderate and severe UL impairment and (2) CC biomarkers relate to UL impairment within individuals with severe impairment poststroke. We hypothesized that CST-AI would differentiate between mild-moderate and severe impairment, but CC microstructure would relate to motor outcome for individuals with severe UL impairment.

METHODS

Seven cohorts with individual diffusion imaging and motor impairment (Fugl-Meyer Upper Limb) data were pooled. Hand-drawn regions-of-interest were used to seed probabilistic tractography for CST (ipsilesional/contralesional) and CC (prefrontal/premotor/motor/sensory/posterior) tracts. Our main imaging measure was mean fractional anisotropy. Linear mixed-effects regression explored relationships between candidate biomarkers and motor impairment, controlling for observations nested within cohorts, as well as age, sex, time poststroke, and lesion volume.

RESULTS

Data from 110 individuals (30 with mild-moderate and 80 with severe motor impairment) were included. In the full sample, greater CST-AI (i.e., lower fractional anisotropy in the ipsilesional hemisphere, p < 0.001) and larger lesion volume (p = 0.139) were negatively related to impairment. In the severe subgroup, CST-AI was not reliably associated with impairment across models. Instead, lesion volume and CC microstructure explained impairment in the severe group beyond CST-AI (p's < 0.010).

DISCUSSION

Within a large cohort of individuals with severe UL impairment, CC microstructure related to motor outcome poststroke. Our findings demonstrate that CST microstructure does relate to UL outcome across the full range of motor impairment but was not reliably associated within the severe subgroup. Therefore, CC microstructure may provide a promising biomarker for severe UL outcome poststroke, which may advance our ability to predict recovery in individuals with severe motor impairment after stroke.

Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: a perspective review

Brain-computer interface (BCI) technology translates brain activity into meaningful commands to establish a direct connection between the brain and the external world. Neuroscientific research in the past two decades has indicated a tremendous potential of BCI systems for the rehabilitation of patients suffering from poststroke impairments. By promoting the neuronal recovery of the damaged brain networks, BCI systems have achieved promising results for the recovery of poststroke motor, cognitive, and language impairments. Also, several assistive BCI systems that provide alternative means of communication and control to severely paralysed patients have been proposed to enhance patients' quality of life. In this article, we present a perspective review of the recent advances and challenges in the BCI systems used in the poststroke rehabilitation of motor, cognitive, and communication impairments.

Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha) - a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke

BACKGROUND

Stroke is one of the most frequent diseases, and half of the stroke survivors are left with permanent impairment. Prediction of individual outcome is still difficult. Many but not all patients with stroke improve by approximately 1.7 times the initial impairment, that has been termed proportional recovery rule. The present study aims at identifying factors predicting motor outcome after stroke more accurately than before, and observe associations of rehabilitation treatment with outcome.

METHODS

The study is designed as a multi-centre prospective clinical observational trial. An extensive primary data set of clinical, neuroimaging, electrophysiological, and laboratory data will be collected within 96 h of stroke onset from patients with relevant upper extremity deficit, as indexed by a Fugl-Meyer-Upper Extremity (FM-UE) score ≤ 50. At least 200 patients will be recruited. Clinical scores will include the FM-UE score (range 0-66, unimpaired function is indicated by a score of 66), Action Research Arm Test, modified Rankin Scale, Barthel Index and Stroke-Specific Quality of Life Scale. Follow-up clinical scores and applied types and amount of rehabilitation treatment will be documented in the rehabilitation hospitals. Final follow-up clinical scoring will be performed 90 days after the stroke event. The primary endpoint is the change in FM-UE defined as 90 days FM-UE minus initial FM-UE, divided by initial FM-UE impairment. Changes in the other clinical scores serve as secondary endpoints. Machine learning methods will be employed to analyze the data and predict primary and secondary endpoints based on the primary data set and the different rehabilitation treatments.

DISCUSSION

If successful, outcome and relation to rehabilitation treatment in patients with acute motor stroke will be predictable more reliably than currently possible, leading to personalized neurorehabilitation. An important regulatory aspect of this trial is the first-time implementation of systematic patient data transfer between emergency and rehabilitation hospitals, which are divided institutions in Germany.

TRIAL REGISTRATION

This study was registered at ClinicalTrials.gov ( NCT04688970 ) on 30 December 2020.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not?

