Grey matter abnormalities are associated only with severe cognitive decline in early stages of Parkinson's disease

Microstructural alterations in white matter and related neurobiology based on the new clinical subtypes of Parkinson's disease

Background and objectives

The advent of new clinical subtyping systems for Parkinson's disease (PD) has led to the classification of patients into distinct groups: mild motor predominant (PD-MMP), intermediate (PD-IM), and diffuse malignant (PD-DM). Our goal was to evaluate the efficacy of diffusion tensor imaging (DTI) in the early diagnosis, assessment of clinical progression, and prediction of prognosis of these PD subtypes. Additionally, we attempted to understand the pathological mechanisms behind white matter damage using single-photon emission computed tomography (SPECT) and cerebrospinal fluid (CSF) analyses.

Methods

We classified 135 de novo PD patients based on new clinical criteria and followed them up after 1 year, along with 45 healthy controls (HCs). We utilized tract-based spatial statistics to assess the microstructural changes of white matter at baseline and employed multiple linear regression to examine the associations between DTI metrics and clinical data at baseline and after follow-up.

Results

Compared to HCs, patients with the PD-DM subtype demonstrated reduced fractional anisotropy (FA), increased axial diffusivity (AD), and elevated radial diffusivity (RD) at baseline. The FA and RD values correlated with the severity of motor symptoms, with RD also linked to cognitive performance. Changes in FA over time were found to be in sync with changes in motor scores and global composite outcome measures. Furthermore, baseline AD values and their rate of change were related to alterations in semantic verbal fluency. We also discovered the relationship between FA values and the levels of α-synuclein and β-amyloid. Reduced dopamine transporter uptake in the left putamen correlated with RD values in superficial white matter, motor symptoms, and autonomic dysfunction at baseline as well as cognitive impairments after 1 year.

Conclusions

The PD-DM subtype is characterized by severe clinical symptoms and a faster progression when compared to the other subtypes. DTI, a well-established technique, facilitates the early identification of white matter damage, elucidates the pathophysiological mechanisms of disease progression, and predicts cognitively related outcomes. The results of SPECT and CSF analyses can be used to explain the specific pattern of white matter damage in patients with the PD-DM subtype.

Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics

Cognitive impairment in Parkinson's disease (PD) is associated with changes in the brain anatomical structures. The objective of this study, is to identify the atrophy patterns based on the severity of cognitive decline and evaluate the disease progression. In this study, gray matter alterations are analysed in 135 PD subjects under 3 cognitive domains (91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D)) by comparing them with 58 Healthy Control (HC) subjects. Voxel Based Morphometry (VBM) is used to segment the gray matter regions in magnetic resonance images and analyse the atrophy patterns statistically. Significant patterns of gray matter variations observed in the middle temporal and medial frontal region differentiate between HC and PD subject groups based on the severity of cognitive decline. Abnormalities in gray matter is substantiated through radiomic features extracted from the significant gray matter clusters. Significant radiomic features of the clusters are able to differentiate between the HC and PD-D subjects with an accuracy of 81.82%. Higher atrophy levels identified in PD-D subjects compared to NC-PD and PD-MCI group enables early diagnosis and treatment procedures. The combined and comprehensive analysis of gray matter alterations through VBM and radiomic features gives better assessment of cognitive impairment in PD.

Systematic review of data-driven cognitive subtypes in Parkinson disease

BACKGROUND AND PURPOSE

Recent application of the mild cognitive impairment concept to Parkinson disease (PD) has proven valuable in identifying patients at risk of dementia. However, it has sparked controversy regarding the existence of cognitive subtypes. The present review evaluates the current literature pertaining to data-driven subtypes of cognition in PD.

METHODS

Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, systematic literature searches for peer-reviewed articles on the topic of cognitive subtyping in PD were performed.

RESULTS

Twenty-two relevant articles were identified in the systematic search. Subtype structures showed either a spectrum of severity or specific domains of impairment. Domain-specific subtypes included amnestic/nonamnestic, memory/executive, and frontal/posterior dichotomies, as well as more complex structures with less definitive groupings. Preliminary longitudinal evidence showed some differences in cognitive progression among subtypes. Neuroimaging evidence provided insight into distinct patterns of brain alterations among subtypes.

CONCLUSIONS

Recurring phenotypes in the literature suggest strong clinical relevance of certain cognitive subtypes in PD. Although the current literature is limited, it raises critical questions about the utility of data-driven methods in cognitive research. The results encourage further integration of neuroimaging research to define the latent neural mechanisms behind divergent subtypes. Although there is no consensus, there appears to be growing consistency and inherent value in identifying cognitive subtypes in PD.

