Mild cognitive impairment in Parkinson's disease is associated with a distributed pattern of brain white matter damage

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

Is white matter hyperintensity burden associated with cognitive and motor impairment in patients with parkinson's disease? A systematic review and meta-analysis

White matter damage quantified as white matter hyperintensities (WMH) may aggravate cognitive and motor impairments, but whether and how WMH burden impacts these problems in Parkinson's disease (PD) is not fully understood. This study aimed to examine the association between WMH and cognitive and motor performance in PD through a systematic review and meta-analysis. We compared the WMH burden across the cognitive spectrum (cognitively normal, mild cognitive impairment, dementia) in PD including controls. Motor signs were compared in PD with low/negative and high/positive WMH burden. We compared baseline WMH burden of PD who did and did not convert to MCI or dementia. MEDLINE and EMBASE databases were used to conduct the literature search resulting in 50 studies included for data extraction. Increased WMH burden was found in individuals with PD compared with individuals without PD (i.e. control) and across the cognitive spectrum in PD (i.e. PD, PD-MCI, PDD). Individuals with PD with high/positive WMH burden had worse global cognition, executive function, and attention. Similarly, PD with high/positive WMH presented worse motor signs compared with individuals presenting low/negative WMH burden. Only three longitudinal studies were retrieved from our search and they showed that PD who converted to MCI or dementia, did not have significantly higher WMH burden at baseline, although no data was provided on WMH burden changes during the follow up. We conclude, based on cross-sectional studies, that WMH burden appears to increase with PD worse cognitive and motor status in PD.

Neuroimaging and plasma evidence of early white matter loss in Parkinson's disease with poor outcomes

Parkinson's disease is a common and debilitating neurodegenerative disorder, with over half of patients progressing to postural instability, dementia or death within 10 years of diagnosis. However, the onset and rate of progression to poor outcomes is highly variable, underpinned by heterogeneity in underlying pathological processes. Quantitative and sensitive measures predicting poor outcomes will be critical for targeted treatment, but most studies to date have been limited to a single modality or assessed patients with established cognitive impairment. Here, we used multimodal neuroimaging and plasma measures in 98 patients with Parkinson's disease and 28 age-matched controls followed up over 3 years. We examined: grey matter (cortical thickness and subcortical volume), white matter (fibre cross-section, a measure of macrostructure; and fibre density, a measure of microstructure) at whole-brain and tract level; structural and functional connectivity; and plasma levels of neurofilament light chain and phosphorylated tau 181. We evaluated relationships with subsequent poor outcomes, defined as development of mild cognitive impairment, dementia, frailty or death at any time during follow-up, in people with Parkinson's disease. We show that extensive white matter macrostructural changes are already evident at baseline assessment in people with Parkinson's disease who progress to poor outcomes (n = 31): with up to 19% reduction in fibre cross-section in multiple tracts, and a subnetwork of reduced structural connectivity strength, particularly involving connections between right frontoparietal and left frontal, right frontoparietal and left parietal and right temporo-occipital and left parietal modules. In contrast, grey matter volumes and functional connectivity were preserved in people with Parkinson's disease with poor outcomes. Neurofilament light chain, but not phosphorylated tau 181 levels were increased in people with Parkinson's disease with poor outcomes, and correlated with white matter loss. These findings suggest that imaging sensitive to white matter macrostructure and plasma neurofilament light chain may be useful early markers of poor outcomes in Parkinson's disease. As new targeted treatments for neurodegenerative disease are emerging, these measures show important potential to aid patient selection for treatment and improve stratification for clinical trials.

Advanced brain aging in Parkinson's disease with cognitive impairment

Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.

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.

White matter changes in Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease (AD). It is characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) and the formation of Lewy bodies (LBs). Although PD is primarily considered a gray matter (GM) disease, alterations in white matter (WM) have gained increasing attention in PD research recently. Here we review evidence collected by magnetic resonance imaging (MRI) techniques which indicate WM abnormalities in PD, and discuss the correlations between WM changes and specific PD symptoms. Then we summarize transcriptome and genome studies showing the changes of oligodendrocyte (OLs)/myelin in PD. We conclude that WM abnormalities caused by the changes of myelin/OLs might be important for PD pathology, which could be potential targets for PD treatment.

