Mild Cognitive Impairment In Parkinson's Disease Research Articles

Mild cognitive impairment in Parkinson's disease: current view

Parkinson's disease (PD), the most common motor movement disorder and second most common neurodegenerative disorder after Alzheimer's disease (AD), is often preceded by a period of mild cognitive impairment (MCI), which is associated with impairment of a variety of cognitive domains including executive function, attention, visuospatial abilities and memory. MCI, a risk factor for developing dementia, affects around 30% of de novo PD patients and can increase to 75% after more than 10 years. While 30–40% remain in the MCI state, up to 60% will convert to dementia. Characteristic findings are slowing of EEG rhythms, frontotemporal hypoperfusion, decreased functional connectivity in the default mode and attentional networks, prefrontal and basal-ganglia-cortical circuits, which often manifests prior to clinical symptoms and overt brain atrophy. The heterogeneity of cognitive phenotypes suggests that a common neurodegenerative process affects multiple functional neuronal networks and neuromodulatory systems that may be superimposed by Lewy body and Alzheimer's-related or other co-pathologies. Sparse neuropathological data for PD-MCI revealed a heterogenous picture with various morphological changes similar to MCI in other diseases. This review highlights the essential epidemiological, clinical, neuroimaging and morphological changes in PD-MCI, available biomarkers, and discusses the heterogenous pathobiological mechanisms involved in its development. In view of its complex pathogenesis, well-designed longitudinal clinico-pathological studies are warranted to clarify the alterations leading to MCI in PD, which may be supported by fluid and neuroimaging biomarkers as a basis for early diagnosis and future adequate treatment modalities of this debilitating disorder.

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.

A fixel-based analysis of white matter reductions early detects Parkinson disease with mild cognitive impairment

BackgroundWhite matter (WM) tract alterations are early signs of cognitive impairment in Parkinson disease (PD) patients. Fixel-based analysis (FBA) has advantages over traditional diffusion tensor imaging in managing complex and crossing fibers. We used FBA to measure fiber-specific changes in patients with PD mild cognitive impairment (PD-MCI) and PD normal cognition (PD-NC). MethodsSeventy-one patients with PD without dementia were included: 39 PD-MCI and 32 PD-NC. All underwent diffusion-weighted imaging, clinical examinations, and tests to evaluate their cognitive function globally and in five cognitive domains. FBA was used to investigate fiber-tract alterations and compare PD-MCI with PD-NC subjects. Correlations with each cognitive test were analyzed. ResultsPatients with PD-MCI were significantly older (P = 0.044), had a higher male-to-female ratio (P = 0.006) and total Unified Parkinson's Disease Rating Scale score (P = 0.001). All fixel-based metrics were significantly reduced within the body of the corpus callosum and superior corona radiata in PD-MCI patients (family-wise error-corrected P value < 0.05) compared with PD-NC patients. The cingulum, superior longitudinal fasciculi, and thalamocortical circuit exhibited predominantly fiber-bundle cross-section (FC) changes. In regression analysis, reduced FC values in cerebellar circuits were associated with poor motor function in PD-MCI patients and poor picture-naming ability in PD-NC patients. ConclusionsPD-MCI patients have significant WM alterations compared with PD-NC patients. FBA revealed these changes in various bundle tracts, helping us to better understand specific WM changes that are functionally implicated in PD cognitive decline. FBA is potentially useful in detecting early cognitive decline in PD.

Subjective cognitive complaints are important in PD-MCI criteria: Associations with CSF markers and cognitive decline

