Cognitive impairment is associated with Hoehn and Yahr stages in early, de novo Parkinson disease patients

Risk of Dementia in Parkinson's Disease: A Systematic Review and Meta-Analysis

Estimates of the risk of dementia in Parkinson's disease (PDD) vary widely. We aimed to review the incidence of PDD and in a meta-analysis estimate the pooled annual incidence and relative risk of PDD while also exploring factors that may contribute to heterogeneity between studies. Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines were followed and MEDLINE and EMBASE were searched for articles reporting the number of cases of dementia in a population, followed longitudinally, with a minimum of 100 dementia-free Parkinson's disease (PD) patients at baseline. Meta-analyses and meta-regressions were used to estimate the pooled incidence rate of PDD and the relative risk of PDD versus healthy controls (HC). A total of 32 studies were identified, 25 reporting the incidence of PDD and 10 reporting the relative risk of PDD versus HC. The pooled incidence rate of PDD was 4.45 (95% confidence interval [CI], 3.91-4.99) per 100 person-years at risk, equating to a 4.5% annual risk of dementia in a PD prevalent population. The relative risk of PDD was estimated to be 3.25 (95% CI, 2.62-4.03) times greater than HC. Factors contributing to study heterogeneity and disparities in the estimated risk of PDD include the age of patients, year of recruitment, and study location. Significant gaps remain with no studies identified in several geographical regions. Future studies should stratify by age and standardize reporting to reduce overall heterogeneity. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Disease progression strikingly differs in research and real-world Parkinson's populations

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Subjective Cognitive Complaints in Parkinson's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Subjective cognitive complaints (SCCs) in Parkinson's disease (PD) are reported frequently, but their prevalence and association with changes on objective testing are not fully known.

OBJECTIVE

We aimed to determine the prevalence, clinical correlates, and predictive value of SCCs in PD.

METHODS

We conducted a systematic review and meta-analysis. From 204 abstracts, we selected 31 studies (n = 3441 patients), and from these, identified the prevalence, clinical features, associations with neuropsychiatric symptoms, and predictive values of SCCs in PD.

RESULTS

The meta-analysis showed an SCC prevalence of 36%. This prevalence, however, was significantly moderated by study heterogeneity regarding female sex, disease severity, levodopa equivalent daily dosage, exclusion from the overall sample of patients with objective cognitive impairment, and measurement instrument. SCC prevalence did not differ between de novo and treated PD patients. SCCs were weakly and negligibly associated with cognitive changes on objective testing in cross-sectional studies. However, in cognitively healthy patients, SCCs had a risk ratio of 2.71 for later cognitive decline over a mean follow-up of 3.16 years. Moreover, SCCs were moderately related to co-occurring symptoms of depression, anxiety, or apathy and were more strongly related to these neuropsychiatric symptoms than objective cognitive functioning.

CONCLUSION

Our analyses suggest that SCCs in patients with and without objective cognitive impairment are frequent, occurring in more than one third of PD patients. Establishing uniform measurement instruments for identifying PD-related SCCs is critical to understand their implications. Even in cases lacking evidence of objective cognitive impairment and where SCCs might reflect underlying neuropsychiatric symptoms, the possibility of later cognitive deterioration should not be excluded. Therefore, SCCs in PD patients warrant close monitoring for opportunities for targeted and effective interventions. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson's disease

BACKGROUND

Early detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease.

OBJECTIVES

To investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods.

METHODS

We included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance.

RESULTS

A total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62-0.68; ACC range: 0.63-0.77; SEN range: 0.45-0.66; SPE range: 0.64-0.84). Models trained with structural connectivity (AUC range, 0.81-0.84; ACC range, 0.75-0.86; SEN range, 0.77-0.91; SPE range, 0.71-0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81-0.85; ACC range, 0.74-0.85; SEN range, 0.79-0.91; SPE range, 0.70-0.89).

CONCLUSIONS

Baseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

Hippocampal subfield abnormalities and biomarkers of pathologic brain changes: from SARS-CoV-2 acute infection to post-COVID syndrome

BACKGROUND

Cognitive deficits are among the main disabling symptoms in COVID-19 patients and post-COVID syndrome (PCS). Within brain regions, the hippocampus, a key region for cognition, has shown vulnerability to SARS-CoV-2 infection. Therefore, in vivo detailed evaluation of hippocampal changes in PCS patients, validated on post-mortem samples of COVID-19 patients at the acute phase, would shed light into the relationship between COVID-19 and cognition.

