UNASSIGNED: The rehabilitation of central post-stroke pain (CPSP) is a complex clinical challenge, and repetitive transcranial magnetic stimulation (rTMS) has been widely applied in the research of neurofunctional recovery following stroke. However, there is currently no reliable evidence-based medicine supporting the efficacy of rTMS in central post-stroke pain. This review aims to evaluate the effects of rTMS on central post-stroke pain.
UNASSIGNED: Following the PRISMA guidelines, we conducted searches on PubMed, Cochrane Library, Embase, Web of Science, CNKI, and Wan Fang Data Knowledge Service Platform. We searched for randomized controlled trials (RCTs) investigating the use of rTMS in treating central post-stroke pain, and conducted screening based on inclusion and exclusion criteria. Characteristics of the included RCTs were extracted. The heterogeneity of the trials was assessed using the I2 statistic. Meta-analysis was performed using Stata 17 software. Bias risk and methodological quality were evaluated using the Cochrane RoB 2 tool and the Pedro scale.
UNASSIGNED: A total of six randomized controlled trials involving 288 patients met our inclusion criteria. In our analysis, rTMS was more effective in treating patients with CPSP compared to the placebo group (SMD=-1.15, 95% CI: -1.69, -0.61, P < 0.001). Furthermore, results from subgroup analysis indicated no statistically significant difference in the improvement of pain for durations exceeding 6 months when comparing rTMS to conventional treatment (SMD=-0.80, 95% CI: -1.63, 0.03, P = 0.059).
UNASSIGNED: TMS can alleviate pain in CPSP patients and improve their motor function, but its effects on depression, anxiety, and MEP-latency are not significant.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/, CRD42024497530.

UNASSIGNED: Enhancing speech-language therapy remains the most effective strategy for improving post-stroke aphasia, However, conventional face-to-face interventions often lack the necessary therapeutic intensity. In recent years, mobile application-based speech-language therapy has emerged progressively, offering new opportunities for independent rehabilitation among aphasic patients. This review aims to evaluate the impact of mobile application-based interventions on post-stroke aphasic.
UNASSIGNED: By conducting a systematic search across five databases (PubMed, Web of Science, EMBASE, CINAHL, and Scopus), we identified and included studies that investigated the utilization of mobile application-based technologies (such as computers, iPads, etc.) for treating post-stroke aphasia.
UNASSIGNED: This study included 15 research investigations, including 10 randomized controlled trials (RCTs), four self-controlled studies and one cross-over experimental design study. Among these, eight studies demonstrated the efficacy of mobile application-based therapy in enhancing overall language functionality for post-stroke aphasia patients, three studies highlighted its potential for improving communication skills, three studies observed its positive impact on spontaneous speech expression. Moreover, four studies indicated its effectiveness in enhancing naming abilities, two studies underscored the positive influence of mobile application-based interventions on the quality of life for individuals with aphasia. Six studies noted that speech improvement effects were maintained during the follow-up period.
UNASSIGNED: The results of this review demonstrate the potential of mobile application-based interventions for improving speech-language function in individuals with aphasia. However, further high-quality research is needed to establish their effects across different domains and to delve into the comparative advantages of various treatment approaches.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=405248.

In recent years, there has been a notable increase in the clinical adoption of instrumental upper limb kinematic assessment. This trend aligns with the rising prevalence of cerebrovascular impairments, one of the most prevalent neurological disorders. Indeed, there is a growing need for more objective outcomes to facilitate tailored rehabilitation interventions following stroke. Emerging technologies, like head-mounted virtual reality (HMD-VR) platforms, have responded to this demand by integrating diverse tracking methodologies. Specifically, HMD-VR technology enables the comprehensive tracking of body posture, encompassing hand position and gesture, facilitated either through specific tracker placements or via integrated cameras coupled with sophisticated computer graphics algorithms embedded within the helmet. This review aims to present the state-of-the-art applications of HMD-VR platforms for kinematic analysis of the upper limb in post-stroke patients, comparing them with conventional tracking systems. Additionally, we address the potential benefits and challenges associated with these platforms. These systems might represent a promising avenue for safe, cost-effective, and portable objective motor assessment within the field of neurorehabilitation, although other systems, including robots, should be taken into consideration.

