Diagnosis Of Parkinson's Disease Research Articles

Application of Deep Learning in the Classification of EEG Signals of Parkinson's Disease Patients

This article summarizes research on classifying electroencephalogram (EEG) signals of Parkinson's disease (PD) patients using deep learning technologies. Parkinson's disease is a common neurodegenerative disorder that severely affects quality of life. EEG signals provide valuable information about brain activity. Therefore, analyzing and classifying the EEG signals of PD patients can aid in early diagnosis and treatment. The paper outlines the assessment metrics and cross-validation methods in the diagnosis of Parkinson's disease, highlighting the effectiveness of deep learning in diagnosis. The key technologies reviewed in the article include Wavelet Packet Transform (WPT) and Deep Residual Neural Networks (DRSN), with the WPT-DRSN method achieving up to 99.92% prediction accuracy in the binary classification task of PD patients. Furthermore, this method has also been applied to more complex classweweification tasks, such as categorizing PD patients, REM sleep behavior disorder patients, and healthy controls, maintaining high accuracy. When analyzing Parkinson's disease EEG with the CRNN model, the accuracy is high, but the sample size is small and the model interpretability is limited, necessitating further validation and improvement. For EEG-based PD diagnosis and classification methods, high accuracy was achieved through ICA preprocessing, CSP feature extraction, and ensemble learning classifiers. Future research should consider the integration of multimodal data fusion, deep learning, and NLP technologies, as well as the development of personalized models to enhance the accuracy of PD diagnosis.

Wavelet-based selection-and-recalibration network for Parkinson’s disease screening in OCT images

Background and Objective:Parkinson’s disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases. Methods:Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening. Results:The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process. Conclusions:Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.

Functional Movement Disorder as a Prodromal Symptom of Parkinson's Disease-Clinical and Pathophysiological Insights.

Functional movement disorder (FMD) is a common manifestation of functional neurological disorder. FMD can occur alongside other neurological conditions, but especially in patients with established Parkinson's disease (PD). An interesting observation emerging across cohort studies and case series is that FMD can precede the diagnosis of PD, suggesting that FMD may itself be a prodromal symptom of neurodegeneration. Such a notion would have significant clinical implications for the assessment and management of people with FMD, particularly with respect to decisions around the use of auxiliary investigations, counselling, and follow-up. In this Viewpoint we review the evidence concerning the temporal relationship between FMD and PD. We discuss the potential explanations and mechanisms for FMD as a prodromal symptom of PD, and highlight clinical considerations and important outstanding questions in the field. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study.

There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia. Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed. Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (β = 84.20, P < .001) and years of education (β = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment. The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Investigation of the utility of smartphone-based gait analyses in discrimination between patients with Alzheimer’s disease and Parkinson’s disease

Objective To reveal the discriminative value of gait parameters between Alzheimer’s disease (AD) and Parkinson’s disease (PD) subjects. Methods We included all consecutive patients with newly diagnosed AD and those with a diagnosis of PD who applied to our polyclinic between March 2022 and June 2022. The demographic and clinical features were evaluated during interviews. The gait analyses were performed using a quantitative, smartphone-based gait analyses program. Using this program, the step time (ST), step length (SL), step number (SN), gait velocity (GV), and cadence were measured in all individuals. Results Overall, 31 patients with AD and 45 with PD were enrolled in the analyses. The mean age of the AD group was higher according to those with PD. As expected, the Mini-Mental State Examination (MMSE) values were lower in the AD group. The comparative analyses of the gait parameters between groups did not reveal differences in any of the measures. The correlation analyses to investigate the possible association between the disease severity and gait parameters revealed that the MDS-UPDRS showed low negative correlations with SL and GV. Conclusion Our findings suggest that the evaluation of gait using the gait analyses program does not contribute to the discrimination between AD and PD in clinical practice.

