Diagnosis Of PD Research Articles

Descriptive study of general practitioner's practices and knowledge about Parkinson's disease in the north of France

BackgroundParkinson'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. MethodsWe 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. ResultsThere 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%. ConclusionsThese 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.

A novel approach for Parkinson’s disease detection using Vold-Kalman order filtering and machine learning algorithms

AbstractParkinson’s disease (PD) is the second most common neurological disorder caused by damage to dopaminergic neurons. Therefore, it is important to develop systems for early and automatic diagnosis of PD. For this purpose, a study that will contribute to the development of systems for the automatic diagnosis of PD is presented. The Electroencephalography (EEG) signals were decomposed into sub-bands using adaptive decomposition methods, such as empirical mode decomposition, variational mode decomposition, and Vold-Kalman order filtering (VKF). Various features were extracted from the sub-band decomposed signals, and the significant ones were determined by Chi-squared test. These important features were applied as input to support vector machine (SVM), fitch neural network (FNN), k-nearest neighbours (KNN), and decision trees (DT), machine learning (ML) models and classification was performed. We analysed the performance of ML models by obtaining accuracy, sensitivity, specificity, positive predictive value, negative predictive values, F1-score, false-positive rate, kappa statistics, and area under the curve. The classification process was performed for two cases: PD ON-HC and PD OFF-HC groups. The most successful method in this study was the VKF method, which was applied for the first time in this field with the approach specified for both cases. In both instances, the SVM algorithm was employed as the ML model, with classifier performance criterion values close to 100%. The results obtained in this study seem to be successful compared to the results of recent research on the diagnosis of PD.

Exercising with a robotic exoskeleton can improve memory and gait in people with Parkinson’s disease by facilitating progressive exercise intensity

People with Parkinson’s disease (PwPD) can benefit from progressive high-intensity exercise facilitated with a lower-extremity exoskeleton, but the mechanisms explaining these benefits are unknown. We explored the relationship between exercise intensity progression and memory and gait outcomes in PwPD who performed 8 weeks (2 × per week) of progressive exercise with and without a lower-extremity powered exoskeleton, as the planned exploratory endpoint analysis of an open-label, parallel, pilot randomized controlled trial. Adults 50–85 years old with a confirmed diagnosis of PD participated. Twenty-seven participants randomized to exercise with (Exo = 13) or without (Nxo = 14) the exoskeleton were included in this exploratory endpoint analysis. Detailed exercise logs were kept and actigraphy was used to measure activity count*min−1 (ACPM) during all exercise sessions. Only the Exo group were able to progressively increase their ACPM over the entire 8-week intervention, whereas the Nxo group plateaued after 4 weeks. Exercise intensity progression correlated with change in the memory sub-scale of the SCOPA-COG and change in gait endurance from the 6MWT, consistent with the prevailing hypotheses linking high-intensity interval exercise to improved muscle and brain function via angiogenic and neurotrophic mechanisms. Facilitating high-intensity exercise with advanced rehabilitation technology is warranted for improving memory and gait endurance in PwPD.Registration: ClinicalTrials.gov, NCT 03583879 (7/10/2018).

Detection of skin α-synuclein using RT-QuIC as a diagnostic biomarker for Parkinson’s disease in the Chinese population

