Enhanced hippocampal TIAM2S expression alleviates cognitive deficits in Alzheimer's disease model mice

BACKGROUND

Dendritic spine dysfunction is a key feature of Alzheimer's disease (AD) pathogenesis. Human T-cell lymphoma invasion and metastasis 2 (TIAM2) is expressed in two isoforms, the full length (TIAM2L) and a short transcript (TIAM2S). Compared to TIAM2L protein, which is undetectable, TIAM2S protein is abundant in human brain tissue, especially the hippocampus, and can promote neurite outgrowth in our previous findings. However, whether enhanced hippocampal TIAM2S expression can alleviate cognitive deficits in Alzheimer's disease model mice remains unclear.

METHODS

We crossbred 3xTg-AD with TIAM2S mice to generate an AD mouse model that carries the human TIAM2S gene (3xTg-AD/TIAM2S mice). The Morris water maze and object location tests assessed hippocampus-dependent spatial memory. Lentiviral-driven shRNA or cDNA approaches were used to manipulate hippocampal TIAM2S expression. Golgi staining and Sholl analysis were utilized to measure neuronal dendrites and dendritic spines in the mouse hippocampi.

RESULTS

Compared to 3xTg-AD mice, 3xTg-AD/TIAM2S mice displayed improved cognitive functions. According to the hippocampus is one of the earliest affected brain regions by AD, we further injected TIAM2S shRNA or TIAM2S cDNA into mouse hippocampi to confirm whether manipulating hippocampal TIAM2S expression could affect AD-related cognitive functions. The results showed that the reduced hippocampal TIAM2S expression in 3xTg-AD/TIAM2S mice abolished the memory improvement effect, whereas increased hippocampal TIAM2S levels alleviated cognitive deficits in 3xTg-AD mice. Furthermore, we found that TIAM2S-mediated memory improvement was achieved by regulating dendritic plasticity.

CONCLUSIONS

These results will provide new insights into connecting TIAM2S with AD and support the notion that TIAM2S should be investigated as potential AD therapeutic targets.

I-BaR: integrated balance rehabilitation framework

Neurological diseases are observed in approximately 1 billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of assist-as-needed paradigm and, with integrated rehabilitation process as whole, that is, ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of virtual reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback.

Systematic review of automatic post-stroke gait classification systems

BACKGROUND

Gait classification is a clinically helpful task performed after a stroke in order to guide rehabilitation therapy. Gait disorders are commonly identified using observational gait analysis in clinical settings, but this approach is limited due to low reliability and accuracy. Data-driven gait classification can quantify gait deviations and categorise gait patterns automatically possibly improving reliability and accuracy; however, the development and clinical utility of current data driven systems has not been reviewed previously.

RESEARCH QUESTION

The purpose of this systematic review is to evaluate the literature surrounding the methodology used to develop automatic gait classification systems, and their potential effectiveness in the clinical management of stroke-affected gait.

METHOD

The database search included PubMed, IEEE Xplore, and Scopus. Twenty-one studies were identified through inclusion and exclusion criteria from 407 available studies published between 2015 and 2022. Development methodology, classification performance, and clinical utility information were extracted for review.

RESULTS AND SIGNIFICANCE

Most of gait classification systems reported a classification accuracy between 80%-100%. However, collated studies presented methodological errors in machine learning (ML) model development. Further, many studies neglected model components such as clinical utility (e.g., predictions don't assist clinicians or therapists in making decisions, interpretability, and generalisability). We provided recommendations to guide development of future post-stroke automatic gait classification systems to better assist clinicians and therapists. Future automatic gait classification systems should emphasise the clinical significance and adopt a standardised development methodology of ML model.

A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control

Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging's region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ( < .001) and increased the prediction coefficient of determination by 20.13% ( < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy.