2332 Feasibility and utility of introducing handgrip strength measurement for outpatients living with Parkinson's disease

Abstract Background Patients living with Parkinson’s disease (PD) who are sarcopenic are at significantly higher risk of falling (Cai et al., Frontiers in Neurology,2021,12,598,035). Handgrip strength is a useful tool to assess for sarcopenia but is not commonly measured in clinical practice, despite the consequences that sarcopenia poses. This study aims to incorporate handgrip strength into the assessment of outpatients living with PD. Secondary objectives are to increase the understanding of whether exercise is associated with increased handgrip strength and to implement interventions for patients who are identified as sarcopenic; to improve their health outcomes. Methods Questionnaires were designed to gather quantitative data about patients’ demographics, how frequently they fall, disease severity and their weekly exercise. These were given to patients attending the movement disorders clinic at Crawley hospital, between February and October 2023. Patients without a diagnosis of PD were excluded. Their grip strength was measured using a standardised technique with a calibrated manometer. Data was input to Microsoft Excel and analysed using Spearman’s rank and Kruskal-Wallis test. Results Handgrip strength was obtained for 125 of 271 patients (46%) attending clinic over this period. Initially healthcare workers took 9.2 minutes to complete the questionnaire, but this improved to 4.3 minutes after updating the form. Sixteen patients were excluded, leaving 51 females and 58 males; both with a mean age of 80. Grip strength reduced with PD severity when adjusted for gender; this was significant in males (H = 51.9, p = 0.00) but not females (H = 4.8,p = 0.31). Grip strength was weakly correlated with exercise, although not significant (r2 = 0.15,p = 0.15) but did not appear to be related to frequency of falls (r2 = 0.01,p = 0.92). Conclusions Handgrip measurement can be successfully implemented into outpatient assessment. Handgrip strength could be used to monitor the effect of lifestyle change in individuals. Limitations include self-reporting bias; which activities each individual classifies as exercise.

Revolutionizing our understanding of Parkinson's disease: Dr. Heinz Reichmann's pioneering research and future research direction.

Millions of individuals around the world are afflicted with Parkinson's disease (PD), a prevalent and incapacitating neurodegenerative disorder. Dr. Reichmann, a distinguished professor and neurologist, has made substantial advancements in the domain of PD research, encompassing both fundamental scientific investigations and practical applications. His research has illuminated the etiology and treatment of PD, as well as the function of energy metabolism and premotor symptoms. As a precursor to a number of neurotransmitters and neuromodulators that are implicated in the pathophysiology of PD, he has also investigated the application of tryptophan (Trp) derivatives in the disease. His principal findings and insights are summarized and synthesized in this narrative review article, which also emphasizes the challenges and implications for future PD research. This narrative review aims to identify and analyze the key contributions of Reichmann to the field of PD research, with the ultimate goal of informing future research directions in the domain. By examining Reichmann's work, the study seeks to provide a comprehensive understanding of his major contributions and how they can be applied to advance the diagnosis and treatment of PD. This paper also explores the potential intersection of Reichmann's findings with emerging avenues, such as the investigation of Trp and its metabolites, particularly kynurenines, which could lead to new insights and potential therapeutic strategies for managing neurodegenerative disorders like PD.

Characteristics, Complications, and Outcomes of Critical Illness in Patients with Parkinson Disease.

Adults with Parkinson disease (PD) are hospitalized at higher rates than age-matched controls, and these hospitalizations are associated with significant morbidity. However, little is known about the consequences of critical illness requiring intensive care unit (ICU)-level care in patients with PD. The aim of this study was to define the characteristics and outcomes of adults with PD admitted to the ICU. We performed a retrospective nested case-control study using the Medical Information Mart for Intensive Care IV data set. Adults with PD were identified, and the index ICU admission for these subjects was matched 1:4 with index ICU admissions without a PD diagnosis based on age, sex, comorbidities, illness severity, ICU type, and need for mechanical ventilation. Primary outcomes were in-hospital mortality and discharge location. Secondary outcomes were length of stay and prespecified complications. A total of 630 adults with PD were identified. Patients with PD were older and were more likely to be male, have more comorbidities, and have higher illness severity at presentation. A matched analysis revealed adults with PD did not have a significant difference in in-hospital mortality but were more likely to be discharged to ahigher level of care. Adults with PD had longer hospital lengths of stay and increased odds of delirium, pressure ulcers, and ileus. During critical illness, patients with PD are at increased risk for longer hospital lengths of stay and complications and require a higher level of care at discharge than matched controls. These findings reveal targets for interventions to improve outcomes for patients with PD and may inform discussions about goals of care in this population.

