Search

This paper presents an efficient deep neural network model for diagnosing Parkinson's disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. The proposed architecture exploits the strengths of both convolutional neural networks and Transformers in a single end-to-end model, where the former is able to extract relevant local features from Vertical Ground Reaction Force (VGRF) signal, while the latter allows to capture long-term spatio-temporal dependencies in data. In this manner, our hybrid architecture achieves an improved performance compared to using either models individually. Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset. Moreover, our method can be generalized and adapted for other classification problems to jointly address the feature relevance and spatio-temporal dependency problems in 1D signals. Our source code and pre-trained models are publicly available at https://github.com/SafwenNaimi. [ABSTRACT FROM AUTHOR]

Background: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation., Methods: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests., Results: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933)., Conclusion: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657., (© 2024. The Author(s).)

Background: The acute levodopa challenge test (ALCT) is a universal method for evaluating levodopa response (LR). Assessment of Movement Disorder Society's Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) is a key step in ALCT, which is some extent subjective and inconvenience., Methods: This study developed a machine learning method based on instrumented Timed Up and Go (iTUG) test to evaluate the patients' response to levodopa and compared it with classic ALCT. Forty-two patients with parkinsonism were recruited and administered with levodopa. MDS-UPDRS III and the iTUG were conducted in both OFF-and ON-medication state. Kinematic parameters, signal time and frequency domain features were extracted from sensor data. Two XGBoost models, levodopa response regression (LRR) model and motor symptom evaluation (MSE) model, were trained to predict the levodopa response (LR) of the patients using leave-one-subject-out cross-validation., Results: The LR predicted by the LRR model agreed with that calculated by the classic ALCT (ICC = 0.95). When the LRR model was used to detect patients with a positive LR, the positive predictive value was 0.94., Conclusions: Machine learning based on wearable sensor data and the iTUG test may be effective and comprehensive for evaluating LR and predicting the benefit of dopaminergic therapy., (© 2024. The Author(s).)

Objective . The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD. Approach. This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification. Main results. Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%). Significance. The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Background: Although pose estimation algorithms have been used to analyze videos of patients with Parkinson's disease (PD) to assess symptoms, their feasibility for differentiating PD from other neurological disorders that cause gait disturbances has not been evaluated yet. We aimed to determine whether it was possible to differentiate between PD and spinocerebellar degeneration (SCD) by analyzing video recordings of patient gait using a pose estimation algorithm., Methods: We videotaped 82 patients with PD and 61 patients with SCD performing the timed up-and-go test. A pose estimation algorithm was used to extract the coordinates of 25 key points of the participants from these videos. A transformer-based deep neural network (DNN) model was trained to predict PD or SCD using the extracted coordinate data. We employed a leave-one-participant-out cross-validation method to evaluate the predictive performance of the trained model using accuracy, sensitivity, and specificity. As there were significant differences in age, weight, and body mass index between the PD and SCD groups, propensity score matching was used to perform the same experiment in a population that did not differ in these clinical characteristics., Results: The accuracy, sensitivity, and specificity of the trained model were 0.86, 0.94, and 0.75 for all participants and 0.83, 0.88, and 0.78 for the participants extracted by propensity score matching., Conclusion: The differentiation of PD and SCD using key point coordinates extracted from gait videos and the DNN model was feasible and could be used as a collaborative tool in clinical practice and telemedicine., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Advancements in smartphone technology provide availability to evaluate movement in a more practical and feasible manner, improving clinicians' ability to diagnose and treat adults at risk for mobility loss. The purpose of this study was to evaluate the validity and reliability of a smartphone application to measure spatiotemporal outcomes during level (primary) and uphill/downhill (secondary) walking with and without an assistive device for older adults (OAs), Parkinson's Disease (PD) and cerebrovascular accident (CVA) populations. Methods: A total of 50 adults (OA = 20; PD = 15; CVA = 15) underwent gait analysis at self-selected gait speeds under 0-degree, 5-degree uphill and 5-degree downhill environments. The validity and reliability of the smartphone outcomes were compared to a motion-capture laboratory. Bland-Altman analysis was used to evaluate limits of agreement between the two systems. Intraclass correlation coefficients (ICCs) were used to determine absolute agreement, and Pearson correlation coefficients (r) were used to assess the strength of the association between the two systems. Results: For level walking, Bland-Altman analysis revealed relatively equal estimations of spatiotemporal outcomes between systems for OAs without an assistive device and slight to mild under- and overestimations of outcomes between systems for PD and CVA with and without an assistive device. Moderate to very high correlations between systems (without an assistive device: OA r-range, 0.72-0.99; PD r-range, 0.87-0.97; CVA r-range, 0.56-0.99; with an assistive device: PD r-range, 0.35-0.98; CVA r-range, 0.50-0.99) were also observed. Poor to excellent ICCs for reliability between systems (without an assistive device: OA ICC range, 0.71-0.99; PD ICC range, 0.73-0.97; CVA ICC range, 0.56-0.99; with an assistive device: PD ICC range, 0.22-0.98; CVA ICC range, 0.44-0.99) were observed across all outcomes. Conclusions: This smartphone application can be clinically useful in detecting most spatiotemporal outcomes in various walking environments for older and diseased adults at risk for mobility loss.

Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson's Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly, ViT highlighted the lateral ventricles associated with cognitive decline, indicating their potential in the Prodromal stage. These findings underscore the potential of these regions as early-stage PD biomarkers and showcase the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis, paving the way for innovative diagnostic tools and precision medicine., (© 2024. The Author(s).)

Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where accurate diagnosis remains challenging. Traditional clinical observation methods often result in misdiagnosis, highlighting the need for biomarker-based diagnostic approaches. This study utilizes ultraperformance liquid chromatography coupled to an electrospray ionization source and quadrupole time-of-flight untargeted metabolomics combined with biochemometrics to identify novel serum biomarkers for PD. Analyzing a Brazilian cohort of serum samples from 39 PD patients and 15 healthy controls, we identified 15 metabolites significantly associated with PD, with 11 reported as potential biomarkers for the first time. Key disrupted metabolic pathways include caffeine metabolism, arachidonic acid metabolism, and primary bile acid biosynthesis. Our machine learning model demonstrated high accuracy, with the Rotation Forest boosting model achieving 94.1% accuracy in distinguishing PD patients from controls. It is based on three new PD biomarkers (downregulated: 1-lyso-2-arachidonoyl-phosphatidate and hypoxanthine and upregulated: ferulic acid) and surpasses the general 80% diagnostic accuracy obtained from initial clinical evaluations conducted by specialists. Besides, this machine learning model based on a decision tree allowed for visual and easy interpretability of affected metabolites in PD patients. These findings could improve the detection and monitoring of PD, paving the way for more precise diagnostics and therapeutic interventions. Our research emphasizes the critical role of metabolomics and machine learning in advancing our understanding of the chemical profile of neurodegenerative diseases.

