Parkinson's Disease Tremor Differentially Responds to Levodopa and Subthalamic Stimulation.

Background: Tremor in Parkinson's disease (PD) has an inconsistent response to levodopa and subthalamic deep brain stimulation (STN-DBS). Objectives: To identify predictive factors of PD tremor responsiveness to levodopa and STN-DBS. Material and Methods: PD patients with upper limb tremor who underwent STN-DBS were included. The levodopa responsiveness of tremor (overall, postural, and rest sub-components), was assessed using the relevant Unified Parkinson's Disease Rating Scale-III items performed during the preoperative assessment. Post-surgical outcomes were similarly assessed ON and OFF stimulation. A score for the rest/postural tremor ratio was used to determine the influence of rest and postural tremor severity on STN-DBS outcome. Factors predictive of tremor responsiveness were determined using multiple linear regression modeling. Volume of tissue activated measurement coupled to voxel-based analysis was performed to identify anatomical clusters associated with motor symptoms improvement. Results: One hundred and sixty five patients were included in this study. Male gender was negatively correlated with tremor responsiveness to levodopa, whereas the ratio of rest/postural tremor was positively correlated with both levodopa responsiveness and STN-DBS tremor outcome. Clusters corresponding to improvement of tremor were in the subthalamic nucleus, the zona incerta and the thalamus, whereas clusters corresponding to improvement for akinesia and rigidity were located within the subthalamic nucleus. Conclusion: More severe postural tremor and less severe rest tremor were associated with both poorer levodopa and STN-DBS response. The different locations of clusters associated with best correction of tremor and other parkinsonian features suggest that STN-DBS effect on PD symptoms is underpinned by the modulation of different networks.

Validation of a comorbidity questionnaire in patients with neurological disorders.

Rational: Several tools exist to assess comorbidities in neurological disorders, the most widely used being the Charlson Comorbidity Index (CCI), but it has several limitations. The Comorbidity and General Health Questionnaire (CGHQ) is a newly designed tool, which includes additional comorbidities associated with health-related quality of life (HR-QOL) and outcomes in neurological disorders. Aims and objectives: To assess the feasibility and validity of the CGHQ in patients with neurological disease. Method: Two hundred patients attending a general neurological clinic were invited to complete the CGHQ along with the EQ-5D-5L questionnaire. The CCI was simultaneously completed by the assessor. CGHQ comorbidity scores were compared with CCI, symptom burden and EQ-5D-5L scores. Results: The CGHQ captured 22 additional comorbidities not included on the CCI and more comorbidities were endorsed on the CGHQ. The CGHQ correlated weakly to moderately with CCI comorbidity scores. While both the CGHQ and CCI correlated negatively with the EQ-5D-5L Visual Analogue Scale, only the CGHQ correlated negatively with the EQ-5D-5L summary index. The CGHQ but not the CCI correlated strongly and positively with symptom burden scores. Conclusion: The CGHQ allows a more comprehensive assessment of comorbidities than the CCI and better correlates with patients' overall symptom burden and HR-QOL in neurological patients.

Computer vision quantification of whole-body Parkinsonian bradykinesia using a large multi-site population.

Parkinson's disease (PD) is a common neurological disorder, with bradykinesia being one of its cardinal features. Objective quantification of bradykinesia using computer vision has the potential to standardise decision-making, for patient treatment and clinical trials, while facilitating remote assessment. We utilised a dataset of part-3 MDS-UPDRS motor assessments, collected at four independent clinical and one research sites on two continents, to build computer-vision-based models capable of inferring the correct severity rating robustly and consistently across all identifiable subgroups of patients. These results contrast with previous work limited by small sample sizes and small numbers of sites. Our bradykinesia estimation corresponded well with clinician ratings (interclass correlation 0.74). This agreement was consistent across four clinical sites. This result demonstrates how such technology can be successfully deployed into existing clinical workflows, with consumer-grade smartphone or tablet devices, adding minimal equipment cost and time.

An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III.