Embodiment of supernumerary robotic limbs in virtual reality

The supernumerary robotic limb system expands the motor function of human users by adding extra artificially designed limbs. It is important for us to embody the system as if it is a part of one's own body and to maintain cognitive transparency in which the cognitive load is suppressed. Embodiment studies have been conducted with an expansion of bodily functions through a "substitution" and "extension". However, there have been few studies on the "addition" of supernumerary body parts. In this study, we developed a supernumerary robotic limb system that operates in a virtual environment, and then evaluated whether the extra limb can be regarded as a part of one's own body using a questionnaire and whether the perception of peripersonal space changes with a visuotactile crossmodal congruency task. We found that the participants can embody the extra-limbs after using the supernumerary robotic limb system. We also found a positive correlation between the perceptual change in the crossmodal congruency task and the subjective feeling that the number of one's arms had increased (supernumerary limb sensation). These results suggest that the addition of an extra body part may cause the participants to feel that they had acquired a new body part that differs from their original body part through a functional expansion.

A Review Regarding Neurorehabilitation Technologies for Hand Motor Functions

The paper deals with a short review regarding neurorehabilitation technologies for regaining human hand mobility functions after a cerebrovascular accident or stroke. The aim of this paper is to form a general understanding of the current technologies used in the field of neurorehabilitation and highlight key characteristics, advantages and disadvantages. Technologies that are studies include robot exoskeletons, electro stimulation, brain computer interfaces (BCI), EEG and limb mounted sensors. After a presenting a summary of current existing technologies, a brief conclusion proposing the future direction of this study is proposed.

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning

Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit decomposition-based method, this work presents a novel neural interface for human gait tracking based on muscle synergy, the high-level neural control information to collaborate muscle groups for performing movements. Three classical synergy extraction approaches include Principle Component Analysis (PCA), Factor Analysis (FA), and Nonnegative Matrix Factorization (NMF), are employed for muscle synergy extraction. A deep regression neural network based on the bidirectional gated recurrent unit (BGRU) is used to extract temporal information from the synergy matrix to estimate joint angles of the lower limb. Eight subjects participated in the experiment while walking at four types of speed: 0.5km/h, 1.0km/h, 2.0km/h, and 3.0km/h. Two machine learning methods based on linear regression (LR) and multilayer perceptron (MLP) are set as the contrast group. The result shows that the synergy-based approach's performance outperforms two contrast methods with R_var² scores of 0.83~0.88. PCA reaches the highest performance of 0.871±0.029, corresponding to RMSE of 3.836°, 6.278°, 2.197° for hip, knee, and ankle, respectively. The effect of walking speed, synergy number, and joint location will be analyzed. The performance shows that muscle synergy has a good correlation will joint angles which can be unearthed by deep learning. The proposed method explores a new way for gait analysis and contributes to building a novel neural interface with muscle synergy and deep learning.

A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work

BACKGROUND

With the remarkable increasing in the numbers of patients, the triaging and prioritizing patients into multi-emergency level is required to accommodate all the patients, save more lives, and manage the medical resources effectively. Triaging and prioritizing patients becomes particularly challenging especially for the patients who are far from hospital and use telemedicine system. To this end, the researchers exploiting the useful tool of machine learning to address this challenge. Hence, carrying out an intensive investigation and in-depth study in the field of using machine learning in E-triage and patient priority are essential and required.

OBJECTIVES

This research aims to (1) provide a literature review and an in-depth study on the roles of machine learning in the fields of electronic emergency triage (E-triage) and prioritize patients for fast healthcare services in telemedicine applications. (2) highlight the effectiveness of machine learning methods in terms of algorithms, medical input data, output results, and machine learning goals in remote healthcare telemedicine systems. (3) present the relationship between machine learning goals and the electronic triage processes specifically on the: triage levels, medical features for input, outcome results as outputs, and the relevant diseases. (4), the outcomes of our analyses are subjected to organize and propose a cross-over taxonomy between machine learning algorithms and telemedicine structure. (5) present lists of motivations, open research challenges and recommendations for future intelligent work for both academic and industrial sectors in telemedicine and remote healthcare applications.