Background and Objective: Parkinson's disease (PD) is the one of the most common neurodegenerative diseases. Many investigators have confirmed the possibility of using circulating miRNAs to diagnose PD. However, the results were inconsistent. Therefore, the aim of this meta-analysis was to systematically evaluate the diagnostic accuracy of circulating miRNAs in the diagnosis of PD., Methods: We carefully searched PubMed, Embase, Web of Science, Cochrane Library, Wanfang database and China National Knowledge Infrastructure for relevant studies (up to January 1, 2022) based on PRISMA statement. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), the diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to test the diagnostic accuracy. Furthermore, subgroup analyses were performed to identify the potential sources of heterogeneity, and the Deeks' funnel plot asymmetry test was used to evaluate the potential publication bias., Results: Forty-four eligible studies from 16 articles (3298 PD patients and 2529 healthy controls) were included in the current meta-analysis. The pooled sensitivity was 0.79 (95% CI: 0.76-0.81), specificity was 0.82 (95% CI: 0.78-0.84), PLR was 4.3 (95% CI: 3.6-5.0), NLR was 0.26 (95% CI: 0.23-0.30), DOR was 16 (95% CI: 13-21), and AUC was 0.87 (95% CI: 0.84-0.90). Subgroup analysis suggested that miRNA cluster showed a better diagnostic accuracy than miRNA simple. Moreover, there was no significant publication bias., Conclusions: Circulating miRNAs have great potential as novel non-invasive biomarkers for PD diagnosis., (Copyright © 2022 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.)

Background: Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by dopaminergic neuron degeneration and diverse motor and nonmotor symptoms. Early diagnosis and intervention are crucial but challenging due to reliance on clinical presentation. Recent research suggests potential biomarkers for early detection, including plasma netrin-1 (NTN-1), a protein implicated in neuronal survival., Methods: This cross-sectional study recruited 105 PD patients and 65 healthy controls, assessing plasma NTN-1 levels and correlating them with clinical characteristics. Statistical analyses explored associations between NTN-1 levels and PD symptoms, considering demographic factors., Results: PD patients exhibited significantly lower plasma NTN-1 levels compared to controls. NTN-1 demonstrated moderate potential as a PD biomarker. Positive correlations were found between NTN-1 levels and motor, depression, and cognitive symptoms. Multiple regression analysis revealed disease duration and NTN-1 levels as key factors influencing symptom severity. Gender also impacted symptom scores., Conclusion: Reduced plasma NTN-1 levels correlate with PD severity, suggesting its potential as a biomarker. However, further research is needed to elucidate the roles of NTN-1 in PD pathophysiology and validate its diagnostic and therapeutic implications. Understanding the involvement of NTN-1 may lead to personalized management strategies for PD., (© 2024 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Parkinson's disease is a neurodegenerative movement disorder associated with motor and non-motor symptoms causing severe disability as the disease progresses. The development of biomarkers for Parkinson's disease to diagnose patients earlier and predict disease progression is imperative. As artificial intelligence and machine learning techniques efficiently process data and can handle multiple data types, we reviewed the literature to determine the extent to which these techniques have been applied to biomarkers for Parkinson's disease and movement disorders. We determined that the most applicable machine learning techniques are support vector machines and neural networks, depending on the size and type of the data being analyzed. Additionally, more complex machine learning techniques showed increased accuracy when compared to less complex techniques, especially when multiple machine learning models were combined. We can conclude that artificial intelligence and machine learning techniques may have the capacity to significantly boost diagnostic capacity in movement disorders and Parkinson's disease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Progressive supranuclear palsy (PSP) is characterized by early onset postural instability and frequent falls. Circular walking necessitates dynamic postural control, which is impaired in patients with PSP. We aimed to explore gait parameters associated with the risk of falls in patients with PSP, focusing on circular walking., Methods: Sixteen drug-naïve patients with PSP, 22 drug-naïve patients with Parkinson's disease (PD), and 23 healthy controls were enrolled. Stride lengths/velocities and their coefficients of variation (CV) during straight and circular walking (walking around a circle of 1-m diameter) were measured under single-task and cognitive dual-task conditions. Correlation analysis was performed between gait parameters and postural instability and gait difficulty (PIGD) motor subscores, representing the risk of falls., Results: Patients with PSP had significantly higher CVs of stride lengths/velocities during circular walking than those during straight walking, and the extent of exacerbation of CVs in patients with PSP was larger than that in patients with PD under single-task conditions. Stride lengths/velocities and their CVs were significantly correlated with PIGD motor subscores in patients with PSP only during single-task circular walking. In addition, patients with PSP showed progressive decrements of stride lengths/velocities over steps only during single-task circular walking., Conclusions: Worse gait parameters during circular walking are associated with an increased risk of falls in patients with PSP. Circular walking is a challenging task to demand the compromised motor functions of patients with PSP, unmasking impaired postural control and manifesting sequence effect. Assessing circular walking is useful for evaluating the risk of falls in patients with early PSP., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

