Parkinson's Disease Individuals Research Articles

Olfactory dysfunction and long-term trajectories of sleep disorders among early Parkinson's disease: findings from a longitudinal cohort.

Previous studies have suggested a connection between impaired olfactory function and an increased risk of rapid eye movement sleep behavior disorder (RBD) in individuals diagnosed with Parkinson's disease (PD). However, there is a gap in knowledge regarding the potential impact of olfactory dysfunction on the long-term patterns of sleep disorders among early PD patients. Data from the Parkinson's Progression Markers Initiative program included 589 participants with assessments of sleep disorders using the Epworth Sleepiness Scale (ESS) and RBD Screening Questionnaire (RBDSQ). Olfactory dysfunction at baseline was measured using the University of Pennsylvania Smell Identification Test. Trajectories of sleep disorders over a 5-year follow-up were identified using group-based trajectory modeling, and the relationship between olfactory dysfunction and sleep disorder trajectories was examined through binomial logistic regression. Two distinct trajectories of sleep disorders over the 5-year follow-up period were identified, characterized by maintaining a low or high ESS score and a low or high RBDSQ score. An inversion association was observed between olfactory function measures and trajectories of excessive daytime sleepiness (EDS) (odds ratio [OR]=0.97, 95% confidence interval [CI] 0.95, 1.00, P=0.038), after controlling for potential covariates. Similarly, olfactory function showed a significant association with lower trajectories of probable RBD (pRBD) (OR=0.96, 95% CI 0.94, 0.98, P=0.001) among early PD individuals. Consistent findings were replicated across alternative analytical models. Our findings indicated that olfactory dysfunction was associated with unfavorable long-term trajectories of sleep disorders among early Parkinson's disease.

Associations of sleep disorders with serum neurofilament light chain levels in Parkinson’s disease

BackgroundSleep disorders are a prevalent non-motor symptom of Parkinson’s disease (PD), although reliable biological markers are presently lacking.ObjectivesTo explore the associations between sleep disorders and serum neurofilament light chain (NfL) levels in individuals with prodromal and early PD.MethodsThe study contained 1113 participants, including 585 early PD individuals, 353 prodromal PD individuals, and 175 healthy controls (HCs). The correlations between sleep disorders (including rapid eye movement sleep behavior disorder (RBD) and excessive daytime sleepiness (EDS)) and serum NfL levels were researched using multiple linear regression models and linear mixed-effects models. We further investigated the correlations between the rates of changes in daytime sleepiness and serum NfL levels using multiple linear regression models.ResultsIn baseline analysis, early and prodromal PD individuals who manifested specific behaviors of RBD showed significantly higher levels of serum NfL. Specifically, early PD individuals who experienced nocturnal dream behaviors (β = 0.033; P = 0.042) and movements of arms or legs during sleep (β = 0.027; P = 0.049) showed significantly higher serum NfL levels. For prodromal PD individuals, serum NfL levels were significantly higher in individuals suffering from disturbed sleep (β = 0.038; P = 0.026). Our longitudinal findings support these baseline associations. Serum NfL levels showed an upward trend in early PD individuals who had a higher total RBDSQ score (β = 0.002; P = 0.011) or who were considered as probable RBD (β = 0.012; P = 0.009) or who exhibited behaviors on several sub-items of the RBDSQ. In addition, early PD individuals who had a high total ESS score (β = 0.001; P = 0.012) or who were regarded to have EDS (β = 0.013; P = 0.007) or who exhibited daytime sleepiness in several conditions had a trend toward higher serum NfL levels.ConclusionSleep disorders correlate with higher serum NfL, suggesting a link to PD neuronal damage. Early identification of sleep disorders and NfL monitoring are pivotal in detecting at-risk PD patients promptly, allowing for timely intervention. Regular monitoring of NfL levels holds promise for tracking both sleep disorders and disease progression, potentially emerging as a biomarker for evaluating treatment outcomes.