IntroductionAccording to the Movement Disorder Society (MDS), subjective cognitive complaints (SCC) are a diagnostic criterion for PD-mild cognitive impairment (PD-MCI); however, studies often do not incorporate SCC when classifying PD-MCI. This inconsistent use may reflect mixed findings regarding the association between SCC and objective measures of cognitive impairment. Our study aimed to describe the extent that inclusion/exclusion of SCC affects the occurrence of PD-MCI, and if the inclusion of SCC is associated with faster cognitive decline and cerebrospinal fluid markers (CSF) of alpha-synuclein, amyloid beta, total tau, and phophorylated-tau. MethodsIndividuals with PD (N = 358) from the PPMI cohort whom completed measures of neuropsychological performance, subjective cognitive complaints, motor severity, and CSF markers were included. Participants were classified as cognitively normal (CN), PD-MCI with subjective cognitive complaints (PD-MCI + SCC) and PD-MCI without subjective cognitive complaints (PD-MCI –SCC). ResultsPD-MCI rates were consistently higher (16.5–19.1%) across the 5 years when SCC was not included in the diagnostic criteria as opposed to when SCC was included (4.4–11.0%). PD-MCI + SCC experienced greater cognitive decline and had significantly higher levels of tau/ab and p-tau/ab relative to both the CN and PD-MCI - SCC groups. ConclusionsInconsistent implementation of an SCC requirement in PD-MCI classifications may have important implications in terms of the occurrence of PD-MCI and its prognostic value. Classifying PD-MCI only using neuropsychological cut-off criterion, without regard to SCC, may lead to higher rates of PD-MCI. Inclusions of SCC in PD-MCI criteria in newly diagnosed PD participants may strengthen the ability to detect individuals at risk for future cognitive decline, though it is possible that this decline is related to Alzheimer's disease changes rather than worse PD pathology.

Machine learning can predict mild cognitive impairment in Parkinson's disease.

Clinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI). Finding significant features associated with PD-MCI through a machine learning approach. Patients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model. Seventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2. This study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.

Can the prediction model using regression with optimal scale improve the power to predict the Parkinson's dementia?

BACKGROUNDEfficiently detecting Parkinson's disease (PD) with dementia (PDD) as soon as possible is an important issue in geriatric medicine.AIMTo develop a model for predicting PDD based on various neuropsychological tests using data from a nationwide survey conducted by the Korean Centers for Disease Control and Prevention and to present baseline data for the early detection of PDD.METHODSThis study comprised 289 patients who were 60 years or older with PD [110 with PDD and 179 Parkinson's Disease-Mild Cognitive Impairment (PD-MCI)]. Regre-ssion with optimal scaling (ROS) was used to identify independent relationships between the neuropsychological test results and PDD.RESULTSIn the ROS analysis, Korean version of mini mental state ex-amination (MMSE) (KOREAN version of MMSE) (b = -0.52, SE = 0.16) and Hoehn and Yahr staging (b = 0.44, SE = 0.19) were significantly effective models for distinguishing PDD from PD-MCI (P < 0.05), even after adjusting for all of the Parkinson's motor symptom and neuropsychological test results. The optimal number of categories (scaling factors) for KOREAN version of MMSE and Hoehn and Yahr Scale was 10 and 7, respectively.CONCLUSIONThe results of this study suggest that among the various neuropsychological tests conducted, the optimal classification scores for KOREAN version of MMSE and Hoehn and Yahr Scale could be utilized as an effective screening test for the early discrimination of PDD from PD-MCI.

Physical inactivity is associated with Parkinson's disease mild cognitive impairment and dementia

Parkinson's disease (PD) is a neurodegenerative disorder marked by both motor symptoms and cognitive impairment. Individuals with PD are generally less physically active relative to healthy older adults. This study examined if physical activity predicted future PD mild cognitive impairment (PD-MCI) and PD dementia (PDD).Participants were part of the Parkinson's Progression Markers Initiative (PPMI), a longitudinal cohort study of individuals newly diagnosed with PD. 307 participants were followed for up to three years. Individuals were classified as cognitively normal (CN), PD mild cognitive impairment (PD-MCI), or PD dementia (PDD) at each year. A self-report measure of physical activity was completed annually. Ordinal multilevel models were computed to examine the longitudinal relationship between physical activity and cognitive status. Less self-reported engagement in physical activities predicted worse cognitive status. Specifically, we found evidence of a significant within-person effect. Individuals with PD who became less active were at greater risk of developing PD-MCI or PDD. This effect was driven by longitudinal decreases in leisure/recreational physical activities. Findings support the hypothesis that longitudinal changes in physical activity are associated with clinically meaningful diagnostic outcomes of cognitive status. Since individuals with PD are prone to sedentary behaviors, future studies are needed to determine the clinical utility of interventions promoting routine physical activity.

Prevalence and affective correlates of subjective cognitive decline in patients with de novo Parkinson's disease.

