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Stepwise linear regression (SLR) is the most common approach to predicting activities of daily living at discharge with the Functional Independence Measure (FIM) in stroke patients, but noisy nonlinear clinical data decrease the predictive accuracies of SLR. Machine learning is gaining attention in the medical field for such nonlinear data. Previous studies reported that machine learning models, regression tree (RT), ensemble learning (EL), artificial neural networks (ANNs), support vector regression (SVR), and Gaussian process regression (GPR), are robust to such data and increase predictive accuracies. This study aimed to compare the predictive accuracies of SLR and these machine learning models for FIM scores in stroke patients. Subacute stroke patients (N = 1,046) who underwent inpatient rehabilitation participated in this study. Only patients' background characteristics and FIM scores at admission were used to build each predictive model of SLR, RT, EL, ANN, SVR, and GPR with 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) values were compared between the actual and predicted discharge FIM scores and FIM gain. Machine learning models (R2 of RT = 0.75, EL = 0.78, ANN = 0.81, SVR = 0.80, GPR = 0.81) outperformed SLR (0.70) to predict discharge FIM motor scores. The predictive accuracies of machine learning methods for FIM total gain (R2 of RT = 0.48, EL = 0.51, ANN = 0.50, SVR = 0.51, GPR = 0.54) were also better than of SLR (0.22). This study suggested that the machine learning models outperformed SLR for predicting FIM prognosis. The machine learning models used only patients' background characteristics and FIM scores at admission and more accurately predicted FIM gain than previous studies. ANN, SVR, and GPR outperformed RT and EL. GPR could have the best predictive accuracy for FIM prognosis.

Cerebellar transcranial direct current stimulation (ctDCS) modulates the primary motor cortex (M1) via cerebellar brain inhibition (CBI), which affects motor control in humans. However, the effects of ctDCS on motor control are inconsistent because of an incomplete understanding of the real-time changes in the M1 excitability that occur during ctDCS, which determines motor output under regulation by the cerebellum. This study investigated changes in corticospinal excitability and motor control during ctDCS in healthy individuals. In total, 37 healthy individuals participated in three separate experiments. ctDCS (2 mA) was applied to the cerebellar hemisphere during the rest condition or a pinch force–tracking task. Motor-evoked potential (MEP) amplitude and the F-wave were assessed before, during, and after ctDCS, and pinch force control was assessed before and during ctDCS. The MEP amplitudes were significantly decreased during anodal ctDCS from 13 min after the onset of stimulation, whereas the F-wave was not changed. No significant changes in MEP amplitudes were observed during cathodal and sham ctDCS conditions. The MEP amplitudes were decreased during anodal ctDCS when combined with the pinch force–tracking task, and pinch force control was impaired during anodal ctDCS relative to sham ctDCS. The MEP amplitudes were not significantly changed before and after all ctDCS conditions. Motor cortical excitability was suppressed during anodal ctDCS, and motor control was unskilled during anodal ctDCS when combined with a motor task in healthy individuals. Our findings provided a basic understanding of the clinical application of ctDCS to neurorehabilitation.

Post-stroke disability affects patients’ lifestyles after discharge, and it is essential to predict functional recovery early in hospitalization to allow time for appropriate decisions. Previous studies reported important clinical indicators, but only a few clinical indicators were analyzed due to insufficient numbers of cases. Although review articles can exhaustively identify many prognostic factors, it remains impossible to compare the contribution of each predictor. This study aimed to determine which clinical indicators contribute more to predicting the functional independence measure (FIM) at discharge by comparing standardized coefficients. In this study, 980 participants were enrolled to build predictive models with 32 clinical indicators, including the stroke impairment assessment set (SIAS). Trunk function had the most significant standardized coefficient of 0.221. The predictive models also identified easy FIM sub-items, SIAS, and grip strength on the unaffected side as having positive standardized coefficients. As for the predictive accuracy of this model, R 2 was 0.741. This is the first report that included FIM sub-items separately in post-stroke predictive models with other clinical indicators. Trunk function and easy FIM sub-items were included in the predictive model with larger positive standardized coefficients. This predictive model may predict prognosis with high accuracy, fewer clinical indicators, and less effort to predict.

