Early Initiation of Exoskeletal Robotic Gait Training Improves Functional Outcomes in the Patients with Stroke: A Retrospective Observational Study.

INTRODUCTION: The effect of early initiation of gait training using hybrid assistive limb (HAL) remains unclear. This observational study aimed to investigate whether early initiation of gait training using HAL improves functional outcomes in patients with stroke. METHODS: We retrospectively analyzed patients with acute stroke admitted to our facility. HAL was used for exoskeletal robotic gait training. Study participants were median split into an early group and a late group based on the days from stroke onset to initiation of gait training using HAL. The functional outcomes, defined by the Brunnstrom recovery stage (BRS), modified Rankin Scale (mRS), and Functional Independence Measure (FIM) at discharge, were compared using propensity score-matched analysis. RESULTS: We performed a propensity score-matched analysis in 63 patients with stroke (31 from the early group and 32 from the late group), and 17 pairs were matched. There were no significant differences in discharge in the BRS of the upper limb and finger in the post-matched cohort. On the other hand, the BRS of the lower limb in the early group was significantly higher than that in the late group. In addition, the mRS, but not FIM scores, was significantly better in the early group than that in the late group. CONCLUSIONS: In conclusion, early initiation of gait training using HAL might improve the motor function of the paralyzed lower limb and disability in patients with stroke.

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.

BACKGROUND: The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is limited, and there are no clear criteria for determining which individuals should receive such treatment. The current study aimed to develop a clinical prediction rule using machine learning to identify the characteristics of patients with stroke who can the achieve minimal clinically important difference of the Fugl-Meyer Upper Extremity Evaluation (FMA-UE) after single-joint hybrid assistive limb (HAL-SJ) rehabilitation. METHODS: This study included 71 patients with subacute stroke who received HAL-SJ rehabilitation. The chi-square automatic interaction detector (CHAID) model was applied to predict improvement in upper limb motor function. Based the analysis using CHAID, age, sex, days from stroke onset to the initiation of HAL-SJ rehabilitation, and upper limb motor and cognitive functions were used as independent variables. Improvement in upper limb motor function was determined based on the minimal clinically important difference of the FMA-UE, which was used as a dependent variable. RESULTS: According to the CHAID model, the FMA-UE score during the initiation of HAL-SJ rehabilitation was the most significant predictive factor for patients who are likely to respond to the intervention. Interestingly, this therapy was more effective in patients with moderate upper limb motor dysfunction and early initiation of HAL-SJ rehabilitation. The accuracy of the CHAID model was 0.89 (95% confidence interval: 0.81-0.96). CONCLUSION: We developed a clinical prediction rule for identifying the characteristics of patients with stroke whose upper limb motor function can improve with HAL-SJ rehabilitation.

Simplified Prehospital Prediction Rule to Estimate the Likelihood of 4 Types of Stroke: The 7-Item Japan Urgent Stroke Triage (JUST-7) Score.

OBJECTIVE: Prehospital prediction models to estimate the likelihood of several types of stroke (large vessel occlusion [LVO], intracranial hemorrhage [ICH], and subarachnoid hemorrhage [SAH], and other types of stroke) should be useful to transfer those with suspected stroke to appropriate facilities. We recently reported Japan Urgent Stroke Triage (JUST) score with 21 items had excellent predictive abilities, and we further tried to simplify the score with parsimonious items and comparable predictive abilities. METHODS: We conducted historical and prospective multicenter cohort studies at 8 centers from June 2015 to March 2018. We developed the prediction rules with select variables from JUST score for LVO, ICH, SAH and other types of stroke in 2236 patients with suspected stroke in historical derivation cohort. We validated the developed prediction rules in 964 patients in prospective validation cohort. RESULTS: There were 1150 stroke, including 235 LVO, 352 ICH, 107 SAH and 456 other types of stroke in the derivation cohort. We developed the scores with 7 items (high blood pressure, arrhythmia, conjugate deviation, headache, dysarthria, disturbance of consciousness, paralysis of upper limbs) and the developed scores had area under the receiver-operating curve (AUC) of 0.84 for any type of stroke, 0.89 for LVO, 0.79 for ICH, and 0.90 for SAH in the derivation cohort. There were 490 stroke, including 102 LVO, 138 ICH, 28 SAH and 222 other types of stroke in the validation cohort. The scores well discriminated these strokes in the validation cohort (AUC of 0.76 for any type of stroke; 0.81 for LVO, 0.73 for ICH, and 0.85 for SAH). CONCLUSIONS: The simplified 7-item JUST (JUST-7) score had good predictive ability and can help healthcare providers to estimate the likelihood of different types of stroke and decide the referral hospital.