BackgroundSeveral studies have indicated that skin holds promise as a potential sample for detecting pathological α-Syn and serving as a diagnostic biomarker for α-synucleinopathies. Despite reports in Chinese PD patients, comprehensive research on skin α-Syn detection using RT-QuIC is lacking.ObjectiveThis study aimed to evaluate the diagnostic performance of skin samples using RT-QuIC from PD patients in the Chinese population.MethodsPatients with sporadic PD and controls were included according to the British PD Association Brain Bank diagnostic criteria. The seeding activity of misfolded α-Syn in these skin samples was detected using the RT-QuIC assay after protein extraction. Biochemical and morphological analyses of RT-QuIC products were conducted by atomic force microscopy, transmission electron microscopy, Congo red staining, and dot blot analysis.Result30 patients clinically diagnosed with PD and 28 controls with non-α-synucleinopathies were included in this study. 28 of 30 PD patients demonstrated positive α-Syn seeding activity by RT-QuIC assay. In contrast, no α-Syn seeding activity was detected in the 28 control samples, with an overall sensitivity and specificity of 93.3% and 100%, respectively (P < 0.001). Biochemical characterization of the RT-QuIC product indicated fibrillary α-Syn species in PD-seeded reactions, while control samples failed in the conversion of recombinant α-Syn substrate.ConclusionThis study applied RT-QuIC technology to identify misfolded α-Syn seeding activity in skin samples from Chinese PD patients, demonstrating high specificity and sensitivity. Skin α-Syn RT-QuIC is expected to be a reliable approach for the diagnosis of PD.

Association of grip strength and walking pace with the risk of incident Parkinson's disease: a prospective cohort study of 422,531 participants.

Muscle weakness is a prominent feature of Parkinson's disease, but whether the occurrence of this deficit in healthy adults is associated with subsequent PD diagnosis remains unclear. This study sought to examine the relationship between muscle strength, represented by grip strength and walking pace, and the risk of incident PD. A total of 422,531 participants from the UK biobank were included in this study. Longitudinal associations of grip strength and walking pace with the risk of incident PD were investigated by Cox proportional hazard models adjusting for several well-established risk factors. Subgroup and sensitivity analyses were also conducted for further validation. After a median follow-up of 9.23years, 2,118 (0.5%) individuals developed incident PD. For per 5kg increment of absolute grip strength, there was a significant 10.2% reduction in the risk of incident PD (HR = 0.898, 95% CI [0.872-0.924], P < 0.001). Similarly, per 0.05kg/kg increment of relative grip strength was related to a 9.2% reduced risk of incident PD (HR = 0.908, 95% CI [0.887-0.929], P < 0.001). Notably, the associations remained consistent when grip strength was calculated as quintiles. Moreover, participants with a slower walking pace demonstrated an elevated risk of incident PD (HR = 1.231, 95%CI [1.075-1.409], P = 0.003). Subgroup and sensitivity analyses further validated the robustness of the observed associations. Our findings showed a negative association of grip strength and walking pace with the risk of incident PD independent of important confounding factors. These results hold potential implications for the early screening of people at high-risk of PD.

Deep vein thrombosis and its risk factors in neurodegenerative diseases: A markedly higher incidence in Parkinson's disease

BackgroundInformation on the incidence and risk factors of deep vein thrombosis (DVT) in neurodegenerative diseases is limited. We aimed to determine the incidence of DVT among neurodegenerative disorders (amyotrophic lateral sclerosis [ALS], Parkinson's disease [PD], multiple system atrophy [MSA], and progressive supranuclear palsy [PSP]-corticobasal syndrome [CBS]) and the risk factors for the development of DVT. MethodsOverall, 229 hospitalized patients with neurodegenerative diseases (65 patients with ALS, 61 with PD, 53 with MSA, and 50 with PSP-CBS) were included in this study. D-dimer value and ultrasonography of the leg vein were assessed to determine the presence or absence of leg DVT. We compared the DVT incidence among each disease group. To identify the risk factors for DVT, a multivariate analysis was performed. ResultsOf 229 patients, 34 had leg DVT; the incidence was significantly higher in patients with PD (38%) than in those with ALS (2%), MSA (5%), or PSP-CBS (4%). Patients with DVT were older, had a smaller waist circumference, had a longer disease duration, and had a high blood pressure (BP) variability. Multivariate analysis revealed that a PD diagnosis and female sex, with a high BP variability were predictive of leg DVT. ConclusionsAmong the neurodegenerative diseases, the DVT incidence was markedly higher in PD than in ALS, MSA, and PSP-CBS. Several risk factors have been identified in patients with leg DVT. Recognition of these risk factors will improve patient care and guide the appropriate use of anticoagulants.

