Cognitive profile and determinants of poor cognition in people without dementia in Parkinson’s disease

An important proportion of patients with Parkinson’s Disease (PD) present signs of cognitive impairment, although this is heterogeneous. In an attempt to classify this, the dual syndrome hypothesis distinguishes between two profiles: one defined by attentional and executive problems with damage in anterior cerebral regions, and another with mnesic and visuospatial alterations, with damage in posterior cerebral regions. The Montreal Cognitive Assessment (MoCA) is one of the recommended screening tools, and one of the most used, to assess cognitive impairment in PD. However, its ability to specifically identify these two profiles of cognitive impairment has not been studied. The aim of this study was, therefore, to analyze the capacity of the MoCA to detect cognitive impairment, and also to identify anterior and posterior profiles defined by the dual syndrome hypothesis. For this purpose, 59 patients with idiopathic PD were studied with the MoCA and a neuropsychological battery of tests covering all cognitive domains. Results of logistic regression analysis with ROC (Receiver Operating Characteristic) curves showed that MoCA detected cognitive impairment and identified patients with a profile of anterior/attentional and executive deficit, with acceptable sensibility and specificity. However, it did not identify patients with a posterior/mnesic-visuospatial impairment. We discuss the reasons for the lack of sensitivity of MoCA in this profile, and other possible implications of these results with regards the usefulness of this tool to assess cognitive impairment in PD.

Cognitive impairment (CI) is common in Parkinson's disease (PD) and an important cause of disability. Screening facilitates early detection of CI and has implications for management. Preclinical disability is when patients have functional limitations but maintain independence through compensatory measures. The objective of this study was to investigate the relationship between scores on the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) with levels of PD severity and disability. PD patients (n = 2,234) in a large observational study were stratified by disease severity, based on Total Unified Parkinson's Disease Rating Scale (Total UPDRS) and Hoehn and Yahr (HY) stage. Using MMSE (n = 1,184) or MoCA (n = 1,050) and basic (ADL) and instrumental activities of daily living (IADL) scales for disability, linear regression analysis examined associations between cognitive status and disability. Cognition and disability were highly correlated, with the strongest correlation between IADL and MoCA. Only 16.0% of mean MMSE scores were below threshold for CI (28) and only in advanced PD (Total UPDRS 60+, HY≥3). MoCA scores fell below CI threshold (26) in 66.2% of the sample and earlier in disease (Total UPDRS 30+, HY≥2), corresponding with impairments in ADLs. In a large clinical dataset, a small fraction of MMSE scores fell below cutoff for CI, reinforcing that MMSE is an insensitive screening tool in PD. MoCA scores indicated CI earlier in disease and coincided with disability. This study shows that MoCA, but not MMSE is sensitive to the emergence of early cognitive impairment in PD and correlates with the concomitant onset of disability.

Objective The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson’s disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI.Methods In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson’s Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method.Results Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87–0.89).Conclusion Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.

Background: Cognitive impairment is prevalent in Parkinson's disease (PD), affecting 15–20% of patients at diagnosis. α-synuclein expression and genetic polymorphisms of Apolipoprotein E (ApoE) have been associated with the presence of cognitive impairment in PD although data have been inconsistent.Objectives: To determine the prevalence of cognitive impairment in patients with PD using Montreal Cognitive Assessment (MoCA), Comprehensive Trail Making Test (CTMT) and Parkinson's disease-cognitive rating scale (PDCRS), and its association with plasma α-synuclein and ApoE genetic polymorphisms.Methods: This was across-sectional study involving 46 PD patients. Patients were evaluated using Montreal cognitive assessment test (MoCA), and detailed neuropsychological tests. The Parkinson's disease cognitive rating scale (PDCRS) was used for cognitive function and comprehensive trail making test (CTMT) for executive function. Blood was drawn for plasma α-synuclein measurements and ApoE genetic analysis. ApoE polymorphism was detected using MutaGELAPoE from ImmunDiagnostik. Plasma α-synuclein was detected using the ELISA Technique (USCN Life Science Inc.) according to the standard protocol.Results: Based on MoCA, 26 (56.5%) patients had mild cognitive impairment (PD-MCI) and 20 (43.5%) had normal cognition (PD-NC). Based on the PDCRS, 18 (39.1%) had normal cognition (PDCRS-NC), 17 (37%) had mild cognitive impairment (PDCRS-MCI), and 11 (23.9%) had dementia (PDCRS-PDD). In the PDCRS-MCI group, 5 (25%) patients were from PD-NC group and all PDCRS-PDD patients were from PD-MCI group. CTMT scores were significantly different between patients with MCI and normal cognition on MoCA (p = 0.003). Twenty one patients (72.4%) with executive dysfunction were from the PD-MCI group; 17 (77.3%) with severe executive dysfunction and 4 (57.1%) had mild to moderate executive dysfunction. There were no differences in the plasma α-synuclein concentration between the presence or types of cognitive impairment based on MoCA, PDCRS, and CTMT. TheApoEe4 allele carrier frequency was significantly higher in patients with executive dysfunction (p = 0.014).Conclusion: MCI was prevalent in our PD population. PDCRS appeared to be more discriminatory in detecting MCI and PDD than MoCA. Plasma α-synuclein level was not associated with presence nor type of cognitive impairment, but the ApoEe4 allele carrier status was significantly associated with executive dysfunction in PD.