Orthostatic Hypotension and Risk of Mild Cognitive Impairment and Dementia in Parkinson's Disease.

Orthostatic hypotension (OH) is a common condition in Parkinson's disease (PD) with a possible link to cognitive decline. The aim was to explore the association between OH and PD-associated mild cognitive impairment (PD-MCI) and dementia (PDD) over 9 years in a population-based incident PD cohort. We prospectively followed up patients from PD diagnosis with serial blood pressure measurements, clinical examinations, and neuropsychological assessments. We defined OH using (1) consensus-based criteria and (2) clinically significant OH by mean arterial pressure (MAP) in standing position ≤75 mmHg. PD-MCI and PDD were diagnosed according to acknowledged criteria. We applied generalized estimating equations models to investigate associations between OH measurements and cognitive impairment over time. Weibull accelerated failure time regression models were used to study if early OH (≤3 years of PD diagnosis) accelerates the time to incident PD-MCI and PDD. Of 186 enrolled patients, consensus-based OH affected 68.8%, clinically significant OH 33.9%, PD-MCI 60.8%, and PDD 31.2%. Consensus-based OH was associated with PD-MCI (odds ratio [OR]: 2.04, 95% confidence interval: 1.44-2.90, P < 0.001), whereas clinically significant OH was associated with both PD-MCI (OR: 1.95, 1.11-3.43, P = 0.020) and PDD (OR: 3.66, 1.95-6.86, P < 0.001). Early clinically significant OH, but not early consensus-based OH, reduced time to incident PD-MCI by 54% (P = 0.021) and time to PDD by 44% (P = 0.003) independently of potential confounders, including supine hypertension and cardiovascular disease. MAP in standing position emerged as a stronger predictor of cognitive decline than OH determined using consensus-based criteria. These findings have implications for both research and clinical practice.

A novel voice classification based on Gower distance for Parkinson disease detection

BackgroundTraditional classifier for the classification of diseases, such as K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM), often struggle with high-dimensional medical datasets. ObjectiveThis study presents a novel classifier to overcome the limitations of traditional classifiers in Parkinson’s disease (PD) detection based on Gower distance. MethodsWe present the Gower distance metric to handle diverse feature sets in voice recordings, which acts as a dissimilarity measure for all feature types, making the model adept at identifying subtle patterns indicative of PD. Additionally, the Cuckoo Search algorithm is employed for feature selection, reducing dimensionality by focusing on key features, thereby lessening the computational load associated with high-dimensional datasets. ResultsThe proposed classifier based on Gower distance resulted in an accuracy rate of 98.3% with feature selection and achieved an accuracy of 94.92% without the feature selection method. It outperforms traditional classifiers and recent studies in PD detection from voice recordings. ConclusionsThis accuracy shows the capability of the approach in the correct classification of instances and points out the potential of the approach as a reliable diagnostic tool for the medical practitioner. The findings state that the proposed approach holds promise for improving the diagnosis and monitoring of PD, both within medical institutions and at homes for the elderly.

Alzheimer's Disease Related Biomarkers Were Associated with Amnestic Cognitive Impairment in Parkinson's Disease: A Cross-Sectional Cohort Study.

Cognitive impairment is common in patients with Parkinson's disease (PD) and occurs through multiple mechanisms, including Alzheimer's disease (AD) pathology and the involvement of α-synucleinopathies. We aimed to investigate the pathological biomarkers of both PD and AD in plasma and neuronal extracellular vesicles (EVs) and their association with different types of cognitive impairment in PD patients. A total of 122 patients with PD and 30 healthy controls were included in this cross-sectional cohort study between March 2021 and July 2023. Non-dementia PD patients were divided into amnestic and non-amnestic groups according to the memory domain of a neuropsychological assessment. Plasma and neuronal EV biomarkers, including α-synuclein (α-syn), beta-amyloid (Aβ), total tau (T-tau), phosphorylated tau181 (p-tau181), and glial fibrillary acidic protein (GFAP), were measured using a single-molecule array and a chemiluminescence immunoassay, respectively. Neuronal EV but not plasma α-syn levels, were significantly increased in PD as compared to healthy controls, and they were positively associated with UPDRS part III scores and the severity of cognitive impairment. A lower plasma Aβ42 level and higher neuronal EV T-tau level were found in the amnestic PD group compared to the non-amnestic PD group. The results of the current study demonstrate that neuronal EV α-syn levels can be a sensitive biomarker for assisting in the diagnosis and disease severity prediction of PD. Both AD and PD pathologies are important factors in cognitive impairment associated with PD, and AD pathologies are more involved in amnestic memory deficit in PD.

