Insights From the Transition to Competency-Based Medical Education in Neurology Programs.

Canadian neurology residency programs recently transitioned to Competency-Based Medical Education (CBME). Iterative evaluation is required to optimize CBME implementation. This study aimed to examine the variability and challenges in uptake of CBME in neurology residency programs and identify its benefits and pitfalls. Neurology residents and faculty participated in respective anonymous surveys. Common barriers to uptake were identified from both perspectives. Orientation to CBME was adequate, but workload was increased and contributed to burnout for faculty and residents. It is premature to draw conclusions regarding benefits of CBME. Future research considerations include standardization of entrustment scales and reduction of stakeholder burden.

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease.

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

Dorsal striatum does not mediate feedback-based, stimulus-response learning: An event-related fMRI study in patients with Parkinson's disease tested on and off dopaminergic therapy.

Learning associations between stimuli and responses is essential to everyday life. Dorsal striatum (DS) has long been implicated in stimulus-response learning, though recent results challenge this contention. We have proposed that discrepant findings arise because stimulus-response learning methodology generally confounds learning and response selection processes. In 19 patients with Parkinson's disease (PD) and 18 age-matched controls, we found that dopaminergic therapy decreased the efficiency of stimulus-response learning, with corresponding attenuation of ventral striatum (VS) activation. In contrast, exogenous dopamine improved response selection accuracy related to enhanced DS BOLD signal. Contrasts between PD patients and controls fully support these within-subject patterns. These double dissociations in terms of behaviour and neural activity related to VS and DS in PD and in response to dopaminergic therapy, strongly refute the view that DS mediates stimulus-response learning through feedback. Our findings integrate with a growing literature favouring a role for DS in decision making rather than learning, and unite two literature that have been evolving independently.

Palliative Care Discussions in Multiple System Atrophy: A Retrospective Review.

OBJECTIVE: Multiple system atrophy (MSA) is an incurable neurodegenerative illness in which progressive symptoms, including stridor and acute laryngeal obstruction, occur. Advanced care planning and palliative care discussions in people living with MSA are not well defined. The aim of the present study is to evaluate advanced care planning and current practices in palliative care in MSA to identify opportunities for improving quality of care. METHODS: The study is a retrospective chart review assessing the focus and timing of palliative care discussions in people living with MSA. Some 22 charts were reviewed. RESULTS: A total of 22 patients were included. The most common symptoms were parkinsonism, orthostatic hypotension, GI/GU dysfunction, ataxia and gait impairment. Six patients had stridor. Of the palliative care discussions that took place, the most common topics were diagnosis, symptoms or symptom management, and prognosis. In the majority of patients who died and who had a do-not-attempt-resuscitation order, discussions surrounding resuscitation and goals of care took place only hours before death. CONCLUSIONS: There is no standard approach to advanced care planning and palliative care discussions in people living with MSA. We propose a framework to guide advanced care planning and palliative care discussions in MSA.

Cueing and gait improvement among people with Parkinson's disease: a meta-analysis.

OBJECTIVE: To compare the relative efficacy of visual versus auditory cueing on gait among individuals with Parkinson's disease (PD). DATA SOURCES: A systematic search was completed up to September 2011, using the following databases: EMBASE, Scopus, Medline, CINAHL, and PubMed. STUDY SELECTION: Four authors searched the databases using the following terms: Parkinson's disease (including abbreviations), gait, cadence, step, pace, cueing, cues, and prompt. All studies that evaluated the effect of cueing on gait in PD were selected by consensus of 2 pairs of authors who reviewed the titles and abstracts. Each pair of authors then applied the inclusion and exclusion criteria to each study, and 25 articles were chosen. Inclusion criteria were cueing studies that reported pre- and postoutcome measures of gait parameters. Exclusion criteria were lack of data and studies that evaluated gait aids. DATA EXTRACTION: Gait measures of cadence, stride length, and velocity, before and after cueing, were collected from each study. If data were represented in graphs, a pair of authors extracted the data points individually, then compared and averaged values. DATA SYNTHESIS: The data were synthesized using a meta-analysis based on cue type. Auditory cueing demonstrated significant improvement of cadence (Hedge g=.556; 95% confidence interval [CI], .291-.893), stride length (Hedge g=.497; 95% CI, .289-.696), and velocity (Hedge g=.544; 95% CI, .294-.795). In contrast, visual cueing significantly improved stride length only (Hedge g=.554; 95% CI, .072-1.036). CONCLUSIONS: The findings suggest that auditory cueing is more effective for treating gait disorders in PD. Further research is needed to determine the optimum auditory cueing strategy for gait improvements.