Detection of mild cognitive impairment in Parkinson's disease using gradient boosting decision tree models based on multilevel DTI indices

BACKGROUND

Cognitive dysfunction is the most common non-motor symptom in Parkinson's disease (PD), and timely detection of a slight cognitive decline is crucial for early treatment and prevention of dementia. This study aimed to build a machine learning model based on intra- and/or intervoxel metrics extracted from diffusion tensor imaging (DTI) to automatically classify PD patients without dementia into mild cognitive impairment (PD-MCI) and normal cognition (PD-NC) groups.

METHODS

We enrolled PD patients without dementia (52 PD-NC and 68 PD-MCI subtypes) who were assigned to the training and test datasets in an 8:2 ratio. Four intravoxel metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and two novel intervoxel metrics, local diffusion homogeneity (LDH) using Spearman's rank correlation coefficient (LDHs) and Kendall's coefficient concordance (LDHk), were extracted from the DTI data. Decision tree, random forest, and eXtreme gradient boosting (XGBoost) models based on individual and combined indices were built for classification, and model performance was assessed and compared via the area under the receiver operating characteristic curve (AUC). Finally, feature importance was evaluated using SHapley Additive exPlanation (SHAP) values.

RESULTS

The XGBoost model based on a combination of the intra- and intervoxel indices achieved the best classification performance, with an accuracy of 91.67%, sensitivity of 92.86%, and AUC of 0.94 in the test dataset. SHAP analysis showed that the LDH of the brainstem and MD of the right cingulum (hippocampus) were important features.

CONCLUSIONS

More comprehensive information on white matter changes can be obtained by combining intra- and intervoxel DTI indices, improving classification accuracy. Furthermore, machine learning methods based on DTI indices can be used as alternatives for the automatic identification of PD-MCI at the individual level.

Specific patterns of coherence and phase lag index in particular regions as biomarkers of cognitive impairment in Parkinson's disease

INTRODUCTION

Cognitive impairment is a persistent and increasingly reported symptom of patients with Parkinson's disease (PD), significantly affecting daily functioning quality. This study aims to evaluate the functional connectivity of the brain network in patients with Parkinson's disease with various severities of cognitive decline using quantitative electroencephalography (EEG) analysis.

METHODS

Based on the EEG recorded in the resting state, the coherence and phase lag index were calculated to evaluate functional connectivity in 108 patients with Parkinson's disease divided into three groups according to their cognitive condition: dementia due to PD (PD-D), PD and mild cognitive impairment (PD-MCI) and cognitively normal patients (PD-CogN).

RESULTS

It was found that there were significantly different coherence values in the PD-D group compared to PD-CogN in different frequency bands. In most cases, there was a decrease in coherence in PD-D compared to PD-CogN. The most specific changes were revealed in the theta frequency band in the temporal right-frontal left and temporal right-frontal right regions. In the alpha frequency band, the most significant decreases were shown in the occipital right-frontal left and occipital left-frontal right areas. There were also statistically significant differences in phase lag index between many areas, especially in the theta frequency range.

CONCLUSIONS

These findings indicate that the functional connectivity patterns of coherence and phase lag index - found in a particular frequency band and region - could become a reliable biomarker for identifying cognitive impairment and differentiating its severity in PD patients.

