Improving Hand Function of Severely Impaired Chronic Hemiparetic Stroke Individuals Using Task-Specific Training With the ReIn-Hand System: A Case Series

Optimizing Stroke Detection Using Evidential Networks and Uncertainty-Based Refinement

Evaluating neurological impairments post-stroke is essential for assessing treatment efficacy and managing subsequent disabilities. Conventional clinical assessment methods depend largely on clinicians' visual and physical evaluations, resulting in coarse rating systems that frequently miss subtle impairments or improvements. Interactive robotic devices, like the Kinarm Exoskeleton system, are transforming the assessment of motor impairments by offering precise and objective movement measurements. In this study, we analyzed kinematic data from 337 stroke patients and 368 healthy controls performing three Kinarm tasks. Using deep learning methods, particularly an evidential network, we distinguished impaired participants from healthy controls while generating measures of prediction uncertainty. By retraining the network with the least uncertain samples and refining the test set by excluding the top 10% most uncertain samples, we improved the sensitivity of detecting subtle impairments in minimally impaired stroke patients (those scoring normal on the CMSA) from 0.55 to 0.75. We further extended the model to detect impairments associated with transient ischemic attack (TIA), resulting in an increased detection accuracy from 0.86 to 0.92. The model's ability to identify subtle motor deficits, even in TIA patients who show no observable symptoms on standard clinical exams, highlights its significant clinical utility. Detecting TIA is critical, as individuals who experience a TIA have a substantially higher risk of recurrent stroke. This work highlights the immense potential of integrating deep learning with uncertainty estimation to enhance the detection of stroke-related impairments, potentially paving the way for personalized post-stroke rehabilitation.

Active, Actuated, and Assistive: a Scoping Review of Exoskeletons for the Hands and Wrists

BACKGROUND

Assistive technology is often incorporated into rehabilitation and support for those impacted by upper limb impairments. When powered, these devices provide additional force to the joints of users with muscle weakness. Actuated devices allow dynamic movement compared to splints, therefore improving the ability to complete activities of daily living. However, these devices are not often prescribed and are underrepresented in research and clinical settings.

OBJECTIVE

This review examined the existing literature on devices developed to support hand and wrist functionality in daily activities. Focusing on active, powered, and actuated devices, to gain a clearer understanding of the current limitations in their design and prescription.

METHODOLOGY

The scoping review was conducted using the PRISMA-ScR guidelines. A systematic search was done on MEDLINE, EMBASE, Scopus, Web of Science, and NHS the Knowledge Network from inception to May 2023. Articles were included if the device was portable; supported the hands and wrist actively using an actuator; and could be used for assistive living during or post-rehabilitation period.

FINDINGS

A total of 135 studies were included in the analysis of which 34 were clinical trials. The design and control methods of 121 devices were analyzed. Electrical stimulation and direct mechanical transmission were popular actuation methods. Electromyography (EMG) and joint movement detection were highly used control methods to translate user intentions to device actuation. A total of 226 validation methods were reported, of which 44% were clinically validated. Studies were often not conducted in operational environments with 69% at technology readiness levels ≤ 6, indicating that further development and testing is required.

CONCLUSION

The existing literature on hand and wrist exoskeletons presents large variations in validation methods and technical requirements for user-specific characteristics. This suggests a need for well-defined testing protocols and refined reporting of device designs. This would improve the significance of clinical outcomes and new assistive technology.

The Effectiveness of Upper Extremity Orthotic Interventions on Performance Skills and Performance of Occupations for Adults after Stroke: A Scoping Review

The objective of this study was to determine the effectiveness of upper extremity orthoses on improving performance skills and performance of occupations after stroke. Databases searched included CINAHL, PubMed, and OT Seeker. Articles were included if published between 2012 to 2022, English, peer-reviewed, level of evidence IB, IIB, or IIIB, and included upper extremity orthoses, adults after a stroke, and performance skill and performance of occupation outcome measures; six studies meet inclusion criteria. Moderate strength of evidence supports the usage of dynamic upper extremity orthoses to improve performance skills, although they do not improve performance of occupations for adults after stroke. Evidence suggests practitioners should utilize dynamic orthoses concurrently with tasks that promote performance skills such as gripping, pinching, grasping, and reaching during interventions to promote upper extremity use after stroke. Additional research is needed to further justify the use of upper extremity orthoses for performance of occupations after stroke.

Motor Activated Auricular Vagus Nerve Stimulation as a Potential Neuromodulation Approach for Post-Stroke Motor Rehabilitation: A Pilot Study

BACKGROUND

Implanted vagus nerve stimulation (VNS), when synchronized with post-stroke motor rehabilitation improves conventional motor rehabilitation training. A non-invasive VNS method known as transcutaneous auricular vagus nerves stimulation (taVNS) has emerged, which may mimic the effects of implanted VNS.

