Predicting Motor Recovery Research Articles

Prediction of Motor Recovery Using Diffusion Tensor Imaging and Regional Cerebral Blood Flow in Postoperative Brain Tumors.

To determine whether diffusion tensor image (DTI) parameters and regional cerebral blood flow (rCBF) serve to preoperatively predict postoperative motor outcomes in patients with brain tumors. We included 81 patients with brain tumors who underwent surgical treatment. Motor function was assessed using the manual muscle test in the upper and lower limbs at admission and discharge. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and their ratios (rFA, rMD, rAD, and rRD) were measured at the corona radiata, internal capsule, and cerebral peduncle of the corticospinal tract (CST). In addition, DTI and single photon emission computed tomography (SPECT) were synthesized to measure rCBF at the CST. Both DTI parameters and rCBF at the CST in the preoperative motor weakness group significantly differed from those of the preoperative normal function group. rFA at the cerebral peduncle and the internal capsule was considerably higher in those showing postoperative motor recovery than in those postoperative unchanged or with deteriorated motor function (P < 0.05). Moreover, there was significantly lower rMD and rRD at the internal capsule in the motor recovery group (P < 0.05, P < 0.01).Furthermore, rCBF was higher at all the cerebral peduncle, internal capsule, and corona radiate in the motor recovery group than in the unchanged and deteriorated motor function group (P < 0.05, P < 0.01, P < 0.01). The analysis of DTI parameters and rCBF is useful in predicting postoperative motor outcomes in patients with brain tumors.

Transcranial Doppler ultrasonography can predict inpatient rehabilitation functional outcome in patients with stroke.

Despite advances in imaging techniques and treatment modalities, tools to predict recovery after stroke remain limited. To determine if transcranial Doppler (TCD) mean flow velocities were predictive of functional recovery following ischemic stroke. Data were collected from patients with stroke admitted to an academic tertiary care facility in the southeastern region of the United States between 2012 and 2019 who had a middle cerebral artery distribution ischemic stroke, TCD, and were discharged to our inpatient rehabilitation facility. Mean flow velocities were categorized as low (<40 cm/s), normal (40-80 cm/s), or high (>80 cm/s). Functional Independence Measure (FIM) scores were collected on admission to and discharge from inpatient rehabilitation. Multiple linear regression models were used to assess the differences in mean FIM score changes by categories of mean flow velocities. We enrolled 57 patients, mean age 60 years, 73.7% male. Compared to the normal velocity group, those with abnormally low velocities had a significantly smaller change in their FIM score (adjusted β = -8.42; p = .01). Compared to the normal velocity group, those with abnormally high velocities experienced a greater change in FIM score, but this association was not statistically significant (beta = 1.12; p = .77). In this limited population, we found that low mean flow velocity as measured by TCD ultrasonography after a middle cerebral artery stroke is associated with poorer functional recovery following inpatient rehabilitation. Our results suggest that the value of TCD ultrasonography as a tool to predict motor recovery after stroke warrants further investigation.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

Evaluating the axonal injury and predicting the motor function recovery in supratentorial acute stroke patients

PurposeThis study aims to evaluate axonal injury in supratentorial acute stroke patients and predict motor function recovery. MethodsA cross-sectional descriptive study was performed on 28 patients with supratentorial acute stroke. All patients underwent brain magnetic resonance imaging (MRI) at Bach Mai Hospital from September 2021 to August 2022. Diffuse tensor imaging (DTI) was conducted using a 3-Tesla MRI machine to evaluate the association between the corticospinal tract and infarct area. Therefore, axonal injury and motor function recovery levels could be predicted. ResultsAlmost patients had no change in the signal of the axons, which did not pass through the infarct lesion (28.6%), and the signals strongly decreased in patients whose axons completely stayed inside the infarct lesion (32.1%). The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) indices of the axons on the infarcted side were lower than those of axons on the contralateral side. The patient group, in which the axons did not pass through the infarct lesion or had no change in axonal signals, had a higher rate of better motor function recovery after 3 months than the other groups (39.3% and 25 %, respectively), whereas the axon group, in which the axons stayed completely inside the infarct lesion or had a remarkably decreased signal, had a very poor rate of recovery (32.1% and 39.3 %, respectively). The FA index of the axons on the infarct side in the poor recovery group was lower than that in the good recovery group. The ADC index did not differ between the groups. ConclusionMRI finding with axonal signal, location, and infarct side could significantly predict motor recovery after 3 months in acute stroke patients.