I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research. Randomized clinical trials investigating stationary gait robots have not shown the superiority of these devices over comparable interventions regarding clinical effectiveness, and there are clinical practice guidelines that even recommend against their use. Nevertheless, these devices are still widely used, and our field needs to find ways to apply these devices more effectively. The authors of the article mentioned above feed different machine learning algorithms with patients’ data from the beginning of a robot-assisted gait training intervention using the robot Lokomat. The output of these algorithms allows predictions of the clinical outcome (i.e., functional ambulation categories) while the patients are still participating in the intervention. Such an analysis based on the collection of the device’s data could optimize the application of these devices. The article provides an example of how our field of research could make progress as we advance, and in this opinion piece, I would like to present my view on the prioritization of upcoming research on robot-assisted gait training. Furthermore, I briefly speculate on some drawbacks of randomized clinical trials in the field of robot-assisted gait training and how the quality and thus the effectiveness of robot-assisted gait training could potentially be improved based on the collection and analysis of clinical training data, a better patient selection and by giving greater weight to the motivational aspects for the participants.

The TWIST Tool Predicts When Patients Will Recover Independent Walking After Stroke: An Observational Study

BACKGROUND

The likelihood of regaining independent walking after stroke influences rehabilitation and hospital discharge planning.

OBJECTIVE

This study aimed to develop and internally validate a tool to predict whether and when a patient will walk independently in the first 6 months post-stroke.

METHODS

Adults with stroke were recruited if they had new lower limb weakness and were unable to walk independently. Clinical assessments were completed one week post-stroke. The primary outcome was time post-stroke by which independent walking (Functional Ambulation Category score ≥ 4) was achieved. Cox hazard regression identified predictors for achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. The cut-off and weighting for each predictor was determined using β-coefficients. Predictors were assigned a score and summed for a final TWIST score. The probability of achieving independent walking at each time point for each TWIST score was calculated.

RESULTS

We included 93 participants (36 women, median age 71 years). Age < 80 years, knee extension strength Medical Research Council grade ≥ 3/5, and Berg Balance Test < 6, 6 to 15, or ≥ 16/56, predicted independent walking and were combined to form the TWIST prediction tool. The TWIST prediction tool was at least 83% accurate for all time points.

CONCLUSIONS

The TWIST tool combines routine bedside tests at one week post-stroke to accurately predict the probability of an individual patient achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. If externally validated, the TWIST prediction tool may benefit patients and clinicians by informing rehabilitation decisions and discharge planning.

Exploring Clinical and Neurophysiological Factors Associated with Response to Constraint Therapy and Brain Stimulation in Children with Hemiparetic Cerebral Palsy

BACKGROUND

Perinatal stroke causes hemiparetic cerebral palsy (HCP) and lifelong disability. Constraint-induced movement therapy (CIMT) and neurostimulation may enhance motor function, but the individual factors associated with responsiveness are undetermined.

OBJECTIVE

We explored the clinical and neurophysiological factors associated with responsiveness to CIMT and/or brain stimulation within a clinical trial.

METHODS

PLASTIC CHAMPS was a randomized, blinded, sham-controlled trial (n = 45) of CIMT and neurostimulation paired with intensive, goal-directed therapy. Primary outcome was the Assisting Hand Assessment (AHA). Classification trees created through recursive partitioning suggested clinical and neurophysiological profiles associated with improvement at 6-months.

RESULTS

Both clinical (stroke side (left) and age >14 years) and neurophysiological (intracortical inhibition/facilitation and motor threshold) were associated with responsiveness across treatment groups with positive predictive values (PPV) approaching 80%.

CONCLUSION

This preliminary analysis suggested sets of variables that may be associated with response to intensive therapies in HCP. Further modeling in larger trials is required.

Beyond Diffusion Tensor MRI Methods for Improved Characterization of the Brain after Ischemic Stroke: A Review

Ischemic stroke is a worldwide problem, with 15 million people experiencing a stroke annually. MR imaging is a valuable tool for understanding and assessing brain changes after stroke and predicting recovery. Of particular interest is the use of diffusion MR imaging in the nonacute stage 1-30 days poststroke. Thousands of articles have been published on the use of diffusion MR imaging in stroke, including several recent articles reviewing the use of DTI for stroke. The goal of this work was to survey and put into context the recent use of diffusion MR imaging methods beyond DTI, including diffusional kurtosis, generalized fractional anisotropy, spherical harmonics methods, and neurite orientation and dispersion models, in patients poststroke. Early studies report that these types of beyond-DTI methods outperform DTI metrics either in being more sensitive to poststroke changes or by better predicting outcome motor scores. More and larger studies are needed to confirm the improved prediction of stroke recovery with the beyond-DTI methods.