BACKGROUND: Parkinson's disease severity is typically measured using the Movement Disorder Society Unified Parkinson's disease rating scale (MDS-UPDRS). While training for this scale exists, users may vary in how they score a patient with the consequence of intra-rater and inter-rater variability. OBJECTIVE: In this study we explored the consistency of an artificial intelligence platform compared with traditional clinical scoring in the assessment of motor severity in PD. METHODS: Twenty-two PD patients underwent simultaneous MDS-UPDRS scoring by two experienced MDS-UPDRS raters and the two sets of accompanying video footage were also scored by an artificial intelligence video analysis platform known as KELVIN. RESULTS: KELVIN was able to produce a summary score for 7 MDS-UPDRS part 3 items with good inter-rater reliability (Intraclass Correlation Coefficient (ICC) 0.80 in the OFF-medication state, ICC 0.73 in the ON-medication state). Clinician scores had exceptionally high levels of inter-rater reliability in both the OFF (0.99) and ON (0.94) medication conditions (possibly reflecting the highly experienced team). There was an ICC of 0.84 in the OFF-medication state and 0.31 in the ON-medication state between the mean Clinician and mean Kelvin scores for the equivalent 7 motor items, possibly due to dyskinesia impacting on the KELVIN scores. CONCLUSION: We conclude that KELVIN may prove useful in the capture and scoring of multiple items of MDS-UPDRS part 3 with levels of consistency not far short of that achieved by experienced MDS-UPDRS clinical raters, and is worthy of further investigation.

The Impact of Type 2 Diabetes in Parkinson's Disease.

BACKGROUND: Type 2 diabetes (T2DM) is an established risk factor for developing Parkinson's disease (PD), but its effect on disease progression is not well understood. OBJECTIVE: The aim of this study was to investigate the influence of T2DM on aspects of disease progression in PD. METHODS: We analyzed data from the Tracking Parkinson's study to examine the effects of comorbid T2DM on PD progression and quality of life by comparing symptom severity scores assessing a range of motor and nonmotor symptoms. RESULTS: We identified 167 (8.7%) patients with PD and T2DM (PD + T2DM) and 1763 (91.3%) patients with PD without T2DM (PD). After controlling for confounders, patients with T2DM had more severe motor symptoms, as assessed by Movement Disorder Society Unified Parkinson's Disease Rating Scale, Part III (25.8 [0.9] vs. 22.5 [0.3] P = 0.002), and nonmotor symptoms, as assessed by Non-Motor Symptoms Scale total (38.4 [2.5] vs. 31.8 [0.7] P < 0.001), and were significantly more likely to report loss of independence (odds ratio, 2.08; 95% confidence interval [CI]: 1.34-3.25; P = 0.001) and depression (odds ratio, 1.62; CI: 1.10-2.39; P = 0.015). Furthermore, over time, patients with T2DM had significantly faster motor symptom progression (P = 0.012), developed worse mood symptoms (P = 0.041), and were more likely to develop substantial gait impairment (hazard ratio, 1.55; CI: 1.07-2.23; P = 0.020) and mild cognitive impairment (hazard ratio, 1.7; CI: 1.24-2.51; P = 0.002) compared with the PD group. CONCLUSIONS: In the largest study to date, T2DM is associated with faster disease progression in Parkinson's, highlighting an interaction between these two diseases. Because it is a potentially modifiable metabolic state, with multiple peripheral and central targets for intervention, it may represent a target for alleviating parkinsonian symptoms and slowing progression to disability and dementia. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Combining biomarkers for prognostic modelling of Parkinson's disease.

BACKGROUND: Patients with Parkinson's disease (PD) have variable rates of progression. More accurate prediction of progression could improve selection for clinical trials. Although some variance in clinical progression can be predicted by age at onset and phenotype, we hypothesise that this can be further improved by blood biomarkers. OBJECTIVE: To determine if blood biomarkers (serum neurofilament light (NfL) and genetic status (glucocerebrosidase, GBA and apolipoprotein E (APOE))) are useful in addition to clinical measures for prognostic modelling in PD. METHODS: We evaluated the relationship between serum NfL and baseline and longitudinal clinical measures as well as patients' genetic (GBA and APOE) status. We classified patients as having a favourable or an unfavourable outcome based on a previously validated model, and explored how blood biomarkers compared with clinical variables in distinguishing prognostic phenotypes . RESULTS: 291 patients were assessed in this study. Baseline serum NfL was associated with baseline cognitive status. Nfl predicted a shorter time to dementia, postural instability and death (dementia-HR 2.64; postural instability-HR 1.32; mortality-HR 1.89) whereas APOEe4 status was associated with progression to dementia (dementia-HR 3.12, 95% CI 1.63 to 6.00). NfL levels and genetic variables predicted unfavourable progression to a similar extent as clinical predictors. The combination of clinical, NfL and genetic data produced a stronger prediction of unfavourable outcomes compared with age and gender (area under the curve: 0.74-age/gender vs 0.84-ALL p=0.0103). CONCLUSIONS: Clinical trials of disease-modifying therapies might usefully stratify patients using clinical, genetic and NfL status at the time of recruitment.