Local white matter abnormalities in Parkinson's disease with mild cognitive impairment: Assessed with neurite orientation dispersion and density imaging

Mild cognitive impairment is a nonmotor complication in Parkinson's disease (PD) that have a high risk of developing dementia. White matter is associated with cognitive function in PD and the alterations may occur before the symptoms of the disease. Previous diffusion tensor imaging (DTI) studies lacked specificity to characterize the concrete contributions of distinct white matter tissue properties. This may lead to inconsistent conclusions about the alteration of white matter microstructure. Here, we used neurite orientation dispersion and density imaging (NODDI) and white matter fiber clustering method to uncover local white matter microstructures in PD with mild cognitive impairment (PD-MCI). This study included 23 PD-MCI and 20 PD with normal cognition (PD-NC) and 21 healthy controls (HC). To probe specific and fine-grained differences, metrics of NODDI and DTI in white matter fiber clusters were evaluated using along-tract analysis. Our results showed that PD-MCI patients had significantly lower neurite density index (NDI) and orientation dispersion index (ODI) in white matter fiber clusters in the prefrontal region. Correlation analysis and receiver operating characteristic (ROC) analysis revealed that the diagnostic performance of NODDI-derived metrics in cingulum bundle (2 clusters) and thalamo-frontal (2 clusters) were superior to DTI metrics. Our study provides a more specific insight to uncover local white matter abnormalities in PD-MCI, which benefit understanding the underlying mechanism of cognitive decline in PD and predicting the disease in advance.

Olfactory Function and Diffusion Tensor Imaging as Markers of Mild Cognitive Impairment in Early Stages of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder that is typified by motor signs and symptoms but can also lead to significant cognitive impairment and dementia Parkinson's Disease Dementia (PDD). While dementia is considered a nonmotor feature of PD that typically occurs later, individuals with PD may experience mild cognitive impairment (PD-MCI) earlier in the disease course. Olfactory deficit (OD) is considered another nonmotor symptom of PD and often presents even before the motor signs and diagnosis of PD. We examined potential links among cognitive impairment, olfactory functioning, and white matter integrity of olfactory brain regions in persons with early-stage PD. Cognitive tests were used to establish groups with PD-MCI and with normal cognition (PD-NC). Olfactory functioning was examined using the University of Pennsylvania Smell Identification Test (UPSIT) while the white matter integrity of the anterior olfactory structures (AOS) was examined using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) analysis. Those with PD-MCI demonstrated poorer olfactory functioning and abnormalities based on all DTI parameters in the AOS, relative to PD-NC individuals. OD and microstructural changes in the AOS of individuals with PD may serve as additional biological markers of PD-MCI.

Functions and dysfunctions of oligodendrocytes in neurodegenerative diseases

Neurodegenerative diseases (NDDs) are characterized by the progressive loss of selectively vulnerable populations of neurons, which is responsible for the clinical symptoms. Although degeneration of neurons is a prominent feature that undoubtedly contributes to and defines NDD pathology, it is now clear that neuronal cell death is by no means mediated solely by cell-autonomous mechanisms. Oligodendrocytes (OLs), the myelinating cells of the central nervous system (CNS), enable rapid transmission of electrical signals and provide metabolic and trophic support to neurons. Recent evidence suggests that OLs and their progenitor population play a role in the onset and progression of NDDs. In this review, we discuss emerging evidence suggesting a role of OL lineage cells in the pathogenesis of age-related NDDs. We start with multiple system atrophy, an NDD with a well-known oligodendroglial pathology, and then discuss Alzheimer's disease (AD) and Parkinson's disease (PD), NDDs which have been thought of as neuronal origins. Understanding the functions and dysfunctions of OLs might lead to the advent of disease-modifying strategies against NDDs.

Chronic effects of different exercise types on brain activity in healthy older adults and those with Parkinson’s disease: A systematic review

Objective: This study aimed to compare the regulation of brain activity by different kinds of long-term exercises (Tai Chi, treadmill training, and dancing) in healthy older adults and those with PD.

Methods: From January 2000 to October 2021, the electronic databases PubMed, Web of Science, and Scopus were searched. All articles were screened throughout the inclusion and exclusion criteria, which was followed by PICOS criteria. Finally, all articles were systematically reviewed with analyses.

Results: 29 studies were identified for this review, 24 of which were finally included in a group of healthy older adults, and five of which in a group of people with PD. All studies showed that significant changes were showed on people with PD and healthy older adults’ brain activity after three terms of the exercises we chose. An inverse change trend on the functional connectivity in people with PD was observed after treadmill training, whereas increased brain activity, cognitive function, memory, and emotion were noticed in healthy older adults.