Investigating the structure-function relationship of the corticomotor system early after stroke using machine learning

BACKGROUND

Motor outcomes after stroke can be predicted using structural and functional biomarkers of the descending corticomotor pathway, typically measured using magnetic resonance imaging and transcranial magnetic stimulation, respectively. However, the precise structural determinants of intact corticomotor function are unknown. Identifying structure-function links in the corticomotor pathway could provide valuable insight into the mechanisms of post-stroke motor impairment. This study used supervised machine learning to classify upper limb motor evoked potential status using MRI metrics obtained early after stroke.

METHODS

Retrospective data from 91 patients (49 women, age 35-97 years) with moderate to severe upper limb weakness within a week after stroke were included in this study. Support vector machine classifiers were trained using metrics from T1- and diffusion-weighted MRI to classify motor evoked potential status, empirically measured using transcranial magnetic stimulation.

RESULTS

Support vector machine classification of motor evoked potential status was 81% accurate, with false positives more common than false negatives. Important structural MRI metrics included diffusion anisotropy asymmetry in the supplementary and pre-supplementary motor tracts, maximum cross-sectional lesion overlap in the sensorimotor tract and ventral premotor tract, and mean diffusivity asymmetry in the posterior limbs of the internal capsule.

INTERPRETATIONS

MRI measures of corticomotor structure are good but imperfect predictors of corticomotor function. Residual corticomotor function after stroke depends on both the extent of cross-sectional macrostructural tract damage and preservation of white-matter microstructural integrity. Analysing the corticomotor pathway using a multivariable MRI approach across multiple tracts may yield more information than univariate biomarker analyses.

Leveraging Factors of Self-Efficacy and Motivation to Optimize Stroke Recovery

The International Classification of Functioning, Disability and Health framework recognizes that an individual's functioning post-stroke reflects an interaction between their health condition and contextual factors encompassing personal and environmental factors. Personal factors significantly impact rehabilitation outcomes as they determine how an individual evaluates their situation and copes with their condition in daily life. A key personal factor is self-efficacy-an individual's belief in their capacity to achieve certain outcomes. Self-efficacy influences an individual's motivational state to execute behaviors necessary for achieving desired rehabilitation outcomes. Stroke rehabilitation practice and research now acknowledge self-efficacy and motivation as critical elements in post-stroke recovery, and increasing evidence highlights their contributions to motor (re)learning. Given the informative value of neuroimaging-based biomarkers in stroke, elucidating the neurological underpinnings of self-efficacy and motivation may optimize post-stroke recovery. In this review, we examine the role of self-efficacy and motivation in stroke rehabilitation and recovery, identify potential neural substrates underlying these factors from current neuroimaging literature, and discuss how leveraging these factors and their associated neural substrates has the potential to advance the field of stroke rehabilitation.

Clinical Imaging-Derived Metrics of Corticospinal Tract Structural Integrity Are Associated With Post-stroke Motor Outcomes: A Retrospective Study

Objective

The primary objective of this study was to retrospectively investigate associations between clinical magnetic resonance imaging-based (MRI) metrics of corticospinal tract (CST) status and paretic upper extremity (PUE) motor recovery in patients that completed acute inpatient rehabilitation (AR) post-stroke.

Methods

We conducted a longitudinal chart review of patients post-stroke who received care in the Emory University Hospital system during acute hospitalization, AR, and outpatient therapy. We extracted demographic information, stroke characteristics, and longitudinal documentation of post-stroke motor function from institutional electronic medical records. Serial assessments of paretic shoulder abduction and finger extension were estimated (E-SAFE) and an estimated Action Research Arm Test (E-ARAT) score was used to quantify 3-month PUE motor function outcome. Clinically-diagnostic MRI were used to create lesion masks that were spatially normalized and overlaid onto a white matter tract atlas delineating CST contributions emanating from six cortical seed regions to obtain the percentage of CST lesion overlap. Metric associations were investigated with correlation and cluster analyses, Kruskal-Wallis tests, classification and regression tree analysis.