ObjectivesThe novel concept of subjective cognitive decline (SCD) in Parkinson's disease (PD) refers to subjective cognitive impairment without concurrent objective cognitive deficits. This study aimed to determine the prevalence and affective correlates of SCD in de novo PD patients.Materials and MethodsA total of 139 de novo PD patients underwent comprehensive neuropsychological evaluation. PD patients with SCD (PD‐SCD) did not meet the diagnostic criteria for mild cognitive impairment in PD (PD‐MCI) based on the Movement Disorder Society Level II Criteria and were defined by a Domain‐5 Score ≥1 on the Non‐Motor Symptoms Questionnaire. Affective symptoms were measured using the Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA).ResultsIn de novo PD cohort, the prevalence of SCD was 28.1%. PD‐SCD patients performed significantly better than PD‐MCI patients on tests of five cognitive domains. The more commonly affected domains in PD‐SCD patients were memory (28.2%) and attention/working memory (25.6%). Multivariable linear regression analysis revealed that PD‐SCD was significantly associated with both HAMD (β = 4.518, 95% CI = 0.754–8.281, p = .019) and HAMA scores (β = 4.259, 95% CI = 1.054–7.464, p = .010). Furthermore, binary logistic regression analysis revealed that higher HAMD (OR = 1.128, 95% CI = 1.019–1.249, p = .020) and HAMA scores (OR = 1.176, 95% CI = 1.030–1.343, p = .017) increased the risk of PD‐SCD.ConclusionsOur findings suggest that SCD is highly prevalent in de novo PD patients. The presence of PD‐SCD is suggestive of an underlying affective disorder.

Dissociable contribution of plasma NfL and p-tau181 to cognitive impairment in Parkinson's disease

BackgroundCognitive dysfunction is a disabling complication in Parkinson's disease (PD). Accuracy of diagnosis of mild cognitive impairment in PD (PD-MCI) depends on the tests performed, which limits results generalization. Blood-based biomarkers could provide additional objective information for PD-MCI diagnosis and progression. Blood neurofilament light chain (NfL), a marker of neuronal injury, has shown good performance for PD disease stratification and progression. While NfL is not disease-specific, phosphorylated-tau at threonine-181 (p-tau181) in blood is a highly specific marker of concomitant brain amyloid-β and tau pathology. MethodsWe investigated the potential of plasma NfL and p-tau181 levels as markers of cognitive impairment in a prospective cohort of 109 PD patients with and without PD-MCI (age 68.1 ± 7 years, education 12.2± 5 years), and 40 comparable healthy controls. After a follow-up of 4 years, we evaluated their predictive value for progression to dementia. ResultsAlthough NfL and p-tau181 levels were significantly increased in PD compared with healthy controls, only NfL levels were significantly higher in PD-MCI compared with PD with normal cognition (PD-NC) at baseline. After a follow-up of 4 years, only NfL predicted progression to dementia (HR 1.23, 95% CI 1.02–1.53; p = 0.038). Significant correlations between fluid biomarkers and neuropsychological examination were only found with NfL levels. ConclusionsPlasma NfL levels objectively differentiates PD-MCI from PD-NC patients, and may serve as a plasma biomarker for predicting progression to dementia in PD. Plasma levels of p-tau181 does not seem to help in differentiating PD-MCI or to predict future cognitive deterioration.

Does transcranial direct current stimulation enhance cognitive performance in Parkinson's disease mild cognitive impairment? An event-related potentials and neuropsychological assessment study.

Parkinson's disease-mild cognitive impairment (PD-MCI) is garnering attention as a key interventional period for cognitive impairment. Currently, there are no approved treatments for PD-MCI and encouraging results of transcranial direct current stimulation (tDCS) combined with other interventions have been proposed, though the efficacy and neural mechanisms of tDCS alone have not been studied in PD-MCI yet. The present double-blind, randomized, sham-controlled study assessed the effects of tDCS over the dorsolateral prefrontal cortex on cognitive functions via neuropsychological and electrophysiological evaluations in individuals with PD-MCI for the first time. Twenty-six individuals with PD-MCI were administered 10 sessions of active (n = 13) or sham (n = 13) prefrontal tDCS twice a day, for 5days. Changes were tested through a comprehensive neuropsychological battery and event-related potential recordings, which were performed before, immediately, and 1month after the administrations. Neuropsychological assessment showed an improvement in delayed recall and executive functions in the active group. N1 amplitudes in response to targets in the oddball test-likely indexing attention and discriminability and NoGo N2 amplitudes in the continuous performance test-likely indexing cognitive control and conflict monitoring increased in the active group. Active stimulation elicited higher benefits 1month after the administrations. The present findings substantiate the efficacy of tDCS on cognitive control and episodic memory, along with the neural underpinnings of cognitive control, highlighting its potential for therapeutic utility in PD-MCI. NCT 04,171,804. Date of registration: 21/11/2019.