The new mechanism of cognitive decline induced by hypertension: High homocysteine-mediated aberrant DNA methylation

The prevalence and severity of hypertension-induced cognitive impairment increase with the prolonging of hypertension. The mechanisms of cognitive impairment induced by hypertension primarily include cerebral blood flow perfusion imbalance, white and gray matter injury with blood-brain barrier disruption, neuroinflammation and amyloid-beta deposition, genetic polymorphisms and variants, and instability of blood pressure. High homocysteine (HHcy) is an independent risk factor for hypertension that also increases the risk of developing early cognitive impairment. Homocysteine (Hcy) levels increase in patients with cognitive impairment induced by hypertension. This review summarizes a new mechanism whereby HHcy-mediated aberrant DNA methylation and exacerbate hypertension. It involves changes in Hcy-dependent DNA methylation products, such as methionine adenosyltransferase, DNA methyltransferases, S-adenosylmethionine, S-adenosylhomocysteine, and methylenetetrahydrofolate reductase (MTHFR). The mechanism also involves DNA methylation changes in the genes of hypertension patients, such as brain-derived neurotrophic factor, apolipoprotein E4, and estrogen receptor alpha, which contribute to learning, memory, and attention deficits. Studies have shown that methionine (Met) induces hypertension in mice. Moreover, DNA hypermethylation leads to cognitive behavioral changes alongside oligodendroglial and/or myelin deficits in Met-induced mice. Taken together, these studies demonstrate that DNA methylation regulates cognitive dysfunction in patients with hypertension. A better understanding of the function and mechanism underlying the effect of Hcy-dependent DNA methylation on hypertension-induced cognitive impairment will be valuable for early diagnosis, interventions, and prevention of further cognitive defects induced by hypertension.

Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms

The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.

Entropy analysis and grey cluster analysis of multiple indexes of 5 kinds of genuine medicinal materials

5 kinds of genuine medicinal materials, including Diding (Latin name: Corydalis bungeana Turcz), Purslane (Latin name: Portulaca oleracea L.), straw sandal board (Latin name: Hoya carnosa (L.f.) R. Br), June snow (Latin name: Serissa japonica (Thunb.) Thunb.), pine vine rattan (Latin name: Lycopodiastrum casuarinoides (Spring) Holub. [Lycopodium casuarinoides Spring]), were selected as the research objects. The combustion heat, thermo gravimetric parameters, and fat content, calcium content, trace element content, ash content of 5 kinds of genuine medicinal materials were measured. The combustion heat, differential thermal gravimetric analysis, fat content, calcium content, trace elements content, and ash content of 5 kinds of genuine medicinal materials were used to build a systematic multi-index evaluation system by gray pattern recognition and grey correlation coefficient cluster analysis, which can make up for the gaps in this area and provide scientific basis and research significance for the study of genuine medicinal materials quality. The results showed that the order of combustion heat of 5 kinds of genuine medicinal materials, including Diding, Purslane, straw sandal board, June snow, pine vine rattan, was Diding > June snow > straw sandal board > Purslane > pine vine rattan, the order of fat content (%) of 5 kinds of genuine medicinal materials was straw sandal board > Diding > pine vine rattan > June snow > Purslane, the order of calcium content (%) was pine vine rattan > June snow > Purslane > straw sandal board > Diding, the order of ash content was June snow > Purslane > straw sandal board > pine vine rattan > Diding. From the analysis of thermogravimetric analysis results and thermogravimetric combustion stability, the order of combustion stability of 5 kinds of genuine medicinal materials was June snow > pine Vine rattan > straw sandal board > Diding > Portulaca oleracea. The order of the content of 12 trace elements in 5 kinds of genuine medicinal materials, in terms of trace element content, June snow contains the highest trace elements in all samples. According to combustion heat, combustibility (combustion stability of genuine medicinal materials), fat, calcium, ash, trace element content, the comprehensive evaluation results of multi-index analysis constructed by gray correlation degree, gray correlation coefficient factor analysis, and gray hierarchical cluster analysis showed that the comprehensive evaluation multi-index order of 5 genuine medicinal materials, including Diding, Purslane, straw sandal board, June snow and pine vine rattan, was June snow > straw sandal board > Diding > Purslane > pine vine rattan. Therefore, the comprehensive evaluation results of the quality of genuine medicinal materials selected in this study were June snow the best, followed by straw sandal board. This research has important theoretical and practical significance for the multi-index measurement and comprehensive evaluation of genuine medicinal materials, and can provide scientific basis and research significance for the research of multi-index quality control of genuine medicinal material.

