Effectiveness of personalized rehabilitation in adults suffering from persistent concussion symptoms as compared to usual care: a randomized control trial protocol.

BACKGROUND: Symptoms reported by patients who sustain a concussion are non-specific. As such, clinicians are better able to manage patients when a standardized clinical exam is performed to sub-type the driver(s) of symptoms. Aerobic exercise and multimodal rehabilitation have consistently shown to be a possibly effective means to manage this population; however, the optimal training prescription is unclear. Thus, there is a need to further examine the effectiveness of personalized rehabilitative treatments. Our primary aim is to evaluate the response to personalized therapy on recovery, as measured by The Rivermead Post-concussion Symptoms Questionnaire (RPQ) when compared to an active control. METHODS: We will conduct a multi-center 12-week case-crossover randomized controlled trial. 50 participants will be recruited from out-patient University Health Network clinics and community-based clinical practices around the greater Toronto area. Participants will be randomized at baseline to Group A: a personalized care program followed by an active control or Group B: an active control followed by a personalized care program. Participants will be included should they be 21 years of age and older and have symptoms that have persisted beyond 4 weeks but less than 1 year. Participants will undergo 6-weeks of care in their respective streams. After 6-weeks, participants will undergo a re-examination. They will then crossover and undertake the alternative treatment for 6 weeks. At the end of 12 weeks, participants will undertake the endpoint examinations. The primary outcome will be the Rivermead Postconcussion Questionnaire (RPQ). The secondary outcomes will be changes in standardized clinical examination, Neck Disability Index (NDI), Patient Health Questionnaire (PHQ-9) and an electroencephalography (EEG) via NeuroCatch™. The statistical analysis to be performed is composed of an adjusted model using an analysis of variance, specifically using an unpaired t-test to test for associations between variables and outcomes. DISCUSSION: Given the recommendations from reviews on the topic of rehabilitation for adults with persistent concussion symptoms, we are undertaking a controlled trial. The documented high costs for patients seeking care for persistent symptoms necessitate the need to evaluate the effectiveness of a personalized rehabilitative program compared to the current standard of care. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT06069700.

A novel functional electrical stimulation sleeve based on textile-embedded dry electrodes.

BACKGROUND: Functional electrical stimulation (FES) is a rehabilitation technique that enables functional improvements in patients with motor control impairments. This study presents an original design and prototyping method for a smart sleeve for FES applications. The article explains how to integrate a carbon-based dry electrode into a textile structure and ensure an electrical connection between the electrodes and the stimulator for effective delivery of the FES. It also describes the materials and the step-by-step manufacturing processes. RESULTS: The carbon-based dry electrode is integrated into the textile substrate by a thermal compression molding process on an embroidered conductive matrix. This matrix is composed of textile silver-plated conductive yarns and is linked to the stimulator. Besides ensuring the electrical connection, the matrix improves the fixation between the textile substrate and the electrode. The stimulation intensity, the perceived comfort and the muscle torque generated by the smart FES sleeve were compared to hydrogel electrodes. The results show a better average comfort and a higher average stimulation intensity with the smart FES sleeve, while there were no significant differences for the muscle torque generated. CONCLUSIONS: The integration of the proposed dry electrodes into a textile is a viable solution. The wearable FES system does not negatively impact the electrodes' performance, and tends to improve it. Additionally, the proposed prototyping method is applicable to an entire garment in order to target all muscles. Moreover, the process is feasible for industrial production and commercialization since all materials and processes used are already available on the market.

Model-based closed-loop control of thalamic deep brain stimulation.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction.

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches model to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.

Neural signatures of indirect pathway activity during subthalamic stimulation in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) produces an electrophysiological signature called evoked resonant neural activity (ERNA); a high-frequency oscillation that has been linked to treatment efficacy. However, the single-neuron and synaptic bases of ERNA are unsubstantiated. This study proposes that ERNA is a subcortical neuronal circuit signature of DBS-mediated engagement of the basal ganglia indirect pathway network. In people with Parkinson's disease, we: (i) showed that each peak of the ERNA waveform is associated with temporally-locked neuronal inhibition in the STN; (ii) characterized the temporal dynamics of ERNA; (iii) identified a putative mesocircuit architecture, embedded with empirically-derived synaptic dynamics, that is necessary for the emergence of ERNA in silico; (iv) localized ERNA to the dorsal STN in electrophysiological and normative anatomical space; (v) used patient-wise hotspot locations to assess spatial relevance of ERNA with respect to DBS outcome; and (vi) characterized the local fiber activation profile associated with the derived group-level ERNA hotspot.

Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning.

BACKGROUND: The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form, a comprehensive dataset named Zivot with patient population clinical feature breakdowns and a baseline for DFU detection using this dataset and a UNet architecture. RESULTS: Following this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset, and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics. CONCLUSIONS: This work and the Zivot database offer a foundation for further exploration of holistic and multimodal approaches to DFU research.

A dry polymer nanocomposite transcutaneous electrode for functional electrical stimulation.