Machine Learning Algorithm Identifies the Importance of Environmental Factors for Hospital Discharge to Home of Stroke Patients using Wheelchair after Discharge.

BACKGROUND AND PURPOSE: Physical environmental factors are generally likely to become barriers for discharge to home of wheelchair users, compared with non-wheelchair users. However, the importance of environmental factors has not been investigated adequately. Application of machine learning technology might efficiently identify the most influential factors, although it is not easy to interpret and integrate various information including individual and environmental factors in clinical stroke rehabilitation. This study aimed to identify the influential factors affecting home discharge in the stroke patients who use a wheelchair after discharge by using machine learning technology. METHODS: This study used the rehabilitation database of our facility, which includes all stroke patients admitted into the convalescence rehabilitation ward. The chi-squared automatic interaction detection (CHAID) algorithm was used to develop a model to classify wheelchair-using stroke patients discharged to home or not-to-home. RESULTS: Among the variables, including basic information, motor functional factor, activities of daily living ability factor, and environmental factors, the CHAID model identified house renovation and the existence of sloping roads around the house as the first and second discriminators for home discharge. CONCLUSIONS: Our present results could scientifically clarify that the clinician need to focus on the physical environmental factors for achieving home discharge in the patients who use a wheelchair after discharge.

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method. METHOD: This was a cohort study on data from the maintained database of all patients with stroke who were admitted to the convalescence rehabilitation ward of our facility. In total, 1125 stroke patients were investigated. We developed three classification and regression tree (CART) models to identify the possibility of home discharge after inpatient rehabilitation. RESULTS: Among three models, CART model incorporating basic information, functional factor, and environmental factor variables achieved the highest accuracy for identification of home discharge. This model identified FIM dressing of the upper body (score of ≤2 or >2) as the first single discriminator for home discharge. Performing house renovation was associated with a high possibility of home discharge even in patients with stroke who had a poor FIM score in the ability to dress the upper body (≤2) at admission into the convalescence rehabilitation ward. Interestingly, many patients who performed house renovation have achieved home discharge regardless of the degree of lower limb paralysis. CONCLUSION: We identified the influential factors for realizing home discharge using the decision tree algorithm, including environmental factors, in patients with convalescent stroke.

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.

OBJECTIVES: Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised machine learning algorithms for the classification of home discharge possibility in stroke patients. MATERIALS AND METHODS: This was a secondary analysis based on the data of 481 stroke patients from the database of our institution. Five models developed by supervised machine learning algorithms, including decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machine (SVM), and random forest (RF) were compared by constructing a classification system based on the same dataset. Several parameters including classification accuracy, area under the curve (AUC), and F1 score (a weighted average of precision and recall) were used for model evaluation. RESULTS: The k-NN model had the best classification accuracy (84.0%) with a moderate AUC (0.88) and F1 score (87.8). The SVM model also showed high classification accuracy (82.6%) along with the highest AUC (0.91), sensitivity (94.4), negative predictive value (87.5), and negative likelihood ratio (0.088). The DT, LDA, and RF models had high classification accuracies (≥ 79.9%) with moderate AUCs (≥ 0.84) and F1 scores (≥ 83.8). CONCLUSIONS: Regarding model performance, the k-NN and SVM seemed the best candidate algorithms for classifying the possibility of home discharge in stroke patients.

Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis.