Conclusion: Our findings suggest that different patterns of brain activity were also observed between healthy older adults and people with PD after treadmill training. However, more robust evidence and comprehensive studies are needed to determine if there is a difference between healthy older adults and people with PD.

The interplay between structural and functional connectivity in early stage Parkinson's disease patients

The mechanisms underlying cognitive disturbances in Parkinson's disease (PD) are poorly understood but likely to depend on the ongoing degenerative processes affecting structural and functional connectivity (FC). This pilot study examined patterns of FC alterations during a cognitive task using EEG and structural characteristics of white matter (WM) pathways connecting these activated regions in early-stage PD. Eleven PD patients and nine healthy controls (HCs) underwent EEG recording during an auditory oddball task and MRI scans. Source localization was performed and Gaussian mixture model was fitted to identify brain regions with high power during task performance. These areas served as seed regions for connectivity analysis. FC among these regions was assessed by measures of magnitude squared coherence (MSC), and phase-locking value (PLV), while structural connectivity was evaluated using fiber tracking based on diffusion tensor imaging (DTI). The paracentral lobule (PL), superior parietal lobule (SPL), superior and middle frontal gyrus (SMFG), parahippocampal gyrus, superior and middle temporal gyri (STG, MTG) demonstrated increased activation during task performance. Compared to HCs, PD showed lower FC between SMFG and PL and between SMFG and SPL in MSC (p = 0.012 and p = 0.036 respectively). No significant differences between the groups were observed in PLV and the measured DTI metrics along WM tracts. These findings demonstrate that in early PD, cognitive performance changes might be attributed to FC alterations, suggesting that FC is affected early on in the degenerative process, whereas structural damage is more prominent in advanced stages as a result of the disease burden accumulation.

Microstructural but not macrostructural cortical degeneration occurs in Parkinson’s disease with mild cognitive impairment

This study aimed to investigate the cortical microstructural/macrostructural degenerative patterns in Parkinson’s disease (PD) patients with mild cognitive impairment (MCI). Overall, 38 PD patients with normal cognition (PD-NC), 38 PD-MCI, and 32 healthy controls (HC) were included. PD-MCI was diagnosed according to the MDS Task Force level II criteria. Cortical microstructural alterations were evaluated with Neurite Orientation Dispersion and Density Imaging. Cortical thickness analyses were derived from T1-weighted imaging using the FreeSurfer software. For cortical microstructural analyses, compared with HC, PD-NC showed lower orientation dispersion index (ODI) in bilateral cingulate and paracingulate gyri, supplementary motor area, right paracentral lobule, and precuneus (PFWE < 0.05); while PD-MCI showed lower ODI in widespread regions covering bilateral frontal, parietal, occipital, and right temporal areas and lower neurite density index in left frontal area, left cingulate, and paracingulate gyri (PFWE < 0.05). Furthermore, compared with PD-NC, PD-MCI showed reduced ODI in right frontal area and bilateral caudate nuclei (voxel P < 0.01 and cluster >100 voxels) and the ODI values were associated with the Montreal Cognitive Assessment scores (r = 0.440, P < 0.001) and the memory performance (r = 0.333, P = 0.004) in the PD patients. However, for cortical thickness analyses, there was no difference in the between-group comparisons. In conclusion, cortical microstructural alterations may precede macrostructural changes in PD-MCI. This study provides insightful evidence for the degenerative patterns in PD-MCI and contributes to our understanding of the latent biological basis of cortical neurite changes for early cognitive impairment in PD.