The functional brain connectome in isolated rapid eye movement sleep behavior disorder and Parkinson’s disease

BackgroundIsolated rapid-eye-movement behavior disorder (iRBD) often precedes the development of alpha-synucleinopathies such as Parkinson’s disease (PD). Magnetic resonance imaging (MRI) studies have revealed structural brain alterations in iRBD partially resembling those observed in PD. However, relatively little is known about whole-brain functional brain alterations in iRBD. Here, we characterize the functional brain connectome of iRBD compared with PD patients and healthy controls (HC) using resting-state functional MRI (rs-fMRI). MethodsEighteen iRBD subjects (67.3 ± 6.6 years), 18 subjects with PD (65.4 ± 5.8 years), and 39 age- and sex-matched HC (64.4 ± 9.2 years) underwent rs-fMRI at 3 T. We applied a graph theoretical approach to analyze the brain functional connectome at the global and regional levels. Data were analyzed using both frequentist and Bayesian statistics. ResultsGlobal connectivity was largely preserved in iRBD and PD individuals. In contrast, both disease groups displayed altered local connectivity mainly in the motor network, temporal cortical regions including the limbic system, and the visual system. There were some group specific alterations, and connectivity changes were pronounced in PD individuals. Overall, however, there was a good agreement of the connectome changes observed in both disease groups. ConclusionsThis study provides evidence for widespread functional brain connectivity alterations in iRBD, including motor circuitry, despite normal motor function. Connectome alterations showed substantial resemblance with those observed in PD, underlining a close pathophysiological relationship of iRBD and PD.

Altered hippocampal doublecortin expression in Parkinson's disease.

Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by motor and non-motor symptoms. Motor symptoms include bradykinesia, resting tremors, muscular rigidity, and postural instability, while non-motor symptoms include cognitive impairments, mood disturbances, sleep disturbances, autonomic dysfunction, and sensory abnormalities. Some of these symptoms may be influenced by the proper hippocampus functioning, including adult neurogenesis. Doublecortin (DCX) is a microtubule-associated protein that plays a pivotal role in the development and differentiation of migrating neurons. This study utilized postmortem human brain tissue of PD and age-matched control individuals to investigate DCX expression in the context of adult hippocampal neurogenesis. Our findings demonstrate a significant reduction in the number of DCX-expressing cells within the subgranular zone (SGZ), as well as a decrease in the nuclear area of these DCX-positive cells in postmortem brain tissue obtained from PD cases, suggesting an impairment in the adult hippocampal neurogenesis. Additionally, we found that the nuclear area of DCX-positive cells correlates with pH levels. In summary, we provide evidence supporting that the process of hippocampal adult neurogenesis is likely to be compromised in PD patients before cognitive dysfunction, shedding light on potential mechanisms contributing to the neuropsychiatric symptoms observed in affected individuals. Understanding these mechanisms may offer novel insights into the pathophysiology of PD and possible therapeutic avenues.

Synaptic loss and its association with symptom severity in Parkinson’s disease

Parkinson’s disease (PD) is the fastest growing neurodegenerative disease, but at present there is no cure, nor any disease-modifying treatments. Synaptic biomarkers from in vivo imaging have shown promise in imaging loss of synapses in PD and other neurodegenerative disorders. Here, we provide new clinical insights from a cross-sectional, high-resolution positron emission tomography (PET) study of 30 PD individuals and 30 age- and sex-matched healthy controls (HC) with the radiotracer [11C]UCB-J, which binds to synaptic vesicle glycoprotein 2A (SV2A), and is therefore, a biomarker of synaptic density in the living brain. We also examined a measure of relative brain perfusion from the early part of the same PET scan. Our results provide evidence for synaptic density loss in the substantia nigra that had been previously reported, but also extend this to other early-Braak stage regions known to be affected in PD (brainstem, caudate, olfactory cortex). Importantly, we also found a direct association between synaptic density loss in the nigra and severity of symptoms in patients. A greater extent and wider distribution of synaptic density loss in PD patients with longer illness duration suggests that [11C]UCB-J PET can be used to measure synapse loss with disease progression. We also demonstrate lower brain perfusion in PD vs. HC groups, with a greater extent of abnormalities in those with longer duration of illness, suggesting that [11C]UCB-J PET can simultaneously provide information on changes in brain perfusion. These results implicate synaptic imaging as a useful PD biomarker for future disease-modifying interventions.

