"Remind-to-Move" Treatment Enhanced Activation of the Primary Motor Cortex in Patients with Stroke

Priming transcranial direct current stimulation for improving hemiparetic upper limb in patients with subacute stroke: study protocol for a randomised controlled trial

INTRODUCTION

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that modulates brain states by applying a weak electrical current to the brain cortex. Several studies have shown that anodal stimulation of the ipsilesional primary motor cortex (M1) may promote motor recovery of the affected upper limb in patients with stroke; however, a high-level clinical recommendation cannot be drawn in view of inconsistent findings. A priming brain stimulation protocol has been proposed to induce stable modulatory effects, in which an inhibitory stimulation is applied prior to excitatory stimulation to a brain area. Our recent work showed that priming theta burst magnetic stimulation demonstrated superior effects in improving upper limb motor function and neurophysiological outcomes. However, it remains unknown whether pairing a session of cathodal tDCS with a session of anodal tDCS will also capitalise on its therapeutic effects.

METHODS AND ANALYSIS

This will be a two-arm double-blind randomised controlled trial involving 134 patients 1-6 months after stroke onset. Eligible participants will be randomly allocated to receive 10 sessions of priming tDCS+robotic training, or 10 sessions of non-priming tDCS+robotic training for 2 weeks. The primary outcome is the Fugl-Meyer Assessment-upper extremity, and the secondary outcomes are the Wolf Motor Function Test and Modified Barthel Index. The motor-evoked potentials, regional oxyhaemoglobin level and resting-state functional connectivity between the bilateral M1 will be acquired and analysed to investigate the effects of priming tDCS on neuroplasticity.

ETHICS AND DISSEMINATION

The study has been approved by the Research Ethics Committee of the Shanghai Yangzhi Rehabilitation Center (reference number: Yangzhi2023-022) and will be conducted in accordance with the Declaration of Helsinki of 1964, as revised in 2013.

TRIAL REGISTRATION NUMBER

ChiCTR2300074681.

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity

This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions. The proposed models consist of two distinct graph-based convolutional neural networks, each leveraging different types of brain connectivities to enhance classification performance and gain a deeper understanding of brain connections. The first model, Adjacency-based Convolutional Neural Network Model (Adj-CNNM), utilizes a graph representation based on structural brain connectivity to embed spatial information, distinguishing it from prior spatial filtering approaches dependent on subjects and tasks. Extensive tests on a benchmark dataset-IV-2a demonstrate that an accuracy of 72.77% is achieved by the Adj-CNNM, surpassing baseline and state-of-the-art methods. The second model, Phase Locking Value Convolutional Neural Network Model (PLV-CNNM), incorporates functional connectivity to overcome structural connectivity limitations and identifies connections between distinct brain regions. The PLV-CNNM achieves an overall accuracy of 75.10% across the 1-51 Hz frequency range. In the preferred 8-30 Hz frequency band, known for motor imagery data classification (including α, μ, and β waves), individual accuracies of 91.9%, 90.2%, and 85.8% are attained for α, μ, and β, respectively. Moreover, the model performs admirably with 84.3% accuracy when considering the entire 8-30 Hz band. Notably, the PLV-CNNM reveals robust connections between different brain regions during motor imagery tasks, including the frontal and central cortex and the central and parietal cortex. These findings provide valuable insights into brain connectivity patterns, enriching the comprehension of brain function. Additionally, the study offers a comprehensive comparative analysis of diverse brain connectivity modeling methods.

Neurobiomechanical mechanism of Tai Chi to improve upper limb coordination function in post-stroke patients: a study protocol for a randomized controlled trial

BACKGROUND

Upper limb dysfunction seriously affects the ability of stroke patients to perform activities of daily living. As a popular exercise therapy, Tai Chi may become an alternative intervention. However, the neurophysiological mechanism by which Tai Chi improves upper limb dysfunction in stroke patients is still unclear, which limits its further promotion and application. Therefore, conducting a strict randomized clinical trial is necessary to observe how Tai Chi affects upper limb dysfunction in stroke patients and to explore its neurophysiological mechanism.

METHODS/DESIGN

This report describes a randomized, parallel-controlled trial with distributive concealment and evaluator blinding. A total of 84 eligible participants will be randomly assigned to the Tai Chi group or the control group in a 1:1 ratio. The participants in the Tai Chi group will receive 4 weeks of Tai Chi training: five 60-min sessions a week for a total of 20 sessions. The participants in the control group will not receive Tai Chi training. Both groups will receive medical treatment and routine rehabilitation training. The primary outcome measure is the mean change in the Fugl-Meyer Assessment Upper Extremity (FMA-UE) scale score between baseline and 4 weeks; the secondary outcomes are the mean changes in kinematic characteristics and the Wolf Motor Function Test (WMFT) and Stroke Impact Scale (SIS) scores. In addition, the corticomuscular coupling level and near-infrared brain functional imaging will be monitored to explore the mechanism by which Tai Chi improves upper limb function of stroke patients.

DISCUSSION

This randomized controlled trial will examine the effectiveness of Tai Chi in stroke patients with upper limb dysfunction and explore the neurophysiological mechanism. Positive results will verify that Tai Chi can improve upper limb function of stroke patients.