Effect of cognitive reserve on cognitive function in Parkinson's disease.

Previous studies showed inconsistent results for the correlation between cognitive reserve (CR) and cognitive function in Parkinson's disease (PD). Additionally, conflicting results were obtained for the association between CR and risk of longitudinal cognitive decline, longitudinal progression to mild cognitive impairment (MCI), and longitudinal progression to dementia in PD patients. Thus, a meta-analysis is essential to summary these inconsistent results. Articles published before November 2021 were searched in databases as follows: PubMed, Web of Science, Medline, EMBASE, and Google Scholar. We computed Fisher's z score and standard error (SE) of each transformation value of correlation coefficient for the correlation between educational level and cognitive function. Additionally, odds ratios (ORs) or hazard ratios (HRs) and their 95% confidence intervals (CIs) were computed as effect sizes for the correlation between educational level and risk of longitudinal progression to MCI. The present study showed that higher educational levels were related to better general cognitive function, executive function, memory, and information processing speed in PD patients, whereas no significant association was showed between educational levels and visuospatial function, language in PD patients. Additionally, included studies reported a negative association between educational level and risk of longitudinal progression to MCI in PD patients. In conclusion, the study demonstrated that higher CR might be correlated with better cognitive function and lower risk of longitudinal progression to MCI in PD. In addition, large-scale prospective studies are necessary to explore the effect of CR on cognitive function in PD.

Resting-State Functional Connectivity in Frontostriatal and Posterior Cortical Subtypes in Parkinson's Disease-Mild Cognitive Impairment.

The "dual syndrome hypothesis" distinguished two subtypes in mild cognitive impairment (MCI) in Parkinson's disease: frontostriatal, characterized by attentional and executive deficits; and posterior cortical, characterized by visuospatial, memory, and language deficits. The aim was to identify resting-state functional modifications associated with these subtypes. Ninety-five nondemented patients categorized as having normal cognition (n=31), frontostriatal (n=14), posterior cortical (n=20), or mixed (n=30) cognitive subtype had a 3 T resting-state functional magnetic resonance imaging scan. Twenty-four age-matched healthy controls (HCs) were also included. A group-level independent component analysis was performed to identify resting-state networks, and the selected components were subdivided into 564 cortical regions in addition to 26 basal ganglia regions. Global intra- and inter-network connectivity along with global and local efficiencies was compared between groups. The network-based statistics approach was used to identify connections significantly different between groups. Patients with posterior cortical deficits had increased intra-network functional connectivity (FC) within the basal ganglia network compared with patients with frontostriatal deficits. Patients with frontostriatal deficits had reduced inter-network FC between several networks, including the visual, default-mode, sensorimotor, salience, dorsal attentional, basal ganglia, and frontoparietal networks, compared with HCs, patients with normal cognition, and patients with a posterior cortical subtype. Similar results were also found between patients with a mixed subtype and HCs. MCI subtypes are associated with specific changes in resting-state FC. Longitudinal studies are needed to determine the predictive potential of these markers regarding the risk of developing dementia. © 2021 International Parkinson and Movement Disorder Society.

A-75 Effects of Rivastigmine on Neurocognitive Deficits in Patients with Parkinson’s Disease

Abstract Objective Cognitive impairments are commonly seen in Parkinson’s disease (PD). However, identification and tracking of cognitive deficits are not always part of treatment plans. Cholinergic treatment with rivastigmine has demonstrated beneficial effects on cognition and gait stability in PD-dementia, but less evidence exists in PD-mild cognitive impairment. We investigated the cognitive effects of rivastigmine treatment in a 3-month open-label pilot study. Method 31 participants with PD and mild–moderate cognitive impairment (24 male; mean age = 71.7; mean years-of-education = 17.2; mean Montreal Cognitive Assessment (MoCA) score = 21.7) completed pre-testing in a single-site, non-randomized study at the University of Maryland Parkinson’s Disease and Movement Disorders Center. A subset of 12 patients returned for follow-up after 12 weeks of rivastigmine treatment. A physical examination, the MoCA, and a computerized cognitive measure (NeuroTrax) were completed at each session. It was hypothesized that rivastigmine would benefit cognition, particularly executive functioning. Results Rivastigmine benefited global cognitive functioning as measured by both the MoCA (t(10) = −2.5, p &amp;lt; 0.05; M(Time 1) = 22.6(2.2), M(Time 2) = 24.9(3.9)) and NeuroTrax (t(11) = −3.0, p &amp;lt; 0.05; M(Time 1) = 88.7(13.6), M(Time 2) = 95.5(11.6)), though no domain-specific changes were evident. Relationships among the two measures were also examined. Moderate correlations were found between MoCA total scores and NeuroTrax measures including Global Cognitive Scale (r = 0.40, p &amp;lt; 0.05), Visuospatial Functioning (r = 0.39, p &amp;lt; 0.05), Executive Functioning (r = 0.50, p &amp;lt; 0.005), and Motor Response (speed/planning; r = 0.51, p &amp;lt; 0.005). Conclusions Although small sample size and practice effects must be considered, results suggest potential global cognitive benefit of rivastigmine for patients with PD experiencing mild–moderate cognitive deficits. Treatment planning for all PD patients should include periodic cognitive screenings and consideration of treatment options.