Results

Thirty-four patients met study eligibility criteria. All CST overlap percentages were correlated with E-ARAT however, ventral premotor tract (PMv) overlap was the only tract that remained significantly correlated after multiple comparisons adjustment. Lesion overlap percentage in CST contributions from all seed regions was significantly different between outcome categories. Using MRI metrics alone, dorsal premotor (PMd) and PMv tracts classified recovery outcome category with 79.4% accuracy. When clinical and MRI metrics were combined, AR E-SAFE, patient age, and overall CST lesion overlap classified patients with 88.2% accuracy.

Conclusions

Study findings revealed clinical MRI-derived CST lesion overlap was associated with PUE motor outcome post-stroke and that cortical projections within the CST, particularly those emanating from non-M1 cortical areas, prominently ventral premotor (PMv) and dorsal premotor (PMd) cortices, distinguished between PUE outcome groups. Exploratory predictive models using clinical MRI metrics, either alone or in combination with clinical measures, were able to accurately identify recovery outcome category for the study cohort during both the acute and early subacute phases of post-stroke recovery. Prospective studies are recommended to determine the predictive utility of including clinical imaging-based biomarkers of white matter tract structural integrity in predictive models of post-stroke recovery.

Changes in synaptic density in the subacute phase after ischemic stroke: A 11C-UCB-J PET/MR study

Functional alterations after ischemic stroke have been described with Magnetic Resonance Imaging (MRI) and perfusion Positron Emission Tomography (PET), but no data on in vivo synaptic changes exist. Recently, imaging of synaptic density became available by targeting synaptic vesicle protein 2 A, a protein ubiquitously expressed in all presynaptic nerve terminals. We hypothesized that in subacute ischemic stroke loss of synaptic density can be evaluated with ¹¹C-UCB-J PET in the ischemic tissue and that alterations in synaptic density can be present in brain regions beyond the ischemic core. We recruited ischemic stroke patients to undergo ¹¹C-UCB-J PET/MR imaging 21 ± 8 days after stroke onset to investigate regional ¹¹C-UCB-J SUVR (standardized uptake value ratio). There was a decrease (but residual signal) of ¹¹C-UCB-J SUVR within the lesion of 16 stroke patients compared to 40 healthy controls (ratio_{lesion/controls} = 0.67 ± 0.28, p = 0.00023). Moreover, ¹¹C-UCB-J SUVR was lower in the non-lesioned tissue of the affected hemisphere compared to the unaffected hemisphere (ΔSUVR = -0.17, p = 0.0035). The contralesional cerebellar hemisphere showed a lower ¹¹C-UCB-J SUVR compared to the ipsilesional cerebellar hemisphere (ΔSUVR = -0.14, p = 0.0048). In 8 out of 16 patients, the asymmetry index suggested crossed cerebellar diaschisis. Future research is required to longitudinally study these changes in synaptic density and their association with outcome.

New Candidates for Biomarkers and Drug Targets of Ischemic Stroke-A First Dynamic LC-MS Human Serum Proteomic Study

(1) Background: The aim of this dynamic-LC/MS-human-serum-proteomic-study was to identify potential proteins-candidates for biomarkers of acute ischemic stroke, their changes during acute phase of stroke and to define potential novel drug-targets. (2) Methods: A total of 32 patients (29–80 years) with acute ischemic stroke were enrolled to the study. The control group constituted 29 demographically-matched volunteers. Subjects with stroke presented clinical symptoms lasting no longer than 24 h, confirmed by neurological-examination and/or new cerebral ischemia visualized in the CT scans (computed tomography). The analysis of plasma proteome was performed using LC-MS (liquid chromatography–mass spectrometry). (3) Results: Ten proteins with significantly different serum concentrations between groups volunteers were: complement-factor-B, apolipoprotein-A-I, fibronectin, alpha-2-HS-glycoprotein, alpha-1B-glycoprotein, heat-shock-cognate-71kDa protein/heat-shock-related-70kDa-protein-2, thymidine phosphorylase-2, cytoplasmic-tryptophan-tRNA-ligase, ficolin-2, beta-Ala-His-dipeptidase. (4) Conclusions: This is the first dynamic LC-MS study performed on a clinical model which differentiates serum proteome of patients in acute phase of ischemic stroke in time series and compares to control group. Listed proteins should be considered as risk factors, markers of ischemic stroke or potential therapeutic targets. Further clinical validation might define their exact role in differential diagnostics, monitoring the course of the ischemic stroke or specifying them as novel drug targets.

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Outcome Prediction of Patients with Intracerebral Hemorrhage by Measurement of Lesion Volume in the Corticospinal Tract on Computed Tomography

Objective:

This study investigated the potential utility of computed tomography for outcome prediction in patients with intracerebral hemorrhage.

