Relationship Between Melatonin Receptor Agonists and Parkinson's Disease.

Parkinson's disease affects millions of people worldwide, and without significant progress in disease prevention and treatment, its incidence and prevalence could increase by more than 30% by 2030. Researchers have focused on targeting sleep and the circadian system as a novel treatment strategy for Parkinson's disease. This study investigated the association between melatonin receptor agonists and Parkinson's disease, using the Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS). The target drugs were melatonin receptor agonists including ramelteon, tasimelteon, and agomelatine. Parkinson's disease cases were defined according to the Medical Dictionary for Regulatory Activities (MedDRA) 25.0; Standardized MedDRA Query (SMQ) using both the "narrow" and "broad" preferred terms (PTs) associated with Parkinson's disease. The association between melatonin receptor agonists (ramelteon, tasimelteon, and agomelatine) and Parkinson's disease was evaluated by the reporting odds ratio. Upon analyzing the data from all patients registered in the FAERS, ramelteon (ROR: 0.66, 95% confidence interval [95% CI]: 0.51-0.84) and tasimelteon (ROR: 0.49, 95% CI: 0.38-0.62) showed negative correlations with Parkinson's disease. Conversely, only agomelatine was positively correlated with Parkinson's disease (ROR: 2.63, 95% CI: 2.04-3.40). These results suggest that among the melatonin receptor agonists, ramelteon and tasimelteon are negatively correlated with Parkinson's disease. In contrast, agomelatine was shown to be positively correlated with Parkinson's disease. These results should be used in research to develop drugs for the treatment of Parkinson's disease, fully considering the limitations of the spontaneous reporting system.

Modulation of α-synuclein aggregation amid diverse environmental perturbation.

Intrinsically disordered protein α-synuclein (αS) is implicated in Parkinson's disease due to its aberrant aggregation propensity. In a bid to identify the traits of its aggregation, here we computationally simulate the multi-chain association process of αS in aqueous as well as under diverse environmental perturbations. In particular, the aggregation of αS in aqueous and varied environmental condition led to marked concentration differences within protein aggregates, resembling liquid-liquid phase separation (LLPS). Both saline and crowded settings enhanced the LLPS propensity. However, the surface tension of αS droplet responds differently to crowders (entropy-driven) and salt (enthalpy-driven). Conformational analysis reveals that the IDP chains would adopt extended conformations within aggregates and would maintain mutually perpendicular orientations to minimize inter-chain electrostatic repulsions. The droplet stability is found to stem from a diminished intra-chain interactions in the C-terminal regions of αS, fostering inter-chain residue-residue interactions. Intriguingly, a graph theory analysis identifies small-world-like networks within droplets across environmental conditions, suggesting the prevalence of a consensus interaction patterns among the chains. Together these findings suggest a delicate balance between molecular grammar and environment-dependent nuanced aggregation behavior of αS.

Relevance of genetic testing in the gene-targeted trial era: the Rostock Parkinson's disease study.