OBJECTIVE: Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI.
METHODS: The retrospective cohort study design was conducted to develop and validate a web-based prediction model. Adults who experienced a stroke between January 1, 2004, and September 30, 2017, were enrolled, and patients with PSCI were followed up from the stroke index date until their last follow-up. The model\'s performance metrics, including accuracy, area under the curve (AUC), recall, precision, and F1 score, were compared.
RESULTS: A total of 3209 stroke patients were included in the study. The model demonstrated an accuracy of 0.8793, AUC of 0.9200, recall of 0.6332, precision of 0.9664, and F1 score of 0.7651. In the external validation phase, the accuracy improved to 0.9039, AUC to 0.9094, recall to 0.7457, precision to 0.9168, and F1 score to 0.8224. The final model can be accessed at https://psci-calculator.my.id/.
CONCLUSIONS: Our results are able to produce a user-friendly interface that is useful for health practitioners to perform early prediction on PSCI. These findings also suggest that the provided AI model is reliable and can serve as a roadmap for future studies using AI models in a clinical setting.

UNASSIGNED: This review aimed to assess the impact of different exercise dosages on cognitive function in individuals with post-stroke cognitive impairment (PSCI).
UNASSIGNED: Four electronic databases-Embase, PubMed, Web of Science, and Cochrane Library-were systematically searched from inception to 01 January 2024, focusing on the impact of exercise therapy on cognitive function in individuals with PSCI. Only randomized controlled trials meeting the criteria were included. The exercise therapy dose and adherence were evaluated following the American College of Sports Medicine (ACSM) guidelines, categorized into a high compliance group with ACSM recommendations and a low or uncertain compliance group. A random-effects model compared the effect of ACSM compliance on cognitive function in individuals with PSCI, with the effect size represented by the standardized mean difference (SMD) and a 95% confidence interval (CI).
UNASSIGNED: In total, 18 studies meeting the criteria were included, with data from 1,742 participants. The findings suggested a beneficial effect of exercise on cognitive function in individuals with PSCI [SMD = 0.42, 95% CI (0.20, 0.65)]. Ten studies were categorized as the \"high adherence group\" and eight in the \"low or uncertain adherence group\" based on the ACSM recommendations. The subgroup analysis revealed that the SMD of the high compliance group was 0.46 (95% CI: 0.10, 0.82) (p = 0.01), while the SMD of the low or uncertain compliance group was 0.38 (95% CI: 0.07, 0.70) (p = 0.02).
UNASSIGNED: Our study indicates the beneficial impact of exercise for patients with PSCI over no exercise. Furthermore, high adherence to the exercise dose recommended by ACSM guidelines demonstrated a more substantial improvement in cognitive function than low or uncertain adherence in patients with PSCI. Systematic Review Registration: https:// www.crd.york.ac.uk/prospero/#myprospero, identifier CRD42023487915.

BACKGROUND: In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.
METHODS: Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson\'s correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.
RESULTS: Parietal-frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.
CONCLUSIONS: In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.

BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significant challenge to classifier performance, particularly for people with stroke who may encounter difficulties repeatedly performing trials. This makes it challenging to create reliable in-home rehabilitation systems that can accurately classify gestures.
METHODS: Twenty individuals who suffered a stroke performed seven different gestures (mass flexion, mass extension, wrist volar flexion, wrist dorsiflexion, forearm pronation, forearm supination, and rest) related to activities of daily living. They performed these gestures while wearing EMG sensors on the forearm, as well as FMG sensors and an IMU on the wrist. We developed a model based on prototypical networks for one-shot transfer learning, K-Best feature selection, and increased window size to improve model accuracy. Our model was evaluated against conventional transfer learning with neural networks, as well as subject-dependent and subject-independent classifiers: neural networks, LGBM, LDA, and SVM.
RESULTS: Our proposed model achieved 82.2% hand-gesture classification accuracy, which was better (P<0.05) than one-shot transfer learning with neural networks (63.17%), neural networks (59.72%), LGBM (65.09%), LDA (63.35%), and SVM (54.5%). In addition, our model performed similarly to subject-dependent classifiers, slightly lower than SVM (83.84%) but higher than neural networks (81.62%), LGBM (80.79%), and LDA (74.89%). Using K-Best features improved the accuracy in 3 of the 6 classifiers used for evaluation, while not affecting the accuracy in the other classifiers. Increasing the window size improved the accuracy of all the classifiers by an average of 4.28%.
CONCLUSIONS: Our proposed model showed significant improvements in hand-gesture recognition accuracy in individuals who have had a stroke as compared with conventional transfer learning, neural networks and traditional machine learning approaches. In addition, K-Best feature selection and increased window size can further improve the accuracy. This approach could help to alleviate the impact of physiological differences and create a subject-independent model for stroke survivors that improves the classification accuracy of wearable sensors.
BACKGROUND: The study was registered in Chinese Clinical Trial Registry with registration number CHiCTR1800017568 in 2018/08/04.