Pose-based tremor type and level analysis for Parkinson's disease from video.

Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73 and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions. We propose to analyze Parkinson's tremor (PT) to support the analysis of PD, since PT is one of the most typical symptoms of PD with broad generalizability. To realize the idea, we present SPA-PTA, a deep learning-based PT classification and severity estimation system that takes consumer-grade videos of front-facing humans as input. The core of the system is a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture that effectively extracts relevant PT information and filters noise. It enhances modeling performance while improving system interpretability. We validate our system via individual-based leave-one-out cross-validation on two tasks: the PT classification task and the tremor severity rating estimation task. Our system presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT class, while providing a 76.4% accuracy and 76.7% F1-score in more complex multiclass tremor rating classification task. Our system offers a cost-effective PT classification and tremor severity estimation results as warning signs of PD for undiagnosed patients with PT symptoms. In addition, it provides a potential solution for supporting PD diagnosis in regions with limited clinical resources.

Challenges and knowledge gaps in the management of non-tuberculous mycobacterial pulmonary disease in sub-Saharan African countries with a high tuberculosis burden: a scoping review

IntroductionIn sub-Saharan African (SSA) countries endemic for tuberculosis (TB), previous TB is a significant risk factor for non-tuberculous mycobacterial pulmonary disease (NTM-PD). The deployment of GeneXpert MTB/RIF in pulmonary TB...

Trajectories of healthy aging and their association with prodromal parkinson disease: The HELIAD study

Background: The aim of the present study was to investigate the association of prodromal PD (pPD) with trajectories of healthy aging, according to its latest definition by the WHO. Methods: In a sample of 1,226 older adults (704 women), PD diagnosis was reached through standard clinical research procedures. Probability of pPD was calculated according to the International Parkinson and Movement Disorder Society’s research criteria for PD-free participants. A healthy aging metric was introduced using an item response theory approach (IRT) based on information from validated questionnaires assessing functionality. Four trajectories of healthy aging were created based on whether the healthy aging status of participants was above or below the median at baseline and follow up: High-High, High-Low, Low-High and Low-Low. Results: 34.3% belonged to the High-High group, 15.7% to the High-Low, 18.6% to the Low-High and 31.4% to the Low-Low group. Participants with possible/probable pPD were 78% less likely to belong in High-High trajectory of healthy aging as compared to those without pPD (OR = 0.22, 95%CI 0.06-0.79, p-value = 0,02). Conclusion: Our findings suggest an inverse association of pPD probability with healthy aging among older adults; Further research is needed to investigate the clinical implications of this association.

Social deprivation and exclusion in Parkinson’s disease: a cross-sectional and longitudinal study

ObjectiveTo describe prevalence and associated factors of social deprivation in people with Parkinson’s disease (PwPD).DesignCross-sectional and longitudinal cohort study.SettingData were taken from the Survey of Health, Ageing and Retirement in...

Personality Disorders and Traits of ABC Clusters in Fibromyalgia in a Neurological Setting.

Evidence suggests that there is substantial comorbidity between fibromyalgia and Axis II pathology (i.e., personality disorders-PDs). The aim of the current study was to find out the exact cluster (A, B, C) of PDs or traits that are more prominent in FM and may be predictors of FM diagnosis. Data from 86 subjects (53 with FM and 33 controls without FM) were analyzed in an observational, cross-sectional, comparative study in a neurological setting. The assessment of categorical PDs and traits was performed independently with the Structured Clinical Interview for Personality Disorders (SCID-II). Binary logistic regression was used to determine FM predictors among PD traits. Compared with controls, FM patients had a higher rate of PD diagnoses (56.7 vs. 18.2%, p < 0.001). However, the rate was significantly higher only for borderline PD diagnosis (28.3% vs. 6.1% p < 0.05). The binary logistic regression analysis showed that schizotypal and schizoid (cluster A), borderline (cluster B), and dependent (cluster C) personality traits may be significant predictors of fibromyalgia (Nagelkerke R2 = 0.415). Our results may reflect the association of FM with personality traits of all three PD clusters: A (eccentric), B (dramatic), and C (anxious). However, the most consistent evidence seems to be for borderline PD.

Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Hypokinetic dysarthria, a motor speech disorder characterized by reduced movement and control in the speech-related muscles, is mostly associated with Parkinson’s disease. Acoustic speech features thus offer the potential for early digital biomarkers to diagnose and monitor the progression of this disease. However, the influence of language on the successful classification of healthy and dysarthric speech remains crucial. This paper explores the analysis of acoustic speech features, both established and newly proposed, in a multilingual context to support the diagnosis of PD. The study aims to identify language-independent and highly discriminative digital speech biomarkers using statistical analysis and machine learning techniques. The study analyzes thirty-three acoustic features extracted from Czech, American, Israeli, Columbian, and Italian PD patients, as well as healthy controls. The analysis employs correlation and statistical tests, descriptive statistics, and the XGBoost classifier. Feature importances and Shapley values are used to provide explanations for the classification results. The study reveals that the most discriminative features, with reduced language dependence, are those measuring the prominence of the second formant, monopitch, and the frequency of pauses during text reading. Classification accuracies range from 67% to 85%, depending on the language. This paper introduces the concept of language robustness as a desirable quality in digital speech biomarkers, ensuring consistent behaviour across languages. By leveraging this concept and employing additional metrics, the study proposes several language-independent digital speech biomarkers with high discrimination power for diagnosing PD.

Interrater reliability of criterion A of the alternative model for personality disorder (Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition-Section III): A meta-analysis.

The alternative model for personality disorder (AMPD) is currently included in Section III of the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition (DSM-5). This review sought to summarize the literature concerning the interrater reliability (IRR) of the AMPD. Despite high heterogeneity, meta-analysis provided tentative support for the IRR of Criterion A of the AMPD, with pooled intraclass correlation coefficients (ICCs) for the Level of Personality Functioning Scale (LPFS) and its domains falling above DSM acceptability levels. Subgroup analysis of the LPFS suggested IRR scores could be improved by using a specific AMPD Structured Clinical Interview (SCI). Further research should in particular consider the IRR of Criterion B elements of the AMPD and overall PD diagnosis, where insufficient data were available to draw conclusions in the present study. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

ResNet-Based Parkinson's Disease Classification

Parkinson&#x0027;s Disease (PD) is a brain disorder that leads to shaking, stiffness, and difficulty with walking, balance, and coordination. The symptoms usually begin gradually and get worse over time. Early diagnosis is very important, because treatments are more effective and easier to perform during the early stages of PD. However, early diagnosis is challenging, because the symptoms start gradually, and at the early stages, they are not very noticeable. In this paper, we propose a method that uses ResNet50, a Residual Network that has 50 layers, to help diagnosis PD. The data used is a collection of frequency features acquired by applying spectral analysis strategies to the speech recordings of the patient. We then convert the frequency features into a 2-dimensional heat map. This heat map is passed to ResNet50, which predicts whether the patient has PD or not. We have conducted experiments and compared the accuracy with several state-of-art methods. The results have demonstrated the feasibility and robustness of the proposed method. <i>Impact Statement</i> &#x2014; Parkinson&#x0027;s Disease (PD) is the most common movement disorder with more than 10 million PD patients worldwide. In the United States, around 60,000 are diagnosed with PD every year. Treatments are more effective at early stage, so early diagnosis is very important. A simple and effective classifier to diagnose PD patients is therefore crucial for doctors and patients. For the last decade, Convolutional Neural Networks (CNN) are the dominant approach for image classifications. Our proposed method benefits from the versatility, solid performance of pretrained CNN architecture by converting the frequency domain features of patients' speech recordings into heat maps. Our method achieved an accuracy of 90.7&#x0025; in diagnosis of PD by only using Tunable Q-Factor Wavelet Transform (TQWT) features, outperforming other state-of-art methods. Therefore, the conversion from non-graphical features to heat maps provides simple, accurate deep network for PD diagnosis that is fast to train, and only requires TQWT features.

Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients

BackgroundMedicare claims and electronic health record data are both commonly used for research and clinical practice improvement; however, it is not known how concordant diagnoses of neurodegenerative diseases (NDD, comprising dementia and Parkinson’s disease) are in these data types. Therefore, our objective was to determine the sensitivity and specificity of neurodegenerative disease (NDD) diagnoses contained in structured electronic health record (EHR) data compared to Medicare claims data.MethodsThis was a retrospective cohort study of 101,980 unique patients seen at a large North Carolina health system between 2013–2017, which were linked to 100% North and South Carolina Medicare claims data, to evaluate the accuracy of diagnoses of neurodegenerative diseases in EHRs compared to Medicare claims data. Patients age > 50 who were enrolled in fee-for-service Medicare were included in the study. Patients were classified as having or not having NDD based on the presence of validated ICD-CM-9 or ICD-CM-10 codes associated with NDD or claims for prescription drugs used to treat NDD. EHR diagnoses were compared to Medicare claims diagnoses.ResultsThe specificity of any EHR diagnosis of NDD was 99.0%; sensitivity was 61.3%. Positive predictive value and negative predictive value were 90.8% and 94.1% respectively. Specificity of an EHR diagnosis of dementia was 99.0%, and sensitivity was 56.1%. Specificity of an EHR diagnosis of PD was 99.7%, while sensitivity was 76.1%.ConclusionsMore research is needed to investigate under-documentation of NDD in electronic health records relative to Medicare claims data, which has major implications for clinical practice (particularly patient safety) and research using real-world data.

Risk of Parkinson's disease in celiac disease: A Swedish population-based study.

Using a population-based matched cohort design, we assessed the association of celiac disease (CeD) with risk of PD by comparing patients with biopsy-confirmed CeD in Sweden to a biopsy-free population and their unaffected siblings, separately. No overall association was observed but CeD diagnosed before age 60 associated positively with incident diagnosis of PD (HR = 1.29; 95% CI: 1.02-1.62), which was mainly attributed to the significantly elevated risk detected after 10-15 years since biopsy (HR = 1.68; 95% CI: 1.05-2.68). Our findings imply an increased vulnerability to long-term PD development among patients with CeD diagnosed before 60s. This article is protected by copyright. All rights reserved.

α-Synuclein-carrying astrocytic extracellular vesicles in Parkinson pathogenesis and diagnosis

BackgroundThe accumulation of α-synuclein (α-syn), an essential step in PD development and progression, is observed not only in neurons but also in glia, including astrocytes. The mechanisms regulating astrocytic α-syn level and aggregation remain unclear. More recently, it has been demonstrated that a part of α-syn spreading occurs through extracellular vesicles (EVs), although it is unknown whether this process is involved in astrocytes of PD. It is known, however, that EVs derived from the central nervous system exist in the blood and are extensively explored as biomarkers for PD and other neurodegenerative disorders.MethodsPrimary astrocytes were transfected with A53T α-syn plasmid or exposed to α-syn aggregates. The level of astrocyte-derived EVs (AEVs) was assessed by nanoparticle tracking analysis and immunofluorescence. The lysosomal function was evaluated by Cathepsin assays, immunofluorescence for levels of Lamp1 and Lamp2, and LysoTracker Red staining. The Apogee assays were optimized to measure the GLT-1+ AEVs in clinical cohorts of 106 PD, 47 multiple system atrophy (MSA), and 103 healthy control (HC) to test the potential of plasma AEVs as a biomarker to differentiate PD from other forms of parkinsonism.ResultsThe number of AEVs significantly increased in primary astrocytes with α-syn deposition. The mechanism of increased AEVs was partially attributed to lysosomal dysfunction. The number of α-syn-carrying AEVs was significantly higher in patients with PD than in HC and MSA. The integrative model combining AEVs with total and aggregated α-syn exhibited efficient diagnostic power in differentiating PD from HC with an AUC of 0.915, and from MSA with an AUC of 0.877.ConclusionsPathological α-syn deposition could increase the astrocytic secretion of EVs, possibly through α-syn-induced lysosomal dysfunction. The α-syn-containing AEVs in the peripheral blood may be an effective biomarker for clinical diagnosis or differential diagnosis of PD.