Estimates of the spectrum and frequency of pathogenic variants in Parkinson's disease (PD) in different populations are currently limited and biased. Furthermore, although therapeutic modification of several genetic targets has reached the clinical trial stage, a major obstacle in conducting these trials is that PD patients are largely unaware of their genetic status and, therefore, cannot be recruited. Expanding the number of investigated PD-related genes and including genes related to disorders with overlapping clinical features in large, well-phenotyped PD patient groups is a prerequisite for capturing the full variant spectrum underlying PD and for stratifying and prioritizing patients for gene-targeted clinical trials. The Rostock Parkinson's disease (ROPAD) study is an observational clinical study aiming to determine the frequency and spectrum of genetic variants contributing to PD in a large international cohort. We investigated variants in 50 genes with either an established relevance for PD or possible phenotypic overlap in a group of 12 580 PD patients from 16 countries [62.3% male; 92.0% White; 27.0% positive family history (FH+), median age at onset (AAO) 59 years] using a next-generation sequencing panel. Altogether, in 1864 (14.8%) ROPAD participants (58.1% male; 91.0% White, 35.5% FH+, median AAO 55 years), a PD-relevant genetic test (PDGT) was positive based on GBA1 risk variants (10.4%) or pathogenic/likely pathogenic variants in LRRK2 (2.9%), PRKN (0.9%), SNCA (0.2%) or PINK1 (0.1%) or a combination of two genetic findings in two genes (∼0.2%). Of note, the adjusted positive PDGT fraction, i.e. the fraction of positive PDGTs per country weighted by the fraction of the population of the world that they represent, was 14.5%. Positive PDGTs were identified in 19.9% of patients with an AAO ≤ 50 years, in 19.5% of patients with FH+ and in 26.9% with an AAO ≤ 50 years and FH+. In comparison to the idiopathic PD group (6846 patients with benign variants), the positive PDGT group had a significantly lower AAO (4 years, P = 9 × 10-34). The probability of a positive PDGT decreased by 3% with every additional AAO year (P = 1 × 10-35). Female patients were 22% more likely to have a positive PDGT (P = 3 × 10-4), and for individuals with FH+ this likelihood was 55% higher (P = 1 × 10-14). About 0.8% of the ROPAD participants had positive genetic testing findings in parkinsonism-, dystonia/dyskinesia- or dementia-related genes. In the emerging era of gene-targeted PD clinical trials, our finding that ∼15% of patients harbour potentially actionable genetic variants offers an important prospect to affected individuals and their families and underlines the need for genetic testing in PD patients. Thus, the insights from the ROPAD study allow for data-driven, differential genetic counselling across the spectrum of different AAOs and family histories and promote a possible policy change in the application of genetic testing as a routine part of patient evaluation and care in PD.

Don't die like me: Which proteins are responsible for the selective neuronal vulnerability within the substantia nigra?

A hallmark of Parkinson's disease is the specific degeneration of dopaminergic neurons in the substantia nigra pars compacta. Interestingly, not all of these neurons are affected to the same extent. Studies revealed that neurons located more ventrally within the substantia nigra pars compacta have a higher prevalence to degenerate than those located in the dorsal tier. The underlying reasons for this selective neuronal vulnerability are still unknown. The aim of the present study was to gain a better understanding of molecular differences between these two neuronal subpopulations that may explain the selective neuronal vulnerability within the human substantia nigra. For this purpose, the neurons from the ventral as well as dorsal tier of the substantia nigra were specifically isolated out of neuropathologically unremarkable human substantia nigra sections with laser microdissection. Following, their proteome was analyzed by data independent acquisition mass spectrometry. The samples were analysed donor-specifically and not pooled for this purpose. A total of 5,391 proteins were identified in the substantia nigra. Of these, 2,453 proteins could be quantified in 100% of the dorsal tier samples. 1,629 could be quantified in 100% of the ventral tier samples. Nine proteins were differentially regulated with a log2 value ≥0.5 and a Qvalue ≤0.05. Of these 7 were higher abundant in the dorsal tier and 2 higher in the ventral tier. These proteins are associated with the cytoskeleton, neuronal plasticity, or calcium homeostasis. With these findings a deeper understanding can be gained of the selective neuronal vulnerability within the substantia nigra and of protective mechanisms against neurodegeneration in specific neuronal subpopulations.

Evaluation of Neuroprotective Effect of Gut Microbe in Parkinson's Disease: An In Silico and In Vivo Approach.