Validity of the Nintendo Wii® balance board for the assessment of standing balance in Parkinson's disease.

BACKGROUND: Impaired postural stability places individuals with Parkinson's at an increased risk for falls. Given the high incidence of fall-related injuries within this population, ongoing assessment of postural stability is important. OBJECTIVE: To evaluate the validity of the Nintendo Wii(®) balance board as a measurement tool for the assessment of postural stability in individuals with Parkinson's. SUBJECTS: Twenty individuals with Parkinson's participated. INTERVENTION: Subjects completed testing on two balance tasks with eyes open and closed on a Wii(®) balance board and biomechanical force platform. MAIN MEASURES: Bland-Altman plots and a two-way, random-effects, single measure intraclass correlation coefficient model were used to assess concurrent validity of centre-of-pressure data. RESULTS: Concurrent validity was demonstrated to be excellent across balance tasks (intraclass correlation coefficients = 0.96, 0.98, 0.92, 0.94). CONCLUSIONS: This study suggests that the Wii(®) balance board is a valid tool for the quantification of postural stability among individuals with Parkinson's.

Clinical milestones as triggers for palliative care intervention in progressive Supranuclear palsy and multiple system atrophy.

not required for reviews.

Predictive language comprehension in Parkinson's disease.

Verb and action knowledge deficits are reported in persons with Parkinson's disease (PD), even in the absence of dementia or mild cognitive impairment. However, the impact of these deficits on combinatorial semantic processing is less well understood. Following on previous verb and action knowledge findings, we tested the hypothesis that PD impairs the ability to integrate event-based thematic fit information during online sentence processing. Specifically, we anticipated persons with PD with age-typical cognitive abilities would perform more poorly than healthy controls during a visual world paradigm task requiring participants to predict a target object constrained by the thematic fit of the agent-verb combination. Twenty-four PD and 24 healthy age-matched participants completed comprehensive neuropsychological assessments. We recorded participants' eye movements as they heard predictive sentences (The fisherman rocks the boat) alongside target, agent-related, verb-related, and unrelated images. We tested effects of group (PD/control) on gaze using growth curve models. There were no significant differences between PD and control participants, suggesting that PD participants successfully and rapidly use combinatory thematic fit information to predict upcoming language. Baseline sentences with no predictive information (e.g., Look at the drum) confirmed that groups showed equivalent sentence processing and eye movement patterns. Additionally, we conducted an exploratory analysis contrasting PD and controls' performance on low-motion-content versus high-motion-content verbs. This analysis revealed fewer predictive fixations in high-motion sentences only for healthy older adults. PD participants may adapt to their disease by relying on spared, non-action-simulation-based language processing mechanisms, although this conclusion is speculative, as the analyses of high- vs. low-motion items was highly limited by the study design. These findings provide novel evidence that individuals with PD match healthy adults in their ability to use verb meaning to predict upcoming nouns despite previous findings of verb semantic impairment in PD across a variety of tasks.

A milder form of molybdenum cofactor deficiency type A presenting as Leigh's syndrome-like phenotype highlighting the secondary mitochondrial dysfunction: a case report.