This opinion piece describes major limitations of using α-synuclein in speculative neuronally enriched for diagnosing or predicting Parkinson's disease risk from prodromal conditions such as REM behaviour disorder. It concludes that such an approach is unreliable and recommends that future researchers divert away to more widely accepted approaches such as seed amplification assays., (© 2024 The Author(s). European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Background: Parkinson's disease (PD), the second most frequent neurodegenerative disease, constitutes a major public health challenge. A guide published by the French National Authority for Health in 2012 and revised in 2016 put forward recommendations for general practitioners (GP) planning care pathways for parkinsonian patients. It is well known that PD can be difficult to diagnose, and that when patients consult their GP, symptoms are often still limited and embedded in clinical uncertainty. This means the pathway to confirmed diagnosis of PD can be lengthy and uncertain. Consequently, it is important to identify the difficulties GPs encounter when caring for PD patients in order to help them better close the gaps in care strategies., Methods: We conducted a descriptive cross-sectional survey in northern France to evaluate GP practices and knowledge about PD and their accordance with care pathway recommendations. The survey was conducted using a 30-item questionnaire sent to a sample of GPs., Results: There were 164 GPs who responded to the study questionnaire. The responding GPs generally followed current care pathway recommendations. In presence of a parkinsonian syndrome, 93.3% of the GPs reported systematically looking for an iatrogenic cause; 57.4% did not announce the diagnosis without the advice of a neurologist; 97.6% referred patients to a neurologist when they suspected PD; and 80.5% asked the neurologist to modify treatments. Our findings also revealed some difficult aspects of GP practices: only 2.5% had had additional training in neurology; only 53.6% felt comfortable with the diagnosis of PD; 63.6% prescribed additional exams for the diagnosis; most of the GPs were unaware of second-line treatments and their indications, and finally existence of PD expert centers was unknown for 85.2%., Conclusions: These findings could be useful to guide implementation of new measures supporting more holistic care for PD patients; PD expert centers in France could provide complementary information and training for GPs., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Parkinson's Disease (PD) body-first subtype is characterized by prodromal autonomic symptoms and REM sleep behavior disorder (RBD), symmetric dopaminergic degeneration, and increased risk of dementia. On the other hand, the PD brain-first subtype has fewer non-motor symptoms and a milder motor phenotype. The temporal relationship between RBD onset and motor symptoms onset may differentiate these two subtypes. We aimed to investigate electrocortical differences between brain-first and body-first PD patients. PD patients with an available routinely collected EEG were retrospectively selected. RBD was diagnosed using the RBD screening questionnaire (≥ 6). According to the onset of RBD patients were classified into PD-RBDpre (RBD onset before motor symptoms) and PD-RBDpost (RBD onset after motor symptoms). Patients without RBD were classified as PD-RBD-. Presence of Mild Cognitive Impairment (MCI) was diagnosed according to the MDS criteria. EEG Spectral analysis was performed in resting state by computing the Power Spectral Density (PSD) of site-specific signal epochs for the common frequency bands (delta, theta, alpha, beta). Thirty-eight PD-RBD-, 14 PD-RBDpre and 31 PD-RBDpost patients were recruited. Comparing both global and site-specific absolute values, we found a significant trend toward beta band reduction going from PD-RBD-, PD-RBDpost and PD-RBDpre. No significant differences were found between PD-RBDpost and PD-RBD- patients. PD-RBDpre patients may represent a different subset of patients as compared to patients without RBD, while patients with later onset have intermediate EEG spectral features. Quantitative EEG may provide new hints in PD subtyping., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Introduction: Dysgraphia, a recognized PD motor symptom, lacks effective clinical assessment. Current evaluation relies on motor assessment scales. Computational methods introduced over the past decade offer an objective dysgraphia assessment, considering size, duration, speed, and handwriting fluency. Objective evaluation of dysgraphia may be of help for early diagnosis of PD., Objective: Computerized assessment of dysgraphia in de novo PD patients and its correlation with clinical scales., Methods: We evaluated 38 recently diagnosed, premedication PD patients and age-matched controls without neurological disorders. Participants wrote "La casa de Pamplona es bonita" three times on paper and once on a Wacom tablet under the paper, totaling four phrases. Writing segments of 5-10 s were analyzed. The Wacom tablet captured kinematic data, including mean velocity, mean acceleration, and pen pressure. Data were saved in.svc format and analyzed using specialized software developed by Tecnocampus Mataró. Standard clinical practice data, Hoehn & Yahr staging, and UPDRS scales were used for evaluation., Results: Significant kinematic differences existed; patients had lower mean speed (27 ± 12 vs. 48 ± 18, p < 0.0001) and mean acceleration (7.2 ± 3.9 vs. 15.01 ± 7, p < 0.0001) than controls. Mean speed and mean acceleration correlated significantly with UPDRS III scores (speed: r = -0.52, p < 0.0007; acceleration: r = 0.60, p < 0.0001), indicating kinematic parameters' potential in PD evaluation., Conclusions: Dysgraphia is identifiable in PD patients, even de novo, indicating an early symptom and correlates with clinical scales, offering potential for objective PD patient evaluation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Parkinson's Disease (PD), a neurodegenerative disorder, significantly impacts the quality of life for millions of people worldwide. PD primarily impacts dopaminergic neurons in the brain's substantia nigra, resulting in dopamine deficiency and gait impairments such as bradykinesia and rigidity. Currently, several well-established tools, such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Hoehn and Yahr (H&Y) Scale, are used for evaluating gait dysfunction in PD. While insightful, these methods are subjective, time-consuming, and often ineffective in early-stage diagnosis. Other methods using specialized sensors and equipment to measure movement disorders are cumbersome and expensive, limiting their accessibility. This study introduces a hierarchical approach to evaluating gait dysfunction in PD through videos. The novel 2-Stream Spatial-Temporal Neural Network (2S-STNN) leverages the spatial-temporal features from the skeleton and silhouette streams for PD classification. This approach achieves an accuracy rate of 89% and outperforms other state-of-the-art models. The study also employs saliency values to highlight critical body regions that significantly influence model decisions and are severely affected by the disease. For a more detailed analysis, the study investigates 21 specific gait attributes for a nuanced quantification of gait disorders. Parameters such as walking pace, step length, and neck forward angle are found to be strongly correlated with PD gait severity categories. This approach offers a comprehensive and convenient solution for PD management in clinical settings, enabling patients to receive a more precise evaluation and monitoring of their gait impairments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by progressive loss of dopamine neurons and aberrant deposits of alpha-synuclein (α-syn) in the brain. The symptomatic treatment is started after the onset of motor manifestations in a late stage of the disease. Preclinical studies with neurotrophic factors (NTFs) show promising results of disease-modifying neuroprotective or even neurorestorative effects. Four NTFs have entered phase I-II clinical trials with inconclusive outcomes. This is not surprising because the preclinical evidence is from acute early-stage disease models, but the clinical trials included advanced PD patients. To conclude the value of NTF therapies, clinical studies should be performed in early-stage patients with prodromal symptoms, that is, before motor manifestations. In this review, we summarize currently available diagnostic and prognostic biomarkers that could help identify at-risk patients benefiting from NTF therapies. Focus is on biochemical and imaging biomarkers, but also other modalities are discussed. Neuroimaging is the most important diagnostic tool today, but α-syn imaging is not yet viable. Modern techniques allow measuring various forms of α-syn in cerebrospinal fluid, blood, saliva, and skin. Digital biomarkers and artificial intelligence offer new means for early diagnosis and longitudinal follow-up of degenerative brain diseases., (© 2024 The Author(s). Basic & Clinical Pharmacology & Toxicology published by John Wiley & Sons Ltd on behalf of Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).)