Parkinson's disease is a progressive neurodegenerative disorder marked by the death of dopaminergic neurons in the substantia nigra region of the brain. Aggregation of alpha-synuclein (α-synuclein) is a contributing factor to Parkinson's disease pathogenesis. The objective of this study is to investigate the neuroprotective effects of gut microbes on α-synuclein aggregation using both in silico and in vivo approaches. We focussed on the interaction between α-synuclein and metabolites released by gut bacteria that protect from PD. We employed three probiotic microbe strains against α-synuclein protein: Lactobacillus casei, Escherichia coli, and Bacillus subtilis, with their chosen PDB IDs being Dihydrofolate reductase (3DFR), methionine synthetase (6BM5), and tryptophanyl-tRNA synthetase (3PRH), respectively. Using HEX Dock 6.0 software, we examined the interactions between these proteins. Among the various metabolites, methionine synthetase produced by E. coli showed potential interactions with α-synuclein. To further evaluate the neuroprotective benefits of E. coli, an in vivo investigation was performed using a rotenone-induced Parkinsonian mouse model. The motor function of the animals was assessed through behavioural tests, and oxidative stress and neurotransmitter levels were also examined. The results demonstrated that, compared to the rotenone-induced PD mouse model, the rate of neurodegeneration was considerably reduced in mice treated with E. coli. Additionally, histopathological studies provided evidence of the neuroprotective effects of E. coli. In conclusion, this study lays the groundwork for future research, suggesting that gut bacteria may serve as potential therapeutic agents in the development of medications to treat Parkinson's disease. fig. 1.

Residues 2 to 7 of α-synuclein regulate amyloid formation via lipid-dependent and lipid-independent pathways.

Amyloid formation by α-synuclein (αSyn) occurs in Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies. Deciphering the residues that regulate αSyn amyloid fibril formation will not only provide mechanistic insight but may also reveal targets to prevent and treat disease. Previous investigations have identified several regions of αSyn to be important in the regulation of amyloid formation, including the non-amyloid-ß component (NAC), P1 region (residues 36 to 42), and residues in the C-terminal domain. Recent studies have also indicated the importance of the N-terminal region of αSyn for both its physiological and pathological roles. Here, the role of residues 2 to 7 in the N-terminal region of αSyn is investigated in terms of their ability to regulate amyloid fibril formation in vitro and in vivo. Deletion of these residues (αSynΔN7) slows the rate of fibril formation in vitro and reduces the capacity of the protein to be recruited by wild-type (αSynWT) fibril seeds, despite cryo-EM showing a fibril structure consistent with those of full-length αSyn. Strikingly, fibril formation of αSynΔN7 is not induced by liposomes, despite the protein binding to liposomes with similar affinity to αSynWT. A Caenorhabditis elegans model also showed that αSynΔN7::YFP forms few puncta and lacks motility and lifespan defects typified by expression of αSynWT::YFP. Together, the results demonstrate the involvement of residues 2 to 7 of αSyn in amyloid formation, revealing a target for the design of amyloid inhibitors that may leave the functional role of the protein in membrane binding unperturbed.

Daytime napping and the incidence of Parkinson's disease: a prospective cohort study with Mendelian randomization.

BACKGROUND: The causal relationship between daytime napping and the risk of Parkinson's disease (PD) remains unclear, with prospective studies providing limited evidence. This study investigated the association between daytime napping frequency and duration and PD incidence and explored the causality relationship between this association by conducting Mendelian randomization (MR) analysis. METHODS: This prospective cohort study included 393,302 participants, and accelerometer-measured daytime napping data were available only for 78,141 individuals. Cox proportional hazards regression was used to estimate the association between the daytime napping frequency and duration and the PD risk. The role of the systemic immune-inflammation index (SII) in the association between daytime napping frequency and PD risk was assessed through mediation analyses. Moreover, the causal association between the daytime napping frequency and the PD risk was preliminarily explored by conducting two-sample MR analyses. RESULTS: The median follow-up duration was 12.18 years. The participants who reported napping sometimes or usually exhibited a significantly higher PD risk than those who never/rarely napped during the day [sometimes: hazard ratio (HR), 1.13; 95% confidence interval (CI), 1.03-1.23; usually: HR, 1.33; 95% CI, 1.14-1.55], and SII played a mediating role in this association. However, the MR analyses did not indicate that the daytime napping frequency and PD risk were significantly associated. The participants napping for over 1 h exhibited a significantly elevated PD risk (HR, 1.54; 95% CI, 1.11-2.16). Moreover, no significant interaction was identified between napping frequency or duration and genetic susceptibility to PD (P for interaction > 0.05). CONCLUSIONS: In this study, increased daytime napping frequency and duration were associated with an increased PD risk, but no causal relationship was observed between napping frequency and PD risk in the MR analysis. Larger GWAS-based cohort studies and MR studies are warranted to explore potential causal relationships.