Background: Molybdenum cofactor deficiency (MoCD) (OMIM# 252150) is an autosomal-recessive disorder caused by mutations in four genes involved in the molybdenum cofactor (MOCO) biosynthesis pathway. Objectives: We report a milder phenotype in a patient with MOCS1 gene mutation who presented with a Leigh-like presentation. Case report: We present the case of a 10-year-old boy who was symptomatic at the age of 5 months with sudden onset of dyskinesia, nystagmus, and extrapyramidal signs following a febrile illness. Initial biochemical, radiological, and histopathological findings a Leigh syndrome-like phenotype; however, whole-exome sequencing detected compound heterozygous mutations in MOCS1 gene, c.1133 G>C and c.217C>T, confirming an underlying MoCD. This was biochemically supported by low uric acid level of 80 (110-282 mmol/L) and low cystine level of 0 (3-49), and a urine S-sulfocysteine at 116 (0-15) mmol/mol creatinine. The patient was administered methionine- and cystine-free formulas. The patient has remained stable, with residual intellectual, speech, and motor sequelae. Conclusion: This presentation expands the phenotypic variability of late-onset MoCD A and highlights the role of secondary mitochondrial dysfunction in its pathogenesis.

Preliminary Assessment of the Safety of a Fault-Tolerant Control-based Wearable Tremor Suppression Glove.

The advent of wearable tremor suppression de-vices (WTSDs) has provided a promising alternative approach for parkinsonian tremor management, especially for individuals whose tremors are not managed by conventional treatment options. Currently, research in WTSDs has shown successful results with a tremor suppression ratio of up to 99 %; however, the user safety of WTSDs has not been properly considered, especially in the occurrence of unexpected events, such as faults and disturbances. In this study, a fault-tolerant control system was developed and integrated into the control system of a WTSD for the first time. The safety and tremor suppression performance of the proposed system under the influence of a measurement loss fault were tested and evaluated on 18 tremor motion datasets, specifically by quantifying the tremor power suppression ratio and the error when tracking voluntary motion. The experimental evaluation showed that the proposed system could remain functional and safe to use in the existence of the fault, with an average user motion tracking error of 1.5º. It was also found that the proposed system achieved significantly improved performance in both metrics when compared to the system without a fault-tolerant controller. Clinical Relevance-This work improves the safety and robustness of WTSDs making them more suitable for use as an additional treatment for parkinsonian tremor.

Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device.

The side effects and complications of traditional treatments for treating pathological tremor have led to a growing research interest in wearable tremor suppression devices (WTSDs) as an alternative approach. Similar to how the human brain coordinates the function of the human system, a tremor estimator determines how a WTSD functions. Although many tremor estimation algorithms have been developed and validated, whether they can be implemented on a cost-effective embedded system has not been studied; furthermore, their effectiveness on tremor signals with multiple harmonics has not been investigated. Therefore, in this study, four tremor estimators were implemented, evaluated, and compared: Weighted-frequency Fourier Linear Combiner (WFLC), WFLC-based Kalman Filter (WFLC-KF), Band-limited Multiple FLC, and enhanced High-order WFLC-KF (eHWFLC-KF). This study aimed to evaluate the performance of each algorithm on a bench-top tremor suppression system with 18 recorded tremor motion datasets; and compare the performance of each estimator. The experimental evaluation showed that the eHWFLC-KF-based WTSD achieved the best performance when suppressing tremor with an average of 89.3% reduction in tremor power, and an average error when tracking voluntary motion of 6.6°/s. Statistical analysis indicated that the eHWFLC-KF-based WTSD is able to reduce the power of tremor better than the WFLC and WFLC-KF, and the BMFLC-based WTSD is better than the WFLC. The performance when tracking voluntary motion is similar among all systems. This study has proven the feasibility of implementing various tremor estimators in a cost-effective embedded system, and provided a real-time performance assessment of four tremor estimators.

Triggers for Referral to Specialized Palliative Care in Advanced Neurologic and Neurosurgical Conditions: A Systematic Review.

