Mild cognitive impairment reverters have a favorable cognitive prognosis and cortical integrity in Parkinson's disease

Hypoperfusion in Alzheimer's Disease-Prone Regions and Dementia Conversion in Parkinson's Disease

PURPOSE OF THE REPORT

Although early detection of individuals at risk of dementia conversion is important in patients with Parkinson's disease (PD), there is still no consensus on neuroimaging biomarkers for predicting future cognitive decline. We aimed to investigate whether cerebral perfusion patterns on early-phase 18 F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ( 18 F-FP-CIT) PET have the potential to serve as a neuroimaging predictor for early dementia conversion in patients with PD.

MATERIALS AND METHODS

In this retrospective analysis, we enrolled 187 patients with newly diagnosed PD who underwent dual-phase 18 F-FP-CIT PET at initial assessment and serial cognitive assessments during the follow-up period (>5 years). Patients with PD were classified into 2 groups: the PD with dementia (PDD)-high-risk (PDD-H; n = 47) and the PDD-low-risk (PDD-L; n = 140) groups according to dementia conversion within 5 years of PD diagnosis. We explored between-group differences in the regional uptake in the early-phase 18 F-FP-CIT PET images. We additionally performed a linear discriminant analysis to develop a prediction model for early PDD conversion.

RESULTS

The PDD-H group exhibited hypoperfusion in Alzheimer's disease (AD)-prone regions (inferomedial temporal and posterior cingulate cortices, and insula) compared with the PDD-L group. A prediction model using regional uptake in the right entorhinal cortex, left amygdala, and left isthmus cingulate cortex could optimally distinguish the PDD-H group from the PDD-L group.

CONCLUSIONS

Regional hypoperfusion in the AD-prone regions on early-phase 18 F-FP-CIT PET can be a useful biomarker for predicting early dementia conversion in patients with PD.

An Examination of Cognitive Heterogeneity in Parkinson Disease: The Dual-Syndrome Hypothesis

PURPOSE

Cognitive impairment is one of the most debilitating nonmotor symptoms in Parkinson disease (PD), and its presentation is heterogeneous. One proposed model to explain cognitive variability in PD is the dual-syndrome hypothesis. This hypothesis delineates two cognitive profiles, a "fronto-striatal" profile and a "posterior cortical" profile according to symptom presentation, associated motor phenotype, and risk for dementia. The current study examined the dual-syndrome hypothesis in individuals with idiopathic PD to evaluate the existence of these profiles, determine the association with the motor phenotype (tremor dominant vs. postural instability/gait disorder), and assess the relative risk for dementia.

METHOD

A retrospective examination was conducted using data from the Parkinson's Progression Markers Initiative database at baseline (within 2 years of diagnosis) and 5 years after baseline. Descriptive categorizations, cluster analyses, generalized linear mixed models, and logistic regressions were used to address the research questions.

RESULTS

There was emerging evidence of cognitive profiles; however, these were not fully supported by cluster analyses. Baseline cognitive profile was associated with later motor phenotype, and as predicted, dementia risk was greatest in persons with baseline posterior cortical impairments.

CONCLUSION

The current results provide mixed support for the dual-syndrome hypothesis, with some evidence that the posterior cortical cognitive profile is associated with postural instability and gait disorder as well as greater dementia risk.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

Predictors of Cognitive Change in Parkinson Disease: A 2-year Follow-up Study

BACKGROUND

Mild cognitive impairment is common in Parkinson disease (PD-MCI). However, instability in this clinical diagnosis and variability in rates of progression to dementia raises questions regarding its utility for longitudinal tracking and prediction of cognitive change in PD. We examined baseline neuropsychological test and cognitive diagnosis predictors of cognitive change in PD.

METHODS

Persons with PD, without dementia PD (N=138) underwent comprehensive neuropsychological assessment at baseline and were followed up to 2 years. Level II Movement Disorder Society criteria for PD-MCI and PD dementia (PDD) were applied annually. Composite global and domain cognitive z -scores were calculated based on a 10-test neuropsychological battery.