BACKGROUND AND PURPOSE: Accurate prediction using simple and changeable variables is clinically meaningful because some known-predictors, such as stroke severity and patients age cannot be modified with rehabilitative treatment. There are limited clinical prediction rules (CPRs) that have been established using only changeable variables to predict the activities of daily living (ADL) dependence of stroke patients. This study aimed to develop and assess the CPRs using machine learning-based methods to identify ADL dependence in stroke patients. METHODS: In total, 1125 stroke patients were investigated. We used a maintained database of all stroke patients who were admitted to the convalescence rehabilitation ward of our facility. The classification and regression tree (CART) methodology with only the FIM subscores was used to predict the ADL dependence. RESULTS: The CART method identified FIM transfer (bed, chair, and wheelchair) (score ≤ 4.0 or > 4.0) as the best single discriminator for ADL dependence. Among those with FIM transfer (bed, chair, and wheelchair) score > 4.0, the next best predictor was FIM bathing (score ≤ 2.0 or > 2.0). Among those with FIM transfer (bed, chair, and wheelchair) score ≤ 4.0, the next predictor was FIM transfer toilet (score ≤ 3 or > 3). The accuracy of the CART model was 0.830 (95% confidence interval, 0.804-0.856). CONCLUSION: Machine learning-based CPRs with moderate predictive ability for the identification of ADL dependence in the stroke patients were developed.

Effects of Exoskeletal Lower Limb Robot Training on the Activities of Daily Living in Stroke Patients: Retrospective Pre-Post Comparison Using Propensity Score Matched Analysis.

PURPOSE: There is limited evidence of gait training using newly developed exoskeletal lower limb robot called Hybrid Assistive Limb (HAL) on the function and ability to perform ADL in stroke patients. In clinical settings, we frequently find it challenging to conduct a randomized controlled trial; thus, a large-scale observational study using propensity score analysis methods is a feasible alternative. The present study aimed to determine whether exoskeletal lower limb robot training improved the ability to perform ADL in stroke patients. MATERIALS AND METHODS: Acute stroke patients who were admitted to our facility from April 2016 to March 2017 were evaluated in the conventional rehabilitation period (CRP) and those admitted from April 2017 to June 2019 were evaluated in the HAL rehabilitation period (HRP). We started a new gait rehabilitation program using HAL at the midpoint of these two periods. The functional outcomes or ADL ability outcomes of the patients in the CRP and the subsequent HRP were compared using propensity score matched analyses. RESULTS: Propensity score matching analysis was performed for 108 stroke patients (63 from the CRP and 45 from the HRP), and 36 pairs were matched. The ADL ability, defined by the FIM scores and FIM score change, was significantly higher in patients admitted during the HRP. In addition, more stroke patients obtained practical walking ability during hospitalization in the HRP. CONCLUSION: Gait training using HAL affects the ADL ability and obtaining of practical walking ability of stroke patients.

Combination of Exoskeletal Upper Limb Robot and Occupational Therapy Improve Activities of Daily Living Function in Acute Stroke Patients.

PURPOSE: Previous studies have suggested that upper limb rehabilitation using therapeutic robots improves motor function of stroke patients. However, the effect of upper limb robotic rehabilitation on improving functioning in activities of daily living (ADL) remains unclear. The present study aimed to determine whether upper limb rehabilitation using single joint Hybrid Assistive Limb (HAL-SJ) affects ADL function and the use of a hemiparetic arm in ADLs of acute stroke patients. MATERIALS AND METHODS: Twelve acute stroke patients participated in the study and were randomly divided into group A or group B. The patients in group A followed an A-B-A-B design and those in group B followed a B-A-B-A design. The patients received combination HAL-SJ and occupational therapy during A and conventional occupational therapy during B. RESULTS: Upper limb motor function and ADLs, in particular, dressing the upper body, were improved during combination HAL-SJ and occupational therapy. Interestingly, the use of a hemiparetic arm in daily life evaluated using the motor activity log was also significantly improved during A in group A. CONCLUSIONS: Combination HAL-SJ and occupational therapy affects ADL function and real use of a hemiparetic arm in the daily life of acute stroke patients.