Background: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear., Objective: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD)., Methods: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs., Results: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics., Conclusions: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD., (© 2024. Fondazione Società Italiana di Neurologia.)

Purpose: Early differentiation between Parkinson's disease (PD) and Multiple system atrophy (MSA), particularly the parkinsonian subtypes (MSA-P), is challenging due to similar clinical symptoms. We aimed to evaluate Sympathetic skin response (SSR) and Cutaneous silent period (CSP) parameters in patients with MSA-P and PD to identify possible biomarkers that could distinguish the two groups of patients in early stage., Methods: 22 individuals with early-stage MSA-P, 29 with early-stage PD, and 28 healthy controls were recruited from Guangdong Provincial People's Hospital. Demographic data was collected for all participants. Their SSR and CSP were evaluated using clinical electromyography equipment. Data were compared between different groups. The diagnostic accuracy of SSR and CSP parameters was calculated using the ROC curve. Logistic regression was used to produce an integration model to enhance diagnostic utility., Results: Foot amplitude, CSP end latency and duration distinguished MSA-P from PD with the area under the curve (AUC) 0.770, 0.806, and 0.776, respectively. Foot and hand SSR amplitude distinguished PD from HC with the AUC 0.871 and 0.768, respectively. Foot SSR amplitude, hand SSR amplitude, and CSP end latency distinguished MSA-P from HC with the AUC 0.964, 0.872, and 0.812, respectively. The combination of SSR and CSP parameters differentiation between MSA-P and PD, PD and HC with the AUC 0.829 and 0.879, respectively., Conclusions: Analysis of SSR and CSP parameters showed excellent diagnostic accuracy in discriminating patients with early-stage MSA-P from HC and good diagnostic accuracy in discriminating patients with MSA-P from PD with early stages., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Chunge Xie reports financial support was provided by Medical Scientific Research Foundation of Guangdong Province, China. Chunge Xie reports administrative support was provided by Traditional Chinese Medicine Bureau of Guangdong Province, China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: We aimed to establish specific biomarkers of Parkinson's disease (PD) by comparing activity of more affected (MA) and less affected (LA) subthalamic nucleus (STN) of patients with prominent clinical asymmetry., Methods: We recorded single unit activity and local field potentials (LFP) of the STN during deep brain stimulation surgeries. Neuronal firing patterns and discharge rate, as well as oscillatory features of both single cells and LFP, were analyzed., Results: We observed notable differences in proportions of irregular-burst and pause-burst, but not tonic neurons, between the hemispheres. Oscillations of pause-burst neurons correlated significantly with the bradykinesia and rigidity scores of the corresponding hemibody. LFP derived from MA STN featured greater power in 12-15 Hz., Conclusions: Our results provide evidence that the increased proportion of units with prolonged pauses may be associated with PD. We also speculate that some of them may gain rhythmicity in the alpha-beta range in relation to hypokinetic symptoms, long-term disease, or both., Significance: Our findings highlight the relation between specific oscillatory features of the STN, predominance of subthalamic pause-burst units and PD pathophysiology., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: The Comprehensive Coordination Scale (CCS) is a valid, reliable scale for evaluating motor coordination, described as the ability to produce context-dependent movements of multiple body parts in both spatial and temporal domains in patients with chronic stroke. The aim of this study was to develop a Turkish version of the CCS that can be used to evaluate coordination in Parkinson's disease patients and to test its validity and reliability., Methods: Thirty-four patients with Parkinson's disease (18 females, 16 males) with a mean age of 67.55± 10.93 years were included. The 9-Hole Peg Test (9 HPT), Timed Up and Go Test (TUG), Berg Balance Scale (BBS), Functional Reach Test (FRT), and Modified Hoehn and Yahr (H&Y) were administered to the patients., Results: In the correlation analysis for convergent validity, the CCS-TR showed a moderate negative correlation with both the 9 HPT and the TUG test (r=-0.684 and -0.641, respectively). There were also moderate positive correlations observed between the CCS-TR and the FRT and BBS (r=0.607 and 0.657, respectively). The total CCS-TR had excellent intrarater reliability (ICC=0.99; 95 % CI: 0.98-0.99) and interrater reliability (ICC=0.90; 95 % CI: 0.81-0.95). The CCS-TR subscales had excellent intrarater reliability (ICC: range 0.98 from 0.99). For intrarater agreement, the ICC was 0.99, and for interrater agreement, the ICC was 0.90 (excellent agreement)., Conclusion: The results of the present study indicate that the Turkish version of the CCS is a reliable and valid coordination scale that can be used in patients with Parkinson's disease., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Freezing of Gait (FoG) is a common symptom of Parkinson's disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient's intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable. In this article, we address this task as a fine-grained human action recognition problem based on vision inputs. A novel deep learning architecture, namely, higher order polynomial transformer (HP-Transformer), is proposed to incorporate pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, a higher order self-attention mechanism is proposed based on higher order polynomials. To this end, linear, bilinear, and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and further fused by a cross-order fusion strategy for FoG detection. Comprehensive experiments on a large in-house dataset collected during clinical assessments demonstrate the effectiveness of the proposed method, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 is achieved for detecting FoG.