Macular structural integrity estimates are associated with Parkinson's disease genetic risk.

BACKGROUND: Optical coherence tomography (OCT) is a non-invasive technique to measure retinal layer thickness, providing insights into retinal ganglion cell integrity. Studies have shown reduced retinal nerve fibre layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness in Parkinson's disease (PD) patients. However, it is unclear if there is a common genetic overlap between the macula and peripapillary estimates with PD and if the genetic risk of PD is associated with changes in ganglion cell integrity estimates in young adults. METHOD: Western Australian young adults underwent OCT imaging. Their pRNFL, GCIPL, and overall retinal thicknesses were recorded, as well as their longitudinal changes between ages 20 and 28. Polygenic risk scores (PRS) were estimated for each participant based on genome-wide summary data from the largest PD genome-wide association study conducted to date. We further evaluated whether PD PRS was associated with changes in thickness at a younger age. To evaluate the overlap between retinal integrity estimates and PD, we annotated and prioritised genes using mBAT-combo and performed colocalisation through the GWAS pairwise method and HyPrColoc. We used a multi-omic approach and single-cell expression data of the retina and brain through a Mendelian randomisation framework to evaluate the most likely causal genes. Genes prioritised were analysed for missense variants that could have a pathogenic effect using AlphaMissense. RESULTS: We found a significant association between the Parkinson's disease polygenic risk score (PD PRS) and changes in retinal thickness in the macula of young adults assessed at 20 and 28 years of age. Gene-based analysis identified 27 genes common to PD and retinal integrity, with a notable region on chromosome 17. Expression analyses highlighted NSF, CRHR1, and KANSL1 as potential causal genes shared between PD and ganglion cell integrity measures. CRHR1 showed consistent results across multiple omics levels. INTERPRETATION: Our findings suggest that retinal measurements, particularly in young adults, could be a potential marker for PD risk, indicating a genetic overlap between retinal structural integrity and PD. The study highlights specific genes and loci, mainly on chromosome 17, as potential shared etiological factors for PD and retinal changes. Our results highlight the importance of further longitudinal studies to validate retinal structural metrics as early indicators of PD predisposition.

Safety and efficiency of deep brain stimulation in the elderly patients with Parkinson's disease.

AIMS: Deep brain stimulation (DBS) is not routinely performed in elderly patients (≥75 years old) to date because of concerns about complications and decreased benefit. This study aimed to evaluate the safety and efficacy of DBS in elderly patients with Parkinson's disease. METHODS: A retrospective analysis was performed using data from 40 elderly patients from four centers who were treated with neurosurgical robot-assisted DBS between September 2016 and December 2021. These patients were followed up for a minimum period of 2 years, with a subgroup of nine patients followed up for 5-7 years. Patient demographic characteristics, surgical information, pre- and postoperative motor scores, non-motor scores, activities of daily living, and complications were retrospectively analyzed. RESULTS: The mean surgical procedure duration was 1.65 ± 0.24 h, with a mean electrode implantation duration of 1.10 ± 0.23 h and a mean pulse generator implantation duration of 0.55 ± 0.07 h. The mean pneumocephalus volume, electrode fusion error, and Tao's DBS surgery scale were 16.23 ± 12.81 cm3, 0.81 ± 0.23 mm, and 77.63 ± 8.08, respectively. One patient developed a skin infection, and the device was removed. The Unified Parkinson's disease rating scale, Unified Parkinson's disease rating scale of Part III, tremor, rigidity, bradykinesia, axial, and Barthel index for activities of daily living (ADL-Barthel) scores significantly improved at the 2-year follow-up (p < 0.05). The levodopa equivalent daily dose (LEDD) was significantly reduced at the 2-year follow-up (p < 0.05). However, the Montreal cognitive assessment, Hamilton depression scale, and Hamilton anxiety scale scores did not significantly change during the 2-year follow-up (p > 0.05). Additionally, in the subgroup with a 5-year follow-up, the motor symptoms, ADL-Barthel score, and cognitive function worsened over time compared to baseline. However, there was still an improvement in motor symptoms and ADL with DBS on-stimulation compared with the off-stimulation state. The LEDD increased 5 years after surgery compared to that at baseline. Eleven patients had passed away during follow-up, the mean survival time was 38.3 ± 17.3 months after surgery, and the mean age at the time of death was 81.2 (range 75-87) years. CONCLUSION: Robot-assisted DBS surgery for the elderly patients with Parkinson's disease is accurate and safe. Motor symptoms and ADL significantly improve and patients can benefit from long-term neuromodulation, which may decrease the risk of death.