Clinical Prediction Rules to Classify Types of Stroke at Prehospital Stage.

Background and Purpose- Endovascular therapy is effective against acute cerebral large vessel occlusion (LVO). However, many patients do not receive such interventions because of the lack of timely identification of the type of stroke. If the types of stroke (any stroke, LVO, intracranial hemorrhage [ICH], and subarachnoid hemorrhage [SAH]) were to be predicted at the prehospital stage, better access to appropriate interventions would be possible. Japan Urgent Stroke Triage (JUST) score was clinical prediction rule to classify suspected patients of acute stroke into different types at the prehospital stage. Methods- We obtained information for signs and symptoms and medical history of consecutive suspected patients of acute stroke at prehospital stage from paramedics and final diagnosis from the receiving hospital. We constructed derivation cohort in the historical multicenter cohort study from June 2015 to March 2016 and validation cohort in the prospective multicenter cohort study from August 2016 to July 2017. The derivation and the validation cohorts included 1229 and 1007 patients, respectively. We constructed multivariate logistic regression models with 21 variables to develop clinical prediction rules, which distinguish between different types of stroke: any stroke, LVO, ICH, and SAH. Results- Among the 1229 patients (median age, 72 years; 55% men) in the derivation cohort, 533 stroke, 104 LVO, 169 ICH, and 57 SAH cases were observed. The developed rules showed that the areas under the receiver operating curves were 0.88 for any stroke, 0.92 for LVO, 0.84 for ICH, and 0.89 for SAH. The validation cohort of 1007 patients (median age, 75 years; 56% men) showed that the areas under the curves of any stroke, LVO, ICH, and SAH were 0.80, 0.85, 0.77, and 0.94, respectively. Conclusions- These clinical prediction rules can help paramedics classify the suspected patients of stroke into any stroke, LVO, ICH, and SAH groups with excellent accuracy.

Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study.

Robot-assisted gait training is effective for walking independence in stroke rehabilitation, the hybrid assistive limb (HAL) is an example. However, gait training with HAL may not be effective for everyone, and it is not clear who is not expected to benefit. Therefore, we aimed to identify the characteristics of stroke patients who have difficulty gaining benefits from gait training with HAL. We conducted a single-institutional retrospective cohort study. The participants were 82 stroke patients who had received gait training with HAL during hospitalization. The dependent variable was the functional ambulation category (FAC) that a measure of gait independence in stroke patients, and five independent [age, National Institutes of Health Stroke Scale, Brunnstrom recovery stage (BRS), days from stroke onset, and functional independence measure total score (cognitive items)] variables were selected from previous studies and analyzed by logistic regression analysis. We evaluated the validity of logistic regression analysis by using several indicators, such as the area under the curve (AUC), and a confusion matrix. Age, days from stroke onset to HAL initiation, and BRS were identified as factors that significantly influenced walking independence through gait training with HAL. The AUC was 0.86. Furthermore, after building a confusion matrix, the calculated binary accuracy, sensitivity (recall), and specificity were 0.80, 0.80, and 0.81, respectively, indicated high accuracy. Our findings confirmed that older age, greater degree of paralysis, and delayed initiation of HAL-assisted training after stroke onset were associated with increased likelihood of walking dependence upon hospital discharge.

Optimal endovascular therapy technique for isolated intracranial atherothrombotic stroke-related large vessel occlusion in the acute to subacute stage.