Spasticity Predicts Motor Recovery for Patients with Subacute Motor Complete Spinal Cord Injury.

A motor complete spinal cord injury (SCI) results in the loss of voluntary motor control below the point of injury. Some of these patients can regain partial motor function through inpatient rehabilitation; however, there is currently no biomarker to easily identify which patients have this potential. Evidence indicates that spasticity could be that marker. Patients with motor complete SCI who exhibit spasticity show preservation of descending motor pathways, the pathways necessary for motor signals to be carried from the brain to the target muscle. We hypothesized that the presence of spasticity predicts motor recovery after subacute motor complete SCI. Spasticity (Modified Ashworth Scale and pendulum test) and descending connectivity (motor evoked potentials) were tested in the rectus femoris muscle in patients with subacute motor complete (n = 36) and motor incomplete (n = 30) SCI. Motor recovery was assessed by using the International Standards for Neurological Classification of Spinal Cord Injury and the American Spinal Injury Association Impairment Scale (AIS). All measurements were taken at admission and discharge from inpatient rehabilitation. We found that motor complete SCI patients with spasticity improved in motor scores and showed AIS conversion to either motor or sensory incomplete. Conversely, patients without spasticity showed no changes in motor scores and AIS conversion. In incomplete SCI patients, motor scores improved and AIS conversion occurred regardless of spasticity. These findings suggest that spasticity represents an easy-to-use clinical outcome that might help to predict motor recovery after severe SCI. This knowledge can improve inpatient rehabilitation effectiveness for motor complete SCI patients. ANN NEUROL 2023.

몸통조절이 뇌졸중 환자의 운동 회복, 자세 이동 및 일상생활의 예측 가능성

Trunk control is an important factor in stroke rehabilitation because it stabilizes the spine and rib cage, enabling individuals to maintain an upright posture, adjust weight, and perform static and dynamic postural adjustments. This study aimed to assess the feasibility of using trunk control as a predictor of motor recovery, postural transfer, and activities of daily living in patients with stroke. Seventeen patients with subacute stroke participated in this study. Four reliable and valid tests were used to examine trunk control, postural transfer, activities of daily living, and motor recovery, including the trunk impairment scale (TIS), 5 times sit-to-stand test (5XSST), modified Barthel index (MBI), and Fugl–Meyer assessment of motor functioning (FMA-motor), respectively. Upon analyzing the corrected data, no statistically significant relationship was found between the TIS score and the 5XSST, MBI, and FMA-motor scores. The 5XSST score, MBI, and FMA-motor also did not show statistically significant relationships. Despite the lack of a significant relationship, the TIS score exhibited a positive relationship with the MBI and FMA-motor scores and a negative relationship with the 5XSST. Based on the results of this study, trunk control in the sitting position is not feasible as a predictor of postural transfer, activities of daily living, and motor recovery after a stroke. This study suggests that Future studies should explore the relationship between trunk control and different positions, such as kneeling, half kneeling, and standing, as predictors of postural transfer, activities of daily living, and motor recovery after stroke.

A practical guide to the evaluation of the functional integrity of the corticospinal tract by transcranial magnetic stimulation in post-stroke patients

INTRODUCTION: Transcranial magnetic stimulation (TMS) can be a particularly useful tool to assess the integrity of corticospinal tract (CST) in post-stroke patients, based on the motor evoked potential (MEP) of which we can determine the extent of brain damage and predict motor recovery after brain injuries. OBJECTIVE: To provide a practical guide to assess the functional integrity of the CST in the hand area of primary motor cortex (Hand-M1) using single-pulse TMS. RESULTS: A step by step procedure should be initiated with markings to find C3 or C4 from the 10-20 system, depending on which hemisphere is damaged, with the proper coil positioning at a 45° angle for we to properly find the MEP navigating from the original point. If no potentials are evoked at rest condition, MEP should be searched during a slight tonic contraction of the target muscle. If no voluntary movement can be produced in the affected muscles, facilitated MEPs should be searched with an isometric recruitment of the contralateral homologous target muscles. MEP will be considered absent if no visible muscle contraction is identified after the pulse. In addition, we can perform MEP search with electromyographic recordings for a peak-to-peak signal analysis. CONCLUSION: We can use this practical guide to assess the functional integrity of CST in Hand-M1 with single pulse TMS to consider a present or absent MEP and determine the extent of brain damage and predict a possible motor recovery after stroke.