RESULTS

Baseline diagnosis of PD-MCI was not associated with a change in global cognitive z -scores. Lower baseline attention and higher executive domain z -scores were associated with greater global cognitive z -score worsening regardless of cognitive diagnosis. Worse baseline domain z -scores in the attention and language domains were associated with progression to MCI or PDD, whereas higher baseline scores in all cognitive domains except executive function were associated with clinical and psychometric reversion to "normal" cognition.

CONCLUSIONS

Lower scores on cognitive tests of attention were predictive of worse global cognition over 2 years of follow-up in PD, and lower baseline attention and language scores were associated with progression to MCI or PDD. However, PD-MCI diagnosis per se was not predictive of cognitive decline over 2 years. The association between higher executive domain z -scores and greater global cognitive worsening is probably a spurious result.

An interpretable multiparametric radiomics model of basal ganglia to predict dementia conversion in Parkinson's disease

Cognitive impairment in Parkinson's disease (PD) severely affects patients' prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve the prediction of dementia development in PD when integrated with clinical profiles. In this retrospective study, 262 patients with newly diagnosed PD (June 2008-July 2017, follow-up >5 years) were included. MRI radiomic features (n = 1284) were extracted from bilateral caudate and putamen. Two models were developed to predict dementia development: (1) a clinical model-age, disease duration, and cognitive composite scores, and (2) a combined clinical and radiomics model. The area under the receiver operating characteristic curve (AUC) were calculated for each model. The models' interpretabilities were studied. Among total 262 PD patients (mean age, 68 years ± 8 [standard deviation]; 134 men), 51 (30.4%), and 24 (25.5%) patients developed dementia within 5 years of PD diagnosis in the training (n = 168) and test sets (n = 94), respectively. The combined model achieved superior predictive performance compared to the clinical model in training (AUCs 0.928 vs. 0.894, P = 0.284) and test set (AUCs 0.889 vs. 0.722, P = 0.016). The cognitive composite scores of the frontal/executive function domain contributed most to predicting dementia. Radiomics derived from the caudate were also highly associated with cognitive decline. Multiparametric MRI radiomics may have an incremental prognostic value when integrated with clinical profiles to predict future cognitive decline in PD.

Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) has been thought of as the transitional stage between normal ageing and Alzheimer's disease, involving substantial changes in brain grey matter structures. As most previous studies have focused on single regions (e.g. the hippocampus) and their changes during MCI development and reversion, the relationship between grey matter covariance among distributed brain regions and clinical development and reversion of MCI remains unclear.

METHODS

With samples from two independent studies (155 from the Beijing Aging Brain Rejuvenation Initiative and 286 from the Alzheimer's Disease Neuroimaging Initiative), grey matter covariance of default, frontoparietal, and hippocampal networks were identified by seed-based partial least square analyses, and random forest models were applied to predict the progression from normal cognition to MCI (N-t-M) and the reversion from MCI to normal cognition (M-t-N).

RESULTS

With varying degrees, the grey matter covariance in the three networks could predict N-t-M progression (AUC = 0.692-0.792) and M-t-N reversion (AUC = 0.701-0.809). Further analyses indicated that the hippocampus has emerged as an important region in reversion prediction within all three brain networks, and even though the hippocampus itself could predict the clinical reversion of M-t-N, the grey matter covariance showed higher prediction accuracy for early progression of N-t-M.

CONCLUSIONS

Our findings are the first to report grey matter covariance changes in MCI development and reversion and highlight the necessity of including grey matter covariance changes along with hippocampal degeneration in the early detection of MCI and Alzheimer's disease.

Cerebral cortical thinning in Parkinson's disease depends on the age of onset

Patients with older-onset Parkinson's disease (PD) have more severe motor symptoms, faster progression, and a worse prognosis. The thinning of the cerebral cortex is one of the causes of these issues. Patients with older-onset PD manifest more extended neurodegeneration associated with α-synuclein deposition in the cerebral cortex; however, the cortical regions that undergo thinning are unclear. We aimed to identify cortical regions with different thinning depending on the age of onset in patients with PD. Sixty-two patients with PD were included in this study. Patients with PD onset at <63 years old were included in the early or middle-onset PD group, and those with PD onset at >63 years old were included in the late-onset PD (LOPD) group. Brain magnetic resonance imaging data of these patients were processed using FreeSurfer to measure their cortical thickness. The LOPD group displayed less cortical thickness in the superior frontal gyrus, middle frontal gyrus, precentral gyrus, postcentral gyrus, superior temporal gyrus, temporal pole, paracentral lobule, superior parietal lobule, precuneus, and occipital lobe than the early or middle-onset PD group. Compared with patients with early and middle-onset PD, elderly patients displayed extended cortical thinning with disease progression. Differences in the clinical manifestations of PD according to the age of onset were partly due to variations in the morphological changes in the brain.