Longitudinal corpus callosum microstructural decline in early-stage Parkinson’s disease in association with akinetic-rigid symptom severity

Previous diffusion tensor imaging (DTI) studies of Parkinson’s disease (PD) show reduced microstructural integrity of the corpus callosum (CC) relative to controls, although the characteristics of such callosal degradation remain poorly understood. Here, we utilized a longitudinal approach to identify microstructural decline in the entire volume of the CC and its functional subdivisions over 2 years and related the callosal changes to motor symptoms in early-stage PD. The study sample included 61 PD subjects (N = 61, aged 45–82, 38 M & 23 F, H&Y ≤ 2) from the Parkinson’s Progressive Markers Initiative database (PPMI). Whole-brain voxel-wise results revealed significant fractional anisotropy (FA) and mean diffusivity (MD) changes in the CC, especially in the genu and splenium. Using individually drawn CC regions of interest (ROI), our analysis further revealed that almost all subdivisions of the CC show significant decline in FA to certain extents over the two-year timeframe. Additionally, FA seemed lower in the right hemisphere of the CC at both time-points, and callosal FA decline was associated with FA and MD decline in widespread cortical and subcortical areas. Notably, multiple regression analysis revealed that across-subject akinetic-rigid severity was negatively associated with callosal FA at baseline and 24 months follow-up, and the effect was strongest in the anterior portion of the CC. These results suggest that callosal microstructure alterations in the anterior CC may serve as a viable biomarker for akinetic-rigid symptomology and disease progression, even in early PD.

Cerebellar Microstructural Abnormalities in Parkinson's Disease: a Systematic Review of Diffusion Tensor Imaging Studies

Diffusion tensor imaging (DTI) is now having a strong momentum in research to evaluate the neural fibers of the CNS. This technique can study white matter (WM) microstructure in neurodegenerative disorders, including Parkinson's disease (PD). Previous neuroimaging studies have suggested cerebellar involvement in the pathogenesis of PD, and these cerebellum alterations can correlate with PD symptoms and stages. Using the PRISMA 2020 framework, PubMed and EMBASE were searched to retrieve relevant articles. Our search revealed 472 articles. After screening titles and abstracts, and full-text review, and implementing the inclusion criteria, 68 papers were selected for synthesis. Reviewing the selected studies revealed that the patterns of reduction in cerebellum WM integrity, assessed by fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity measures can differ symptoms and stages of PD. Cerebellar diffusion tensor imaging (DTI) changes in PD patients with "postural instability and gait difficulty" are significantly different from "tremor dominant" PD patients. Freezing of the gate is strongly related to cerebellar involvement depicted by DTI. The "reduced cognition," "visual disturbances," "sleep disorders," "depression," and "olfactory dysfunction" are not related to cerebellum microstructural changes on DTI, while "impulsive-compulsive behavior" can be linked to cerebellar WM alteration. Finally, higher PD stages and longer disease duration are associated with cerebellum white matter alteration depicted by DTI. Depiction of cerebellar white matter involvement in PD is feasible by DTI. There is an association with disease duration and severity and several clinical presentations with DTI findings. This clinical-imaging association may eventually improve disease management.

Simulating the progression of brain structural alterations in Parkinson's disease

Considering brain structural alterations as neurodegenerative consequences of Parkinson's disease (PD), we sought to infer the progression of PD via the ordering of brain structural alterations from cross-sectional MRI observations. Having measured cortical thinning in gray matter (GM) regions and disintegrity in white matter (WM) regions as MRI markers of structural alterations for 130 patients with PD (69 ± 10 years, 72 men), stochastic simulation based on the probabilistic relationship between the brain regions was conducted to infer the ordering of structural alterations across all brain regions and the staging of structural alterations according to changes in clinical status. The ordering of structural alterations represented WM disintegrity tending to occur earlier than cortical thinning. The staging of structural alterations indicated structural alterations happening mostly before major disease complications such as postural instability and dementia. Later disease states predicted by the sequence of structural alterations were significantly related to more severe clinical symptoms. The relevance of the ordering of brain structural alterations to the severity of clinical symptoms suggests the clinical feasibility of predicting PD progression states.