Prediction of Post-stroke Motor Recovery Benefits from Measures of Sub-acute Widespread Network Damages (P12-8.002)

<h3>Objective:</h3> Developing a more accurate post-stroke motor recovery prediction model leveraging brain network connectivity measurements. <h3>Background:</h3> Following an acute ischemic stroke, some patients recover almost completely, while others barely recover. It is known that lesion volume, initial motor impairment, and cortico-spinal tract asymmetry (benchmark features) significantly impact motor changes over time. However, accurate prediction models of patients’ recovery are still missing. Recent work suggested that disabilities arise not only from focal structural changes but also from widespread alterations in inter-regional connectivity. Here, we built on this hypothesis to develop a more accurate post-stroke motor recovery prediction model. <h3>Design/Methods:</h3> We evaluated a dataset of 37 patients (17 women) with upper extremity motor strokes. Their impairment was assessed at 2 weeks (baseline) and 3 months after the stroke using the Fugl-Meyer-Assessment. 18 graph measures of global structural connectivity were obtained using the patients’ T2 lesion mask to virtually reproduce the lesions in 60 healthy streamline tractography connectomes. We added these indirect measures to the benchmark features, and we used a ridge regression regularization in a leave-one-out cross-validation framework to predict motor recovery at 3 months post-injury. <h3>Results:</h3> Prediction accuracy significantly increased when using brain connectivity measures (R²=0.68, 95% CI: 0.64–0.74) as compared to benchmark features alone (R²=0.38, 95% CI: 0.31–0.51). <h3>Conclusions:</h3> We report that global measures of brain connectivity estimated by introducing virtual lesions in healthy structural connectomes significantly increase the accuracy of motor recovery prediction in stroke patients compared to focal and clinical measures used until now. This indirect estimation of the stroke impact on the whole brain connectome is more readily available than direct measures of structural connectivity obtained with magnetic resonance imaging (MRI) and would, therefore, have a more substantial clinical impact. <b>Disclosure:</b> An immediate family member of Dr. Pirondini has stock in Reach Neuro. The institution of Dr. Pirondini has received research support from NIH. The institution of an immediate family member of Dr. Pirondini has received research support from NIH. Dr. Pirondini has received intellectual property interests from a discovery or technology relating to health care. An immediate family member of Dr. Pirondini has received intellectual property interests from a discovery or technology relating to health care. Maria Giulia Preti has nothing to disclose. Dimitri Van De Ville has nothing to disclose. Prof. Guggisberg has nothing to disclose. Dr. Rivier has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Pyxis Partners.

Predictive Factors and Interventional Modalities of Post-stroke Motor Recovery: An Overview.

Stroke is the most common cause of motor impairment worldwide. Therefore, many factors are being investigated for their predictive and facilitatory effects on recovery of motor function after stroke.Motor recovery can be predicted through several factors, such as clinical assessment, clinical biomarkers, and gene-based variations. As for interventions, many methods are under experimental investigation that aim to improve motor recovery, including different types of pharmacological interventions, non-invasive stimulation, and rehabilitation training by inducing cortical reorganization, neuroplasticity, angiogenesis, changing the levels of neurotransmitters in the brain, and altering the inflammatory and apoptotic processes occurring after stroke. Studies have shown that clinical biomarkers combined with clinical assessment and gene-based variations are reliable factors for predicting motor recovery after stroke. Moreover, different types of interventions such as pharmacological agents (selective serotonin reuptake inhibitors {SSRI}, noradrenaline reuptake inhibitors {NARIs}, levodopa, and amphetamine), non-invasive stimulation, and rehabilitation training have shown significant results in improving functional and motor recovery.

Prediction of post-stroke motor recovery benefits from measures of sub-acute widespread network damages.