Volitional Control of Brain Motor Activity and Its Therapeutic Potential.

BACKGROUND: Neurofeedback training is a closed-loop neuromodulatory technique in which real-time feedback of brain activity and connectivity is provided to the participant for the purpose of volitional neural control. Through practice and reinforcement, such learning has been shown to facilitate measurable changes in brain function and behavior. OBJECTIVES: In this review, we examine how neurofeedback, coupled with motor imagery training, has the potential to improve or normalize motor function in neurological diseases such as Parkinson disease and chronic stroke. We will also explore neurofeedback in the context of brain-machine interfaces (BMIs), discussing both noninvasive and invasive methods which have been used to power external devices (eg, robot hand orthosis or exoskeleton) in the context of motor neurorehabilitation. CONCLUSIONS: The published literature provides mounting high-quality evidence that neurofeedback and BMI control may lead to clinically relevant changes in brain function and behavior.

Long-term success of low-frequency subthalamic nucleus stimulation for Parkinson's disease depends on tremor severity and symptom duration.

Patients with Parkinson's disease can develop axial symptoms, including speech, gait and balance difficulties. Chronic high-frequency (>100 Hz) deep brain stimulation can contribute to these impairments while low-frequency stimulation (<100 Hz) may improve symptoms but only in some individuals. Factors predicting which patients benefit from low-frequency stimulation in the long term remain unclear. This study aims to confirm that low-frequency stimulation improves axial symptoms, and to go further to also explore which factors predict the durability of its effects. We recruited patients who developed axial motor symptoms while using high-frequency stimulation and objectively assessed the short-term impact of low-frequency stimulation on axial symptoms, other aspects of motor function and quality of life. A retrospective chart review was then conducted on a larger cohort to identify which patient characteristics were associated with not only the need to trial low-frequency stimulation, but also those which predicted its sustained use. Among 20 prospective patients, low-frequency stimulation objectively improved mean motor and axial symptom severity and quality of life in the short term. Among a retrospective cohort of 168 patients, those with less severe tremor and those in whom axial symptoms had emerged sooner after subthalamic nucleus deep brain stimulation were more likely to be switched to and remain on long-term low-frequency stimulation. These data suggest that low-frequency stimulation results in objective mean improvements in overall motor function and axial symptoms among a group of patients, while individual patient characteristics can predict sustained long-term benefits. Longer follow-up in the context of a larger, controlled, double-blinded study would be required to provide definitive evidence of the role of low-frequency deep brain stimulation.

Exenatide once weekly over 2 years as a potential disease-modifying treatment for Parkinson's disease: protocol for a multicentre, randomised, double blind, parallel group, placebo controlled, phase 3 trial: The 'Exenatide-PD3' study.

INTRODUCTION: Parkinson's disease (PD) is a common neurodegenerative disorder with substantial morbidity. No disease-modifying treatments currently exist. The glucagon like peptide-1 receptor agonist exenatide has been associated in single-centre studies with reduced motor deterioration over 1 year. The aim of this multicentre UK trial is to confirm whether these previous positive results are maintained in a larger number of participants over 2 years and if effects accumulate with prolonged drug exposure. METHODS AND ANALYSIS: This is a phase 3, multicentre, double-blind, randomised, placebo-controlled trial of exenatide at a dose of 2 mg weekly in 200 participants with mild to moderate PD. Treatment duration is 96 weeks. Randomisation is 1:1, drug to placebo. Assessments are performed at baseline, week 12, 24, 36, 48, 60, 72, 84 and 96 weeks.The primary outcome is the comparison of Movement Disorders Society Unified Parkinson's Disease Rating Scale part 3 motor subscore in the practically defined OFF medication state at 96 weeks between participants according to treatment allocation. Secondary outcomes will compare the change between groups among other motor, non-motor and cognitive scores. The primary outcome will be reported using descriptive statistics and comparisons between treatment groups using a mixed model, adjusting for baseline scores. Secondary outcomes will be summarised between treatment groups using summary statistics and appropriate statistical tests to assess for significant differences. ETHICS AND DISSEMINATION: This trial has been approved by the South Central-Berkshire Research Ethics Committee and the Health Research Authority. Results will be disseminated in peer-reviewed journals, presented at scientific meetings and to patients in lay-summary format. TRIAL REGISTRATION NUMBERS: NCT04232969, ISRCTN14552789.