Background: Dementia is caused by neurodegenerative conditions and characterized by cognitive decline. Diagnostic accuracy for dementia subtypes, such as Alzheimer's Disease (AD), Dementia with Lewy Bodies (DLB) and Parkinson's Disease with dementia (PDD), remains challenging., Methods: Here, different methods of quantitative electroencephalography (qEEG) analyses were employed to assess their effectiveness in distinguishing dementia subtypes from healthy controls under eyes closed (EC) and eyes open (EO) conditions., Results: Classic Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses differentiated between all conditions for the 4-8 Hz theta range. Only individuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for the wider 4-15 Hz frequency range, encompassing the actual dominant frequency of all individuals. FFT results for this range yielded wider significant discriminators vs AR, also detecting differences between AD and DLB. Analyses of the inclusive dominant / peak frequency range (4-15 Hz) indicated slowing and reduced variability, also discriminating between synucleinopathies vs controls and AD. Dissociation of periodic oscillations and aperiodic components of AR spectra using Fitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable and subtype-specific slowing of brain oscillatory peaks during EC and EO for all groups. Distinct and robust differences were particularly strong for aperiodic parameters, suggesting their potential diagnostic power in detecting specific changes resulting from age and cognitive status., Conclusion: Our findings indicate that qEEG methods can reliably detect dementia subtypes. Spectral analyses comprising an integrated, multi-parameter EEG approach discriminating between periodic and aperiodic components under EC and EO conditions may enhance diagnostic accuracy in the future., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024. Published by Elsevier B.V.)

Background: Neurodegenerative diseases (NDDs) pose significant challenges due to their debilitating nature and limited therapeutic options. Accurate and timely diagnosis is crucial for optimizing patient care and treatment strategies. Gait analysis, utilizing wearable sensors, has shown promise in assessing motor abnormalities associated with NDDs., Research Question: Research Question 1 To what extent can analyzing the interaction of both limbs in the time-frequency domain serve as a suitable methodology for accurately classifying NDDs? Research Question 2 How effective is the utilization of color-coded images, in conjunction with deep transfer learning models, for the classification of NDDs?, Methods: GaitNDD database was used, comprising recordings from patients with Huntington's disease, amyotrophic lateral sclerosis, Parkinson's disease, and healthy controls. The gait signals underwent signal preparation, wavelet coherence analysis, and principal component analysis for feature enhancement. Deep transfer learning models (AlexNet, GoogLeNet, SqueezeNet) were employed for classification. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using 5-fold cross-validation., Results: The classification performance of the models varied depending on the time window used. For 5-second gait signal segments, AlexNet achieved an accuracy of 95.91 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.49 % and 92.73 %, respectively. For 10-second segments, AlexNet outperformed other models with an accuracy of 99.20 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.75 % and 95.00 %, respectively. Statistical tests confirmed the significance of the extracted features, indicating their discriminative power for classification., Significance: The proposed method demonstrated superior performance compared to previous studies, offering a non-invasive and cost-effective approach for the automated diagnosis of NDDs. By analyzing the interaction between both legs during walking using wavelet coherence, and utilizing deep transfer learning models, accurate classification of NDDs was achieved., Competing Interests: Declaration of Competing Interest We declare that we have no financial or personal relationships with other people or organizations that can inappropriately influence our work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Nance: Dr. Nance serves on the steering committees of PD GENEration and ACTIVATE-PD. Dr. Nance is co-chair of the Genetics/Environment Working Group of the Parkinson Study Group. Dr. Phillips: Dr. Phillips has no conflicts of interest to declare. Dr. Tropea: Dr. Tropea has received research grants from the NIH (K23NS114167, P30AG072979), and research support from The Michael J Fox Foundation (FOX BIONET, site investigator) and the Parkinson Foundation (PD GENEration, site investigator). Dr. Tropea serves on the steering Committee of ACTIVATE-PD (Bial) and is co-chair of the Genetics/Environmental Working Group of the Parkinson Study Group. He has received travel expense reimbursement from the Parkinson Study Group and the Parkinson Foundation.

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients ( n = 82) and age-matched comparison participants ( n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

Introduction: While there is an interest in defining longitudinal change in people with chronic illness like Parkinson's disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort., Methods: In this retrospective longitudinal analysis of 802 people with typical Parkinson's disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models., Results: Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p < 0.001)., Conclusion: The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation., (© 2024. The Author(s).)

A continuous levodopa sensor can improve the quality of life for patients suffering with Parkinson's disease by enhancing levodopa titration and treatment effectiveness; however, its development is currently hindered by the absence of a specific levodopa molecular recognition element and limited insights into how real-time monitoring might affect clinical outcomes. This gap in research contributes to clinician uncertainty regarding the practical value of continuous levodopa monitoring data. This paper examines the current state of levodopa sensing and the inherent limitations in today's methods. Further, these challenges are described, including aspects such as interference from the metabolic pathway and adjunct medications, temporal resolution, and clinical questions, with a specific focus on a comprehensive selection of molecules, such as adjunct medications and structural isomers, as an interferent panel designed to assess and validate future levodopa sensors. We review insights and lessons from previously reported levodopa sensors and present a comparative analysis of potential molecular recognition elements, discussing their advantages and drawbacks.

Introduction: Smartphone applications (apps) are instruments that assist with objective measurements during the clinical assessment of patients with movement disorders. We aim to test the hypothesis that Parkinson's disease (PD) patients will exhibit an increase in tapping variability and a decrease in tapping speed over a one-year period, compared to healthy controls (HC)., Methods: Data was prospectively collected from participants enrolled in our Cincinnati Cohort Biomarker Program, in 2021-2023. Participants diagnosed with PD and age-matched HC were examined over a one-year-interval with a tapping test performed with customized smartphone app. Tapping speed (taps/s), inter-tap intervals and variability (movement regularity), and sequence effect were measured., Results: We included 295 PD patients and 62 HC. At baseline, PD subjects showed higher inter-tap variability than HC (coefficient-of-variation-CV, 37 ms [22-64] vs 26 ms [8-51]) (p = 0.007). Conversely, there was no difference in inter-tap intervals (411 ms [199-593] in PD versus 478 ms [243-618] in HC) and tapping speed (3.42[2.70-4.76] taps/s in PD versus 3.21 taps/s [2.57-4.54] in HC) (p > 0.05). Only PD subjects (n = 135), at the one-year follow-up, showed a decreased tapping speed vs baseline (3.44 taps/s [2.86-4.81] versus 3.39 taps/s [2.58,4.30]) (p = 0.036), without significant changes in inter-tap variability (CV, 32 ms [18,55] baseline versus 34 ms [22,59] follow-up) (p = 0.142). No changes were found in HC at the one-year follow up (all p values>0.05)., Conclusions: Inter-tap variability (dysrhythmia) but no inter-tap intervals or tapping speed are reliably distinctive feature of an app-based bradykinesia assessment in PD., Competing Interests: Declaration of competing interest Dr. Luca Marsili has received honoraria from the International Association of Parkinsonism and Related Disorders (IAPRD) Society for social media and web support, and personal compensation as a consultant/scientific advisory board member for Acadia. Dr. Marsili has received a grant (collaborative research agreement) from the International Parkinson's and Movement Disorders Society for the MDS-UTRS Validation Program (Role: PI), Non-Profit. Dr. Alberto J. Espay has received grant support from the NIH and the Michael J Fox Foundation; personal compensation as a consultant/scientific advisory board member for Abbvie, Neuroderm, Neurocrine, Amneal, Adamas, Acadia, Acorda, Kyowa Kirin, Sunovion, Lundbeck, and USWorldMeds; publishing royalties from Lippincott Williams & Wilkins, Cambridge University Press, and Springer; and honoraria from USWorldMeds, Acadia, and Sunovion. Dr. Espay is cofounder of REGAIN Therapeutics, owner of a provisional patent on compositions and methods for treatment and/or prophylaxis of proteinopathies. Drs. Abanto, Mahajan, Duque, Chinchihualpa Paredes, Deraz, and Bologna report no conflicts of interest. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors., (Copyright © 2024. Published by Elsevier B.V.)