Emerging wearable technologies for multisystem monitoring and treatment of Parkinson's disease: a narrative review.

Parkinson's disease (PD) is a chronic movement disorder characterized by a variety of motor and nonmotor comorbidities, including cognitive impairment, gastrointestinal (GI) dysfunction, and autonomic/sleep disturbances. Symptoms typically fluctuate with different settings and environmental factors and thus need to be consistently monitored. Current methods, however, rely on infrequent rating scales performed in clinic. The advent of wearable technologies presents a new avenue to track objective measures of PD comorbidities longitudinally and more frequently. This narrative review discusses and proposes emerging wearable technologies that can monitor manifestations of motor, cognitive, GI, and autonomic/sleep comorbidities throughout the daily lives of PD individuals. This can provide more wholistic insight into real-time physiological versus pathological function with the potential to better assess treatments during clinical trials and allow physicians to optimize treatment regimens. Additionally, this narrative review briefly examines novel applications of wearables as therapy for PD patients.

Laser-light cueing shoes with integrated foot pressure and inertial sensing for investigating the impact of visual cueing on gait characteristics in Parkinson's disease individuals.

Gait disorders are a fundamental challenge in Parkinson's disease (PD). The use of laser-light visual cues emitted from shoes has demonstrated effective in improving freezing of gait within less restrictive environments. However, the effectiveness of shoes-based laser-light cueing may vary among individuals with PD who have different types of impairments. We introduced an innovative laser-light visual shoes system capable of producing alternating visual cues for the left and right feet through one-side cueing at a time, while simultaneously recording foot inertial data and foot pressures. The effects of this visual cueing system on gait patterns were assessed in individuals with PD, both those with well-gait and those with worse-gait. Our device successfully quantified gait characteristics, including the asymmetry in the center of pressure trajectory, in individuals with PD. Furthermore, visual cueing prolonged stride times and increased the percentage of stance phase, while concurrently reducing stride length in PD individuals with well-gait. Conversely, in PD individuals with worse-gait, visual cueing resulted in a decreased freeze index and a reduction in the proportion of intervals prone to freezing episodes. The effects of visual cueing varied between PD individuals with well-gait and those with worse-gait. Visual cueing slowed down gait in the well-gait group while it appeared to mitigate freezing episodes in worse-gait group. Future researches, including enhancements to extend the projection distance of visual cues and clinical assessments conducted in real-world settings, will help establish the clinical utility of our proposed visual cueing system.

Microstructural white matter changes underlying speech deficits in Parkinson’s disease

Speech impairments are one of the common symptoms of individuals with Parkinson’s disease (PD). However, little is known about the underlying neuroanatomical structural deficits specifically in the basal ganglia-thalamocortical (BGTC) loop in the speech deficits of PD. Here we investigated white matter differences in PD using probabilistic tractography. Diffusion tensor imaging data were downloaded from the Parkinson’s Progression Markers Initiative database. We included three groups of participants: 20 PD individuals with speech deficits, 20 PD individuals without speech deficits, and 20 age- and gender-matched control participants. Overall, PD individuals with speech deficits had higher mean diffusivity in the BGTC pathway in the left hemisphere compared with PD individuals without speech deficits. The present study exhibits that there may be a distinct pathophysiological profile of white matter for speech deficits in PD.

Predicting the occurrence of mild cognitive impairment in Parkinson's disease using structural MRI data.

Mild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson's disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early anatomical brain changes that precede the transition from healthy cognition to MCI in PD. Structural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson's Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC, n = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, n = 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier. The average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain. Our machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.

Effects of non-invasive brain stimulation over supplementary motor area in people with Parkinson’s disease: a protocol for a systematic review and meta-analysis of randomised controlled trials

IntroductionIndividuals with Parkinson’s disease (PD) often experience initial hesitation, slowness of movements, decreased balance and impaired standing ability, which can significantly impact their independence. Transcranial magnetic stimulation and transcranial direct...