Regional Neural Activity Changes in Parkinson’s Disease-Associated Mild Cognitive Impairment and Cognitively Normal Patients

PurposeThe aim of this study was to compare regional homogeneity (ReHo) changes in Parkinson’s disease mild cognitive impairment (PD-MCI) patients with respect to normal controls (NC) and those with cognitively normal PD (PD-CN). Further, the study investigated the relationship between ReHo changes in PD patients and neuropsychological variation.Patients and MethodsThirty PD-MCI, 19 PD-CN, and 21 NC subjects were enrolled. Resting state functional magnetic resonance imaging data of all subjects were collected, and regional brain activity was measured for ReHo. Analysis of covariance for ReHo was determined between the PD-MCI, PD-CN, and NC groups. Spearman rank correlations were assessed using the ReHo maps and data from the neuropsychological tests.ResultsIn comparison with NC, PD-CN patients showed significantly higher ReHo values in the right middle frontal gyrus (MFG) and lower ReHo values in the left supramarginal gyrus, bilateral inferior parietal lobule (IPL), and the right postcentral gyrus (PCG). In comparison with PD-CN patients, PD-MCI patients displayed significantly higher ReHo values in the right PCG, left middle occipital gyrus (MOG) and IPL. No significant correlation between ReHo indices and the neuropsychological scales was observed.ConclusionOur finding revealed that decreases in ReHo in the default mode network (DMN) may appear before PD-related cognitive impairment. In order to preserve executive attention capacity, ReHo in the right MFG in PD patients lacking cognition impairment increased for compensation. PD-MCI showed increased ReHo in the left MOG, which might have been caused by visual and visual-spatial dysfunction, and increased ReHo in the left IPL, which might reflect network disturbance and induce cognition deficits.

Multiculturalism: A Challenge for Cognitive Screeners in Parkinson's Disease.

The Montreal Cognitive Assessment (MoCA) and the Dementia Rating Scale-2 (DRS-2) are recommended screeners for Parkinson's disease mild cognitive impairment (PD-MCI). Cross-cultural studies examining their diagnostic precision have not addressed cultural bias in a multicultural setting. To compare DRS-2 and MoCA performance between patients born in Canada, the USA, and the UK (Anglosphere group) and immigrant patients born elsewhere (International group). To identify sources of cultural bias by comparing group characteristics, and by assessing the relationships between performance and immigration and socio-development variables. To examine the diagnostic precision of both tools in detecting PD-MCI in each group. We conducted a clinical chart review of advanced PD patients who completed cognitive screeners (MoCA: n=288, 30% International group; DRS-2: n=426, 31% International group). All completed a comprehensive neuropsychological assessment to apply Level II PD-MCI diagnostic criteria. The International group performed worse than the Anglosphere group on the MoCA and DRS-2, and the only variable that accounted for some of the group difference was the Historical Index of Human Development, a societal variable, which fully mediated the group effect on the DRS-2. Diagnostic precision of the MoCA was at chance level in the International group, and was poorer than that of the DRS-II in this group and that of the MoCA in the Anglosphere group, although these were considered poor. Our results support the recommendation to exert caution in using cognitive screeners to capture PD-MCI in all patients and particularly with first generation immigrants.

Mild Cognitive Impairment in de novo Parkinson's Disease: Selective Attention Deficit as Early Sign of Neurocognitive Decay.