Background: The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures., Objectives: (1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model., Methods: 152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips., Results: For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group., Conclusions: A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features., Competing Interests: Declaration of competing interest No specific funding was received for this work. The authors declare that there are no conflicts of interest relevant to this work., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background and Objectives: Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables., Methods: Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB., Results: A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD., Discussion: Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Introduction: Functional movement disorders (FMD) can overlap with Parkinson's disease (PD), and distinguishing between the two clinical conditions can be complex. Framing social cognition (theory of mind) (TOM) disorder, attention deficit, and psychodynamic features of FMD and PD may improve diagnosis., Methods: Subjects with FMD and PD and healthy controls (HC) were administered tasks assessing TOM abilities and attention. The psychodynamic hypothesis of conversion disorder was explored by a questionnaire assessing dissociative symptoms. A comprehensive battery of neuropsychological tasks was also administered to FMD and PD., Results: Although both FMD and PD scored lower than HC on all TOM tests, significant correlations between TOM and neuropsychological tasks were found only in PD but not in FMD. Only PD showed a reduction in attentional control. Dissociative symptoms occurred only in FMD., Discussion: Cognitive-affective disturbances are real in FMD, whereas they are largely dependent on cognitive impairment in PD. Attentional control is preserved in FMD compared to PD, consistent with the hypothesis that overload of voluntary attentional orientation may be at the basis of the onset of functional motor symptoms. On a psychodynamic level, the confirmation of dissociative symptoms in FMD supports the conversion disorder hypothesis., Conclusion: FMD and PD can be distinguished on an affective and cognitive level. At the same time, however, the objective difficulty often encountered in distinguishing between the two pathologies draws attention to how blurred the boundary between 'organic' and 'functional' can be., (© 2024. Fondazione Società Italiana di Neurologia.)