Identifying prodromal symptoms at high specificity for Parkinson's disease.

To test drugs with the potential to prevent the onset of Parkinson's disease (PD), it is key to identify individuals in the general population at high risk of developing PD. This is often difficult because most of the clinical markers are non-specific, common in PD but also common in older adults (e.g., sleep problems). We aimed to identify the clinical markers at high specificity for developing PD by comparing individuals with PD or prodromal PD to healthy controls. We investigated motor and non-motor symptoms (Movement Disorder Society Unified Parkinson's Disease Rating Scale Part 1 and 2 items) in 64 prodromal PD and 422 PD individuals calculating the odds ratios, adjusting for age and gender, for PD and prodromal PD versus 195 healthy controls. Symptoms at high specificity were defined as having an adjusted odds ratio ≥ 6. Constipation had an adjusted odds ratio, 6.14 [95% CI: 2.94-12.80] showing high specificity for prodromal PD, and speech difficulties had an adjusted odds ratio, 9.61 [95% CI: 7.88-48.81] showing high specificity for PD. The proportion of participants showing these specific markers was moderate (e.g., prevalence of constipation was 43.75% in prodromal PD, and speech difficulties was 33.89% in PD), suggesting these symptoms may make robust predictors of prodromal PD and PD, respectively. Clinical markers at high specificity for developing PD could be used as tools in the screening of general populations to identify individuals at higher risk of developing PD.

Prognoza: Parkinson’s Disease Prediction Using Classification Algorithms

Parkinson's Disease (PD) is a persistent neurological condition that has a global impact on a significant number of individuals. The timely detection of PD is imperative for the efficacious treatment and control of the condition. Machine learning (ML) methods have demonstrated significant potential in forecasting Parkinson's disease (PD) based on diverse data sources in recent times. The present research paper outlines a study that employs machine learning [ML]techniques to predict Parkinson's disease. A dataset comprising clinical and demographic characteristics of both patients diagnosed with PD and healthy individuals was taken from Kaggle. The aforementioned dataset was utilized to train and assess multiple machine learning models. The experimental findings indicate that the CatBoost model exhibited superior performance compared to the other models, achieving an accuracy rate of 95.1% and a root mean squared error of of 0.34.In summary, our research showcases the capabilities of machine learning methodologies in forecasting Parkinson's disease and offers valuable insights into the crucial predictors for PD prognosis. The results of our study could potentially contribute to the advancement of diagnostic methods for the timely identification of PD, with increased precision and efficacy.

Detecting Minor Symptoms of Parkinson's Disease in the Wild Using Bi-LSTM with Attention Mechanism.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients' quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.

Effects of HiBalance training program on physical function of individuals with idiopathic Parkinson disease: a systematic review and meta-analysis.

The effectiveness of the HiBalance training program for individuals with Parkinson disease (PD) remains debated, prompting a systematic investigation. This study aims to assess whether the HiBalance training program yields favorable outcomes on physical function and self-reported function measures in PD individuals. A systematic search across PubMed, Cochrane Library, Ovid, Scopus, and PEDro databases identified studies exploring HiBalance training's impact on physical function in idiopathic PD. Publication date restrictions were not applied. Two independent reviewers evaluated bias risk using the Cochrane Risk of Bias tool and study quality using the PEDro scale. Effect size (standardized mean difference, SMD) and heterogeneity (Higgins I2) were determined. Six studies underwent qualitative analysis, with two randomized-controlled trials and one multi-center clinical trial being included in the meta-analysis. HiBalance training exhibited a significant impact on physical function (SMD = 0.49; P = 0.0003). The Mini-BESTest score and gait velocity displayed improvements with moderate-effect sizes. However, solely gait velocity showed clinical enhancement. Yet, these benefits did not remain at the 6- and 12-month follow-ups post-intervention. Self-reported function measures showed no alteration post-HiBalance training. Publication bias was absent. HiBalance training led to clinically significant improvements solely in gait velocity, though these gains waned over time. The findings suggest the necessity of refining the HiBalance program to sustain positive outcomes and ensure lasting enhancements. This underscores the importance of post-HiBalance training exercise programs to maintain benefits in the long term. The protocol of this review and meta-analysis was registered at PROSPERO (CRD42022325649). Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022325649 .