METHODS

Hippocampal subfields volume, microstructure, and perfusion were evaluated in 84 PCS patients and compared to 33 controls. Associations with blood biomarkers, including glial fibrillary acidic protein (GFAP), myelin oligodendrocyte glycoprotein (MOG), eotaxin-1 (CCL11) and neurofilament light chain (NfL) were evaluated. Besides, biomarker immunodetection in seven hippocampal necropsies of patients at the acute phase were contrasted against eight controls.

FINDINGS

In vivo analyses revealed that hippocampal grey matter atrophy is accompanied by altered microstructural integrity, hypoperfusion, and functional connectivity changes in PCS patients. Hippocampal structural and functional alterations were related to cognitive dysfunction, particularly attention and memory. GFAP, MOG, CCL11 and NfL biomarkers revealed alterations in PCS, and showed associations with hippocampal volume changes, in selective hippocampal subfields. Moreover, post mortem histology showed the presence of increased GFAP and CCL11 and reduced MOG concentrations in the hippocampus in post-mortem samples at the acute phase.

INTERPRETATION

The current results evidenced that PCS patients with cognitive sequalae present brain alterations related to cognitive dysfunction, accompanied by a cascade of pathological alterations in blood biomarkers, indicating axonal damage, astrocyte alterations, neuronal injury, and myelin changes that are already present from the acute phase.

FUNDING

Nominative Grant FIBHCSC 2020 COVID-19. Department of Health, Community of Madrid. Instituto de Salud Carlos III through the project INT20/00079, co-funded by European Regional Development Fund "A way to make Europe" (JAMG). Instituto de Salud Carlos III (ISCIII) through Sara Borrell postdoctoral fellowship Grant No. CD22/00043) and co-funded by the European Union (MDC). Instituto de Salud Carlos III through a predoctoral contract (FI20/000145) (co-funded by European Regional Development Fund "A way to make Europe") (MVS). Fundación para el Conocimiento Madri+d through the project G63-HEALTHSTARPLUS-HSP4 (JAMG, SOM).

A fully automated approach involving neuroimaging and deep learning for Parkinson's disease detection and severity prediction

Three-dimensional magnetic resonance imaging has been proved to detect and predict the severity of progressive neurodegenerative disorders such as Parkinson's disease. The application of pre-processing with neuroimaging methods plays a vital role in post-processing for these problems. The development of technology over the years has enabled the use of deep learning methods such as convolutional neural networks (CNN) on magnetic resonance imaging (MRI) . In this study, the detection of Parkinson's disease and the prediction of disease severity were studied with 2D and 3D CNN using T1-weighted MRIs that were pre-processed with FLIRT image registration and BET non-brain tissue scraper. For 2D CNN, the median slices of the MR images in the sagittal, coronal, and axial planes were used separately and in combination. In addition, the whole brain for 3D CNN has been downsized. Considering the performance of the proposed methods, the highest results achieved for detecting Parkinson's disease were measured as 0.9620, 0.9452, 0.9407, and 0.9536 for Accuracy, F1 score, precision, and Recall, respectively. The highest result achieved for estimating the severity of Parkinson's disease was that 3D CNN was fed three times with a downsized whole MRI, which were measured for R, and R2 as 0.9150 and 0.8372, respectively. When the results obtained with the methods suggested within the scope of the study were examined, it was observed that the applied methods yielded promising performance.

Psychometric properties and construct validity of the Parkinson’s Disease-Cognitive Rating Scale (PD-CRS) in Colombia

Background

Cognitive impairment is frequent among people living with Parkinson’s disease: up to 40% of patients exhibit symptoms of mild cognitive impairment and 25% meet the criteria for dementia. Parkinson’s Disease Cognitive Rating Scale (PD-CRS) is one of the recommended scales by the Movement Disorders Society Task Force for level 1 screening of dementia. However, its psychometric properties have not been studied in the Colombian population.

Methods

A cross-sectional study was conducted on 100 patients with Parkinson’s disease diagnosed by a movement disorders neurologist. Patients were evaluated with PD-CRS and MoCA. Principal component analysis was conducted, and then confirmatory factor analysis was implemented through the maximum-likelihood method. Internal consistency was evaluated using Cronbach α. Convergent and divergent validity were also calculated and concurrent validity with the MoCA was assessed.