TRIAL REGISTRATION

Chinese Clinical Trial Registration Center, ChiCTR2200061376 (retrospectively registered). Registered June 22, 2022. http://www.chictr.org.cn/listbycreater.aspx . Manuscript Version: 3.0 Manuscript Date: October 10, 2023.

Prefrontal cortex activity of active motion, cyclic electrical muscle stimulation, assisted motion, and imagery of wrist extension in stroke using fNIRS

OBJECTIVES

This study aimed to determine whether the prefrontal cortex (PFC) was activated during four training approaches for wrist extension in patients with stroke, including active motion, cyclic electrical muscle stimulation (EMS), assisted motion, and motor imagery (MI).

MATERIALS AND METHODS

We conducted a cross-sectional study involving 16 patients with stroke, and adopted functional near-infrared spectroscopy (fNIRS) to observe PFC activity during four treatment paradigms. The beta value of 53 channels in fNIRS under each paradigm, compared to the baseline, was evaluated using single sample t-test. The one-way analysis of variance with post hoc analysis was employed to compare the difference of significantly activated channels among four treatment paradigms.

RESULTS

This study revealed that the active motion (t values ranging from 2.399 to 4.368, p values <0.05), as well as MI of wrist extension (t values ranging from 2.161 to 4.378, p values <0.05), significantly increased HBO concentration across the entire PFC. The cyclic EMS enhanced the activation of Broca's area and frontal pole (FP) (t values ranging from -2.540 to 2.303, p values <0.05). The assisted motion induced significant activation in Broca's area, dorsolateral prefrontal cortex, and FP (t values ranging from -2.226 to 3.056, p values <0.05). The difference in ΔHBO among the four tasks was seen in Broca's area, FP, and frontal eye field.

CONCLUSIONS

Active wrist extension and MI activate most PFC areas, whereas assisted motion and single-use of cyclic EMS have limited effectiveness for PFC activation in stroke patients.

Motor Rehabilitation of Upper Limbs Using a Gesture-Based Serious Game: Evaluation of Usability and User Experience

Objective: Gesture-based serious games can be based on playful and interactive scenarios to enhance user engagement and experience during exercises, thereby increasing efficiency in the motor rehabilitation process. This study aimed to develop the Rehabilite Game (RG) as a complementary therapy tool for upper limb rehabilitation in clinics and home environments and to evaluate aspects of usability and user experience of it. Materials and Methods: The evaluation consisted of the use of a gesture-based serious game with motor rehabilitation sessions managed in a web platform. Thirty-three participants were recruited (21 physiotherapists and 12 patients). The protocol allowed each participant to have the experience of playing sessions with different combinations of settings. The User Experience Questionnaire (UEQ) was used to evaluate aspects of usability and user experience. The study was approved by the Research Ethics Board of the Federal University of Piaui (number 3,429,494). Results: The level of satisfaction with the RG was positive, with an excellent Net Promoter Score for 85.7% of physiotherapists and 100% of patients. All six UEQ scales (attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty) reflected acceptance. Conclusion: The study demonstrated that, according to the results obtained in the experiments, the RG had positive feedback from physiotherapists and patients, indicating that the game can be used in a clinical trial to be compared with other rehabilitation techniques.

Determining Factors that Influence Adoption of New Post-Stroke Sensorimotor Rehabilitation Devices in the USA

Rehabilitation device efficacy alone does not lead to clinical practice adoption. Previous literature identifies drivers for device adoption by therapists but does not identify the best settings to introduce devices, the roles of different stakeholders including rehabilitation directors, or specific criteria to be met during device development. The objective of this work was to provide insights into these areas to increase clinical adoption of post-stroke restorative rehabilitation devices. We interviewed 107 persons including physical/occupational therapists, rehabilitation directors, and stroke survivors and performed content analysis. Unique to this work, care settings in which therapy goals are best aligned for restorative devices were found to be outpatient rehabilitation, followed by inpatient rehabilitation. Therapists are the major influencers for adoption because they typically introduce new rehabilitation devices to patients for both clinic and home use. We also learned therapists' utilization rate of a rehabilitation device influences a rehabilitation director's decision to acquire the device for facility use. Main drivers for each stakeholder are identified, along with specific criteria to add details to findings from previous literature. In addition, drivers for home adoption of rehabilitation devices by patients are identified. Rehabilitation device development should consider the best settings to first introduce the device, roles of each stakeholder, and drivers that influence each stakeholder, to accelerate successful adoption of the developed device.

Rehabilitation Treatment of Motor Dysfunction Patients Based on Deep Learning Brain-Computer Interface Technology

In recent years, brain-computer interface (BCI) is expected to solve the physiological and psychological needs of patients with motor dysfunction with great individual differences. However, the classification method based on feature extraction requires a lot of prior knowledge when extracting data features and lacks a good measurement standard, which makes the development of BCI. In particular, the development of a multi-classification brain-computer interface is facing a bottleneck. To avoid the blindness and complexity of electroencephalogram (EEG) feature extraction, the deep learning method is applied to the automatic feature extraction of EEG signals. It is necessary to design a classification model with strong robustness and high accuracy for EEG signals. Based on the research and implementation of a BCI system based on a convolutional neural network, this article aims to design a brain-computer interface system that can automatically extract features of EEG signals and classify EEG signals accurately. It can avoid the blindness and time-consuming problems caused by the machine learning method based on feature extraction of EEG data due to the lack of a large amount of prior knowledge.