Following a stroke in regions of the brain responsible for motor activity, patients can lose their ability to control parts of their body. Over time, some patients recover almost completely, while others barely recover at all. It is known that lesion volume, initial motor impairment and cortico-spinal tract asymmetry significantly impact motor changes over time. Recent work suggested that disabilities arise not only from focal structural changes but also from widespread alterations in inter-regional connectivity. Models that consider damage to the entire network instead of only local structural alterations lead to a more accurate prediction of patients' recovery. However, assessing white matter connections in stroke patients is challenging and time-consuming. Here, we evaluated in a data set of 37 patients whether we could predict upper extremity motor recovery from brain connectivity measures obtained by using the patient's lesion mask to introduce virtual lesions in 60 healthy streamline tractography connectomes. This indirect estimation of the stroke impact on the whole brain connectome is more readily available than direct measures of structural connectivity obtained with magnetic resonance imaging. We added these measures to benchmark structural features, and we used a ridge regression regularization to predict motor recovery at 3 months post-injury. As hypothesized, accuracy in prediction significantly increased (R 2 = 0.68) as compared to benchmark features (R 2 = 0.38). This improved prediction of recovery could be beneficial to clinical care and might allow for a better choice of intervention.

A new score combining compound muscle action potential (CMAP) amplitudes and motor score is predictive of motor outcome after AVXS-101 (Onasemnogene Abeparvovec) SMA therapy

Spinal muscular atrophy 1 (SMA1) is a severe early genetic disease with degeneration of motor neurons. Motor development is still suboptimal after gene replacement therapy in symptomatic patients. In this study, compound muscle action potential (CMAP) amplitudes were explored as predictors of motor recovery after gene therapy. Thirteen symptomatic SMA1 patients were prospectively included at the Necker Enfants Malades Hospital, Paris, France (Cohort 1) and 12 at the other pediatric neuromuscular reference centers of the French Filnemus network (Cohort 2). In Cohort 1, median CMAP amplitudes showed the best improvement between baseline and the 12 months visit compared to the other tested nerves (ulnar, fibular and tibial). High median CMAP amplitudes at baseline was associated with unaided sitting achievement at M6 (AUC 90%). None of the patients with CHOPINTEND at M0 < 30/64 and median CMAP < 0.5 mV achieved unaided sitting at M6 and this result was confirmed on Cohort 2 used as an independent validation data. Thus, median CMAP amplitude is a valid biomarker for routine practice to predict sitting at M6. A median CMAP amplitude over 0.5 mV at baseline may predict better motor recovery.

Abstract 7: Prediction Of Post-stroke Motor Recovery Benefits From Measures Of Sub-acute Widespread Network Damages

Background: Following an acute ischemic stroke, some patients recover almost completely, while others barely recover. It is known that lesion volume, initial motor impairment, and cortico-spinal tract asymmetry (benchmark features) significantly impact motor changes over time. However, accurate prediction models of patients’ recovery are still missing. Recent work suggested that disabilities arise not only from focal structural changes, but also from widespread alterations in inter-regional connectivity. Here, we built on this hypothesis to develop a more accurate post-stroke motor recovery prediction model. Methods: We evaluated a dataset of 37 patients (17 women) with upper extremity motor strokes. Their impairment was assessed at 2 weeks (baseline) and 3 months after the stroke using the Fugl-Meyer-Assessment. 18 graph measures of global structural connectivity were obtained using the patients’ T2 lesion mask to virtually reproduce the lesions in 60 healthy streamline tractography connectomes. We added these indirect measures to the benchmark features, and we used a ridge regression regularization in a leave-one-out cross-validation framework to predict motor recovery at 3 months post-injury. Results: Accuracy in prediction significantly increased when using brain connectivity measures (R 2 =0.68, 95% CI: 0.64-0.74) as compared to benchmark features alone (R 2 =0.38, 95% CI: 0.31-0.51). Conclusion: We reported that global measures of brain connectivity estimated by introducing virtual lesions in healthy structural connectomes significantly increase the accuracy of motor recovery prediction in stroke patients compared to focal and clinical measures used until now. This indirect estimation of the stroke impact on the whole brain connectome is more readily available than direct measures of structural connectivity obtained with magnetic resonance imaging (MRI) and would, therefore, have a more substantial clinical impact.

Prediction of Post Stroke recovery: Artificial intelligence could be a key of success.