Results

62% were males. Their median age was 68 years (IQR 57–74) and the median disease duration was 4 years (IQR 2–9). 77% were classified in early stages (Hoehn and Yahr stage ≤ 2), while the MDS-UPDRS part III score was 25 (IQR 15.5–38). In the principal component factor analysis, the pattern matrix unveiled a mnesic and a non-mnesic domain. Confirmatory factor analysis showed similar explanatory capacity (λ ≥ 0.50) for items other than naming (λ = 0.34). Cronbach’s α for the full 9-items instrument was 0.74. MoCA and PD-CRS total scores were correlated (ρ = 0.71, p = 0.000). Assuming a cut-off score of 62 points, there is an agreement of 89% with the definition of dementia by MoCA for Colombia (κ = 0.59; p = 0.000).

Conclusion

PD-CRS has acceptable psychometric properties for the Colombian population and has significant correlation and agreement with a validated scale (MoCA).

Prediction of Cognitive Degeneration in Parkinson's Disease Patients Using a Machine Learning Method

This study developed a predictive model for cognitive degeneration in patients with Parkinson's disease (PD) using a machine learning method. The clinical data, plasma biomarkers, and neuropsychological test results of patients with PD were collected and utilized as model predictors. Machine learning methods comprising support vector machines (SVMs) and principal component analysis (PCA) were applied to obtain a cognitive classification model. Using 32 comprehensive predictive parameters, the PCA-SVM classifier reached 92.3% accuracy and 0.929 area under the receiver operating characteristic curve (AUC). Furthermore, the accuracy could be increased to 100% and the AUC to 1.0 in a PCA-SVM model using only 13 carefully chosen features.

Associations of cognitive dysfunction with motor and non-motor symptoms in patients with de novo Parkinson's disease

The risk factors of mild cognitive impairment (MCI) in patients with de novo Parkinson’s disease (PD) remain unclear. Therefore, the objective of this study was to compare motor and non-motor symptoms between de novo patients with PD with and without MCI. Moreover, detailed relationships between each cognitive deficit and other clinical characteristics in de novo patients with PD were investigated. Consecutive patients with de novo PD were retrospectively enrolled in this study. Motor symptoms were assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS) part-III and the Hoehn and Yahr (HY) stage. Non-motor symptoms including depression, anxiety, fatigue, and autonomic dysfunction were measured using representative questionnaires. Motor symptoms, depression, and dysautonomia were associated with MCI in de novo patients with PD. Compared with the non-MCI group with PD, the MCI group with PD had higher scores of UPDRS-III, HY stage, depression, and dysautonomia, but not fatigue or anxiety. Both UPDRS-III and HY stage were significantly linked to all cognitive deficits except attention. Logistic regression analysis showed that depression was associated with memory, visuospatial, and executive impairment, and dysautonomia was related to visuospatial and executive impairment. The results of this study suggest that cognitive impairment in PD might have a different relationship pattern to the motor and some non-motor symptoms.

Graded peak cycle ergometer test for cognitively impaired patients with Parkinson's disease: a pilot study

BACKGROUND

Cognitive decline affects up to 50% of patients with Parkinson's disease (PD) in the course of the disease and may be amenable to exercise interventions. To accurately set adequate training intensities, standardized exercise testing is required but such testing takes considerable time and effort. The aim of this pilot study was to investigate the feasibility of a graded peak cycle ergometer exercise test in cognitively impaired patients with Parkinson's Disease (PD), and to define whether age-predicted maximal heart rate (HRmax) matched measured HRmax.

METHODS

A convenience sample of seven patients with PD (Hoehn and Yahr: 2-4, and cognitive impairment (Montreal Cognitive Assessment (MoCA) ≤ 26) completed a graded peak cycle ergometer test to voluntary exhaustion. Borg Rating of Perceived Exertion was used to record the individual's perception of exertion. Pre-defined age-predicted HRmax (calculated as 208-(0.7 × age) was compared with the measured HRmax using Bland-Altman plot and a two-one-sided test.

RESULTS

All PD patients completed the graded exercise test between 8-12 minutes, showing therefore 100% compliance to the test protocol. No adverse events occurred. Predicted HRmax and measured HRmax did not differ.

CONCLUSION

We demonstrate feasibility of graded peak cycle ergometer testing in PD patients with cognitive impairment. The good correspondence of age-predicted HRmax equation with measured HRmax, in this small sample, may in the future provide clinicians with a tool to define training intensities in cognitively impaired PD, without cardiac disease. However, further research is needed to confirm these results.