Transcranial magnetic stimulation (TMS) has been found to be promising in the neurorehabilitation of post-stroke patients. Aphasia and cognitive impairment (CI) are prevalent post-stroke; however, there is still a lack of consensus about the characteristics of interventions based on TMS and its neuropsychological and anatomical-functional benefits. Therefore, studies that contribute to creating TMS protocols for these neurological conditions are necessary. To analyze the evidence of the neuropsychological and anatomical-functional TMS effects in post-stroke patients with CI and aphasia and determine the characteristics of the most used TMS in research practice. The present study followed the PRISMA guidelines and included articles from PubMed, Scopus, Web of Science, ScienceDirect, and EMBASE databases, published between January 2010 and March 2023. In the 15 articles reviewed, it was found that attention, memory, executive function, language comprehension, naming, and verbal fluency (semantic and phonological) are the neuropsychological domains that improved post-TMS. Moreover, TMS in aphasia and post-stroke CI contribute to greater frontal activation (in the inferior frontal gyrus, pars triangularis, and opercularis). Temporoparietal effects were also found. The observed effects occur when TMS is implemented in repetitive modality, at a frequency of 1 Hz, in sessions of 30 min, and that last more than 2 weeks in duration. The use of TMS contributes to the neurorehabilitation process in post-stroke patients with CI and aphasia. However, it is still necessary to standardize future intervention protocols based on accurate TMS characteristics.

BACKGROUND: This study aimed to investigate whether moxibustion could affect PI3K/Akt pathway to regulate Transforming acidic coiled-coil containing protein 3 (TACC3) and promote axonal regeneration to improve learning and memory function in middle cerebral artery occlusion (MCAO) rats.
METHODS: Sixty SD rats were randomly divided into 4 groups: sham-operated control group (SC), model control group (MC), model + moxibustion group (MM), and model + inhibitor + moxibustion group (MIM). The rats in MC, MM, and MIM groups were made into MCAO models, and PI3K inhibitor LY294002 was injected into the rats in MIM group before modeling; while the rats in SC group were only treated with artery separation without monofilament inserting. After that, the rats in MM and MIM groups were intervented with moxibustion. We used the Zea-Longa scale, micro-Magnetic Resonance Imaging (micro-MRI), Morris water maze (MWM), TUNEL, western blot (WB), immunofluorescence and immunohistochemistry to evaluate the neurological deficits, cerebral infarct volume, learning and memory, apoptotic cell percentage in the hippocampal, the expression level of axonal regeneration and PI3K/AKt related proteins, the expression level of TACC3. The detection of 2 h after surgery showed the result before moxibustion and 7 days after the intervention showed the results after moxibustion.
RESULTS: After 7 d of intervention, the scores of Zea-Longa and the cerebral infarct volume, the escape latency, the percentage of apoptosis cells of MM group were lower than that of MC and MIM groups; the frequency of rats crossed the previous platform location, PI3K, p-Akt/t-Akt and TACC3, the level of GAP-43 in MM group was more than MC and MIM groups (P < 0.05). While no statistical difference existed between MIM group and MC group (P > 0.05).
CONCLUSIONS: Moxibustion can promote axonal regeneration and improve learning and memory of Post-stroke cognitive impairment via activating the PI3K/AKT signaling pathway and TACC3.

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients\' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area\'s advancement towards precision PSCI treatment.