Madam, Stroke is a leading cause of death and disability around the globe and particularly in low- and middle-income countries, and this burden is increasing. (1) Its incidence in Pakistan, is also increasing daily and shares a significant burden by contributing to an exponential expenditure of resources, finances, community manpower, health services and overall economy. (2) Overall disability burden can be reduced remarkably, if early recovery prediction can be formulated for stroke parameters such as upper limb impairment, swallowing, Shoulder Abduction and Finger Extension (SAFE) score, Motor Evoked Potential (MEP) status, National Institute of Health Stroke Scale (NIHSS) scoring. Existing relevant evidences for the early prediction of stroke recovery, reported the use of blood biomarker as an objective indicator. And among them, some serve as a guide in decision-making for clinical practice, such as: Brain natriuretic peptide (BNP), D-Dimer, and have potential in improving the diagnosis and the management of patients with stroke. MRI findings have also made an accurate prognosis about behavioral outcomes after stroke based on the severity of cognitive impairments. (3) For predicting recovery after stroke, various algorithms approaches have also been done since last 10 years and among Predict Recovery Potential (PREP2), (GRAVo) and (PRESS) models of prediction, studies have supported that the PREP2 algorithm was regarded as potentially valid. To date, only one approach has combined biomarkers within the first few days after stroke to make predictions for individual patients. The Predict Recovery Potential (PREP) algorithm predicts upper-limb functional outcomes by combining biomarkers, neurophysiological and neuroimaging measures to make a prognosis. PREP2 algorithm is probably the easiest approach to operationalize among predictive models and serves as a benchmark for predicting motor recovery after stroke. (4) So, till now, there is no consensus among both clinicians and scientists on how to apply a specific predictive model in clinical routine or research protocols, in which biological and psycho-social factors can be collectively incorporated with Artificial intelligence. Hence these steps are mandatory to be implemented in predictive models considering all the factors mentioned above- and including other factors like cost, knowledge, interface development, resources, time and expertise of both scientists and clinicians. ---Continue

Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke

Background: Machine learning (ML) is an artificial intelligence technique in which a system learns patterns and rules from a given data. Objectives: The objective of the study was to investigate the potential of ML for predicting motor recovery in stroke patients. Methods: We analyzed data from 833 consecutive stroke patients using 3 ML algorithms: deep neural network (DNN), random forest, and logistic regression. We created a practical ML model using the most common data measured in almost all rehabilitation hospitals as input data. Demographic and clinical data, including modified Brunnstrom classification (MBC) and functional ambulation classification (FAC), were collected when patients were transferred to the rehabilitation unit (8–30 days) and 6 months after stroke onset and were used as input data. Motor outcomes at 6 months after stroke onset of the affected upper and lower extremities were classified according to MBC and FAC, respectively. Patients with an MBC of <5 and an FAC of <4 at 6 months after stroke onset were considered to have a “poor” outcome, whereas those with MBC ≥5 and FAC ≥4 were considered to have a “good” outcome. Results: The area under the curve (AUC) for the DNN model for predicting motor function was 0.836 for the upper and lower limb motor functions. For the random forest and logistic regression models, the AUCs were 0.736 and 0.790 for the upper and lower limb motor functions, respectively. The AUCs for the random forest and logistic regression models were 0.741 and 0.795 for the upper and lower limb motor functions, respectively. Conclusion: Although we used simple and common data that can be obtained in clinical practice as variables, our DNN algorithm was useful for predicting motor recovery of the upper and lower extremities in stroke patients during the recovery phase.

Prediction of motor recovery after ischemic stroke: Clinical and diffusion tensor imaging study

BackgroundThe severity of stroke-induced disruption to the corticospinal tract (CST) would be predictable to affect motor outcome. Diffusion tensor imaging (DTI) is a noninvasive technique that can be applied to assess the structural integrity of the CST. Aim of the workTo assess the value of DTI in patients early presenting with acute ischemic stroke as a prognostic modality to predict the clinical outcome Patients and methodsThirty-four patients with acute ischemic stroke underwent clinical assessment using the National Institutes of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), Medical Research Council (MRC) score, Morticity Index (MI), and DTI to detect the degree of reduction of fractional anisotropy (FA), and pattern of CST at baseline and after 6 months follow up. Seventeen age, sex matched controls underwent DTI assessment. ResultsThe stroke patients showed a significant reduction in the baseline FA values of the CSTs on the affected sides compared to the contralateral sides and controls. Moreover, they showed lower mean baseline FA lesion side and FA ratio(rFA) compared to follow up. The patients with high baseline FA, rFA showed good recovery response with cut off values of 0.483, 0.948 respectively. There was a significant negative correlation between baseline FA on the lesion side, rFA and follow up NIHSS, and MRS scores and they had a significant positive correlation with follow up MI scores. Conclusion: Patients with higher baseline FA, rFA values were correlated with better motor recovery, and could predict the motor recovery in ischemic stroke patients.