The role of the medial prefrontal cortex in cognition, ageing and dementia

Humans require a plethora of higher cognitive skills to perform executive functions, such as reasoning, planning, language and social interactions, which are regulated predominantly by the prefrontal cortex. The prefrontal cortex comprises the lateral, medial and orbitofrontal regions. In higher primates, the lateral prefrontal cortex is further separated into the respective dorsal and ventral subregions. However, all these regions have variably been implicated in several fronto-subcortical circuits. Dysfunction of these circuits has been highlighted in vascular and other neurocognitive disorders. Recent advances suggest the medial prefrontal cortex plays an important regulatory role in numerous cognitive functions, including attention, inhibitory control, habit formation and working, spatial or long-term memory. The medial prefrontal cortex appears highly interconnected with subcortical regions (thalamus, amygdala and hippocampus) and exerts top-down executive control over various cognitive domains and stimuli. Much of our knowledge comes from rodent models using precise lesions and electrophysiology readouts from specific medial prefrontal cortex locations. Although, anatomical disparities of the rodent medial prefrontal cortex compared to the primate homologue are apparent, current rodent models have effectively implicated the medial prefrontal cortex as a neural substrate of cognitive decline within ageing and dementia. Human brain connectivity-based neuroimaging has demonstrated that large-scale medial prefrontal cortex networks, such as the default mode network, are equally important for cognition. However, there is little consensus on how medial prefrontal cortex functional connectivity specifically changes during brain pathological states. In context with previous work in rodents and non-human primates, we attempt to convey a consensus on the current understanding of the role of predominantly the medial prefrontal cortex and its functional connectivity measured by resting-state functional MRI in ageing associated disorders, including prodromal dementia states, Alzheimer's disease, post-ischaemic stroke, Parkinsonism and frontotemporal dementia. Previous cross-sectional studies suggest that medial prefrontal cortex functional connectivity abnormalities are consistently found in the default mode network across both ageing and neurocognitive disorders such as Alzheimer's disease and vascular cognitive impairment. Distinct disease-specific patterns of medial prefrontal cortex functional connectivity alterations within specific large-scale networks appear to consistently feature in the default mode network, whilst detrimental connectivity alterations are associated with cognitive impairments independently from structural pathological aberrations, such as grey matter atrophy. These disease-specific patterns of medial prefrontal cortex functional connectivity also precede structural pathological changes and may be driven by ageing-related vascular mechanisms. The default mode network supports utility as a potential biomarker and therapeutic target for dementia-associated conditions. Yet, these associations still require validation in longitudinal studies using larger sample sizes.

Informant-Reported Cognitive Decline is Associated with Objective Cognitive Performance in Parkinson's Disease

OBJECTIVE

The utility of informant-based measures of cognitive decline to accurately describe objective cognitive performance in Parkinson's disease (PD) without dementia is uncertain. Due to the clinical relevance of this information, the purpose of this study was to examine the relationship between informant-based reports of patient cognitive decline via the Informant Questionnaire of Cognitive Decline in the Elderly (IQCODE) and objective cognition in non-demented PD controlling for cognitive status (i.e., mild cognitive impairment; PD-MCI and normal cognition; PD-NC).

METHOD

One-hundred and thirty-nine non-demented PD participants (PD-MCI n = 38; PD-NC n = 101) were administered measures of language, executive function, attention, learning, delayed recall, visuospatial function, mood, and motor function. Each participant identified an informant to complete the IQCODE and a mood questionnaire.

RESULTS

Greater levels of informant-based responses of patient cognitive decline on the IQCODE were significantly associated with worse objective performance on measures of global cognition, attention, learning, delayed recall, and executive function in the overall sample, above and beyond covariates and cognitive status. However, the IQCODE was not significantly associated with language or visuospatial function.

CONCLUSIONS

Results indicate that informant responses, as measured by the IQCODE, may provide adequate information on a wide range of cognitive abilities in non-demented PD, including those with MCI and normal cognition. Findings have important clinical implications for the utility of the IQCODE in the identification of PD patients in need of further evaluation, monitoring, and treatment.

Mild Motor Signs Matter in Typical Brain Aging: The Value of the UPDRS Score Within a Functionally Intact Cohort of Older Adults

Objectives: To characterize the clinical correlates of subclinical Parkinsonian signs, including longitudinal cognitive and neural (via functional connectivity) outcomes, among functionally normal older adults.

Methods: Participants included 737 functionally intact community-dwelling older adults who performed prospective comprehensive evaluations at ~15-months intervals for an average of 4.8 years (standard deviation 3.2 years). As part of these evaluations, participants completed the Unified Parkinson's Disease Rating Scale (UPDRS) longitudinally and measures of processing speed, executive functioning and verbal episodic memory. T1-weighted structural scans and task-free functional MRI scans were acquired on 330 participants. We conducted linear mixed-effects models to determine the relationship between changes in UPDRS with cognitive and neural changes, using age, sex, and education as covariates.