Working memory (WM) can influence selective attention. However, the effect of WM load on postural standing tasks has been poorly understood, even though these tasks require attentional resources. The purpose of this study was to examine whether WM load would impact anticipatory postural adjustments (APAs) during step initiation. Sixteen healthy young adults performed stepping tasks alone or concurrently with a WM task in a dual-task design. The stepping tasks involved volitional stepping movements in response to visual stimuli and comprised of simple and choice reaction time tasks and the Flanker task which consisted of congruent and incongruent (INC) conditions. In the dual-task condition, subjects were required to memorize either one or six digits before each stepping trial. Incorrect weight transfer prior to foot-lift, termed APA errors, reaction time (RT), and foot-lift time were measured from the vertical force data. The results showed that APA error rate was significantly higher when memorizing six-digit than one-digit numerals in the INC condition. In addition, RT and foot-lift time were significantly longer in the INC condition compared to the other stepping conditions, while there was no significant effect of WM load on RT or foot-lift time. These findings suggest that high WM load reduces the cognitive resources needed for selective attention and decision making during step initiation.

Background Upper-limb spasticity frequently occurs after stroke and there is a clinical need for more effective therapies. The Phase III J-PURE study assessed the efficacy and safety of incobotulinumtoxinA up to 400 U for post-stroke upper-limb spasticity in Japan. Methods In the 12-week main period (MP) of this double-blind, placebo-controlled study, Japanese subjects with upper-limb spasticity received one injection cycle of incobotulinumtoxinA 400 U, 250 U, or matching placebo. Eligible subjects enrolled in an open-label extension (OLEX) period of three injection cycles of incobotulinumtoxinA 400 U (32–40 weeks). The primary objective was to establish the efficacy of a single incobotulinumtoxinA injection using the Modified Ashworth Scale (MAS) wrist score. Secondary efficacy outcomes and safety were also assessed. Results Among 100 treated subjects, AUCs for incobotulinumtoxinA 400 and 250 U were significantly different versus placebo ( p  = 0.0014 and p  = 0.0031, respectively) for change from baseline in MAS wrist score to the end of the MP, with similar results from baseline to week 4. IncobotulinumtoxinA 400 U was superior versus placebo across other spasticity patterns and at most study visits. Improvements were maintained throughout the OLEX period. Disability Assessment Scale and Investigator’s Clinical Global Impression scores improved significantly for incobotulinumtoxinA 400 U versus placebo from baseline to week 4 ( p  = 0.0067 and p  < 0.0001, respectively). IncobotulinumtoxinA was well tolerated up to 52 weeks, with no unexpected adverse events. Conclusion IncobotulinumtoxinA reduced (pathologically) increased muscle tone, improved functionality and was well tolerated in Japanese subjects with post-stroke upper-limb spasticity.

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms—specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms ( p  < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893–0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable.

Reciprocal inhibition (RI) between leg muscles is crucial for smooth movement. Pedaling is a rhythmic movement that can increase RI in healthy individuals. Transcutaneous spinal cord stimulation (tSCS) stimulates spinal neural circuits by targeting the afferent fibers. Pedaling with simultaneous tSCS may modulate the plasticity of the spinal neural circuit and alter neural activity based on movement and muscle engagement. This study investigated the RI changes after pedaling and tSCS and determined the phase of pedaling in which tSCS should be applied for optimal RI modulation in healthy individuals. Eleven subjects underwent three interventions: pedaling combined with tSCS during the early phase of lower extension (phase 1), pedaling combined with tSCS during the late phase of lower flexion (phase 4) of the pedaling cycle, and pedaling combined with sham tSCS. The RI from the tibialis anterior to the soleus muscle was assessed before, immediately after, 15 min, and 30 min after the intervention. RI increased immediately after phase 4 and pedaling combined with sham tSCS, whereas no changes were observed after phase 1. These results demonstrate that tSCS modulates RI changes induced by pedaling in a stimulus phase-dependent manner in healthy individuals. However, the mechanism involved in this intervention needs to be explored to achieve higher efficacy.