BACKGROUND AND PURPOSE: Reocclusion after treatment is a concern in endovascular therapy (EVT) for isolated intracranial atherothrombotic stroke-related large vessel occlusion (AT-LVO). However, the optimal EVT technique for AT-LVO has not yet been investigated. This study evaluated the optimal EVT technique for AT-LVO in a real-world setting. MATERIALS AND METHODS: We conducted a historical multicenter registry study at 51 centers that enrolled patients with AT-LVO. We divided the patients into three groups based on the EVT technique: mechanical thrombectomy alone (MT-only), percutaneous transluminal angioplasty (PTA), and stent deployment (Stent). MT alone was classified into the MT-only group, PTA and MT-PTA into the PTA group, and MT-Stent, MT-PTA-Stent, PTA-Stent, and Stent-only into the Stent group. The primary outcome was the incidence of reocclusion of the treated vessels within 90 days of EVT completion. RESULTS: We enrolled 770 patients and analyzed 509 patients. The rates in the MT-only, PTA, and Stent groups were 40.7, 44.4, and 14.9%, respectively. The incidence rate of residual stenosis >70% of final angiography was significantly higher in the MT-only group than in the PTA and Stent groups (MT-only vs. PTA vs. Stent: 34.5% vs. 26.3% vs. 13.2%, p=0.002). The reocclusion rate was significantly lower in the PTA group than in the MT-only group (adjusted hazard ratio [95% confidence interval], 0.48 [0.29-0.80]). Of the patients, 83.5% experienced reocclusion within 10 days after EVT. Alarmingly, a substantial subset (approximately 62.0%) of patients underwent reocclusion within 2 days of EVT. The incidence of modified Rankin scale scores of 0-2 90 days after EVT was not significantly different among the three groups. The incidences of symptomatic intracranial hemorrhage (ICH), any other ICH, and death were not significantly different. CONCLUSIONS: The incidence rate of reocclusion was significantly lower in the PTA group than in the MT-only group. We found no significant difference in reocclusion rates between the Stent and MT-only groups. In Japan, GP IIb/IIIa inhibitors are not reimbursed. Therefore, PTA might be the preferred choice for AT-LVOs due to the higher reocclusion risk with MT-only. Reocclusion was likely to occur within 10 days, particularly within 2 days post-EVT. ABBREVIATIONS: EVT = endovascular treatment; LVO = large vessel occlusion; MT = mechanical thrombectomy; PTA = percutaneous transluminal angioplasty; ICH = intracranial hemorrhage; SD = standard deviation; IQR = interquartile range; HRs = hazard ratios; BMI = body mass index; LDL = low-density lipoprotein; HDL = high-density lipoprotein; DAPT = dual antiplatelet therapy; TAPT = triple antiplatelet therapy.

Does frequent use of an exoskeletal upper limb robot improve motor function in stroke patients?

PURPOSE: To determine how differences in frequency of the single-joint hybrid assistive limb (HAL-SJ) use affect the improvement of upper limb motor function and activities of daily living (ADL) in stroke patients. MATERIALS AND METHODS: Subacute stroke patients were divided into the high or low frequency of HAL-SJ use groups. The two groups were matched by propensity score, and the degree of changes 30 days after initiating HAL-SJ use was compared. A logistic regression analysis was performed to examine whether frequent use would increase the number of subjects experiencing the efficacy of more than the minimal clinically important difference (MCID) of Fugl-Meyer assessment (FMA). RESULTS: Twenty-five stroke patients were matched by propensity score, and nine pairs were matched. The high-frequency group showed a significantly superior increase to total FMA shoulder, elbow, forearm, and Barthel index compared with the low-frequency group. Logistic regression analysis revealed no significant associations between frequent use and MCID. CONCLUSIONS: The frequency of HAL-SJ use may affect the improvement of motor function and ADL ability of the upper limb with exception of the fingers and wrist. However, the frequency of intervention was not effective enough to further increase the number of subjects with clinically meaningful changes in upper limb motor function.IMPLICATIONS FOR REHABILITATIONThe current study aimed to clarify how differences in the frequency of single-joint hybrid assistive limb (HAL-SJ) use can affect the improvement of upper-limb motor functions and ADL in subacute stroke patients.Our results implied that the frequency of HAL-SJ use may influence the recovery of upper limb function.However, even if HAL-SJ is used frequently, it does not mean that more patients will achieve clinically meaningful recovery.

Endovascular therapy for acute intracranial large vessel occlusion due to atherothrombosis: Multicenter historical registry.