Video-Based Analyses of Parkinson's Disease Severity: A Brief Review.

Remote and objective assessment of the motor symptoms of Parkinson's disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson's disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson's disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards.

The Future of Incretin-Based Approaches for Neurodegenerative Diseases in Older Adults: Which to Choose? A Review of their Potential Efficacy and Suitability.

The current treatment options for neurodegenerative diseases in older adults rely mainly on providing symptomatic relief. Yet, it remains imperative to identify agents that slow or halt disease progression to avoid the most disabling features often associated with advanced disease stages. A potential overlap between the pathological processes involved in diabetes and neurodegeneration has been established, raising the question of whether incretin-based therapies for diabetes may also be useful in treating neurodegenerative diseases in older adults. Here, we review the different agents that belong to this class of drugs (GLP-1 receptor agonists, dual/triple receptor agonists, DPP-4 inhibitors) and describe the data supporting their potential role in treating neurodegenerative conditions including Parkinson's disease and Alzheimer's disease. We further discuss whether there are any distinctive properties among them, particularly in the context of safety or tolerability and CNS penetration, that might facilitate their successful repurposing as disease-modifying drugs. Proof-of-efficacy data will obviously be of the greatest importance, and this is most likely to be demonstrable in agents that reach the central nervous system and impact on neuronal GLP-1 receptors. Additionally, however, the long-term safety and tolerability (including gastrointestinal side effects and unwanted weight loss) as well as the route of administration of this class of agents may also ultimately determine success and these aspects should be considered in prioritising which approaches to subject to formal clinical trial evaluations.

Seizures triggered by eating: a rare form of reflex epilepsy.

Eating epilepsy is rare and comprises reflex seizures induced by food intake presenting with broad clinical manifestations. Despite this heterogeneity, a unique focal impaired awareness seizure semiology localizing to specific brain regions has been noted. Here, we present a case with video-EEG depicting this characteristic clinical presentation and its informative electrographic correlate. [Published with video sequence].

Diabetes medications and risk of Parkinson's disease: a cohort study of patients with diabetes.

The elevated risk of Parkinson's disease in patients with diabetes might be mitigated depending on the type of drugs prescribed to treat diabetes. Population data for risk of Parkinson's disease in users of the newer types of drugs used in diabetes are scarce. We compared the risk of Parkinson's disease in patients with diabetes exposed to thiazolidinediones (glitazones), glucagon-like peptide-1 (GLP-1) receptor agonists and dipeptidyl peptidase 4 (DPP4) inhibitors, with the risk of Parkinson's disease of users of any other oral glucose lowering drugs. A population-based, longitudinal, cohort study was conducted using historic primary care data from The Health Improvement Network. Patients with a diagnosis of diabetes and a minimum of two prescriptions for diabetes medications between January 2006 and January 2019 were included in our study. The primary outcome was the first recording of a diagnosis of Parkinson's disease after the index date, identified from clinical records. We compared the risk of Parkinson's disease in individuals treated with glitazones or DPP4 inhibitors and/or GLP-1 receptor agonists to individuals treated with other antidiabetic agents using a Cox regression with inverse probability of treatment weighting based on propensity scores. Results were analysed separately for insulin users. Among 100 288 patients [mean age 62.8 years (standard deviation 12.6)], 329 (0.3%) were diagnosed with Parkinson's disease during the median follow-up of 3.33 years. The incidence of Parkinson's disease was 8 per 10 000 person-years in 21 175 patients using glitazones, 5 per 10 000 person-years in 36 897 patients using DPP4 inhibitors and 4 per 10 000 person-years in 10 684 using GLP-1 mimetics, 6861 of whom were prescribed GTZ and/or DPP4 inhibitors prior to using GLP-1 mimetics. Compared with the incidence of Parkinson's disease in the comparison group (10 per 10 000 person-years), adjusted results showed no evidence of any association between the use of glitazones and Parkinson's disease [incidence rate ratio (IRR) 1.17; 95% confidence interval (CI) 0.76-1.63; P = 0.467], but there was strong evidence of an inverse association between use of DPP4 inhibitors and GLP-1 mimetics and the onset of Parkinson's disease (IRR 0.64; 95% CI 0.43-0.88; P < 0.01 and IRR 0.38; 95% CI 0.17-0.60; P < 0.01, respectively). Results for insulin users were in the same direction, but the overall size of this group was small. The incidence of Parkinson's disease in patients diagnosed with diabetes varies substantially depending on the treatment for diabetes received. The use of DPP4 inhibitors and/or GLP-1 mimetics is associated with a lower rate of Parkinson's disease compared to the use of other oral antidiabetic drugs.