Objective: To investigate the accuracy of the SARC-F questionnaire to identify sarcopenia in patients with Parkinson's disease (PD)., Methods: We prospectively recruited patients with PD who had a score of 3 or lower on the Hoehn and Yahr (H&Y) scale. Appendicular skeletal muscle mass (ASM), hand grip strength, and the SARC-F were used to assess sarcopenia. The cutoffs for the ASM index and hand grip strength to diagnose sarcopenia were based on the Asian Working Group for Sarcopenia 2019 consensus. A score ≥4 on the SARC-F was considered at risk for sarcopenia., Results: A total of 365 patients with PD were included (mean age, 71.1 years; men, 53.2 %), and 73 (20.0 %) were diagnosed with sarcopenia. The area under the receiver operating characteristic curve of the SARC-F was 0.702 (95 % confidence interval, 0.634-0.770). Using the recommended cutoff score of ≥4, the SARC-F showed a sensitivity of 38.4 %, specificity of 85.6 %, positive predictive value (PPV) of 40.0 %, and negative predictive value (NPV) of 84.7 %. The Youden's index was the highest at a cutoff score of ≥2, in which the SARC-F showed a sensitivity of 67.1 %, specificity of 65.4 %, PPV of 32.7 %, and NPV of 88.8 %. These predictive values were similar to those obtained using a cutoff score of ≥2.5 or 3 on the H&Y scale., Conclusion: The application of the SARC-F to the mild-to moderate PD population is not appropriate as a first-step screening tool to diagnose sarcopenia. Given the comparable predictive values of the SARC-F and H&Y scale, this questionnaire may be considered only for ruling out sarcopenia in patients with similar disease severity., Competing Interests: Declaration of competing interest The authors have no conflicts of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Parkinson's disease (PD) is estimated to impact up to 1 % of the global population aged 60 years and older. Among the non-motor manifestations of idiopathic PD, radicular neuropathic pain emerges as a noteworthy concern due to its potential for debility in affected individuals. In, this systematic review and meta-analysis we aimed to evaluate the prevalence of radicular neuropathic pain and thus provide evidence of how this painful symptom affects the lives of patients with idiopathic PD. We registered the research protocol for this study in PROSPERO (CRD42022327220). We searched the Embase, Scopus, and PubMed platforms for studies on PD and neuropathic pain until April 2023. The search yielded 36 articles considered to have a low risk of bias. The prevalence of radicular neuropathic pain in patients with PD was 12.7 %, without a difference when we consider the duration of diagnosis (cut-off < 7 years) or levodopa dosage (cut-off <600 mg/dL). Moreover, there was no variation in the prevalence of radicular neuropathic pain regarding a Hoehn and Yahr stage cut-off of <2.5 or >2.5. Of note, a limited number of patients received pain treatment (21.5 %). We also found that the source of publication bias is the use of the Ford criteria (FC), suggesting that this type of diagnostic criteria may contribute to an underdiagnosis of radicular neuropathic pain in patients with PD. This study underlines the necessity for a more discerning and comprehensive approach to the diagnosis and management of radicular neuropathic pain in patients with idiopathic PD., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gabriela Trevisan reports financial support was provided by Coordination of Higher Education Personnel Improvement. Gabriela Trevisan reports a relationship with Coordination of Higher Education Personnel Improvement that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Variants in seven genes (LRRK2, GBA1, PRKN, SNCA, PINK1, PARK7 and VPS35) have been formally adjudicated as causal contributors to Parkinson's disease; however, individuals with Parkinson's disease are often unaware of their genetic status since clinical testing is infrequently offered. As a result, genetic information is not incorporated into clinical care, and variant-targeted precision medicine trials struggle to enrol people with Parkinson's disease. Understanding the yield of genetic testing using an established gene panel in a large, geographically diverse North American population would help patients, clinicians, clinical researchers, laboratories and insurers better understand the importance of genetics in approaching Parkinson's disease. PD GENEration is an ongoing multi-centre, observational study (NCT04057794, NCT04994015) offering genetic testing with results disclosure and genetic counselling to those in the US (including Puerto Rico), Canada and the Dominican Republic, through local clinical sites or remotely through self-enrolment. DNA samples are analysed by next-generation sequencing including deletion/duplication analysis (Fulgent Genetics) with targeted testing of seven major Parkinson's disease-related genes. Variants classified as pathogenic/likely pathogenic/risk variants are disclosed to all tested participants by either neurologists or genetic counsellors. Demographic and clinical features are collected at baseline visits. Between September 2019 and June 2023, the study enrolled 10 510 participants across >85 centres, with 8301 having received results. Participants were: 59% male; 86% White, 2% Asian, 4% Black/African American, 9% Hispanic/Latino; mean age 67.4 ± 10.8 years. Reportable genetic variants were observed in 13% of all participants, including 18% of participants with one or more 'high risk factors' for a genetic aetiology: early onset (<50 years), high-risk ancestry (Ashkenazi Jewish/Basque/North African Berber), an affected first-degree relative; and, importantly, in 9.1% of people with none of these risk factors. Reportable variants in GBA1 were identified in 7.7% of all participants; 2.4% in LRRK2; 2.1% in PRKN; 0.1% in SNCA; and 0.2% in PINK1, PARK7 or VPS35 combined. Variants in more than one of the seven genes were identified in 0.4% of participants. Approximately 13% of study participants had a reportable genetic variant, with a 9% yield in people with no high-risk factors. This supports the promotion of universal access to genetic testing for Parkinson's disease, as well as therapeutic trials for GBA1 and LRRK2-related Parkinson's disease., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Introduction: Cognitive decline frequently occurs in individuals with Parkinson's disease (PD), but the clinical methods to predict the onset of cognitive changes are limited. Given preliminary evidence of the link between gait and cognition, the purpose of this study was to determine if dual task (DT) gait was related to declines in cognition over two years in PD., Methods: A retrospective two-year longitudinal study of 48 individuals with PD using data from the Parkinson's Progression Markers Initiative of the Michael J. Fox Foundation. The following data were extracted at baseline: spatiotemporal gait (during single and DT), demographics (age, sex), PD disease duration (time since diagnosis), motor function (Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS)), and cognition (Montreal Cognitive Assessment (MoCA)), with MoCA scores also extracted after two years., Results: A binomial logistic regression was conducted, with all covariates (above) in block 1 and DT effect (DTE) of gait characteristics in block 2 entered in a stepwise fashion. The final model was statistically significant (χ 2 (6)  = 23.20, p < 0.001) and correctly classified 78.7 % of participants by cognitive status after two years. Only DTE of arm swing asymmetry (ASA) (p = 0.030) was included in block 2 such that a 1 % decline in DTE resulted in 1.6 % increased odds of cognitive decline., Conclusions: Individuals with greater change in arm swing asymmetry from single to DT gait may be more likely to experience a decline in cognition within two years. These results suggested that reduced automaticity or poor utilization of attentional resources may be indicative of subtle changes in cognition and indicate that DT paradigms may hold promise as a marker of future cognitive decline., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Verbal fluency (VF) tasks are known as suitable for detecting cognitive impairment (CI) in Parkinson's disease (PD). This study thus aimed to evaluate the psychometrics and diagnostics of the Alternate Verbal Fluency Battery (AVFB) by Costa et al. (2014) in an Italian cohort of non-demented PD patients, as well as to derive disease-specific cut-offs for it., Methods: N = 192 non-demented PD patients were screened with the Montreal Cognitive Assessment (MoCA) and underwent the AVFB-which includes phonemic, semantic and alternate VF tests (PVF; SVF; AVF), as well as a Composite Shifting Index (CSI) reflecting the "cost" of shifting from a single- to a double-cued VF task. Construct validity and diagnostics were assessed for each AVFB measure against the MoCA. Internal reliability and factorial validity were also tested., Results: The MoCA proved to be strongly associated with PVF, SVF and AVF scores, whilst moderately with the CSI. The AVFB was internally consistent and underpinned by a single component; however, an improvement in both internal reliability and fit to its factorial structure was observed when dropping the CSI. Demographically adjusted scores on PVF, SVF and AVF tests were diagnostically sound in detecting MoCA-defined cognitive impairment, whilst this was not true for the CSI. Disease-specific cut-offs for PVF, SVF and AVF tests were derived., Discussion: In conclusion, PVF, SVF and AVF tests are reliable, valid and diagnostically sound instruments to detect cognitive impairment in non-demented PD patients and are therefore recommended for use in clinical practice and research., (© 2024. The Author(s).)

Advancements in optical coherence control 1-5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography 6-8 . Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities 9-11 . Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically)., (© 2024. The Author(s).)