Results: Cognitive outcomes were processing speed, executive functioning, and episodic memory. Greater within-person increases in UPDRS were associated with more cognitive slowing over time. Although higher average UPDRS scores were significantly associated with overall poorer executive functions, there was no association between UPDRS and executive functioning longitudinally. UPDRS scores did not significantly relate to longitudinal memory performances. Regarding neural correlates, greater increases in UPDRS scores were associated with reduced intra-subcortical network connectivity over time. There were no relationships with intra-frontoparietal or inter-subcortical-frontoparietal connectivity.

Conclusions: Our findings add to the aging literature by indicating that mild motor changes are negatively associated with cognition and network connectivity in functionally intact adults.

Cognitive effects of rhythmic auditory stimulation in Parkinson's disease: A P300 study

Rhythmic auditory stimulation (RAS) may compensate dysfunctions of the basal ganglia (BG), involved with intrinsic evaluation of temporal intervals and action initiation or continuation. In the cognitive domain, RAS containing periodically presented tones facilitates young healthy participants' attention allocation to anticipated time points, indicated by better performance and larger P300 amplitudes to periodic compared to random stimuli. Additionally, active auditory-motor synchronization (AMS) leads to a more precise temporal encoding of stimuli via embodied timing encoding than stimulus presentation adapted to the participants' actual movements. Here we investigated the effect of RAS and AMS in Parkinson's disease (PD). 23 PD patients and 23 healthy age-matched controls underwent an auditory oddball task. We manipulated the timing (periodic/random/adaptive) and setting (pedaling/sitting still) of stimulation. While patients elicited a general timing effect, i.e., larger P300 amplitudes for periodic versus random tones for both, sitting and pedaling conditions, controls showed a timing effect only for the sitting but not for the pedaling condition. However, a correlation between P300 amplitudes and motor variability in the periodic pedaling condition was obtained in control participants only. We conclude that RAS facilitates attentional processing of temporally predictable external events in PD patients as well as healthy controls, but embodied timing encoding via body movement does not affect stimulus processing due to BG impairment in patients. Moreover, even with intact embodied timing encoding, such as healthy elderly, the effect of AMS depends on the degree of movement synchronization performance, which is very low in the current study.

The effect of small vessel disease on motor and cognitive function in Parkinson's disease

OBJECTIVES

Small vessel disease (SVD) has been associated with motor and cognitive impairments in neurodegenerative diseases. We investigated SVD markers using brain magnetic resonance imaging (MRI) and the global SVD score in Parkinson's disease (PD).

PATIENTS AND METHODS

Seventy-one patients with PD were assessed for vascular risk factors, motor severity, and motor phenotype. Global cognition was evaluated using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Based on the MoCA score, we categorized cases into normal (>23) or cognitively impaired (≤23). We calculated the total SVD score (range, 0-4) based on white matter hyper intensities (WMHs), lacunae, cerebral microbleeds (MBs), and enlarged perivascular spaces (PVSs). In addition, we evaluated global brain atrophy.

RESULTS

There were no significant associations with total SVD score and vascular risk factors, PD severity, and motor phenotype. Increasing age and reduced MMSE and MoCA scores were associated with increased SVD burden. Logistic regression analyses demonstrated that periventricular WMH (PVH), PVS in the basal ganglia (BG-PVS), and atrophy were predictors of cognitive impairment in PD.

CONCLUSION

The contribution of SVD may be important in elderly patients with PD. Impaired cognition due to SVD-related brain changes was associated with BG-PVS and PVH. These measures suggest that PD with PVS can provide novel insights into SVD.

The clinical characteristics and cognitive features of mild cognitive impairment in Parkinson's disease and the analysis of relevant factors