BACKGROUND: Atherothrombotic stroke-related large vessel occlusion (AT-LVO) is caused by two etiologies, the intracranial artery occlusion due to in situ occlusion (intracranial group) or due to embolism from cervical carotid occlusion or stenosis (tandem group). The prognosis and reocclusion rate of each etiology after endovascular therapy (EVT) is unclear. METHODS: We conducted a historical multicenter registry study at 51 Japanese centers to compare the prognoses of AT-LVO between two etiologies. The primary outcome was the incidence of recurrent ischemic stroke or reocclusion of the treated vessels within 90 days after EVT. Each of the primary outcome means the incidence of recurrent ischemic stroke and reocclusion of the treated vessels within 90 days after EVT. RESULTS: We analyzed 582 patients (338 in the intracranial group and 244 in the tandem group). Patients in the intracranial group were younger (mean 71.9 vs 74.5, p=0.003), more of them were female and fewer of them were current smokers than those in the tandem group. In the tandem group, the patients' National Institutes of Health Stroke Scale score on admission was higher (13 vs 15, p=0.006), onset to puncture time was shorter (299 [145-631] vs 232 [144-459] minutes, p=0.03) and Alberta Stroke Program Early CT Score (ASPECTS) was lower (8 [7-9] vs 8 [6-9], p=0.0002). The primary outcome was higher in the intracranial group (22.5% vs 8.2%, p<0.0001). However, any ICH and death were not significantly different in the two groups. CONCLUSIONS: The incidence of recurrent ischemic stroke or reocclusion after EVT for AT-LVO was higher in the intracranial group.

Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML).

In conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task.

Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.

Effect of region-wide use of prehospital stroke triage scale on management of patients with acute stroke.

BACKGROUND: Prehospital stroke triage scales help with the decision to transport patients with suspected stroke to suitable hospitals. OBJECTIVE: To explore the effect of the region-wide use of the Japan Urgent Stroke Triage (JUST) score, which can predict several types of stroke: large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction other than LVO (CI). METHODS: We implemented the JUST score and conducted a retrospective and prospective multicenter cohort study at 13 centers in Hiroshima from April 1, 2018, to March 31, 2020. We investigated the success rate of the first request to the hospital, on-scene time, and transport time to hospital. We evaluated the door-to-puncture time, puncture-to-reperfusion time, and 90-day outcome among patients with final diagnoses of LVO. RESULTS: The cohort included 5141 patients (2735 before and 2406 after JUST score implementation). Before JUST score implementation, 1269 strokes (46.4%) occurred, including 140 LVO (5.1%), 394 ICH (14.4%), 120 SAH (4.4%), and 615 CI (22.5%). The JUST score was used in 1484 (61.7%) of the 2406 patients after implementation, which included 1267 (52.7%) cases of stroke (186 LVO (7.7%), 405 ICH (16.8%), 109 SAH (4.5%), and 567 CI (23.6%)). Success rate of the first request to the hospital significantly increased after JUST score implementation (76.3% vs 79.7%, p=0.004). JUST score implementation significantly shortened the door-to-puncture time (84 vs 73 min, p=0.03), but the prognosis remained unaltered among patients with acute LVO. CONCLUSIONS: Use of prehospital stroke triage scales improved prehospital management and preparation time of intervention among patients with acute stroke.

Primary angiitis of the central nervous system mimicking dissemination from brainstem neoplasm: a case report.

BACKGROUND: Diagnosis of PACNS is very difficult because of the variety of clinical manifestation, especially neurologic and MRI findings. We report the case of a 68-year-old woman with PACNS. CASE DESCRIPTION: Her clinical symptoms mimicked malignant brainstem neoplasm disseminated to third and fourth ventricles and cervical spinal cord. Brain biopsy could not be correctly diagnostic. In spite of a trial of high-dose steroids, repeated MRI showed an increasing number of lesions. Two weeks after surgery, she died of respiratory dysfunction. Postmortem examination suggested PACNS, after consideration of histories, laboratory and radiologic findings, and the lack of systemic disease. CONCLUSION: Although brain biopsy represents the gold standard measure, combination with several other examinations might be necessary to arrive at an early and definitive diagnosis. PACNS should be considered the differential diagnosis of intracranial tumors.