Background: In the present study, we aimed to better investigate attention system profile of Parkinson's disease-Mild Cognitive Impairment (PD-MCI) patients and to determine if specific attentional deficits are associated with 123I-FP-CIT SPECT.Methods: A total of 44 de novo drug-naïve PD patients [(27) with normal cognition (PD-NC) and 17 with MCI (PD-MCI)], 23 MCI patients and 23 individuals with subjective cognitive impairment (SCI) were recruited at the Clinical Neurology Unit of Santa Chiara hospital (Pisa University Medical School, Italy). They were assessed by a wide neuropsychological battery, including Visual Search Test (VST) measuring selective attention. Performances among groups were compared by non-parametric tests (i.e., Kruskal-Wallis and Mann-Whitney, Bonferroni corrected). Further, Spearman's rank correlations were performed to explore the association between neuropsychological variables and 123I-FP-CIT SPECT data in PD subgroup.Results: PD-MCI patients performed worse on VST than patients with PD-NC (p = 0.002), patients with MCI and individuals with SCI (p < 0.001). The performance of PD-MCI patients on VST significantly correlated with caudate nucleus 123I-FP-CIT SPECT uptake (rho = 0.582, p < 0.05), whereas a negative correlation between such test and 123I-FP-CIT SPECT uptake in the left putamen (rho = −0.529, p < 0.05) was found in PD-NC patients.Conclusions: We suggest that selective attention deficit might be a trigger of cognitive decay in de novo PD-MCI patients. The VST should be routinely used to detect attentional deficits in hospital clinical practice, in the light of its closely association with dopamine depletion of basal ganglia in mildly impaired PD patients.

Exploring Parkinson’s Disease Predictors based on Basic Intelligence Quotient and Executive Intelligence Quotient

It is important to identify the risk factors of dementia and prevent them for the health of patients and caregivers. This study (1) explored sampling methods that could minimize overfitting due to data imbalance using a data-level approach, (2) developed nine ensemble learning models for predicting Parkinson's Disease–Mild Cognitive Impairment (PD-MCI) ((undersampling, oversampling, and SMOTE) × (boosting, bagging, and random forest)=9), and (3) compared the accuracies, sensitivities, and specificities of these models to understand the prediction performance of the developed models. We examined 368 subjects: 320 healthy elderly people (≥60 and ≤74 years old) without Parkinson's disease (168 men and 152 women) and 48 subjects with PD-MCI (20 men and 28 women). This study used the Cognition Scale for Olde Adults (CSOA), which could measure cognitive functions comprehensively while considering age and education level, to determine the specific cognitive level of the subject. Our study developed nine prediction models ((undersampling, oversampling, and SMOTE) × (boosting, bagging, and random forest)=9) for developing a model to predict PD-MCI based on basic intelligence quotient and executive intelligence quotient. The analysis results showed that a random forest classifier with SMOTE had the best prediction performance with a sensitivity of 69.2%, a specificity of 75.7%, and a mean overall accuracy of 74.0%. In this final model, digit span test-backward, stroop test-interference trial, verbal memory test-delayed recall, verbal fluency test, and confrontation naming test were identified as the key variables with high weight in predicting PD-MCI. The results of this study implied that a random forest classifier with SMOTE could produce models with higher accuracy than a bagging classifier with SMOTE or a boosting classifier with SMOTE when analyzing imbalanced data.

Slow Wave Sleep and EEG Delta Spectral Power are Associated with Cognitive Function in Parkinson's Disease.

Cognitive and sleep dysfunction are common non-motor symptoms in Parkinson's disease (PD). Determine the relationship between slow wave sleep (SWS) and cognitive performance in PD. Thirty-two PD participants were evaluated with polysomnography and a comprehensive level II neurocognitive battery, as defined by the Movement Disorders Society Task Force for diagnosis of PD-mild cognitive impairment. Raw scores for each test were transformed into z-scores using normative data. Z-scores were averaged to obtain domain scores, and domain scores were averaged to determine the Composite Cognitive Score (CCS), the primary outcome. Participants were grouped by percent of SWS into High SWS and Low SWS groups and compared on CCS and other outcomes using 2-sided t-tests or Mann-Whitney U. Correlations of cognitive outcomes with sleep architecture and EEG spectral power were performed. Participants in the High SWS group demonstrated better global cognitive function (CCS) (p = 0.01, effect size: r = 0.45). In exploratory analyses, the High SWS group showed better performance in domains of executive function (effect size: Cohen's d = 1.05), language (d = 0.95), and processing speed (d = 1.12). Percentage of SWS was correlated with global cognition and executive function, language, and processing speed. Frontal EEG delta power during N3 was correlated with the CCS and executive function. Cognition was not correlated with subjective sleep quality. Increased SWS and higher delta spectral power are associated with better cognitive performance in PD. This demonstrates the significant relationship between sleep and cognitive function and suggests that interventions to improve sleep might improve cognition in individuals with PD.