Morphological basis of Parkinson disease-associated cognitive impairment: an update

Cognitive impairment is one of the most salient non-motor symptoms of Parkinson disease (PD) that poses a significant burden on the patients and carers as well as being a risk factor for early mortality. People with PD show a wide spectrum of cognitive dysfunctions ranging from subjective cognitive decline and mild cognitive impairment (MCI) to frank dementia. The mean frequency of PD with MCI (PD-MCI) is 25.8% and the pooled dementia frequency is 26.3% increasing up to 83% 20 years after diagnosis. A better understanding of the underlying pathological processes will aid in directing disease-specific treatment. Modern neuroimaging studies revealed considerable changes in gray and white matter in PD patients with cognitive impairment, cortical atrophy, hypometabolism, dopamine/cholinergic or other neurotransmitter dysfunction and increased amyloid burden, but multiple mechanism are likely involved. Combined analysis of imaging and fluid markers is the most promising method for identifying PD-MCI and Parkinson disease dementia (PDD). Morphological substrates are a combination of Lewy- and Alzheimer-associated and other concomitant pathologies with aggregation of α-synuclein, amyloid, tau and other pathological proteins in cortical and subcortical regions causing destruction of essential neuronal networks. Significant pathological heterogeneity within PD-MCI reflects deficits in various cognitive domains. This review highlights the essential neuroimaging data and neuropathological changes in PD with cognitive impairment, the amount and topographical distribution of pathological protein aggregates and their pathophysiological relevance. Large-scale clinicopathological correlative studies are warranted to further elucidate the exact neuropathological correlates of cognitive impairment in PD and related synucleinopathies as a basis for early diagnosis and future disease-modifying therapies.

Development and validation of a nomogram for freezing of gait in patients with Parkinson's Disease

OBJECTIVES

Freezing of gait (FOG) is a common and complex disabling episodic gait disturbance in patients with Parkinson's disease (PD). Currently, the treatment of FOG remains a challenge for clinicians. The aim of our study was to develop a nomogram for FOG risk based on data collected from Chinese patients with PD.

MATERIALS & METHODS

A total of 379 PD patients (197 with FOG) from Kunming Medical University were recruited as a training cohort. Additionally, 339 PD patients (166 with FOG) were recruited from West China Hospital of Sichuan University, to serve as the validation cohort. The least absolute shrinkage and selection operator regression model was used to select clinical and demographic characteristics as well as blood markers, which were incorporated into a predictive model using multivariate logistic regression to predict the risk of developing FOG. The model was validated using the validation dataset, and model performance was evaluated using the C-index, calibration plot, and decision curve analyses.

RESULTS

The final predictive model included the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) score, Parkinson's Disease Questionnaire (PDQ39), H-Y stage, and visuospatial function. The model showed good calibration and good discrimination, with a C-index value of 0.772 against the training cohort and 0.766 against the validation cohort. Decision curve analysis demonstrated the clinical utility of the nomogram.

CONCLUSION

A nomogram incorporating RBDSQ, PDQ39, H-Y stage, and visuospatial function may reliably predict the risk of FOG in PD patients.

R2* relaxometry analysis for mapping of white matter alteration in Parkinson's disease with mild cognitive impairment

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R2* relaxometry analysis combined with QSM revealed detail of WM alteration in PD-MCI.

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R2* relaxometry analysis can detect slight demyelination in PD-MCI.

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R2* value shows potential for early evaluation of cognitive decline in PD.

Background

R2* relaxometry analysis combined with quantitative susceptibility mapping (QSM), which has high sensitivity to iron deposition, can distinguish microstructural changes of the white matter (WM) and iron deposition, thereby providing a sensitive and biologically specific measure of the WM owing to the changes in myelin and its surrounding environment. This study aimed to explore the microstructural WM alterations associated with cognitive impairment in patients with Parkinson’s disease (PD) using R2* relaxometry analysis combined with QSM.

Materials and methods

We enrolled 24 patients with PD and mild cognitive impairment (PD-MCI), 22 patients with PD and normal cognition (PD-CN), and 19 age- and sex-matched healthy controls (HC). All participants underwent Montreal Cognitive Assessment (MoCA) and brain magnetic resonance imaging, including structural three-dimensional T1-weighted images and multiple spoiled gradient echo sequence (mGRE). The R2* and susceptibility maps were estimated from the multiple magnitude images of mGRE. The susceptibility maps were used for verifying iron deposition in the WM. The voxel-based R2* of the entire WM and its correlation with cognitive performance were analyzed.