Background and Objectives: To systematically review the literature for the most suitable trigger criteria for referral to specialist palliative care services in life-limiting and life-threatening neurologic and neurosurgical conditions. Methods: Literature searches were conducted in Ovid MEDLINE and EMBASE (1990-December 2020). To be included, studies must have trigger/referral criteria clearly outlined, a ≥75% nononcology neurosciences population, and consensus or guidelines documents regarding palliative neurosciences or trigger/referral criteria. We excluded studies that had an oncologic or non-neurosciences population as the main focus of study, trigger and referral criteria not clearly outlined, and no primary or duplicative data. The protocol was registered with PROSPERO (CRD4202013579), and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The American Academy of Neurology Clinical Practice Guidelines Process Manual was used to assess for risk of bias. Results: Our search identified 1,748 publications, of which 22 articles met the eligibility criteria. Studies were considered in 2 main groups: (A) studies designed specifically to identify trigger criteria for referral to specialized neuropalliative care services (n = 9) and (B) studies that retrospectively reported the reason for referral to specialized palliative care or reflected a consensus statement among people with advanced neurologic illness (n = 13). Overall, the results suggest that several published referral triggers for specialized neuropalliative care are based on expert consensus. However, there is a growing body of literature providing evidence-based condition-specific triggers for multiple sclerosis, parkinsonism, amyotrophic lateral sclerosis, and dementia. Discussion: There is a growing body of research that outlines evidence-based referral triggers for neuropalliative care. The ambiguity of nomenclature surrounding referral triggers in the current literature and field of neuropalliative care was a limitation to this study. We suggest that condition-specific triggers are likely to be the most effective for identifying the appropriate patients and timing for referral to specialist palliative care. (PROSPERO registration number: CRD42020135791, crd.york.ac.uk/prospero).

Survey-based identification of design requirements and constraints for a wearable tremor suppression device.

Introduction: Parkinsonian tremor has severely impacted the lives of 65% of individuals with Parkinson's disease, and nearly 25% do not respond to traditional treatments. Although wearable tremor suppression devices (WTSDs) have become a promising alternative approach, this technology is still in the early stages of development, and no studies have reported the stakeholders' opinions on this technology and their desired design requirements. Methods: An online survey was distributed to affected Canadians and Canadian movement disorder specialists (MDS) to acquire information on demographics, the current state of treatments, opinions on the WTSDs, and the desired design requirements of future WTSDs. Results: A total of 101 affected individuals and 24 MDS completed the survey. It was found that both groups are generally open to using WTSDs to manage tremor. The most important design requirement to end users is the adaptability to lifestyle, followed by weight and size, accurate motion, comfort, safety, quick response, and cost. Lastly, most of the participants (65%) think that the device should cost under $500. Conclusions: The findings from this study can be used as guidelines for the development of future WTSDs, such that the future generations could be evaluated and accepted by the end users.

Real-Time Voluntary Motion Prediction and Parkinson's Tremor Reduction Using Deep Neural Networks.

Wearable tremor suppression devices (WTSD) have been considered as a viable solution to manage parkinsonian tremor. WTSDs showed their ability to improve the quality of life of individuals suffering from parkinsonian tremor, by helping them to perform activities of daily living (ADL). Since parkinsonian tremor has been shown to be nonstationary, nonlinear, and stochastic in nature, the performance of the tremor models used by WTSDs is affected by their inability to adapt to the nonlinear behaviour of tremor. Another drawback that the models have is their limitation to estimate or predict one step ahead, which introduces delay when used in real time with WTSDs, which compromises performance. To address these issues, this work proposes a deep neural network model that learns the correlations and nonlinearities of tremor and voluntary motion, and is capable of multi-step prediction with minimal delay. A generalized model that is task and user-independent is presented. The model achieved an average estimation percentage accuracy of 99.2%. The average future voluntary motion prediction percentage accuracy with 10, 20, 50, and 100 steps ahead was 97.0%, 94.0%, 91.6%, and 89.9%, respectively, with prediction time as low as 1.5 ms for 100 steps ahead. The proposed model also achieved an average of 93.8% ± 1.5% in tremor reduction when it was tested in an experimental setup in real time. The tremor reduction showed an improvement of 25% over the Weighted Fourier Linear Combiner (WFLC), an estimator commonly used with WTSDs.