Black and hispanic patients with movement disorders less likely to undergo deep brain stimulation

BackgroundDBS is an effective surgical treatment for ET, PD, and dystonia. Racial disparities in DBS utilization in PD have been documented demonstrating that Black patients receive DBS at lower rates than White patients. To our knowledge, no studies have investigated if this pattern of non-use persists in other movement disorders with FDA-approval. ObjectiveTo identify racial and ethnic disparities in DBS utilization in those hospitalized for ET, PD, and dystonia. MethodsThe NIS database was queried for US hospitalizations from 2012 to 2018 with a primary diagnosis of ET, PD, or dystonia, and a total of 3,363, 21,963, and 1,835 discharges were recorded, respectively. Within that sample, treatment with DBS was identified. Sex, race, age, payment method, income quartile, year, mortality risk, hospital size, urban/rural setting, teaching status, and geographic region were extracted. A multivariate logistic regression was performed to identify predictors for use and non-use of DBS. ResultsBetween 2012 and 2018, Black patients with PD, ET, and dystonia were less likely to receive DBS than White patients. Black patients with PD were 7 times less likely to receive DBS (OR = 0.145, CI = 0.111–0.189), and Black patients with ET and dystonia were 5 times less likely to receive DBS than White patients (OR = 0.188, CI = 0.124–0.285; OR = 0.186, CI = 0.084–0.414). Compared to White patients, Hispanic patients with PD (OR = 0.631, OR = 0.539–0.740) and ET (OR = 0.438, CI = 0.277–0.695) were less likely to undergo DBS. When controlling for patient and hospital level characteristics, racial and ethnic disparities remained. ConclusionsOur data suggest that Black patients with a diagnosis of ET, PD, or dystonia and Hispanic patients with a diagnosis of ET or PD were less likely to be treated with DBS than White patients between 2012 and 2018.

Integrated PET/MRI With 11C-CFT and 18F-FDG for levodopa response difference in Parkinson’s disease