Convention vs. Innovation II: Skin biopsy for synuclein inclusions will replace dopamine transporter imaging for Parkinson disease diagnosis

Convention vs. Innovation II: Skin biopsy for synuclein inclusions will replace dopamine transporter imaging for Parkinson disease diagnosis

Exploring circular RNAs as biomarkers for Parkinson's disease and their expression changes after aerobic exercise rehabilitation.

Circular RNAs (circRNAs) are circularized single-stranded ribonucleic acids that interacts with DNA, RNA, and proteins to play critical roles in cell biology. CircRNAs regulate microRNA content, gene expression, and may code for specific peptides. Indeed, circRNAs are differentially expressed in neurodegenerative disorders like Parkinson's disease (PD), playing a potential role in the mechanisms of brain pathology. The RNA molecules with aberrant expression in the brain can cross the blood-brain barrier and reach the bloodstream, which enable their use as non-invasive PD disease biomarker. Promising targets with valuable discriminatory ability in combined circRNA signatures include MAPK9_circ_0001566, SLAIN1_circ_0000497, SLAIN2_circ_0126525, PSEN1_circ_0003848, circ_0004381, and circ_0017204. On the other hand, regular exercises are effective therapy for mitigating PD symptoms, promoting neuroprotective effects with epigenetic modulation. Aerobic exercises slow symptom progression in PD by improving motor control, ameliorating higher functions, and enhancing brain activity and neuropathology. These improvements are accompanied by changes circRNA expression, including hsa_circ_0001535 (circFAM13B) and hsa_circ_0000437 (circCORO1C). The sensitivity of current methods for detecting circulating circRNAs is considered a limitation. While amplification kits already exist for low-abundant microRNAs, similar kits are needed for circRNAs. Alternatively, the use of digital PCR can help overcome this constraint. The current review examines the potential use of circRNAs as non-invasive biomarkers of PD and to assess the effects of rehabilitation. Although circRNAs hold promise as targets for PD diagnosis and therapeutics, further validation is needed before their clinical implementation.

Alpha Synuclein Toxicity and Non-Motor Parkinson's.

Parkinson's disease (PD) is a common multisystem neurodegenerative disorder affecting 1% of the population over the age of 60 years. The main neuropathological features of PD are the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of alpha synuclein (αSyn)-rich Lewy bodies both manifesting with classical motor signs. αSyn has emerged as a key protein in PD pathology as it can spread through synaptic networks to reach several anatomical regions of the body contributing to the appearance of non-motor symptoms (NMS) considered prevalent among individuals prior to PD diagnosis and persisting throughout the patient's life. NMS mainly includes loss of taste and smell, constipation, psychiatric disorders, dementia, impaired rapid eye movement (REM) sleep, urogenital dysfunction, and cardiovascular impairment. This review summarizes the more recent findings on the impact of αSyn deposits on several prodromal NMS and emphasizes the importance of early detection of αSyn toxic species in biofluids and peripheral biopsies as prospective biomarkers in PD.

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

PurposeThis study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.Design/methodology/approachMultiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.FindingsThe study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.Originality/valueThis study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