Methods:

Patients with putaminal and/or thalamic hemorrhage for whom computed tomography images were acquired in our hospital emergency room soon after onset were retrospectively enrolled. Outcome measurements were obtained at discharge from the convalescent rehabilitation ward of our affiliated hospital. Hemiparesis was evaluated using the total score of the motor component of the Stroke Impairment Assessment Set (SIAS-motor; null to full, 0 to 25), the motor component of the Functional Independence Measure (FIM-motor; null to full, 13 to 91), and the total length of hospital stay. After registration of the computed tomography images to the standard brain, the volumes of the hematoma lesions located in the corticospinal tract were calculated. The correlation between the corticospinal tract lesion volumes and the outcome measurements was assessed using Spearman’s rank correlation test.

Results:

Thirty patients were entered into the final analytical database. Corticospinal tract lesion volumes ranged from 0.002 to 4.302 ml (median, 1.478). SIAS-motor scores ranged from 0 to 25 (median, 20), FIM-motor scores ranged from 15 to 91 (median, 80.5), and the total length of hospital stay ranged from 31 to 194 days (median, 106.5). All correlation tests were statistically significant (P <0.01). The strongest correlation was for SIAS-motor total (R=–0.710), followed by FIM-motor (R=–0.604) and LOS (R=0.493).

Conclusions:

These findings suggest that conventional computed tomography images may be useful for outcome prediction in patients with intracerebral hemorrhage.

Robotic Kinematic measures of the arm in chronic Stroke: part 1 - Motor Recovery patterns from tDCS preceding intensive training

Background

Effectiveness of robotic therapy and transcranial direct current stimulation is conventionally assessed with clinical measures. Robotic metrics may be more objective and sensitive for measuring the efficacy of interventions on stroke survivor’s motor recovery. This study investigated if robotic metrics detect a difference in outcomes, not seen in clinical measures, in a study of transcranial direct current stimulation (tDCS) preceding robotic therapy. Impact of impairment severity on intervention response was also analyzed to explore optimization of outcomes by targeting patient sub-groups.

Methods

This 2020 study analyzed data from a double-blind, sham-controlled, randomized multi-center trial conducted from 2012 to 2016, including a six-month follow-up. 82 volunteers with single chronic ischemic stroke and right hemiparesis received anodal tDCS or sham stimulation, prior to robotic therapy. Robotic therapy involved 1024 repetitions, alternating shoulder-elbow and wrist robots, for a total of 36 sessions. Shoulder-elbow and wrist kinematic and kinetic metrics were collected at admission, discharge, and follow-up.

Results

No difference was detected between the tDCS or sham stimulation groups in the analysis of robotic shoulder-elbow or wrist metrics. Significant improvements in all metrics were found for the combined group analysis. Novel wrist data showed smoothness significantly improved (P < ·001) while submovement number trended down, overlap increased, and interpeak interval decreased. Post-hoc analysis showed only patients with severe impairment demonstrated a significant difference in kinematics, greater for patients receiving sham stimulation.

Conclusions

Robotic data confirmed results of clinical measures, showing intensive robotic therapy is beneficial, but no additional gain from tDCS. Patients with severe impairment did not benefit from the combined intervention. Wrist submovement characteristics showed a delayed pattern of motor recovery compared to the shoulder-elbow, relevant to intensive intervention-related recovery of upper extremity function in chronic stroke.

Trial registration

http://www.clinicaltrials.gov. Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42234-021-00081-9.

Transcranial magnetic stimulation implementation on stroke prognosis

INTRODUCTION

Stroke represents a major cause of functional disability with increasing prevalence. Thus, it is imperative that stroke prognosis be both timely and valid. Up to today, several biomarkers have been investigated in an attempt to forecast stroke survivors' potential for motor recovery, transcranial magnetic stimulation (TMS) being among them.

METHODS

A literature research of two databases (MEDLINE and Scopus) was conducted in order to trace all relevant studies published between 1990 and 2021 that focused on the potential utility of TMS implementation on stroke prognosis. Only full-text articles published in the English language were included.

RESULTS

Thirty-nine articles have been traced and included in this review.