Results

In the voxel-based group comparisons, the R2* in the PD-MCI group was lower in some WM regions, including the corpus callosum, than R2* in the PD-CN and HC groups. The mean susceptibility values in almost all brain regions were negative and close-to-zero values, indicating no detectable paramagnetic iron deposition in the WM of all subjects. There was a significant positive correlation between R2* and MoCA in some regions of the WM, mainly the corpus callosum and left hemisphere.

Conclusion

R2* relaxometry analysis for WM microstructural changes provided further biologic insights on demyelination and changes in the surrounding environment, supported by the QSM results demonstrating no iron existence. This analysis highlighted the potential for the early evaluation of cognitive decline in patients with PD.

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.

Magnetic Resonance Imaging Markers for Cognitive Impairment in Parkinson’s Disease: Current View

Cognitive impairment (CI) ranging from mild cognitive impairment (MCI) to dementia is a common and disturbing complication in patients with Parkinson’s disease (PD). Numerous studies have focused on neuropathological mechanisms underlying CI in PD, along with the identification of specific biomarkers for CI. Magnetic resonance imaging (MRI), a promising method, has been adopted to examine the changes in the brain and identify the candidate biomarkers associated with CI. In this review, we have summarized the potential biomarkers for CI in PD which have been identified through multi-modal MRI studies. Structural MRI technology is widely used in biomarker research. Specific patterns of gray matter atrophy are promising predictors of the evolution of CI in patients with PD. Moreover, other MRI techniques, such as MRI related to small-vessel disease, neuromelanin-sensitive MRI, quantitative susceptibility mapping, MR diffusion imaging, MRI related to cerebrovascular abnormality, resting-state functional MRI, and proton magnetic resonance spectroscopy, can provide imaging features with a good degree of prediction for CI. In the future, novel combined biomarkers should be developed using the recognized analysis tools and predictive algorithms in both cross-sectional and longitudinal studies.

The Myelin-Weighted Connectome in Parkinson's Disease

Background

Even though Parkinson's disease (PD) is typically viewed as largely affecting gray matter, there is growing evidence that there are also structural changes in the white matter. Traditional connectomics methods that study PD may not be specific to underlying microstructural changes, such as myelin loss.

Objective

The primary objective of this study is to investigate the PD‐induced changes in myelin content in the connections emerging from the basal ganglia and the brainstem. For the weighting of the connectome, we used the longitudinal relaxation rate as a biologically grounded myelin‐sensitive metric.

Methods

We computed the myelin‐weighted connectome in 35 healthy control subjects and 81 patients with PD. We used partial least squares to highlight the differences between patients with PD and healthy control subjects. Then, a ring analysis was performed on selected brainstem and subcortical regions to evaluate each node's potential role as an epicenter for disease propagation. Then, we used behavioral partial least squares to relate the myelin alterations with clinical scores.

Results

Most connections (~80%) emerging from the basal ganglia showed a reduced myelin content. The connections emerging from potential epicentral nodes (substantia nigra, nucleus basalis of Meynert, amygdala, hippocampus, and midbrain) showed significant decrease in the longitudinal relaxation rate (P < 0.05). This effect was not seen for the medulla and the pons.

Conclusions

The myelin‐weighted connectome was able to identify alteration of the myelin content in PD in basal ganglia connections. This could provide a different view on the importance of myelination in neurodegeneration and disease progression. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Brain connectivity markers in advanced Parkinson's disease for predicting mild cognitive impairment

OBJECTIVES

Mild cognitive impairment (MCI) is a well-defined non-motor manifestation and a harbinger of dementia in Parkinson's disease. This study is to investigate brain connectivity markers of MCI using diffusion tensor imaging and resting-state functional MRI, and help MCI diagnosis in PD patients.