May Bradykinesia Features Aid in Distinguishing Parkinson's Disease, Essential Tremor, And Healthy Elderly Individuals?

Bradykinesia is the hallmark feature of Parkinson's disease (PD); however, it can manifest in other conditions, including essential tremor (ET), and in healthy elderly individuals. Here we assessed whether bradykinesia features aid in distinguishing PD, ET, and healthy elderly individuals. We conducted simultaneous video and kinematic recordings of finger tapping in 44 PD patients, 69 ET patients, and 77 healthy elderly individuals. Videos were evaluated blindly by expert neurologists. Kinematic recordings were blindly analyzed. We calculated the inter-raters agreement and compared data among groups. Density plots assessed the overlapping in the distribution of kinematic data. Regression analyses and receiver operating characteristic curves determined how the kinematics influenced the likelihood of belonging to a clinical score category and diagnostic group. The inter-rater agreement was fair (Fleiss K = 0.32). Rater found the highest clinical scores in PD, and higher scores in ET than healthy elderly individuals (p < 0.001). In regard to kinematic analysis, the groups showed variations in movement velocity, with PD presenting the slowest values and ET displaying less velocity than healthy elderly individuals (all ps < 0.001). Additionally, PD patients showed irregular rhythm and sequence effect. However, kinematic data significantly overlapped. Regression analyses showed that kinematic analysis had high specificity in differentiating between PD and healthy elderly individuals. Nonetheless, accuracy decreased when evaluating subjects with intermediate kinematic values, i.e., ET patients. Despite a considerable degree of overlap, bradykinesia features vary to some extent in PD, ET, and healthy elderly individuals. Our findings have implications for defining bradykinesia and categorizing patients.

Comparing the hippocampal volumetric atrophy between demented and nondemented individuals with Parkinson's disease: A systematic review and meta-analysis.

Parkinson's disease (PD) is one of the most common neurodegenerative conditions in the world and, when combined with dementia, can lead to immense cerebral volume loss. Of significant importance among all cerebral regions, is the hippocampus. This region plays a pivotal role in memory, and understanding its pathological alterations can answer vital questions regarding dementia. As such, we designed this study to compare the hippocampal volumes of PD patients with dementia (PDD) versus PD without dementia. PubMed, Web of Science, Scopus, and Embase were searched for relevant studies. We also searched the references sections of all included studies. The original search began in March 2022 and was extended to the end of July 2022. All related data were extracted from the studies. If the studies were conducted on patients from comparable patient groups, the most recent study with the most extensive data set would be included. A statistically significant difference was observed comparing the raw hippocampal volumes in participants with PDD and PD (p value = 0.01). In a comparison of normalized hippocampal volume between PDD and PD, there was a statistically significant difference (p value < 0.01), as well. Although further research is required to illuminate the temporal relation between the onset of dementia and hippocampal atrophy in demented PD individuals, the present study highlights the importance of utilizing volumetric studies on memory-related cerebral regions to diagnose the initiation of dementia in the early stages.

Resting-state functional connectivity is modulated by cognitive reserve in early Parkinson's disease.