The purpose of this work is to investigate the clinical characteristics, cognitive impairment features, and subgroup types of Parkinson's disease (PD) subjects with mild cognitive impairment (PD-MCI) in the Chinese population and to analyze relevant risk factors for PD-MCI. A total of 234 non-dementia PD subjects were collected. Standardized neuropsychological assessments of overall cognitive level and four cognitive domains (memory, executive function, attention and visuospatial function) were performed using MDS Task Force diagnostic criteria for PD-MCI. PD-MCI subjects were further divided into four subgroups: nonamnestic single-domain impairment type (PD-naMCI-SD), nonamnestic multiple-domain impairment type (PD-naMCI-MD), amnestic single-domain impairment type (PD-aMCI-SD), and amnestic multiple-domain impairment type (PD-aMCI-MD). The clinical characteristics of and risk factors for all subgroups were analyzed. PD-MCI was found in 45.3% of the non-dementia PD subjects. Differences between the PD-MCI and PD with normal cognition groups with respect to age, age of onset, years of education, and motor symptom severity were significant (P < 0.05). The single-domain impairment type was the largest PD-MCI subgroup (52.83%). Memory and executive function impairment were most frequent (22.64% and 20.75%, respectively). Among the four subgroups, the number of years of education was significantly different (P = 0.003). The overall cognitive function in amnestic multiple-domain impairment type was significantly worse compared with that in those with single-domain impairment type. Regression analysis results showed that old age, high UPDRS-III score, and hyperhomocysteinemia were risk factors for PD-MCI, whereas high education level was a protective factor. Early prevention of MCI-related risk factors provides effective means to retard cognitive decline in PD patients.

Changes in Cortical Thickness in Patients With Early Parkinson's Disease at Different Hoehn and Yahr Stages

Objectives: This study was designed to explore changes in cortical thickness in patients with early Parkinson’s disease (PD) at different Hoehn and Yahr (H-Y) stages and to demonstrate the association of abnormally altered brain regions with part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III).

Materials and Methods: Sixty early PD patients and 29 age- and gender-matched healthy controls (HCs) were enrolled in this study. All PD patients underwent comprehensive clinical and neuropsychological evaluations and 3.0 T magnetic resonance scanning. Patients with H-Y stage ≤1.5 were included in the mild group, and all other patients were included in the moderate group. FreeSurfer software was used to calculate cortical thickness. We assessed the relationship between UPDRS-III and regional changes in cortical thinning, including the bilateral fusiform and the temporal lobe.

Results: The average cortical thickness of the temporal pole, fusiform gyrus, insula of the left hemisphere and fusiform gyrus, isthmus cingulate cortex, inferior temporal gyrus, middle temporal cortex and posterior cingulate cortex of the right hemisphere exhibited significant decreasing trends in HCs group and PD groups (i.e., the mild group and moderate group). After controlling for the effects of age, gender, and disease duration, the UPDRS-III scores in patients with early PD were correlated with the cortical thickness of the left and right fusiform gyrus and the left temporal pole (p < 0.05).

Conclusion: The average cortical thickness of specific brain regions reduced with increasing disease severity in early PD patients at different H-Y stages, and the UPDRS-III scores of early PD patients were correlated with cortical thickness of the bilateral fusiform gyrus and the left temporal pole.

Motor, behavioural, and cognitive correlates of fatigue in early, de novo Parkinson disease patients

INTRODUCTION

Fatigue is one of the most common and disabling non-motor symptoms in Parkinson's disease (PD). The objective of this study was to determine prevalence and motor, behavioural, and cognitive correlates of distressing fatigue in early, de novo PD patients.

METHODS

Eighty-one consecutive de novo PD patients (64% men; mean age 65.73 ± 8.26 years) underwent a comprehensive examination, including Parkinson's disease Fatigue Scale (PFS), Epworth Sleepiness Scale (ESS), Parkinson's Disease Sleep Scale (PDSS), Beck Depression Inventory (BDI), Parkinson's Anxiety Scale (PAS), and Apathy Evaluation Scale (AES). Moreover, all patients underwent a detailed neuropsychological evaluation exploring attention and working memory, executive functions, memory, visuospatial abilities and language. Score of patients with or without distressing fatigue (defined as a PFS score ≥ 8) were compared by Student's t-test or Pearson's chi-square test. Logistic regression analyses were performed to search for motor and non-motor features independently associated with presence of distressing fatigue.

RESULTS

Twelve (15%) patients presented distressing fatigue. Logistic regression identified sleepiness (p = 0.04), "episodic anxiety" subscale of PAS (p = 0.005), and "cognitive apathy" subscale of AES (p = 0.017) as the main factors associated with distressing fatigue. No significant association was found between diagnosis of Mild Cognitive Impairment and distressing fatigue (p = 0.745).

CONCLUSION

In a sample of consecutive de novo PD patients, distressing fatigue is associated with episodic anxiety, cognitive apathy and sleepiness, but not with cognitive impairment. Our findings suggest possible shared pathogenic mechanisms underlying these non-motor symptoms and foster development of early combined therapeutic approaches.