METHODS

An intensive research is carried out by reviewing all articles related to the field of E-triage and remote priority systems that utilise machine learning algorithms and sensors. We have searched all related keywords to investigate the databases of Science Direct, IEEE Xplore, Web of Science, PubMed, and Medline for the articles, which have been published from January 2012 up to date.

RESULTS

A new crossover matching between machine learning methods and telemedicine taxonomy is proposed. The crossover-taxonomy is developed in this study to identify the relationship between machine learning algorithm and the equivalent telemedicine categories whereas the machine learning algorithm has been utilized. The impact of utilizing machine learning is composed in proposing the telemedicine architecture based on synchronous (real-time/ online) and asynchronous (store-and-forward / offline) structure. In addition to that, list of machine learning algorithms, list of the performance metrics, list of inputs data and outputs results are presented. Moreover, open research challenges, the benefits of utilizing machine learning and the recommendations for new research opportunities that need to be addressed for the synergistic integration of multidisciplinary works are organized and presented accordingly.

DISCUSSION

The state-of-the-art studies on the E-triage and priority systems that utilise machine learning algorithms in telemedicine architecture are discussed. This approach allows the researchers to understand the modernisation of healthcare systems and the efficient use of artificial intelligence and machine learning. In particular, the growing worldwide population and various chronic diseases such as heart chronic diseases, blood pressure and diabetes, require smart health monitoring systems in E-triage and priority systems, in which machine learning algorithms could be greatly beneficial.

CONCLUSIONS

Although research directions on E-triage and priority systems that use machine learning algorithms in telemedicine vary, they are equally essential and should be considered. Hence, we provide a comprehensive review to emphasise the advantages of the existing research in multidisciplinary works of artificial intelligence, machine learning and healthcare services.

Exoskeleton robots for lower limb assistance: A review of materials, actuation, and manufacturing methods

The field of robot-assisted physical rehabilitation and robotics technology for providing support to the elderly population is rapidly evolving. Lower limb robot aided rehabilitation and assistive technology have been a focus for the engineering community during the last three decades as several robotic lower limb exoskeletons have been proposed in the literature as well as some being commercially available. Numerous manufacturing techniques and materials have been developed for lower limb exoskeletons during the last two decades, resulting in the design of a variety of robot exoskeletons for gait assistance for elderly and disabled people. One of the most important aspects of developing exoskeletons is the selection of the most appropriate proper material. The material selection strongly influences the overall weight and performance of the exoskeleton robot. The most suitable fabrication method for material is also an important parameter for the development of lower limb robot exoskeletons. In addition to the materials and manufacturing methods, the actuation method plays a vital role in the development of these robot exoskeletons. Even though various materials, manufacturing methods and actuators are reported in the literature for these lower limb robot exoskeletons, there are still avenues of improvement in these three domains. In this review, we have examined various lower limb robotic exoskeletons, concentrating on the three main aspects of material, manufacturing, and actuation. We have focused on the advantages and drawbacks of various materials and manufacturing practices as well as actuation methods. A discussion on future directions of research is provided for the engineering community covering the material, manufacturing and actuation methods.

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.

Robot Assisted Ankle Neuro-Rehabilitation: State of the art and Future Challenges

Introduction: Robot-assisted neuro-rehabilitation is gaining acceptability among the physical therapy community. The ankle is one of the most complicated anatomical joints in the human body and neurologic injuries such as stroke often result in ankle and foot disabilities. Areas covered: Robotic solutions for the ankle joint physical therapy have extensively been researched. Significant research has been conducted on the mechanism design, actuation as well as control of these ankle rehabilitation robots. Also, the experimental evaluations of these robots have been conducted with healthy and neurologically impaired subjects. This paper presents a comprehensive review of the recent developments in the field of robot-assisted ankle rehabilitation. Mechanism design, actuation, and various types of control strategies are discussed. Also, the experimental evaluations of these ankle rehabilitation robots are discussed in the context of the evaluation of robotic hardware with healthy subjects as well as motor function outcomes with neurologically impaired subjects. Expert opinion: Significant progress in the mechanism design, control, and experimental evaluations of the ankle rehabilitation robots have been reported. However, more sensing and reference trajectory generation methods need to be developed as well as more objective quantitive evaluations that need to be conducted for establishing the clinical significance of these robots.