Correlation of Body Composition via Bioelectrical Impedance Analysis and Motor Function and Recovery of Upper Extremity in Patients Undergoing Stroke Rehabilitation.

Bioelectrical impedance analysis (BIA) has been used to investigate the body compositions and predict functional outcomes in patients with stroke, while the role of BIA to predict motor function or recovery in stroke has not been clarified. This study aimed to investigate relationship between body composition measured by BIA and upper limb motor function and recovery. Body compositions (soft tissue lean mass, phase angle, body fat mass and body water) of fifty patients who are admitted to the stroke rehabilitation unit were segmentally analyzed via BIA. The motor recovery of upper extremity (UE) was evaluated via Fugl-Meyer Assessment (UE-FMA) at the time of transfer and discharge. Correlations between body composition and UE-FMA at discharge were analyzed using Spearman correlation coefficient. Multiple regression analysis was used to determine the regression between body composition and motor function and recovery. The Δ Phase angle, the difference of both sides was significantly linearly inversely correlated with UE-FMA at discharge. However, in multiple regression analysis, body compositions including phase angle did not significantly predict motor function at discharge or motor recovery. The Δ Phase angle is related to the severity of upper limb motor function at discharge in subacute stroke patients, and further studies are needed to determine its value as a predictor for motor recovery.

Modeling functional network topology following stroke through graph theory: functional reorganization and motor recovery prediction.

The study of functional reorganization following stroke has been steadily growing supported by advances in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). Concomitantly, graph theory has been increasingly employed in neuroscience to model the brain's functional connectivity (FC) and to investigate it in a variety of contexts. The aims of this study were: 1) to investigate the reorganization of network topology in the ipsilesional (IL) and contralesional (CL) hemispheres of stroke patients with (motor stroke group) and without (control stroke group) motor impairment, and 2) to predict motor recovery through the relationship between local topological variations of the functional network and increased motor function. We modeled the brain's FC as a graph using fMRI data, and we characterized its interactions with the following graph metrics: degree, clustering coefficient, characteristic path length, and betweenness centrality (BC). For both patient groups, BC yielded the largest variations between the two analyzed time points, especially in the motor stroke group. This group presented significant correlations (P<0.05) between average BC changes and the improvements in upper-extremity Fugl-Meyer (UE-FM) scores at the primary sensorimotor cortex and the supplementary motor area for the CL hemisphere. These regions participate in processes related to the selection, planning, and execution of movement. Generally, higher increases in average BC over these areas were related to larger improvements in UE-FM assessment. Although the sample was small, these results suggest the possibility of using BC as an indication of brain plasticity mechanisms following stroke.

Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers.

Background: The efficacy of upper-limb Robot-assisted Therapy (ulRT) in stroke subjects is well-established. The robot-measured kinematic data can assess the biomechanical changes induced by ulRT and the progress of patient over time. However, literature on the analysis of pre-treatment kinematic parameters as predictive biomarkers of upper limb recovery is limited.Objective: The aim of this study was to calculate pre-treatment kinematic parameters from point-to-point reaching movements in different directions and to identify biomarkers of upper-limb motor recovery in subacute stroke subjects after ulRT.Methods: An observational retrospective study was conducted on 66 subacute stroke subjects who underwent ulRT with an end-effector robot. Kinematic parameters were calculated from the robot-measured trajectories during movements in different directions. A Generalized Linear Model (GLM) was applied considering the post-treatment Upper Limb Motricity Index and the kinematic parameters (from demanding directions of movement) as dependent variables, and the pre-treatment kinematic parameters as independent variables.Results: A subset of kinematic parameters significantly predicted the motor impairment after ulRT: the accuracy in adduction and internal rotation movements of the shoulder was the major predictor of post-treatment Upper Limb Motricity Index. The post-treatment kinematic parameters of the most demanding directions of movement significantly depended on the ability to execute elbow flexion-extension and abduction and external rotation movements of the shoulder at baseline.Conclusions: The multidirectional analysis of robot-measured kinematic data predicts motor recovery in subacute stroke survivors and paves the way in identifying subjects who may benefit more from ulRT.