AimParkinson's disease is one of the most common neurodegenerative diseases. Excellent levodopa responsiveness has been proposed as a characteristic supporting feature in substantiating the PD diagnosis. However, a small portion of clinically established PD patients shows poor levodopa response. This study aims to investigate brain function alterations of PD patients with poor levodopa responsiveness by PET/MRI. MethodA total of 46 PD patients were recruited. They all completed 11C-CFT PET/MRI scans and the acute levodopa challenge test. Among these 46 PD patients, 42 participants further underwent 18F-FDG PET/MRI scans. Clinical variables regarding demographic data, disease features and cognition scales were also collected. Based on the improvement rate of UPDRS‐III, PD patients were divided into non-responders (improvement rate < 33 %) and responders (improvement rate ≥ 33 %). Statistical parametric zapping was performed to analyze molecular imaging. Dopaminergic uptake and metabolism of 70 brain regions were converted to quantitative values and expressed as standard uptake value (SUV). SUV was further normalized by the cerebellum. The resulting SUV ratios and clinical variables were then compared by SPSS. ResultsThe difference between levodopa non-responders (n = 17) and responders (n = 29) in the UPDRS III baseline was statistically significant and the former had a lower UPDRS III baseline (19 (10, 32), p＜0.05). In contrast, no statistical difference between these two groups was found in age, gender, disease duration, cognition, motor subtype and Hoehn-Yahr stage. Dopaminergic uptake differences between levodopa non-responders (n = 17) and responders (n = 29) were shown in the left inferior frontal cortex (1.00 ± 0.09 vs 1.07 ± 0.08, p < 0.05 and FDR < 0.2), the right posterior cingulum (1.10 ± 0.10 vs 1.20 ± 0.13, p < 0.05 and FDR < 0.2) and the right insula (1.21 ± 0.12 vs 1.30 ± 0.10, p < 0.05 and FDR < 0.2). The metabolic alterations between levodopa non-responders (n = 16) and responders (n = 26) were shown in the right supplementary motor area (1.30 (1.18, 1.39) vs 1.41 (1.31, 1.53), p < 0.05 and FDR < 0.2), right precuneus (1.37 ± 0.10 vs 1.47 ± 0.18, p < 0.05 and FDR < 0.2), right parietal cortex (1.14 ± 0.15 vs 1.27 ± 0.21, p < 0.05 and FDR < 0.2), right supramarginal gyrus (1.16 (1.12, 1.26) vs 1.25 (1.14, 1.46), p < 0.05 and FDR < 0.2), right postcentral gyrus (1.15 (1.08, 1.32) vs 1.24 (1.17, 1.39), p < 0.05 and FDR < 0.2), medulla (0.75 ± 0.07 vs 0.80 ± 0.07, p < 0.05 and FDR < 0.2), right rolandic operculum (1.25 (1.18, 1.32) vs 1.33 (1.25, 1.50), p < 0.05 and FDR < 0.2), right olfactory (0.95 (0.91, 1.01) vs 1.01 (0.95, 1.15), p < 0.05 and FDR < 0.2), the right insula (1.15 (1.06, 1.22) vs 1.21 (1.12, 1.35), p < 0.05 and FDR < 0.2) and the left cerebellum crus (0.96 (0.91, 1.01) vs 0.92 (0.86, 0.96), p < 0.05 and FDR < 0.2). ConclusionsPD patients with poor response to levodopa showed less severe impairment of baseline motor symptoms, more severe dopaminergic deficits in the left inferior frontal, right posterior cingulate cortex and the right insula, and lower metabolism in the right supplementary motor area, right precuneus, right parietal cortex, right supramarginal gyrus, right postcentral gyrus, medulla, right rolandic operculum, right olfactory, the right insula and higher metabolism in the left cerebellum crus.

Impaired facial emotion recognition in relation to social behaviours in de novo Parkinson's disease.

Facial emotion recognition (FER) is a crucial component of social cognition and is essential in social-interpersonal behaviour regulation. Although FER impairment is well-established in advanced PD, data about FER at the time of diagnosis and its relationship with social behavioural problems in daily life are lacking. The aim was to examine FER at the time of PD diagnosis compared to a matched healthy control (HC) group and to associate FER with indices of social behavioural problems. In total, 142 de novo, treatment-naïve PD patients and 142 HC were included. FER was assessed by the Ekman 60 faces test (EFT). Behavioural problems in PD patients were assessed using the Dysexecutive Questionnaire (DEX-self and DEX-proxy) and the Apathy Evaluation Scale (AES-self). PD patients had significantly lower EFT-total scores (p = .001) compared to HC, with worse recognition of Disgust (p = .001) and Sadness (p = .016). Correlational analyses yielded significant correlations between AES-self and both EFT-total (rs = .28) and Fear (rs = .22). Significant negative correlations were found between DEX-proxy and both EFT-total (rs = -.28) and Anger (rs = -.26). Analyses of DEX-subscales showed that proxy ratings were significantly higher than patient-ratings for the Social Conventions subscale (p = .047). This DEX-proxy subscale had the strongest correlation with EFT-total (rs = -.29). Results show that de novo PD patients already show impaired FER compared to HC. In addition, lower FER is linked to self-reported apathy and proxy-reported social-behavioural problems, especially concerning social conventions. These findings validate the importance of the inclusion of social cognition measures in the neuropsychological assessment even in early PD.