Indirect Control of an Autonomous Wheelchair Using SSVEP BCI

Having the capability to control a wheelchair using brain signals would be a major benefit to patients suffering from motor disabling diseases. However, one major challenge such systems are facing is the amount of input needed over time by the patient for control. Such a navigation control system results in a significant mental burden for the patient. The objective of this study is to develop a BCI system that requires a low number of inputs from a subject to operate. We propose an autonomous wheelchair that uses steady-state visual evoked potential based brain computer interfaces to achieve the objective. A dual mode system was implemented in this study to allow the autonomous wheelchair to work in both unknown and known environments. Robot operating system is used as the middleware in this study for the development of the algorithm to operate the wheelchair. The mental task for the subject using this wheelchair is reduced by relegating the responsibility of navigation control from the subject to the navigation software.

Assessing the Involvement of Users During Development of Lower Limb Wearable Robotic Exoskeletons: A Survey Study

OBJECTIVE

To explore user-centered design methods currently implemented during development of lower limb wearable robots and how they are utilized during different stages of product development.

BACKGROUND

Currently, there appears to be a lack of standardized frameworks for evaluation methods and design requirements to implement effective user-centered design for safe and effective clinical or ergonomic system application.

METHOD

Responses from a total of 191 experts working in the field of lower limb exoskeletons were analyzed in this exploratory survey. Descriptive statistics were used to present responses and measures of frequency, and chi-square tests were used to contrast the answers of respondents who identified as clinicians versus engineers.

RESULTS

A vast majority of respondents involve users in their development, in particular at the initial and iterative stages, although some differences were found between disciplines. A variety of methods and metrics are used to capture feedback from users and test devices, and although valuable, some methods used may not be based on validated measures. Guidelines regarding tests on safety of exoskeletons also lack standardization.

CONCLUSION

There seems to be a consensus among experts regarding the importance of a user-centered approach in exoskeleton development; however, standardized frameworks with regard to appropriate testing methods and design approaches are lacking. Such frameworks should consider an interdisciplinary focus on the needs and safety of the intended user during each iteration of the process.

APPLICATION

This exploratory study provides an overview of current practice among engineers and clinicians regarding the user-centered design of exoskeletons. Limitations and recommendations for future directions are identified.

Meta-Analysis of Integrated Therapeutic Methods in Noninvasive Lower Back Pain Therapy (LBP): The Role of Interdisciplinary Functional Diagnostics

Introduction. Lower back pain (LBP) is almost a problem of civilizations. Quite often, it is a consequence of many years of disturbed distribution of tension within the human body caused by local conditions (injuries, hernias, stenoses, spondylolisthesis, cancer, etc.), global factors (postural defects, structural integration disorders, lifestyle, type of activity, etc.), or systemic diseases (connective tissue, inflammation, tumours, abdominal aneurysm, and kidney diseases, including urolithiasis, endometriosis, and prostatitis). Therefore, LBP rehabilitation requires the use of integrated therapeutic methods, combining the competences of interdisciplinary teams, both in the process of diagnosis and treatment. Aim of the Study. Given the above, the authors of the article conducted meta-analysis of the literature in terms of integrated therapeutic methods, indicating the techniques focused on a holistic approach to the patient. The aim of the article is to provide the reader with comprehensive knowledge about treating LBP using noninterventional methods. Material and Methods. An extensive search for the materials was conducted online using PubMed, the Cochrane database, and Embase. The most common noninterventional methods have been described, as well as the most relevantly updated and previously referenced treatment of LBP. The authors also proposed noninvasive (measurable) diagnostic procedures for the functional assessment of the musculoskeletal system, including initial, systematic, and cross-sectional control. All figures and images have been prepared by the authors and are their property.