Seizures triggered by eating - A rare form of reflex epilepsy: A systematic review.

Eating epilepsy is a rare disorder, characterised by reflex seizures induced by food intake. It is highly heterogenous, with clinical signs and EEG findings varying between patients. However, common features do emerge from the reported literature. The aim of this systematic review was to bring together this information to facilitate understanding and recognition. We therefore searched electronic databases (PubMed, Scopus, Medline) for relevant studies using keywords 'epilepsy', 'seizure' and 'eating' in March 2020. Human studies, written in English, that reported on cohorts of patients with eating epilepsy were included. Fifty-two unique papers were consequently identified, describing seizure characteristics and diagnostic features in 378 patients. Eating seizures began in the second decade of life, with a higher incidence in males. They were typically focal-onset, and most commonly of the focal impaired awareness type. Pharmacological therapy with one or multiple agents was noted in 80 % of cases, with poor control reported in approximately 25 % of patients. While this retrospective work highlights key features, it is important that future studies implicate video EEG to fully evaluate this highly unique and interesting disorder.

Automated PDF highlighting to support faster curation of literature for Parkinson's and Alzheimer's disease.

Neurodegenerative disorders such as Parkinson's and Alzheimer's disease are devastating and costly illnesses, a source of major global burden. In order to provide successful interventions for patients and reduce costs, both causes and pathological processes need to be understood. The ApiNATOMY project aims to contribute to our understanding of neurodegenerative disorders by manually curating and abstracting data from the vast body of literature amassed on these illnesses. As curation is labour-intensive, we aimed to speed up the process by automatically highlighting those parts of the PDF document of primary importance to the curator. Using techniques similar to those of summarisation, we developed an algorithm that relies on linguistic, semantic and spatial features. Employing this algorithm on a test set manually corrected for tool imprecision, we achieved a macro F 1 -measure of 0.51, which is an increase of 132% compared to the best bag-of-words baseline model. A user based evaluation was also conducted to assess the usefulness of the methodology on 40 unseen publications, which reveals that in 85% of cases all highlighted sentences are relevant to the curation task and in about 65% of the cases, the highlights are sufficient to support the knowledge curation task without needing to consult the full text. In conclusion, we believe that these are promising results for a step in automating the recognition of curation-relevant sentences. Refining our approach to pre-digest papers will lead to faster processing and cost reduction in the curation process. Database URL: https://github.com/KHP-Informatics/NapEasy.

Neural correlates of facial motion perception.

Several neuroimaging studies have revealed that the superior temporal sulcus (STS) is highly implicated in the processing of facial motion. A limitation of these investigations, however, is that many of them utilize unnatural stimuli (e.g., morphed videos) or those which contain many confounding spatial cues. As a result, the underlying mechanisms may not be fully engaged during such perception. The aim of the current study was to build upon the existing literature by implementing highly detailed and accurate models of facial movement. Accordingly, neurologically healthy participants viewed simultaneous sequences of rigid and nonrigid motion that was retargeted onto a standard computer generated imagery face model. Their task was to discriminate between different facial motion videos in a two-alternative forced choice paradigm. Presentations varied between upright and inverted orientations. In corroboration with previous data, the perception of natural facial motion strongly activated a portion of the posterior STS. The analysis also revealed engagement of the lingual gyrus, fusiform gyrus, precentral gyrus, and cerebellum. These findings therefore suggest that the processing of dynamic facial information is supported by a network of visuomotor substrates.

Categorizing identity from facial motion.

Advances in marker-less motion capture technology now allow the accurate replication of facial motion and deformation in computer-generated imagery (CGI). A forced-choice discrimination paradigm using such CGI facial animations showed that human observers can categorize identity solely from facial motion cues. Animations were generated from motion captures acquired during natural speech, thus eliciting both rigid (head rotations and translations) and nonrigid (expressional changes) motion. To limit interferences from individual differences in facial form, all animations shared the same appearance. Observers were required to discriminate between different videos of facial motion and between the facial motions of different people. Performance was compared to the control condition of orientation-inverted facial motion. The results show that observers are able to make accurate discriminations of identity in the absence of all cues except facial motion. A clear inversion effect in both tasks provided consistency with previous studies, supporting the configural view of human face perception. The accuracy of this motion capture technology thus allowed stimuli to be generated that closely resembled real moving faces. Future studies may wish to implement such methodology when studying human face perception.