Association Between White Matter Networks and the Pattern of Striatal Dopamine Depletion in Patients With Parkinson Disease

OBJECTIVES

Individual variability in nigrostriatal dopaminergic denervation is an important factor underlying clinical heterogeneity in Parkinson disease (PD). This study aimed to explore whether the pattern of striatal dopamine depletion was associated with white matter (WM) networks in PD.

METHODS

A total of 240 newly diagnosed patients with PD who underwent 18F-FP-CIT PET scans and brain diffusion tensor imaging at initial assessment were enrolled in this study. We measured 18F-FP-CIT tracer uptake as an indirect marker for striatal dopamine depletion. Factor analysis-derived striatal dopamine loss patterns were estimated in each patient to calculate the composite scores of 4 striatal subregion factors (caudate, more-affected and less-affected sensorimotor striata, and anterior putamen) based on the availability of striatal dopamine transporter. The WM structural networks that were correlated with the composite scores of each striatal subregion factor were identified using a network-based statistical analysis.

RESULTS

A higher composite score of caudate (i.e., relatively preserved dopaminergic innervation in the caudate) was associated with a strong structural connectivity in a single subnetwork comprising the left caudate and left frontal gyri. Selective dopamine loss in the caudate was associated with strong connectivity in the structural subnetwork whose hub nodes were bilateral thalami and left insula, which were connected to the anterior cingulum. However, no subnetworks were correlated with the composite scores of other striatal subregion factors. The connectivity strength of the network with a positive correlation with the composite score of caudate affected the frontal/executive function either directly or indirectly through the mediation of dopamine depletion in the caudate.

CONCLUSIONS

Our findings indicate that different patterns of striatal dopamine depletion are closely associated with WM structural alterations, which may contribute to heterogeneous cognitive profiles in individuals with PD.

Brain functional network abnormalities in parkinson's disease with mild cognitive impairment

Mild cognitive impairment in Parkinson's disease (PD-M) is related to a high risk of dementia. This study explored the whole-brain functional networks in early-stage PD-M. Forty-one patients with PD classified as cognitively normal (PD-N, n = 17) and PD-M (n = 24) and 24 demographically matched healthy controls (HC) underwent clinical and neuropsychological evaluations and resting-state functional magnetic resonance imaging. The global, regional, and modular topological characteristics were assessed in the brain functional networks, and their relationships to cognitive scores were tested. At the global level, PD-M and PD-N exhibited higher characteristic path length and lower clustering coefficient, local and global efficiency relative to HC. At the regional level, PD-M and PD-N showed lower nodal centrality in sensorimotor regions relative to HC. At the modular level, PD-M showed lower intramodular connectivity in default mode and cerebellum modules, and lower intermodular connectivity between default mode and frontoparietal modules than PD-N, correlated with Montreal Cognitive Assessment scores. Early-stage PD patients showed weaker small-worldization of brain networks. Modular connectivity alterations were mainly observed in patients with PD-M. These findings highlight the shared and distinct brain functional network dysfunctions in PD-M and PD-N, and yield insight into the neurobiology of cognitive decline in PD.