Neural correlates of walking post-stroke: neuroimaging insights from the past decade.

Walking dysfunction such as slow walking speed and reduced independent mobility are common impairments following stroke. Neural mechanisms of upper limb impairment and motor recovery have been highly studied, while less is known about the neural correlates of walking dysfunction and rehabilitation after stroke. Our objective was to review the literature on neuroimaging correlates of walking and walking recovery post-stroke to provide a more comprehensive picture of neurological regions of interest. We searched the databases PubMed, CINAHL, Web of Science, and Cochrane Trials for articles published in English between January 1, 2010 and November 30, 2020 that assessed walking after stroke through neuroimaging and various clinical measures. The following key words were used: stroke, gait, walking, rehabilitation, brain mapping, neuroimaging, neural control of walking, motor recovery and motor function, and resulted in eighteen articles included in this review. These articles revealed regions of interest associated with lower extremity impairment and walking post-stroke to include the putamen, caudate, insula, pallidum, superior temporal gyrus, internal capsule, superior longitudinal fasciculus, corticospinal tract, corona radiata, and white matter associated with the pedunculopontine nucleus. This information strengthens our understanding of supraspinal control of walking post-stroke. However, future research on lesion location, functional and structural connectivity, and walking deficits is needed to confidently associate specific brain regions and white matter tracts/connectivity with specific impairments. Greater insight into neuromechanisms associated with response to neurorehabilitation post-stroke could improve treatment selection and prediction of motor recovery.

Functional brain networks assessed with surface electroencephalography for predicting motor recovery in a neural guided intervention for chronic stroke.

Predicting whether a chronic stroke patient is likely to benefit from a specific intervention can help patients establish reasonable expectations. It also provides the basis for candidates selecting for the intervention. Recent convergent evidence supports the value of network-based approach for understanding the relationship between dysfunctional neural activity and motor deficits after stroke. In this study, we applied resting-state brain connectivity networks to investigate intervention-specific predictive biomarkers of motor improvement in 22 chronic stroke participants who received either combined action observation with EEG-guided robot-hand training (Neural Guided-Action Observation Group, n = 12, age: 34–68 years) or robot-hand training without action observation and EEG guidance (non-Neural Guided-text group, n = 10, age: 42–57 years). The robot hand in Neural Guided-Action Observation training was activated only when significant mu suppression (8–12 Hz) was detected from participant’s EEG signals in ipsilesional hemisphere while it was randomly activated in non-Neural Guided-text training. Only the Neural Guided-Action Observation group showed a significant long-term improvement in their upper-limb motor functions (P < 0.5). In contrast, no significant training effect on the paretic motor functions was found in the non-Neural Guided-text group (P > 0.5). The results of brain connectivity estimated via EEG coherence showed that the pre-training interhemispheric connectivity of delta, theta, alpha and contralesional connectivity of beta were motor improvement related in the Neural Guided-Action Observation group. They can not only differentiate participants with good and poor recovery (interhemispheric delta: P = 0.047, Hedges’ g = 1.409; interhemispheric theta: P = 0.046, Hedges’ g = 1.333; interhemispheric alpha: P = 0.038, Hedges’ g = 1.536; contralesional beta: P = 0.027, Hedges’ g = 1.613) but also significantly correlated with post-training intervention gains (interhemispheric delta: r = −0.901, P < 0.05; interhemispheric theta: r = −0.702, P < 0.05; interhemispheric alpha: r = −0.641, P < 0.05; contralesional beta: r = −0.729, P < 0.05). In contrast, no EEG coherence was significantly correlated with intervention gains in the non-Neural Guided-text group (all ). Partial least square regression showed that the combination of pre-training interhemispheric and contralesional local connectivity could precisely predict intervention gains in the Neural Guided-Action Observation group with a strong correlation between predicted and observed intervention gains (r = 0.82) and between predicted and observed intervention outcomes (r = 0.90). In summary, EEG-based resting-state brain connectivity networks may serve clinical decision-making by offering an approach to predicting Neural Guided-Action Observation training-induced motor improvement.