Clinical and neurophysiological effects of bilateral repetitive transcranial magnetic stimulation and EEG-guided neurofeedback in Parkinson's disease: a randomized, four-arm controlled trial.

BACKGROUND: Repetitive Transcranial Magnetic Stimulation (rTMS) and EEG-guided neurofeedback techniques can reduce motor symptoms in Parkinson's disease (PD). However, the effects of their combination are unknown. Our objective was to determine the immediate and short-term effects on motor and non-motor symptoms, and neurophysiological measures, of rTMS and EEG-guided neurofeedback, alone or combined, compared to no intervention, in people with PD. METHODS: A randomized, single-blinded controlled trial with 4 arms was conducted. Group A received eight bilateral, high-frequency (10 Hz) rTMS sessions over the Primary Motor Cortices; Group B received eight 30-minute EEG-guided neurofeedback sessions focused on reducing average bilateral alpha and beta bands; Group C received a combination of A and B; Group D did not receive any therapy. The primary outcome measure was the UPDRS-III at post-intervention and two weeks later. Secondary outcomes were functional mobility, limits of stability, depression, health-related quality-of-life and cortical silent periods. Treatment effects were obtained by longitudinal analysis of covariance mixed-effects models. RESULTS: Forty people with PD participated (27 males, age = 63 ± 8.26 years, baseline UPDRS-III = 15.63 ± 6.99 points, H&Y = 1-3). Group C showed the largest effect on motor symptoms, health-related quality-of-life and cortical silent periods, followed by Group A and Group B. Negligible differences between Groups A-C and Group D for functional mobility or limits of stability were found. CONCLUSIONS: The combination of rTMS and EEG-guided neurofeedback diminished overall motor symptoms and increased quality-of-life, but this was not reflected by changes in functional mobility, postural stability or depression levels. TRIAL REGISTRATION: NCT04017481.

Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort.

AIMS: Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson's disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI). METHODS: A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson's Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event. RESULTS: Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004). CONCLUSIONS: PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.

Exploring Causal Links Between Gut Microbiota and Geriatric Syndromes: A Two-Sample Mendelian Randomization Analysis.

Background: Both observational studies and clinical trials have demonstrated a link between the gut microbiota and the geriatric syndrome. Nevertheless, the exact nature of this relationship, particularly concerning causality, remains elusive. Mendelian randomization (MR) is a method of inference based on genetic variation to assess the causal relationship between an exposure and an outcome. In this study, we conducted a two-sample Mendelian randomization (TSMR) study to fully reveal the potential genetic causal effects of gut microbiota on geriatric syndromes. Methods: This study used data from genome wide association studies (GWAS) to investigate causal relationships between the gut microbiota and geriatric syndromes, including frailty, Parkinson's disease (PD), delirium, insomnia, and depression. The primary causal relationships were evaluated using the inverse-variance weighted method, MR Egger, simple mode, weighted mode and weighted median. To assess the robustness of the results, horizontal pleiotropy was examined through MR-Egger intercept and MR-presso methods. Heterogeneity was assessed using Cochran's Q test, and sensitivity was evaluated via the leave-one-out method. Results: We identified 41 probable causal relationships between gut microbiota and five geriatric syndrome-associated illnesses using the inverse-variance weighted method. Frailty showed five positive and two negative causal relationships, while PD revealed three positive and four negative causal connections. Delirium showed three positive and two negative causal relationships. Similarly, insomnia demonstrated nine positive and two negative causal connections, while depression presented nine positive and two negative causal relationships. Conclusions: Using the TSMR method and data from the public GWAS database and, we observed associations between specific microbiota groups and geriatric syndromes. These findings suggest a potential role of gut microbiota in the development of geriatric syndromes, providing valuable insights for further research into the causal relationship between gut microbiota and these syndromes.