Design and Preliminary Performance Assessment of a Wearable Tremor Suppression Glove.

OBJECTIVE: Approximately 25% of individualsliving with parkinsonian tremor do not respond to traditional treatments. Wearable tremor suppression devices (WTSD) provide an alternative approach, however, tremor in the fingers has not been given as much attention as tremor in the elbow and the wrist. Therefore, the objective of this study is to design a wearable tremor suppression glove that can suppress tremor simultaneously, but independently, in multiple hand joints without restricting the user's voluntary motion. METHODS: A WTSD was designed for managing tremor in the index finger metacarpophalangeal (MCP) joint, thumb MCP joint, and the wrist. The prototype was tested and assessed on a participant living with parkinsonian tremor. RESULTS: The experimental evaluation showed an overall suppression of 73.1%, 80.7%, and 85.5% in resting tremor, 70.2%, 79.5%, and 81% in postural tremor, and 60.0%, 58.7%, and 65.0% in kinetic tremor in the index finger MCP joint, the thumb MCP joint, and the wrist, respectively. CONCLUSION: This first assessment of a WTSD for people living with Parkinson's disease provides confirmation of the feasibility of the approach. The next step requires a comprehensive validation on a broader population in order to evaluate the performance of the WTSD. SIGNIFICANCE: This study demonstrates the feasibility of using a WTSD to manage hand and finger tremor. The device enriches the field of upper-limb tremor management, as the first WTSD for multiple joints of the hand.

An exploration of the burden experienced by spousal caregivers of individuals with Parkinson's disease.

Although previous research has attempted to identify the needs of caregivers for individuals with Parkinson's disease (PD), most has focused on the demands associated with the physical needs of the patient, and not on "mental burden." This study used the repertory grid method to capture the full range of caregivers' subjective experience, quantify their perceptions, and to acquire information that might be useful in directing remediation attempts. Within this sample, caregivers reported far greater burden from "mental stress" (e.g., worrying about individual's safety) than from "physical stress" (e.g., lifting individual into bed). Specifically, caregivers were primarily concerned about spousal safety, as this requires continuous vigilance and constant worry. Caregivers also reported experiencing "little deaths" as the disease progresses, related to a loss of independence for the couple, and the steady diminishment of social networks. Increasing attention on the mental burden experienced by spousal caregivers promises to increase quality of care, and quality of life for individuals with PD, by improving quality of life for the caregiver.

The Design of a Parkinson's Tremor Predictor and Estimator Using a Hybrid Convolutional-Multilayer Perceptron Neural Network.

Parkinson's Disease (PD) is considered to be the second most common age-related neuroegenerative disorder, and it is estimated that seven to ten million people worldwide have PD. One of the symptoms of PD is tremor, and studies have shown that wearable assistive devices have the potential to assist in suppressing it. However, despite the progress in the development of these devices, their performance is limited by the tremor estimators they use. Thus, a need for a tremor model that helps the wearable assistive devices to increase tremor suppression without impeding voluntary motion remains. In this work, a user-independent and task-independent tremor and voluntary motion detection method based on neural networks is proposed. Inertial measurement units (IMUs) were used to measure acceleration and angular velocity from participants with PD, these data were then used to train the neural network. The achieved estimation percentage accuracy of voluntary motion was 99.0%, and the future prediction percentage accuracy was 97.3%, 93.7%, 91.4% and 90.3% for 10 ms, 20 ms, 50 ms and 100 ms ahead, respectively. The root mean squared error (RMSE) achieved for tremor estimation was an average of 0.00087°/s on new unseen data, and the future prediction average RMSE across the different tasks achieved was 0.001°/s, 0.002°/s, 0.020°/s and 0.049°/s for 1 ms, 2 ms, 5 ms, and 10 ms ahead, respectively. Therefore, the proposed method shows promise for use in wearable suppression devices.