BACKGROUND: Functional electrical stimulation (FES) can be used in rehabilitation to aid or improve function in people with paralysis. In clinical settings, it is common practice to use transcutaneous electrodes to apply the electrical stimulation, since they are non-invasive, and can be easily applied and repositioned as necessary. However, the current electrode options available for transcutaneous FES are limited and can have practical disadvantages, such as the need for a wet interface with the skin for better comfort and performance. Hence, we were motivated to develop a dry stimulation electrode which could perform equivalently or better than existing commercially available options. METHODS: We manufactured a thin-film dry polymer nanocomposite electrode, characterized it, and tested its performance for stimulation purposes with thirteen healthy individuals. We compared its functionality in terms of stimulation-induced muscle torque and comfort level against two other types of transcutaneous electrodes: self-adhesive hydrogel and carbon rubber. Each electrode type was also tested using three different stimulators and different intensity levels of stimulation. RESULTS: We found the proposed dry polymer nanocomposite electrode to be functional for stimulation, as there was no statistically significant difference between its performance to the other standard electrodes. Namely, the proposed dry electrode had comparable muscle torque generated and comfort level as the self-adhesive hydrogel and carbon rubber electrodes. From all combinations of electrode type and stimulators tested, the dry polymer nanocomposite electrode with the MyndSearch stimulator had the most comfortable average rating. CONCLUSIONS: The dry polymer nanocomposite electrode is a durable and flexible alternative to existing self-adhesive hydrogel and carbon rubber electrodes, which can be used without the addition of a wet interfacing agent (i.e., water or gel) to perform as well as the current electrodes used for stimulation purposes.

Effectiveness of Non-Pharmacological Therapy on Physical Symptoms in Patients With Persistent Concussion Symptoms: A Systematic Review.

This systematic review provides a comprehensive overview on the effectiveness of rehabilitation on physical symptoms in patients of all ages with persistent concussion symptoms. PubMed, MEDLINE®, Cochrane library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Embase were searched from January 1, 2012 to September 1, 2023 using terms related to physical post-concussion symptoms. Eligible articles were critically appraised using the Scottish Intercollegiate Guidelines Network (SIGN) and the Quality Assessment Tool. The Grading of Recommendations Assessment, Development, and Evaluation system was applied to rate the quality of evidence. Thirty-two articles were included. Preliminary evidence suggests that transcranial magnetic stimulation improves symptoms in adults, specifically headaches. Young adults reported a significant decrease in physical symptoms following sub-symptom aerobic training as well as cervical spine manual therapy. Tentatively, adults demonstrated improvements in headache symptoms following neurofeedback sessions, and progressive muscle relaxation resulted in a decrease in monthly headaches. Multimodal therapy in adults produced significant change in physical symptoms when compared with usual care. However, no further reduction in physical symptoms was observed when adult patients received a program of care that afforded cervicovestibular rehabilitation with symptom-limited exercise compared with a symptom-limited exercise program alone. Cognitive behavioral therapy demonstrated inconsistent findings for its effects on physical symptoms, specifically headaches. Veterans had a significant change in post-concussive symptoms, specifically headaches, following 3-month use of an interactive smartphone application as compared with standard care. Finally, in a pediatric population, the use of melatonin did not produce any changes in physical persistent concussion symptoms as compared with placebo. Preliminary evidence suggests that various forms of rehabilitative therapies can improve persistent physical concussive symptoms. However, given the methodological limitations in the majority of trials, the results need to be interpreted with caution.

The Orthotic Effects of Different Functional Electrical Stimulation Protocols on Walking Performance in Individuals with Incomplete Spinal Cord Injury: A Case Series.

Background: Functional electrical stimulation (FES) of paralyzed muscles can facilitate walking after spinal cord injury (SCI). Objectives: To test the orthotic effects of different FES walking protocols on lower joint kinematics and walking speed. Methods: Three adults with incomplete SCI participated in this study. Their lower extremity motor scores and 10-meter walk test results were as follows: subject A: 50, 1.05 m/s, subject B: 44, 0.29 m/s, and subject C: 32, 0.27 m/s. Participants completed four conditions of over-ground walking including no FES and three bilateral FES-walking protocols as follows: multi-muscle stimulation (stimulation of quadriceps and gastrocnemius in the stance phase, and hamstring and tibialis anterior in the swing phase), drop foot (tibialis anterior stimulation), and flexor withdrawal (common peroneal nerve stimulation). The FES system obtained gait phase information from foot switches located under the individuals' heels. Three-dimensional kinematic analysis was undertaken to measure minimum toe clearance (MTC); ankle, knee, and hip range of motion (ROM); stride length; and stride speed. Results: Compared to no-FES walking, MTC increased during drop foot (all subjects), flexor withdrawal (subjects A and B), and multi-muscle stimulation (subjects B and C) protocols. A significant decrease in ankle ROM was seen with drop foot (all subjects), flexor withdrawal (subjects A), and multi-muscle stimulation (subjects A and C) protocols. Hip ROM increased with drop foot (subjects B and C), flexor withdrawal (subject B), and multi-muscle stimulation (subject C) protocols. Conclusion: Three FES walking protocols induced positive kinematic changes as indicated by increased MTC, decreased ankle ROM, and increased hip ROM during walking in subjects with incomplete SCI.