DISCUSSION

Motor evoked potentials (MEPs) recording is indicative of a favorable prognosis concerning the motor recovery of upper and lower extremities' weakness, swallowing and speech difficulties, and the patient's general functional outcome. On the contrary, MEP absence is usually associated with poor prognosis. Relative correlations have also been made among other TMS variants (motor threshold, MEP amplitude, central motor conduction time) and the expected recovery rate. Overall, TMS represents a non-invasive, fast, safe, and reproducible prognostic tool poststroke that could resolve prognostic uncertainties in cases of stroke.

Neuromechanical Biomarkers for Robotic Neurorehabilitation

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

Corticoreticular Pathway in Post-Stroke Spasticity: A Diffusion Tensor Imaging Study

One of the pathophysiologies of post-stroke spasticity (PSS) is the imbalance of the reticulospinal tract (RST) caused by injury to the corticoreticular pathway (CRP) after stroke. We investigated the relationship between injuries of the CRP and PSS using MR diffusion tensor imaging (DTI). The subjects were divided into spasticity and control groups. We measured the ipsilesional fractional anisotropy (iFA) and contralesional fractional anisotropy (cFA) values on the reticular formation (RF) of the CRP were on the DTI images. We carried out a retrospective analysis of 70 patients with ischemic stroke. The cFA values of CRP in the spasticity group were lower than those in the control group (p = 0.04). In the sub-ROI analysis of CRP, the iFA values of pontine RF were lower than the cFA values in both groups (p < 0.05). The cFA values of medullary RF in the spasticity group were lower than the iFA values within groups, and also lower than the cFA values in the control group (p < 0.05). This results showed the CRP injury and that imbalance of RST caused by CRP injury was associated with PSS. DTI analysis of CRP could provide imaging evidence for the pathophysiology of PSS.

Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes

Background. Neuroimaging biomarkers are valuable predictors of motor improvement after stroke, but there is a gap between published evidence and clinical usage. Objective. In this work, we aimed to investigate whether machine learning techniques, when applied to a combination of baseline whole brain volumes and clinical data, can accurately predict individual motor outcome after stroke. Methods. Upper extremity Fugl-Meyer Assessments (FMA-UE) were conducted 1 week and 12 weeks, and structural MRI was performed 1 week, after onset in 56 patients with subcortical infarction. Proportional recovery model residuals were employed to assign patients to proportional and poor recovery groups (34 vs 22). A sophisticated machine learning scheme, consisting of conditional infomax feature extraction, synthetic minority over-sampling technique for nominal and continuous, and bagging classification, was employed to predict motor outcomes, with the input features being a combination of baseline whole brain volumes and clinical data (FMA-UE scores). Results. The proposed machine learning scheme yielded an overall balanced accuracy of 87.71% in predicting proportional vs poor recovery outcomes, a sensitivity of 93.77% in correctly identifying poor recovery outcomes, and a ROC AUC of 89.74%. Compared with only using clinical data, adding whole brain volumes can significantly improve the classification performance, especially in terms of the overall balanced accuracy (from 80.88% to 87.71%) and the sensitivity (from 92.23% to 93.77%). Conclusions. Experimental results suggest that a combination of baseline whole brain volumes and clinical data, when equipped with appropriate machine learning techniques, may provide valuable information for personalized rehabilitation planning after subcortical infarction.

Intrahemispheric EEG: A New Perspective for Quantitative EEG Assessment in Poststroke Individuals

The ratio between slower and faster frequencies of brain activity may change after stroke. However, few studies have used quantitative electroencephalography (qEEG) index of ratios between slower and faster frequencies such as the delta/alpha ratio (DAR) and the power ratio index (PRI; delta + theta/alpha + beta) for investigating the difference between the affected and unaffected hemisphere poststroke. Here, we proposed a new perspective for analyzing DAR and PRI within each hemisphere and investigated the motor impairment-related interhemispheric frequency oscillations. Forty-seven poststroke subjects and twelve healthy controls were included in the study. Severity of upper limb motor impairment was classified according to the Fugl-Meyer assessment in mild/moderate (n = 25) and severe (n = 22). The qEEG indexes (PRI and DAR) were computed for each hemisphere (intrahemispheric index) and for both hemispheres (cerebral index). Considering the cerebral index (DAR and PRI), our results showed a slowing in brain activity in poststroke patients when compared to healthy controls. Only the intrahemispheric PRI index was able to find significant interhemispheric differences of frequency oscillations. Despite being unable to detect interhemispheric differences, the DAR index seems to be more sensitive to detect motor impairment-related frequency oscillations. The intrahemispheric PRI index may provide insights into therapeutic approaches for interhemispheric asymmetry after stroke.