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.

Morphological analysis of subcortical structures for assessment of cognitive dysfunction in Parkinson's disease using multi-atlas based segmentation

Cognitive impairment in Parkinson's Disease (PD) is the most prevalent non-motor symptom that requires analysis of anatomical associations to cognitive decline in PD. The objective of this study is to analyse the morphological variations of the subcortical structures to assess cognitive dysfunction in PD. In this study, T1 MR images of 58 Healthy Control (HC) and 135 PD subjects categorised as 91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D) subjects, based on cognitive scores are utilised. The 132 anatomical regions are segmented using spatially localized multi-atlas model and volumetric analysis is carried out. The morphological alterations through textural features are captured to differentiate among the HC and PD subjects under different cognitive domains. The volumetric differences in the segmented subcortical structures of accumbens, amygdala, caudate, putamen and thalamus are able to predict cognitive impairment in PD. The volumetric distribution of the subcortical structures in PD-MCI subjects exhibit an overlap with the HC group due to lack of spatial specificity in their atrophy levels. The 3D GLCM features extracted from the significant subcortical structures could discriminate HC, NC-PD, PD-MCI and PD-D subjects with better classification accuracies. The disease related atrophy levels of the subcortical structures captured through morphological analysis provide sensitive evaluation of cognitive impairment in PD.

Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer's disease neuroimaging initiative

Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.

Disrupted rich-club organization of brain structural networks in Parkinson's disease

Parkinson's disease (PD) can be considered as the dysfunction in segregation and integration of large-scale structural networks in the late stage of disease progression. However, the altered patterns in the early stage have not been extensively investigated, especially the altered structural rich-club patterns, which is proved powerful to detect the altered patterns of structural networks in Alzheimer's disease and schizophrenia. To this end, we investigated the rich-club organization of the structural networks derived from diffusion tensor imaging (DTI) data in the early stage of PD, and further investigated the relationship between rich-club organization and clinicopathological measures, including motor and non-motor scales and cerebrospinal fluid (CSF) biomarkers. Two datasets were included for validation in this study. The first one included 41 healthy controls (HC) and 64 PD patients from Parkinson's Disease Progression Marker Initiative (PPMI) dataset, and the second one included 24 HC and 26 PD patients. Results revealed that PD patients in early stage had disrupted rich-club organization, with abnormal connectivity strength between peripheral regions (two-sample t-test between PD and HC: p < 0.001), whereas connectivity strength between rich-club regions remained relatively stable (two-sample t-test between PD and HC: p = 0.108). The classification accuracies on three types of connections were 59.93%, 73.96% and 77.44% for rich-club, feeder and local connections. Furthermore, abnormal local and feeder connections showed significant correlation with poor clinical scales and CSF biomarkers. In summary, a selective disruption of non-rich-club connections here could be regarded as a potential marker in the early diagnosis of PD.

Posterior Cortical Cognitive Deficits Are Associated With Structural Brain Alterations in Mild Cognitive Impairment in Parkinson's Disease

Context: Cognitive impairments are common in patients with Parkinson's disease (PD) and are heterogeneous in their presentation. The "dual syndrome hypothesis" suggests the existence of two distinct subtypes of mild cognitive impairment (MCI) in PD: a frontostriatal subtype with predominant attentional and/or executive deficits and a posterior cortical subtype with predominant visuospatial, memory, and/or language deficits. The latter subtype has been associated with a higher risk of developing dementia. Objective: The objective of this study was to identify structural modifications in cortical and subcortical regions associated with each PD-MCI subtype. Methods: One-hundred and fourteen non-demented PD patients underwent a comprehensive neuropsychological assessment as well as a 3T magnetic resonance imaging scan. Patients were categorized as having no cognitive impairment (n = 41) or as having a frontostriatal (n = 16), posterior cortical (n = 25), or a mixed (n = 32) MCI subtype. Cortical regions were analyzed using a surface-based Cortical thickness (CTh) method. In addition, the volumes, shapes, and textures of the caudate nuclei, hippocampi, and thalami were studied. Tractometric analyses were performed on associative and commissural white matter (WM) tracts. Results: There were no between-group differences in volumetric measurements and cortical thickness. Shape analyses revealed more abundant and more extensive deformations fields in the caudate nuclei, hippocampi, and thalami in patients with posterior cortical deficits compared to patients with no cognitive impairment. Decreased fractional anisotropy (FA) and increased mean diffusivity (MD) were also observed in the superior longitudinal fascicle, the inferior fronto-occipital fascicle, the striato-parietal tract, and the anterior and posterior commissural tracts. Texture analyses showed a significant difference in the right hippocampus of patients with a mixed MCI subtype. Conclusion: PD-MCI patients with posterior cortical deficits have more abundant and more extensive structural alterations independently of age, disease duration, and severity, which may explain why they have an increased risk of dementia.