Background: Advanced stage of Parkinson's disease (APD) diagnosis is challenging for general neurologists. The 5-2-1 Criteria and the Cuestionario De Enfermedad de Parkinson Avanzada (CDEPA) have been validated for screening for APD., Objective: This article reports the period-prevalence of APD defined by a movement disorder expert, the 5-2-1 Criteria, and CDEPA and to improve the screening performance of the 5-2-1 Criteria METHODS: A cross-sectional retrospective study at the Parkinson's disease (PD) clinic of a tertiary hospital in Bangkok, Thailand amongst all PD patients aged ≥ 18 years was performed from January 2016 to January 2020. We compared the characteristics of APD and non-APD patients. We externally validated the 5-2-1 Criteria and CDEPA. We explored improving the 5-2-1 Criteria., Results: Of 480 PD patients with complete data, the period-prevalence of APD by the movement disorder expert, the 5-2-1 Criteria and CDEPA were 37.1 %, 48.5 %, and 27.5 %, respectively. Adding requiring help with an activity of daily living and freezing of gait to the original 5-2-1 Criteria enhanced the sensitivity from 86.5 % (95 %CI 80.6, 91.2) to 94.9 % (95 %CI 90.6, 97.7) and negative predictive value (NPV) from 90.3 % (95 %CI 85.9, 93.7) to 96 % (95 %CI 92.6, 98.2). However, the CDEPA had a sensitivity of 62.9 % (95 %CI 55.4, 70) and NPV of 81.0 (95 %CI 76.5, 85)., Conclusion: The 5-2-1 Criteria had a good screening tool performance for general neurologists to refer APD patients for optimal treatments. The modified 5-2-1 Criteria had better performance than the original one. External validation is needed., Competing Interests: Declaration of Competing Interest Weerawat Saengphatrachai reports a relationship with Mahidol University Faculty of Medicine Siriraj Hospital that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background and Purpose: Subtle executive dysfunction is common in people newly diagnosed with Parkinson disease (PD), even when general cognitive abilities are intact. This study examined the Short Weekly Calendar Planning Activity (WCPA-10)'s known-group construct validity, comparing persons with PD to healthy controls (HCs) and nonmanifesting carriers of LRRK2 and GBA gene mutations to HCs. Additionally, convergent and ecological validity was examined., Methods: The study included 73 participants: 22 with idiopathic PD (iPD) who do not carry any of the founder GBA mutations or LRRK2-G2019S, 29 nonmanifesting carriers of the G2019S-LRRK2 (n = 14) and GBA (n = 15) mutations, and 22 HCs. Known-group validity was determined using the WCPA-10, convergent validity by also using the Montreal Cognitive Assessment (MoCA) and Color Trails Test (CTT), and ecological validity by using the WCPA-10, Schwab and England Activities of Daily Living Scale (SE ADL), and Physical Activity Scale for the Elderly (PASE)., Results: Known-group validity of the WCPA-10 was established for the iPD group only; they followed fewer rules (p = 0.020), were slower (p = 0.003) and less efficient (p = 0.001), used more strategies (p = 0.017) on the WCPA-10, and achieved significantly lower CTT scores (p < 0.001) than the HCs. The nonmanifesting carriers and HCs were similar on all cognitive tests. Convergent and ecological validity of the WCPA-10 were partially established, with few correlations between WCPA-10 outcome measures and the MoCA (r = 0.50, r = 0.41), CTT-2 (r = 0.43), SE ADL (r = 0.41), and PASE (r = 0.54, r = 0.46, r = 0.31)., Conclusions: This study affirms the known-group validity for most (four) WCPA-10 scores and partially confirms its convergent and ecological validity for PD., (© 2024 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Objective: Evidence of abnormal α-synuclein (α-Syn) deposition in the brain is required for definitive diagnosis of synucleinopathies, which remains challenging. The seed amplification assay (SAA) is an innovative technique that can detect the seeding activity of misfolded α-Syn, enabling the amplification and detection of minute quantities of pathogenic α-Syn aggregates. This study aimed to evaluate oral mucosa α-Syn SAA as possible diagnostic and prodromal biomarkers for synucleinopathies., Methods: A total of 107 Parkinson's disease (PD) patients, 99 multiple system atrophy (MSA) patients, 33 patients with isolated rapid eye movement sleep behavior disorder (iRBD) and 103 healthy controls (HC) were included. The SAA was applied to detect the seeding activity of α-Syn from oral mucosa. A combination of morphological, biochemical, and biophysical methods was also used to analyze the fibrils generated from the oral mucosa α-Syn SAA., Results: Structured illumination microscopy images revealed the increased α-Syn species in oral mucosa of PD, MSA, and iRBD patients than in HCs. Oral mucosa α-Syn SAA distinguished patients with PD from HC with 67.3% sensitivity and 90.3% specificity. Oral mucosa was α-Syn SAA positive in 53.5% MSA patients and 63.6% iRBD patients. Furthermore, the α-Syn fibrils generated from MSA demonstrated greater resistance to proteinase K digestion and exhibited stronger cytotoxicity compared to those from PD patients., Conclusion: Oral mucosa α-Syn seeding activity may serve as novel non-invasive diagnostic and prodromal biomarkers for synucleinopathies. The α-Syn aggregates amplified from the oral mucosa of PD and MSA exhibited distinct biochemical and biophysical properties. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

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.

Parkinson's disease (PD) is a common neurodegenerative disease characterized pathologically by dopaminergic neuron loss and the formation of Lewy bodies, which are enriched with aggregated α-synuclein (α-syn). PD currently has no cure, but therapeutic strategies are available to alleviate symptoms. Early diagnosis can greatly improve therapeutic interventions, but the clinical diagnosis of PD remains challenging and depends mainly on clinical features and imaging tests. Efficient and specific biomarkers are crucial for the diagnosis, monitoring, and evaluation of PD. Here, we reviewed the biomarkers of PD in different tissues and biofluids, along with the current clinical biochemical detection methods. We found that the sensitivity and specificity of single biomarkers are limited, and selecting appropriate indicators for combined detection can improve the diagnostic accuracy of PD.

Background: Identifying individuals with Parkinson's disease (PD) already in the prodromal phase of the disease has become a priority objective for opening a window for early disease-modifying therapies., Objective: The aim was to evaluate a blood-based α-synuclein seed amplification assay (α-syn SAA) as a novel biomarker for diagnosing PD in the prodromal phase., Methods: In the TREND study (University of Tuebingen) biennial blood samples of n = 1201 individuals with/without increased risk for PD were taken prospectively over 4 to 10 years. We retrospectively analyzed blood samples of 12 participants later diagnosed with PD during the study to detect and amplify pathological α-syn conformers derived from neuronal extracellular vesicles using (1) immunoblot analyses with an antibody against these conformers and (2) an α-syn-SAA. Additionally, blood samples of n = 13 healthy individuals from the TREND cohort and n = 20 individuals with isolated rapid eye movement sleep behavior disorder (iRBD) from the University Hospital Cologne were analyzed., Results: All individuals with PD showed positive immunoblots and a positive α-syn SAA at the time of diagnosis. Moreover, all PD patients showed a positive α-syn SAA 1 to 10 years before clinical diagnosis. In the iRBD cohort, 30% showed a positive α-syn SAA. All healthy controls had a negative SAA., Conclusions: We here demonstrate the possibility to detect and amplify pathological α-syn conformers in peripheral blood up to 10 years before the clinical diagnosis of PD in individuals with and without iRBD. The findings of this study indicate that this blood-based α-syn SAA assay has the potential to serve as a diagnostic biomarker for prodromal PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)