Beta amyloid deposition and cognitive decline in Parkinson's disease: a study of the PPMI cohort

The accumulation of beta amyloid in the brain has a complex and poorly understood impact on the progression of Parkinson's disease pathology and much controversy remains regarding its role, specifically in cognitive decline symptoms. Some studies have found increased beta amyloid burden is associated with worsening cognitive impairment in Parkinson's disease, especially in cases where dementia occurs, while other studies failed to replicate this finding. To better understand this relationship, we examined a cohort of 25 idiopathic Parkinson's disease patients and 30 healthy controls from the Parkinson's Progression Marker Initiative database. These participants underwent [¹⁸F]Florbetaben positron emission tomography scans to quantify beta amyloid deposition in 20 cortical regions. We then analyzed this beta amyloid data alongside the longitudinal Montreal Cognitive Assessment scores across 3 years to see how participant's baseline beta amyloid levels affected their cognitive scores prospectively. The first analysis we performed with these data was a hierarchical cluster analysis to help identify brain regions that shared similarity. We found that beta amyloid clusters differently in Parkinson's disease patients compared to healthy controls. In the Parkinson's disease group, increased beta amyloid burden in cluster 2 was associated with worse cognitive ability, compared to deposition in clusters 1 or 3. We also performed a stepwise linear regression where we found an adjusted R² of 0.495 (49.5%) in a model explaining the Parkinson's disease group's Montreal Cognitive Assessment score 1-year post-scan, encompassing the left gyrus rectus, the left anterior cingulate cortex, and the right parietal cortex. Taken together, these results suggest regional beta amyloid deposition alone has a moderate effect on predicting future cognitive decline in Parkinson's disease patients. The patchwork effect of beta amyloid deposition on cognitive ability may be part of what separates cognitive impairment from cognitive sparing in Parkinson's disease. Thus, we suggest it would be more useful to measure beta amyloid burden in specific brain regions rather than using a whole-brain global beta amyloid composite score and use this information as a tool for determining which Parkinson's disease patients are most at risk for future cognitive decline.

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

BACKGROUND

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.

OBJECTIVES

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.

METHOD

Twenty-six individuals with PD-MCI were administered 10 sessions of active (n = 13) or sham (n = 13) prefrontal tDCS twice a day, for 5 days. Changes were tested through a comprehensive neuropsychological battery and event-related potential recordings, which were performed before, immediately, and 1 month after the administrations.

RESULTS

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 1 month after the administrations.

CONCLUSION

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.

TRIAL REGISTRATION

NCT 04,171,804. Date of registration: 21/11/2019.

Association Between White Matter Connectivity and Early Dementia in Patients With Parkinson Disease

OBJECTIVES

Several clinical and neuroimaging biomarkers have been proposed to identify individuals with Parkinson's disease (PD) who are at risk for ongoing cognitive decline. This study aimed to explore whether white matter (WM) connectivity disruption is associated with dementia conversion in patients with newly diagnosed PD with mild cognitive impairment (PD-MCI).

METHODS

Seventy-five patients with drug-naïve PD-MCI who underwent serial cognitive assessments during the follow-up period (>5 years) were enrolled for the neuroimaging analyses. The patients were classified into either the PD with dementia (PDD) high-risk group (PDD-H, n = 38) or low-risk group (PDD-L, n = 37), depending on whether they converted to dementia within 5 years of PD diagnosis. We conducted degree-based statistic analyses based on a graph-theoretical concept to identify the subnetworks whose WM connectivity was disrupted in the PDD-H group compared with the PDD-L group.

RESULTS

The PDD-H group showed poorer cognitive performance on frontal/executive, visual memory/visuospatial, and attention/working memory/language function than the PDD-L group at baseline assessment. The PDD-H group exhibited more severely disrupted WM connectivity in both frontal and posterior cortical regions with eight hub nodes in the degree-based statistic analysis. The strength of structural connectivity within the identified subnetworks was correlated with the composite scores of frontal/executive function domain (γ = 0.393) and the risk score of PDD conversion within 5 years (γ = -0.480).

CONCLUSIONS

This study demonstrated that disrupted WM connectivity in frontal and posterior cortical regions, which correlated with frontal/executive dysfunction, is associated with early dementia conversion in PD-MCI.

T-MoCA: A valid phone screen for cognitive impairment in diverse community samples

INTRODUCTION

There is an urgent need to validate telephone versions of widely used general cognitive measures, such as the Montreal Cognitive Assessment (T-MoCA), for remote assessments.