OBJECTIVE

To determine whether taVNS paired with motor rehabilitation improves post-stroke motor function, and whether synchronization with movement and amount of stimulation is critical to outcomes.

METHODS

We developed a closed-loop taVNS system for motor rehabilitation called motor activated auricular vagus nerve stimulation (MAAVNS) and conducted a randomized, double-blind, pilot trial investigating the use of MAAVNS to improve upper limb function in 20 stroke survivors. Participants attended 12 rehabilitation sessions over 4-weeks, and were assigned to a group that received either MAAVNS or active unpaired taVNS concurrently with task-specific training. Motor assessments were conducted at baseline, and weekly during rehabilitation training. Stimulation pulses were counted for both groups.

RESULTS

A total of 16 individuals completed the trial, and both MAAVNS (n = 9) and unpaired taVNS (n = 7) demonstrated improved Fugl-Meyer Assessment upper extremity scores (Mean ± SEM, MAAVNS: 5.00 ± 1.02, unpaired taVNS: 3.14 ± 0.63). MAAVNS demonstrated greater effect size (Cohen's d = 0.63) compared to unpaired taVNS (Cohen's d = 0.30). Furthermore, MAAVNS participants received significantly fewer stimulation pulses (Mean ± SEM, MAAVNS: 36 070 ± 3205) than the fixed 45 000 pulses unpaired taVNS participants received (P < .05).

CONCLUSION

This trial suggests stimulation timing likely matters, and that pairing taVNS with movements may be superior to an unpaired approach. Additionally, MAAVNS effect size is comparable to that of the implanted VNS approach.

Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review

Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.

Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.

Peripheral serum metabolomic profiles inform central cognitive impairment

The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value < 0.05). Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models, respectively, that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median area under the receiver operating characteristic curve (AUC) = 0.772 for the metabolite level model; median AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline in the later years (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.

Data-driven analyses of motor impairments in animal models of neurological disorders

Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. The analysis of such movement abnormalities is notoriously difficult and requires a trained evaluator. Here, we show that a deep neural network is able to score behavioral impairments with expert accuracy in rodent models of stroke. The same network was also trained to successfully score movements in a variety of other behavioral tasks. The neural network also uncovered novel movement alterations related to stroke, which had higher predictive power of stroke volume than the movement components defined by human experts. Moreover, when the regression network was trained only on categorical information (control = 0; stroke = 1), it generated predictions with intermediate values between 0 and 1 that matched the human expert scores of stroke severity. The network thus offers a new data-driven approach to automatically derive ratings of motor impairments. Altogether, this network can provide a reliable neurological assessment and can assist the design of behavioral indices to diagnose and monitor neurological disorders.

Effectiveness of interventions to improve hand motor function in individuals with moderate to severe stroke: a systematic review protocol

INTRODUCTION

The human hand is extremely involved in our daily lives. However, the rehabilitation of hand function after stroke can be rather difficult due to the complexity of hand structure and function, as well as neural basis that supports hand function. Specifically, in individuals with moderate to severe impairment following a stroke, previous evidence for effective treatments that recover hand function in this population is limited, and thus has never been reviewed. With the progress of rehabilitation science and tool development, results from more and more clinical trials are now available, thereby justifying conducting a systematic review.

METHODS AND ANALYSIS

This systematic review protocol is consistent with the methodology recommended by the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols and the Cochrane handbook for systematic reviews of interventions. Electronic searches will be carried out in the PubMed, CINAHL, Physiotherapy Evidence Database and Cochrane Library databases, along with manual searches in the reference lists from included studies and published systematic reviews. The date range parameters used in searching all databases is between January 1999 and January 2019. Randomised controlled trials (RCTs) published in English, with the primary outcome focusing on hand motor function, will be included. Two reviewers will screen all retrieved titles, abstracts and full texts, perform the evaluation of the risk bias and extract all data independently. The risk of bias of the included RCTs will be evaluated by the Cochrane Collaboration's tool. A qualitative synthesis will be provided in text and table, to summarise the main results of the selected publications. A meta-analysis will be considered if there is sufficient homogeneity across outcomes. The quality of the included publications will be evaluated by the Grading of Recommendations Assessment, Development and Evaluation system from the Cochrane Handbook for Systematic Reviews of Interventions.

ETHICS AND DISSEMINATION

No ethical approval is needed, and the results of this review will be disseminated via peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

CRD42019128285.