Dementia is an important and increasingly recognized problem in Parkinson's disease (PD). The mini-mental state examination (MMSE) often fails to detect early cognitive decline. The Montreal cognitive assessment (MoCA) is a brief tool developed to detect mild cognitive impairment that assesses a broader range of domains frequently affected in PD. The scores on the MMSE and the MoCA were compared in 88 patients with PD. A pronounced ceiling effect was observed with the MMSE but not with the MoCA. The range and standard deviation of scores was larger with the MoCA(7-30, 4.26) than with the MMSE(16-30, 2.55). The percentage of subjects scoring below a cutoff of 26/30 (used by others to detect mild cognitive impairment) was higher on the MoCA (32%) than on the MMSE (11%) (P < 0.000002). Compared to the MMSE, the MoCA may be a more sensitive tool to identify early cognitive impairment in PD.

Cognitive and gait deficits are common symptoms in Parkinson's disease (PD). Motor-cognitive dual tasks (DTs) are used to explore the interplay between gait and cognition. However, it is unclear if DT gait performance is indicative for cognitive impairment. Therefore, the aim of this study was to investigate if cognitive deficits are reflected by DT costs of spatiotemporal gait parameters. Cognitive function, single task (ST) and DT gait performance were investigated in 67 PD patients. Cognition was assessed by the Montreal Cognitive Assessment (MoCA) followed by a standardized, sensor-based gait test and the identical gait test while subtracting serial 3's. Cognitive impairment was defined by a MoCA score <26. DT costs in gait parameters [(DT - ST)/ST × 100] were calculated as a measure of DT effect on gait. Correlation analysis was used to evaluate the association between MoCA performance and gait parameters. In a linear regression model, DT gait costs and clinical confounders (age, gender, disease duration, motor impairment, medication, and depression) were correlated to cognitive performance. In a subgroup analysis, we compared matched groups of cognitively impaired and unimpaired PD patients regarding differences in ST, DT, and DT gait costs. Correlation analysis revealed weak correlations between MoCA score and DT costs of gait parameters (r/rSp ≤ 0.3). DT costs of stride length, swing time variability, and maximum toe clearance (|r/rSp| > 0.2) were included in a regression analysis. The parameters only explain 8% of the cognitive variance. In combination with clinical confounders, regression analysis showed that these gait parameters explained 30% of MoCA performance. Group comparison revealed strong DT effects within both groups (large effect sizes), but significant between-group effects in DT gait costs were not observed. These findings suggest that DT gait performance is not indicative for cognitive impairment in PD. DT effects on gait parameters were substantial in cognitively impaired and unimpaired patients, thereby potentially overlaying the effect of cognitive impairment on DT gait costs. Limits of the MoCA in detecting motor-function specific cognitive performance or variable individual response to the DT as influencing factors cannot be excluded. Therefore, DT gait parameters as marker for cognitive performance should be carefully interpreted in the clinical context.