METHODS

In the Einstein Aging Study, a diverse community cohort (n = 428; mean age = 78.1; 66% female; 54% non-White), equivalence testing was used to examine concordance between the T-MoCA and the corresponding in-person MoCA assessment. Receiver operating characteristic analyses examined the diagnostic ability to discriminate between mild cognitive impairment and normal cognition. Conversion methods from T-MoCA to the MoCA are presented.

RESULTS

Education, race/ethnicity, gender, age, self-reported cognitive concerns, and telephone administration difficulties were associated with both modes of administration; however, when examining the difference between modalities, these factors were not significant. Sensitivity and specificity for the T-MoCA (using Youden's index optimal cut) were 72% and 59%, respectively.

DISCUSSION

The T-MoCA demonstrated sufficient psychometric properties to be useful for screening of MCI, especially when clinic visits are not feasible.

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

Parkinson's disease (PD) is a common age-related neurodegenerative disease that affects the structural architecture of the cerebral cortex. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis is a popular measure to assess brain structural alterations in the gray matter in PD. However, the results of CTh analysis in PD lack consistency and have not been systematically reviewed. We conducted a comprehensive coordinate-based meta-analysis (CBMA) of 38 CTh studies (57 comparison datasets) in 1,843 patients with PD using the latest seed-based d mapping software. Compared with 1,172 healthy controls, no significantly consistent CTh alterations were found in patients with PD, suggesting CTh as an unreliable neuroimaging marker for PD. The lack of consistent CTh alterations in PD could be ascribed to the heterogeneity in clinical populations, variations in imaging methods, and underpowered small sample sizes. These results highlight the need to control for potential confounding factors to produce robust and reproducible CTh results in PD.

Cortical thickness in Parkinson disease: A coordinate-based meta-analysis

BACKGROUND

A growing number of studies have used surface-based morphometry (SBM) analyses to investigate gray matter cortical thickness (CTh) abnormalities in Parkinson disease (PD). However, the results across studies are inconsistent and have not been systematically reviewed. A clear picture of CTh alterations in PD remains lacked. Coordinate-based meta-analysis (CBMA) is a powerful tool to quantitatively integrate the results of individual voxel-based neuroimaging studies to identify the functional or structural neural substrates of particular neuropsychiatric disorders. Recently, CBMA has been updated for integrating SBM studies.

METHODS

The online databases PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed were comprehensively searched without language limitations from the database inception to February 2, 2020. We will include all SBM studies that compared regional CTh between patients with idiopathic PD and healthy control subjects at the whole-cortex level using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI). In addition to the main CBMA, we will conduct several supplementary analyses to test the robustness of the results, such as jackknife analyses, subgroup analyses, heterogeneity analyses, publication bias analyses, and meta-regression analyses.

RESULTS

This CBMA will offer the latest evidence of CTh alterations in PD.

CONCLUSIONS

Consistent and robust evidence of CTh alterations will feature brain morphometry of PD and may facilitate biomarker development.

PROSPERO REGISTRATION NUMBER

CRD42020148775.

Frontal atrophy as a marker for dementia conversion in Parkinson's disease with mild cognitive impairment

This study aimed to investigate the cortical neural correlates of dementia conversion in Parkinson's disease with mild cognitive impairment (PD-MCI). We classified 112 patients with drug-naïve early stage PD meeting criteria for PD-MCI into either PD with dementia (PDD) converters (n = 34) or nonconverters (n = 78), depending on whether they developed dementia within 4 years of PD diagnosis. Cortical thickness analyses were performed in 34 PDD converters and 34 matched nonconverters. Additionally, a linear discriminant analysis was performed to distinguish PDD converters from nonconverters using cortical thickness of the regions that differed between the two groups. The PDD converters had higher frequencies of multiple domain MCI and amnestic MCI with storage failure, and poorer cognitive performances on frontal/executive, memory, and language function domains than did the nonconverters. Cortical thinning extending from the posterior cortical area into the frontal region was observed in PDD converters relative to nonconverters. The discriminant analysis showed that the prediction model with two cortical thickness variables in the right medial superior frontal and left olfactory cortices optimally distinguished PDD converters from nonconverters. Our data suggest that cortical thinning in the frontal areas including the olfactory cortex is a marker for early dementia conversion in PD-MCI.