Increased Plasma Heme Oxygenase-1 Levels in Patients With Early-Stage Parkinson's Disease

Introduction: Heme oxygenase-1 (HO-1) is a 32 kDa stress-response protein implicated in the pathogenesis of Parkinson’s disease (PD). Biliverdin is derived from heme through a reaction mediated by HO-1 and protects cells from oxidative stress. However, iron and carbon monoxide produced by the catabolism of HO-1 exert detrimental effects on patients with PD. The purpose of this study was to determine whether plasma HO-1 levels represent a biomarker of PD and to further explore the underlying mechanism of increased HO-1 levels by applying voxel-based morphometry (VBM).Methods: We measured plasma HO-1 levels using an enzyme-linked immunosorbent assay (ELISA) in 156 subjects, including 81 patients with early- and advanced-stage PD and 75 subjects without PD. The analyses were adjusted to control for confounders such as age, sex, and medication. We analyzed T1-weighted magnetic resonance imaging (MRI) data from 74 patients with PD using VBM to elucidate the association between altered brain volumes and HO-1 levels. Then, we compared performance on MMSE sub-items between PD patients with low and high levels of HO-1 using Mann-Whitney U tests.Results: Plasma HO-1 levels were significantly elevated in PD patients, predominantly those with early-stage PD, compared with controls (p < 0.05). The optimal cutoff value for patients with early PD was 2.245 ng/ml HO-1 [area under the curve (AUC) = 0.654]. Plasma HO-1 levels were unaffected by sex, age, and medications (p > 0.05). The right hippocampal volume was decreased in the subset of PD patients with high HO-1 levels (p < 0.05). A weak correlation was observed between right hippocampal volume and plasma HO-1 levels (r = −0.273, p = 0.018). There was no difference in total MMSE scores between the low- and high-HO-1 groups (p > 0.05), but the high-HO-1 group had higher language scores than the low-HO-1 group (p < 0.05).Conclusions: Plasma HO-1 levels may be a promising biomarker of early PD. Moreover, a high plasma concentration of the HO-1 protein is associated with a reduction in right hippocampal volume.

Tau pathology associates with in vivo cortical thinning in Lewy body disorders

OBJECTIVES

To investigate the impact of Alzheimer's disease (AD) co-pathology on an in vivo structural measure of neurodegeneration in Lewy body disorders (LBD).

METHODS

We studied 72 LBD patients (Parkinson disease (PD) = 2, PD-MCI = 25, PD with dementia = 10, dementia with Lewy bodies = 35) with either CSF analysis or neuropathological examination and structural MRI during life. The cohort was divided into those harboring significant AD co-pathology, either at autopsy (intermediate/high AD neuropathologic change) or with CSF signature indicating AD co-pathology (t-tau/Aβ1-42  > 0.3) (LBD+AD, N = 19), and those without AD co-pathology (LBD-AD, N = 53). We also included a reference group of 25 patients with CSF biomarker-confirmed amnestic AD. We investigated differences in MRI cortical thickness estimates between groups, and in the 21 autopsied LBD patients (LBD-AD = 14, LBD+AD = 7), directly tested the association between antemortem MRI and post-mortem burdens of tau, Aβ, and alpha-synuclein using digital histopathology in five representative neocortical regions.

RESULTS

The LBD+AD group was characterized by cortical thinning in anterior/medial and lateral temporal regions (P < 0.05 FWE-corrected) relative to LBD-AD. In LBD+AD, cortical thinning was most pronounced in temporal neocortex, whereas the AD reference group showed atrophy that equally encompassed temporal, parietal and frontal neocortex. In autopsied LBD, we found an inverse correlation with cortical thickness and post-mortem tau pathology, while cortical thickness was not significantly associated with Aβ or alpha-synuclein pathology.

INTERPRETATION

LBD+AD is characterized by temporal neocortical thinning on MRI, and cortical thinning directly correlated with post-mortem histopathologic burden of tau, suggesting that tau pathology influences the pattern of neurodegeneration in LBD.

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Background

In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging.

Objective

This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies.

Methods

We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process.

Results

Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification.

Conclusions

This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.