The early diagnosis of mild cognitive impairment (PD-MCI) in Parkinson's disease (PD) is essential as it increases the future risk for PD dementia (PDD). Recently, a novel weighting algorithm for the Montreal Cognitive Assessment (MoCA) subtests has been reported, to best discriminate between those with and without cognitive impairment in PD. The aim of our study was to validate this scoring algorithm in a large sample of non-demented PD patients, hypothesizing that the weighted MoCA would have a higher diagnostic accuracy for PD-MCI than the original MoCA. In 202 non-demented PD patients, we evaluated cognitive status, clinical and demographic data, as well as the MoCA with a weighted and unweighted score. Receiver operating characteristic (ROC) curve analysis was used to evaluate discriminative ability of the MoCA. Group comparisons and ROC analysis were performed for PD-MCI classifications with a cut-off ≤ 1, 1.5, and 2 standard deviation (SD) below appropriate norms. PD-MCI patients scored lower on the weighted than the original MoCA version (p < 0.001) compared to PD patients with normal cognitive function. Areas under the curve only differed significantly for the 2 SD cut-off, leading to an increased sensitivity of the weighted MoCA score (72.9% vs. 70.5%) and specificity compared to the original version (79.0% vs. 65.4%). Our results indicate better discriminant power for the weighted MoCA compared to the original for more advanced stages of PD-MCI (2 SD cut-off). Future studies are needed to evaluate the predictive value of the weighted MoCA for PDD.

Little is known about the impact of the dopamine system on development of cognitive impairment (CI) in Parkinson disease (PD). We used data from a multi-site, international, prospective cohort study to explore the impact of dopamine system-related biomarkers on CI in PD. PD participants were assessed annually from disease onset out to 7 years, and CI determined by applying cut-offs to four measures: (1) Montreal Cognitive Assessment; (2) detailed neuropsychological test battery; (3) Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) cognition score; and (4) site investigator diagnosis of CI (mild cognitive impairment or dementia). The dopamine system was assessed by serial Iodine-123 Ioflupane dopamine transporter (DAT) imaging, genotyping, and levodopa equivalent daily dose (LEDD) recorded at each assessment. Multivariate longitudinal analyses, with adjustment for multiple comparisons, determined the association between dopamine system-related biomarkers and CI, including persistent impairment. Demographic and clinical variables associated with CI were higher age, male sex, lower education, non-White race, higher depression and anxiety scores and higher MDS-UPDRS motor score. For the dopamine system, lower baseline mean striatum dopamine transporter values (P range 0.003-0.005) and higher LEDD over time (P range <0.001-0.01) were significantly associated with increased risk for CI. Our results provide preliminary evidence that alterations in the dopamine system predict development of clinically-relevant, cognitive impairment in Parkinson's disease. If replicated and determined to be causative, they demonstrate that the dopamine system is instrumental to cognitive health status throughout the disease course. Parkinson's Progression Markers Initiative is registered with ClinicalTrials.gov (NCT01141023).

The impact of Parkinson’s disease (PD) dementia is substantial and has major functional and socioeconomic consequences. Early prediction of future cognitive impairment would help target future interventions. The Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), and fluency tests were administered to 486 patients with PD within 3.5 years of diagnosis, and the results were compared with those from 141 controls correcting for age, sex, and educational years. Eighteen-month longitudinal assessments were performed in 155 patients with PD. The proportion of patients classified with normal cognition, mild cognitive impairment (MCI), and dementia varied considerably, depending on the MoCA and MMSE thresholds used. With the MoCA total score at screening threshold, 47.7%, 40.5%, and 11.7% of patients with PD were classified with normal cognition, MCI, and dementia, respectively; by comparison, 78.7% and 21.3% of controls had normal cognition and MCI, respectively. Cognitive impairment was predicted by lower education, increased age, male sex, and quantitative motor and non-motor (smell, depression, and anxiety) measures. Longitudinal data from 155 patients with PD over 18 months showed significant reductions in MoCA scores, but not in MMSE scores, with 21.3% of patients moving from normal cognition to MCI and 4.5% moving from MCI to dementia, although 13.5% moved from MCI to normal; however, none of the patients with dementia changed their classification. The MoCA may be more sensitive than the MMSE in detecting early baseline and longitudinal cognitive impairment in PD, because it identified 25.8% of those who experienced significant cognitive decline over 18 months. Cognitive decline was associated with worse motor and non-motor features, suggesting that this reflects a faster progressive phenotype.