Results

This review article goes beyond combining a detailed description of each procedure with full references, as well as a comprehensive discussion of this very complex and troublesome problem.

Conclusions

Lower back pain is a serious health problem, and this review article will help educate physicians and physiotherapists dealing with LBP in the options of evidence-based treatment. Ultimately, the article introduces and postulates the need to systematize therapeutic procedures in LBP therapy, with a long-term perspective.

Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI

The left and right foot representation area is located within the interhemispheric fissure of the sensorimotor cortex and share spatial proximity. This makes it difficult to visualize the cortical lateralization of event-related (de)synchronization (ERD/ERS) during left and right foot motor imageries. The aim of this study is to investigate the possibility of using ERD/ERS in the mu, low beta, and high beta bandwidth, during left and right foot dorsiflexion kinaesthetic motor imageries (KMI), as unilateral control commands for a brain-computer interface (BCI). EEG was recorded from nine healthy participants during cue-based left-right foot dorsiflexion KMI tasks. The features were analysed for common average and bipolar references. With each reference, mu and beta band-power features were analysed using time–frequency (TF) maps, scalp topographies, and average time course for ERD/ERS. The cortical lateralization of ERD/ERS, during left and right foot KMI, was confirmed. Statistically significant features were classified using LDA, SVM, and KNN model, and evaluated using the area under ROC curves. An increase in high beta power following the end of KMI for both tasks was recorded, from right and left hemispheres, respectively, at the vertex. The single trial analysis and classification models resulted in high discrimination accuracies, i.e. maximum 83.4% for beta ERS, 79.1% for beta ERD, and 74.0% for mu ERD. With each model the features performed above the statistical chance level of 2-class discrimination for a BCI. Our findings indicate these features can evoke left-right differences in single EEG trials. This suggests that any BCI employing unilateral foot KMI can attain classification accuracy suitable for practical implementation. Given results stipulate the novel utilization of mu and beta as independent control features for discrimination of bilateral foot KMI in a BCI.

Assessment of Human Dynamic Gait Stability With a Lower Extremity Assistive Device

This paper focuses on assessing gait stability by metrics derived from dynamical systems theory to understand the influence of unilateral robot assistance on the human walking pattern. A motorized assistive robot is applied to the right knee joint to provide stance support. The metrics related to global stability (the maximum Floquet multiplier, max FM), local stability (short-term and long-term divergence exponents, [Formula: see text] and [Formula: see text]), and variability (median absolute deviation, MAD) are considered. These metrics are derived for bilateral hip, knee, and ankle joint angles. Additionally, a biomechanical metric, the minimum margin of stability is assessed. Experiments are conducted on 11 healthy participants with different robot controllers. The max FM and [Formula: see text] yield statistically significant results, showing that the unassisted (left) leg is more stable in right knee assistance conditions when compared to the normal walking condition due to inter-limb coordination. Moreover, MAD and [Formula: see text] show that the variability and chaotic order of walking pattern during assistance are lower than those of normal walking. The proposed control strategy (automatic impedance tuning, AIT) improves local and orbital gait stability compared to existing controllers such as finite-state machine (FSM). The assessment of dynamic gait stability presented in this paper provides insights for further improving control strategies of assistive robots to help a user reach improved gait stability while maintaining appropriate variability.

EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It is suggested to structure EEG-BCI controlled LL assistive devices within the presented framework, for future generation of intent-based multifunctional controllers. Despite the development of controllers, for BCI-based wearable or assistive devices that can seamlessly integrate user intent, practical challenges associated with such systems exist and have been discerned, which can be constructive for future developments in the field.