Fronto-striatal disconnection is thought to be at the basis of dysexecutive symptoms in patients with Parkinson's disease (PD). Multiple reserve-related processes may offer resilience against functional decline. Among these, cognitive reserve (CR) refers to the adaptability of cognitive processes. To test the hypothesis that functional connectivity of pathways associated with executive dysfunction in PD is modulated by CR. Twenty-six PD patients and 24 controls underwent resting-state functional magnetic resonance imaging. Functional connectivity was explored with independent component analysis and seed-based approaches. The following networks were selected from the outcome of the independent component analysis: default-mode (DMN), left and right fronto-parietal (l/rFPN), salience (SalN), sensorimotor (SMN), and occipital visual (OVN). Seed regions were selected in the substantia nigra and in the dorsolateral and ventromedial prefrontal cortex for the assessment of seed-based functional connectivity maps. Educational and occupational attainments were used as CR proxies. Compared with their counterparts with high CR, PD individuals with low CR had reduced posterior DMN functional connectivity in the anterior cingulate and basal ganglia, and bilaterally reduced connectivity in fronto-parietal regions within the networks defined by the dorsolateral and ventrolateral prefrontal seeds. Hyper-connectivity was detected within medial prefrontal regions when comparing low-CR PD with low-CR controls. CR may exert a modulatory effect on functional connectivity in basal ganglia and executive-attentional fronto-parietal networks. In PD patients with low CR, attentional control networks seem to be downregulated, whereas higher recruitment of medial frontal regions suggests compensation via an upregulation mechanism. This upregulation might contribute to maintaining efficient cognitive functioning when posterior cortical function is progressively reduced.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification.

18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

What Do Arithmetic Errors in the Financial Context Reveal? A Preliminary Study of Individuals with Neurocognitive Disorders

Objectives: Arithmetic errors in the financial context have been investigated mainly in cognitively normal Parkinson’s disease (PD) patients and mildly impaired PD (PD-MCI) individuals. The aim of this study was to examine arithmetic errors in the financial context across neurocognitive disorders. Methods: Four hundred and twenty older adults from Greece were divided into four groups (110 patients with a diagnosis of Alzheimer’s disease (AD), 107 patients with a diagnosis of mild cognitive impairment (MCI), 109 healthy controls and 94 Parkinson’s disease dementia (PDD) patients). Their ages ranged from 65 to 98 years (M = 73.96, SD = 6.68), and the sample had a mean of 8.67 (SD = 4.08) years of education. For each of the AD patients, a counterpart matched by age, educational attainment and gender was selected from a larger group of participants. Results: Overall, the results reveal that healthy older adults did not commit arithmetic errors, but AD patients reported procedural errors in their responses to both questions. A high frequency of procedural errors was found in MCI patients’ responses to the first question, while the errors in their responses to the second question cannot be categorized. Finally, in PDD patients, place value errors were reported for the first question, while more magnitude errors were made when responding to the second question. Conclusions: These findings support that arithmetic errors within financial contexts are not the same across neurocognitive disorders, and numerical representations are not impaired not only in PDD, but also in AD and MCI. This information could be useful in cognitive assessments performed by neurologists and neuropsychologists as these types of errors may be indicators of specific brain pathologies.

Prevalence of Temporomandibular Disorders in subjects affected by Parkinson Disease: a systematic review and metanalysis.

While Temporomandibular disorders (TMDs) are not typically considered a primary symptom of Parkinson disease (PD), recent studies have documented a correlation between aging and TMDs, a cluster of conditions affecting the temporomandibulr joint and surrounding musculature, thereby underscoring the potential for an increased frequency of TMDs in the aging PD population. The purpose of this review is to systematically evaluate the existing literature on the topic and provide a comprehensive overview of the prevalence of TMDs in patients with PD. To determine eligibility, we utilized the Population (human subjects), Exposure (PD), Comparator (PD vs non-PD subjects), and Outcomes (TMD) (PECO) model. To conduct this systematic review, we searched for articles published in PubMed, Web of Science, and Lilacs from the beginning until April150, 2023. Three study were selected. All together they comprised a total of 55828 subjects, out of which 12629 were affected by PD or Parkinsonism, and the remaining 43199 were non-PD subjects matched for age and sex. The study aimed to evaluate the prevalence of TMDs in subjects affected by PD. The results showed that 193/12629 (1,53%) of PD individuals presented with TMD symptoms or were diagnosed with TMD, while 260/43199 (0,6%) of non-PD subjects were diagnosed with the same. The meta-analysis, showed that there was a higher TMD prevalence in PD subjects compared to non-PD subjects (RR 1.57; 95% CI: 1.30- 1.89). The studies reviewed suggest a possible association between TMD and PD. Further research is needed to clarify the relationship between TMD and PD and to identify possible mechanisms and treatment strategies.