Importance of health history analysis in Parkinson's disease

The objective of this research article is to investigate the impact of various health history factors on the risk of developing Parkinson's disease (PD). From the medical history we can identify PD Symptoms and this also help to detect the progression of PD symptoms. By conducting statistical analyses, the study seeks to identify independent risk and protective factors for Parkinson's disease (PD), considering variations in impact across genders and BMI categories. IntroductionIn the diagnosis of PD the analysis of previous health history is very rare in practice while the main diagnosis have been done through the different motor and non-motor symptoms taking in consideration besides the cardinal symptoms of PD for identification and determination the stages of PD. Here we have analyzed the impact of 56 different diseases, symptoms, and surgeries which a subject may have experienced in their life before PD, considered as a health history. MethodsThe behavioral impact for each types of health history have been analyzed statistically with 31,265 subjects including PD, and Control. In this analysis we have calculated the variation of impact for both the Male, and Female, as well as subjects BMI. Results98.12 % PD patients, where 97.63 % Male PD, and 98.71 % Female PD were found with at least one health history record. Coronary heart disease odds ratio (OR) 2.15 (1.85–2.51), Colon Cancer OR 2.11 (1.45–3.05), Cranial brain surgery OR 6.21 (5.11–7.56) have the higher risks to PD. Having the history of Asthma OR 0.66 (0.6–0.72), Anemia OR 0.56 (0.51–0.63), Cirrhosis in Liver OR 0.7 (0.57–0.86), Cosmetic surgery OR 0.7 (0.64–0.77), and Gastritis OR 0.78 (0.71–0.87) have been found to be protective to PD. The risk of developing PD varies between male, and female including subjects BMI for each individual health history types. The diseases which reduce the oxygen saturation in blood like, anemia, asthma, and thalassemia act as protective to PD. ConclusionsIn this study we have analyzed fifty six diseases which include surgeries as a health history of PD patients. Study suggests that a thorough health history could greatly aid in understanding the onset and progression of Parkinson's disease (PD).

Multi-Scale Feature and Multi-Channel Selection toward Parkinson's Disease Diagnosis with EEG.

Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%.

Automatic Parkinson’s Disease Diagnosis with Wearable Sensor Technology for Medical Robot

The clinical diagnosis of Parkinson’s disease (PD) has been the subject of medical robotics research. Currently, a hot research topic is how to accurately assess the severity of Parkinson’s disease patients and enable medical robots to better assist patients in the rehabilitation process. The walking task on the Unified Parkinson’s Disease Rating Scale (UPDRS) is a well-established diagnostic criterion for PD patients. However, the clinical diagnosis of PD is determined based on the clinical experience of neurologists, which is subjective and inaccurate. Therefore, in this study, an automated diagnostic method for PD based on an improved multiclass support vector machine (MCSVM) is proposed in which wearable sensors are used. Kinematic analysis was performed to extract gait features, both spatiotemporal and kinematic, from the installed IMU and pressure sensors. Comparison experiments of three different kernel functions and linear trajectory experiments were designed. The experimental results show that the accuracies of the three kernel functions of the proposed improved MCSVM are 92.43%, 93.45%, and 95.35%. The simulation trajectories of the MCSVM are the closest to the real trajectories, which shows that the technique performs better in the clinical diagnosis of PD.

Survey on Parkinson's Disease Diagnosis Impact: Patients, Caregivers and Health Care Professionals' Perspectives.

Background: The announcement of Parkinson's disease (PD) diagnosis may provoke negative feelings that impact the ability to cope with the disease and all life changes related to this new condition. There are scarce data on how to improve communication about PD diagnosis and which factors may influence this outcome. Methods: We performed a national French survey, investigating the diagnosis announcement impact on a large population of people living with PD (PwPD), who recently received the diagnosis (≤1 year since PD diagnosis), and on related caregivers and health care professionals (HCPs), from tertiary and community-based hospitals. Results: A total of 397 PwPD (45% female and 82% > 50 years old), 192 caregivers and 120 HCPs (69% neurologists) completed the questionnaire. The diagnosis was not expected by about 60% of PwPD and induced negative feelings in the majority (82%) of them. Negative feelings that PwPD experience in the moment of the diagnosis announcement were related with male gender [OR = 2.034, CI 95% 1.09-3.78; p = 0.025] and older age [OR = 1.05, CI 95% 1.01-1.08; p = 0.004], while tremor as the first symptom had a threshold significance [OR = 1.78, CI 95% 0.994-3.187; p = 0.052]. Half of the PwPD and caregivers considered that they did not receive enough information and one third had a short-term appointment to rediscuss the diagnosis. A total of 82% of PwPD expressed the willingness to have a multidisciplinary follow-up (PD nurse, psychologists). Only 24% of the HCPs had been trained for PD announcement. Conclusions: The way a PD diagnosis is delivered represents a pivotal moment in the journey of PwPD and caregivers. This process requires improvement in addressing the gaps expressed by PwPD, caregivers, and HCPs through a participatory approach.

Exploring the complexity of EEG patterns in Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20Hz) and beta 2 (20-30Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.