Alpha synuclein PET imaging, a step closer to in vivo neuropathology in Parkinson's disease and related disorders.

In this issue of Neuron, Endo et al.1 develop a PET tracer capable of detecting alpha-synuclein (ɑ-syn). With validation in animal models and humans, this tracer brings us closer to being able to monitor the synuclein aggregation process and associated pathological changes in Parkinson's disease (PD) and other synucleinopathies.

Absence of motor impairments or pathological changes in TMEM230 knockout rats.

Parkinson's disease (PD), which is the second most common neurodegenerative disorder, is characterized by progressive movement impairment and loss of midbrain dopaminergic neurons in the substantia nigra. Although mutations in TMEM230 are linked to familial PD, the pathogenic mechanism underlying TMEM230-associated PD remains to be elucidated. To explore the effect of TMEM230 depletion in vivo, we created TMEM230 knockout rats using CRISPR-Cas9 technology. TMEM230 knockout rats did not exhibit any core features of PD, including impaired motor function, loss of dopaminergic neurons in the substantia nigra, or altered expression of proteins related to autophagy, the Rab family, or vesicular trafficking. In addition, no glial reactions were observed in TMEM230 knockout rats. These results indicate that depletion of TMEM230 may not lead to dopaminergic neuron degeneration in rats, further supporting that PD-associated TMEM230 mutations lead to dopaminergic neuron death by gain-of-toxic function.

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use.

Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center.

Tremor, defined as an "involuntary, rhythmic, oscillatory movement of a body part", is a key feature of many neurological conditions including Parkinson's disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson's disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81-0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Purpose: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group. Methods: We trained a CNN to receive retinal image inputs of optical coherence tomography (OCT) ganglion cell-inner plexiform layer (GC-IPL) thickness color maps, OCT angiography 6 × 6-mm en face macular images of the superficial capillary plexus, and ultra-widefield (UWF) fundus color and autofluorescence photographs to classify the retinal imaging as PD or control. The model consists of a shared pretrained VGG19 feature extractor and image-specific feature transformations which converge to a single output. Model results were assessed using receiver operating characteristic (ROC) curves and bootstrapped 95% confidence intervals for area under the ROC curve (AUC) values. Results: In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory. Conclusions: Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms. Translational Relevance: Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.

Loss of primary cilia and dopaminergic neuroprotection in pathogenic LRRK2-driven and idiopathic Parkinson's disease.

Activating leucine-rich repeat kinase 2 (LRRK2) mutations cause Parkinson's and phosphorylation of Rab10 by pathogenic LRRK2 blocks primary ciliogenesis in cultured cells. In the mouse brain, LRRK2 blockade of primary cilia is highly cell type specific: For example, cholinergic interneurons and astrocytes but not medium spiny neurons of the dorsal striatum lose primary cilia in LRRK2-pathway mutant mice. We show here that the cell type specificity of LRRK2-mediated cilia loss is also seen in human postmortem striatum from patients with LRRK2 pathway mutations and idiopathic Parkinson's. Single nucleus RNA sequencing shows that cilia loss in mouse cholinergic interneurons is accompanied by decreased glial-derived neurotrophic factor transcription, decreasing neuroprotection for dopamine neurons. Nevertheless, LRRK2 expression differences cannot explain the unique vulnerability of cholinergic neurons to LRRK2 kinase as much higher LRRK2 expression is seen in medium spiny neurons that have normal cilia. In parallel with decreased striatal dopaminergic neurite density, LRRK2 G2019S neurons show increased autism-linked CNTN5 adhesion protein expression; glial cells show significant loss of ferritin heavy chain. These data strongly suggest that loss of cilia in specific striatal cell types decreases neuroprotection for dopamine neurons in mice and human Parkinson's.