Background Patients with stroke in rehabilitation wards are at an increased risk of falling. Although patients’ participation in establishing medical safety is considered crucial, there is limited evidence on their perspectives of falls. This study aims to comprehensively elucidate the subjective falling experience of patients with stroke who have been admitted to rehabilitation wards. Methods Twenty-three consecutive patients with stroke (44 to 90 years) who experienced a fall during hospitalisation were interviewed within 1 week after the fall, and thematic analysis was used to analyse the data. Results Five themes surrounding fall events were extracted from the narratives: ‘Psychological background before the action’, ‘Support for the action’, ‘Direct causes of the fall’, ‘Patients’ awareness after the fall’, and ‘Changes in attitudes and behaviours after the fall’. ‘Psychological background before the action’ comprised hastiness or hesitation to call for help. Participants often took an action based on ‘Support for the action’ derived from their past experiences of moving safely, their confidence, and/or motivation to challenge themselves to move. ‘Direct causes of the fall’ consisted of unfamiliar actions, training fatigue, the surrounding environment, reduced physical function due to paralysis, lack of attention, overconfidence in their ability, and insufficient prediction of falls. ‘Patients’ awareness after the fall’ consisted of re-affirming difficult movements, the need for rehabilitation, a reduced ability to move, an increased risk of falling, the need for attention while moving, a fear of falling, and a lack of lessons learned from falling. Finally, patients demonstrated ‘Changes in attitudes and behaviours after the fall’ such as embodying a positive attitude to cope with the risk of falling or behavioural changes to reduce the risk of falling. Conclusions Comprehensive information on patients’ perspectives before and after the fall was elucidated, uncovering many aspects including the psychological background for why patients engaged in risky behaviours resulting in falls, presence of positive thinking, and behaviour after the fall. By incorporating the patients’ views on fall incidences and their assessment, we can develop appropriate prevention strategies against falls.

Background Robot-assisted rehabilitation for patients with stroke is promising. However, it is unclear whether additional balance training using a balance-focused robot combined with conventional rehabilitation programs supplements the balance function in patients with stroke. The purpose of this study was to compare the effects of Balance Exercise Assist Robot (BEAR) training combined with conventional inpatient rehabilitation training to those of conventional inpatient rehabilitation only in patients with hemiparetic stroke. We also aimed to determine whether BEAR training was superior to intensive balance training. Methods This assessor-blinded randomized controlled trial included 60 patients with first-ever hemiparetic stroke, admitted to rehabilitation wards between December 2016 and February 2019. Patients were randomly assigned to one of three groups, robotic balance training and conventional inpatient rehabilitation (BEAR group), intensive balance training and conventional inpatient rehabilitation (IBT group), or conventional inpatient rehabilitation-only (CR group). The intervention duration was 2 weeks, with assessments conducted pre- and post-intervention, and at 2 weeks follow-up. The primary outcome measure was a change in the Mini-Balance Evaluation Systems Test (Mini-BESTest) score from baseline. Results In total, 57 patients completed the intervention, and 48 patients were evaluated at the follow-up. Significant improvements in Mini-BESTest score were observed in the BEAR and IBT groups compared with in the CR group post-intervention and after the 2-week follow-up period (P < 0.05). Conclusions The addition of balance exercises using the BEAR alongside conventional inpatient rehabilitation improved balance in patients with subacute stroke. Trial registration https://www.umin.ac.jp/ctr ; Unique Identifier: UMIN000025129. Registered on 2 December 2016.

Background Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has the potential to induce motor cortical plasticity in humans. It is well known that motor cortical plasticity plays an essential role in motor learning and recovery in patients with stroke and neurodegenerative disorders. However, it remains unclear how cognitive function influences motor cortical plasticity induced by tDCS. The present study aimed to investigate whether anodal tDCS combined with attention to a target muscle could enhance motor cortical plasticity and improve motor learning in healthy individuals. Methods Thirty-three healthy volunteers were assigned to two experiments. In experiment 1, there were three interventional conditions: 1) anodal tDCS was applied while participants paid attention to the first dorsal interosseous (FDI) muscle, 2) anodal tDCS was applied while participants paid attention to the sound, and 3) anodal tDCS was applied without the participants paying attention to the FDI muscle or the sound. Anodal tDCS (2 mA, 10 min) was applied over the primary motor cortex (M1). Changes in motor evoked potentials (MEPs), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF) were assessed before and immediately after (0 min), and then 10 min, 30 min, and 60 min after each intervention. In experiment 2, we investigated whether the combination of anodal tDCS and attention to the abductor pollicis brevis (APB) muscle could facilitate the learning of a ballistic thumb movement. Results Anodal tDCS increased cortical excitability in all conditions immediately after the stimulation. Significant increases in MEPs and significant decreases in SICI were observed for at least 60 min after anodal tDCS, but only when participants paid attention to the FDI muscle. In contrast, no significant changes in ICF were observed in any condition. In experiment 2, the combination of tDCS and attention to the APB muscle significantly enhanced the acquisition of a ballistic thumb movement. The higher performance was still observed 7 days after the stimulation. Conclusions This study shows that anodal tDCS over M1 in conjunction with attention to the target muscle enhances motor cortex plasticity and improves motor learning in healthy adults. These findings suggest that a combination of attention and tDCS may be an effective strategy to promote rehabilitation training in patients with stroke and neurodegenerative disorders. Trial registration Retrospectively registered ( UMIN000036848 ).