Objective To investigate the risk factors of Parkinson＇s disease（PD）with mild cognitive impairment and mode of cerebral glucose metabolism. Methods One hundred and one non-dementia PD patients were assessed with Montreal Cognitive Assessment（MoCA）and divided into the PD with mild cognitive impairment （PD-MCI）group and the PD non-cognitive impairment（PD-NC）group. The demographic details, clinical features,Unified Pakinson＇s Disease Rating Scale（UPDRS）, Hohen-Yahr rank and Hamilton Depression Scale（HAMD）were compared between the two groups. Patients in Hohen-Yahr stage 1 underwent positron emission tomography（PET）with 18F-fluorodeoxyglucose（18F-FDG）to show glucose metabolism. Results Seventy-seven（74. 3%）PD patients had mild cognitive impairment PD-MCI group had higher score in UPDRS 1st（mentation ,behavior and mood）,2nd （activity of daily living）and 3rd（motor examination）subscale（2.48 ± 1.51,10. 71 ± 4. 88,22.31 ± 12.70）than PD-NC group（1.65 ± 1.29,8.15 ±2. 20,15.92 ±7.56,P 〈0.05）respectively. The FDG metabolism ratio of frontal cortex,parietal cortex and occipital cortex decreased more significantly in PD-MCI than in PD-NC（P 〈 0.05）.Conclusions The risk factors of mild cognitive impairment in PD include moter dysfunction, clinical stage and depression. The metabolic dysfunction of cortex may be the mechanism of mild cognitive impairment in PD. Key words: Parkinson＇s disease; Cognitive impairment; Positron emission tomography

Correlation between motor and cognitive functions in the progressive course of Parkinson's disease

Recognition of early cognitive impairment in Parkinsons disease (PD) is important since it represents a risk factor for developing Parkinson's disease dementia and psychosis. The Mini- Mental State Examination (MMSE) remains the most commonly used screening instrument for global cognition, even though it has not been specifically validated for use in PD subjects. More recently, the Montreal Cognitive Assessment (MoCA) test has been recommended as a better screening tool in PD. Most of these studies have been done in countries with a highly-educated population. The objective of the study is to compare the performance between the MMSE and the MoCA to screen for mild cognitive impairment in subjects with Parkinson's disease and a low education background. The MMSE and MoCA were applied to 128 subjects using a cut-off score of 26 points for cognitive impairment. Fifty-five percent were classified with cognitive impairment according to the MoCA. Forty-one percent of subjects with a normal MMSE were classified with cognitive impairment by MoCA. Results from our analysis could be directly applied to other populations with a high proportion of poorly educated subjects.

Objective: To establish the diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) when screening externally validated cognition in Parkinson disease (PD), by comparison with a PD-focused test (Scales for Outcomes in Parkinson disease–Cognition [SCOPA-COG]) and the standardized Mini-Mental State Examination (S-MMSE) as benchmarks. Methods: A convenience sample of 114 patients with idiopathic PD and 47 healthy controls was examined in a movement disorders center. The 21 patients with dementia (PD-D) were diagnosed using Movement Disorders Society criteria, externally validated by detailed independent functional and neuropsychological tests. The 21 patients with mild cognitive impairment (PD-MCI) scored 1.5 SD or more below normative data in at least 2 measures in 1 of 4 cognitive domains. Other patients had normal cognition (PD-N). Results: Primary outcomes using receiver operating characteristic (ROC) curve analyses showed that all 3 mental status tests produced excellent discrimination of PD-D from patients without dementia (area under the curve [AUC], 87%–91%) and PD-MCI from PD-N patients (AUC, 78%–90%), but the MoCA was generally better suited across both assessments. The optimal MoCA screening cutoffs were Conclusions: The MoCA is a suitably accurate, brief test when screening all levels of cognition in PD.

Prediction of cognitive progression in Parkinson's disease using three cognitive screening measures

Cognitive impairment is common in Parkinson's disease (PD). There is a critical need for a brief, standard cognitive screening measure for use in PD trials whose primary focus is not on cognition. The Parkinson Study Group (PSG) Cognitive/Psychiatric Working Group formed a Task Force to make recommendations for a cognitive scale that could screen for dementia and mild cognitive impairment in clinical trials of PD where cognition is not the primary outcome. This Task Force conducted a systematic literature search for cognitive assessments previously used in a PD population. Scales were then evaluated for their appropriateness to screen for cognitive deficits in clinical trials, including brief administration time (<15 minutes), assessment of the major cognitive domains, and potential to detect subtle cognitive impairment in PD. Five scales of global cognition met the predetermined screening criteria and were considered for review. Based on the Task Force's evaluation criteria the Montreal Cognitive Assessment (MoCA), appeared to be the most suitable measure. This Task Force recommends consideration of the MoCA as a minimum cognitive screening measure in clinical trials of PD where cognitive performance is not the primary outcome measure. The MoCA still requires further study of its diagnostic utility in PD populations but appears to be the most appropriate measure among the currently available brief cognitive assessments. Widespread adoption of a single instrument such as the MoCA in clinical trials can improve comparability between research studies on PD.