A novel dopamine D3R agonist SK609 with norepinephrine transporter inhibition promotes improvement in cognitive task performance in rodent and non-human primate models of Parkinson's disease

Mild cognitive impairment is present in a number of neurodegenerative disorders including Parkinson's disease (PD). Mild cognitive impairment in PD (PD-MCI) often manifests as deficits in executive functioning, attention, and spatial and working memory. Clinical studies have suggested that the development of mild cognitive impairment may be an early symptom of PD and may even precede the onset of motor impairment by several years. Dysfunction in several neurotransmitter systems, including dopamine (DA), norepinephrine (NE), may be involved in PD-MCI, making it difficult to treat pharmacologically. In addition, many agents used to treat motor impairment in PD may exacerbate cognitive impairment. Thus, there is a significant unmet need to develop therapeutics that can treat both motor and cognitive impairments in PD. We have recently developed SK609, a selective, G-protein biased signaling agonist of dopamine D3 receptors. SK609 was successfully used to treat motor impairment and reduce levodopa-induced dyskinesia in a rodent model of PD. Further characterization of SK609 suggested that it is a selective norepinephrine transporter (NET) inhibitor with the ability to increase both DA and NE levels in the prefrontal cortex. Pharmacokinetic analysis of SK609 under systemic administration demonstrated 98% oral bioavailability and high brain distribution in striatum, hippocampus and prefrontal cortex. To evaluate the effects of SK609 on cognitive deficits of potential relevance to PD-MCI, we used unilateral 6-hydroxydopamine (6-OHDA) lesioned rats and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated cynomolgus macaques, with deficits in performance in a sustained attention and an object retrieval task, respectively. SK609 dose dependently improved the performance of 6-OHDA-lesioned rats, with peak performance achieved using a 4 mg/kg dose. This improvement was predominantly due to a significant reduction in the number of misses and false alarm errors, contributing to an increase in sustained attention. In MPTP-lesioned monkeys, this same dose also improved performance in an object retrieval task, significantly reducing cognitive errors (barrier reaches) and motor errors (fine motor dexterity problems). These data demonstrate that SK609 with its unique pharmacological effects on modulating both DA and NE can ameliorate cognitive impairment in PD models and may provide a therapeutic option to treat both motor and cognitive impairment in PD patients.

Urinary kynurenine as a biomarker for Parkinson's disease.

To examine whether urine kynurenine (KYN) levels were associated with early-stage Parkinson's disease (PD), as well as the value of urine KYN as a potential biomarker in early-stage PD. Eighty-two participants including 41 PD patients and 41 healthy controls were enrolled into this study. Urine KYN levels were measured with a KYN enzyme-linked immunoassay kit. In order to explore the correlation between some clinical parameters and urine KYN, the clinical parameters for these participants were recorded. Diagnostic value and clinical relevance of urine KYN were assessed by using receiver operator characteristic (ROC) curve and correlation analysis. Urine KYN levels were significantly higher in the PD group than in the healthy group (891.95 ± 276.65pg/ml vs. 640.11 ± 122.37pg/ml, p = 0.000). The correlations between urine KYN levels and clinical parameters are as follows: Hoehn-Yahr stage (r = 0.676, p = 0.000), disease duration (r = 0.772, p = 0.000), Mini-Mental State Examination scores (r = -0.434, p = 0.005). There was no statistically significant correlation between urine KYN with age, low-density cholesterol (LDL), triglycerides (TG), cholesterol (TC), homocysteine (HCY), uric acid (UA), and glomerular filtration rate (GFR). The ROC analysis showed that urine KYN optimal cutoff value of 751.88pg/ml had a sensitivity of 65.9% and a specificity of 90.2% for distinguishing between PD and controls, with an area under the curve (AUC) of 0.776. Urine KYN were significantly associated with PD severity and mild cognitive impairment. Urine KYN may be a new biomarker for early-stage PD.