METHODS

We evaluated 131 advanced PD patients (disease duration > 5 years; 59 patients with MCI) and 48 healthy control subjects who underwent a diffusion-weighted and resting-state functional MRI scanning. The patients were randomly assigned to training (n = 100) and testing (n = 31) groups. According to the Brainnetome Atlas, ROI-based structural and functional connectivity analysis was employed to extract connectivity features. To identify features with significant discriminative power for patient classification, all features were put into an all-relevant feature selection procedure within cross-validation loops.

RESULTS

Nine features were identified to be significantly relevant to patient classification. They showed significant differences between PD patients with and without MCI and positively correlated with the MoCA score. Five of them did not differ between general MCI subjects and healthy controls from the ADNI database, which suggested that they could uniquely play a part in the MCI diagnosis of PD. On basis of these relevant features, the random forest model constructed from the training group achieved an accuracy of 83.9% in the testing group, to discriminate patients with and without MCI.

CONCLUSIONS

The results of our study provide preliminary evidence that structural and functional connectivity abnormalities may contribute to cognitive impairment and allow to predict the outcome of MCI diagnosis in PD.

KEY POINTS

• Nine MCI markers were identified using an all-relevant feature selection procedure. • Five of nine markers differed between MCI and NC in PD, but not in general persons. • A random forest model achieved an accuracy of 83.9% for MCI diagnosis in PD.

Imaging Cognitive Impairment and Impulse Control Disorders in Parkinson's Disease

Dementia and mild forms of cognitive impairment as well as neuropsychiatric symptoms (i. e., impulse control disorders) are frequent and disabling non-motor symptoms of Parkinson's disease (PD). The identification of changes in neuroimaging studies for the early diagnosis and monitoring of the cognitive and neuropsychiatric symptoms associated with Parkinson's disease, as well as their pathophysiological understanding, are critical for the development of an optimal therapeutic approach. In the current literature review, we present an update on the latest structural and functional neuroimaging findings, including high magnetic field resonance and radionuclide imaging, assessing cognitive dysfunction and impulse control disorders in PD.

Cholinergic basal forebrain and hippocampal structure influence visuospatial memory in Parkinson's disease

Visuospatial impairment in Parkinson's disease (PD) heralds the onset of a progressive dementia syndrome and might be associated with cholinergic dysfunction. It remains unclear however, whether degeneration of the cholinergic basal forebrain is directly related to cognitive decline, or whether relationships between this region and cognitive function are mediated by closely related brain structures such as those in the medial temporal lobe. To evaluate relationships between structure of the cholinergic basal forebrain, medial temporal lobe and cognition, 27 PD patients without dementia and 20 controls underwent neuropsychological assessment and MRI. Volumes of the cholinergic basal forebrain nuclei, the entorhinal cortex, the hippocampus and its subfields were measured. Regression models utilised basal forebrain and hippocampal volumetric measures to predict cognitive performance. In PD, visuospatial memory (but not verbal memory or executive function) was correlated with hippocampal volume, particularly CA2-3, and basal forebrain subregion Ch1-2, but not Ch4. In addition, hippocampal volume was correlated with Ch1-2 in PD. The relationship between Ch1-2 and visuospatial memory was mediated by CA2-3 integrity. There were no correlations between cognitive and volumetric measures in controls. Our data imply that the integrity of the cholinergic basal forebrain is associated with subregional hippocampal volume. Additionally, a relationship between visuospatial function and cholinergic nuclei does exist, but is fully mediated by variations in hippocampal structure. These findings are consistent with the recent hypothesis that forebrain cholinergic system degeneration results in cognitive deficits via cholinergic denervation, and subsequent structural degeneration, of its target regions.

Lewy Body Dementias: A Coin with Two Sides?

Lewy body dementias (LBDs) consist of dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), which are clinically similar syndromes that share neuropathological findings with widespread cortical Lewy body deposition, often with a variable degree of concomitant Alzheimer pathology. The objective of this article is to provide an overview of the neuropathological and clinical features, current diagnostic criteria, biomarkers, and management of LBD. Literature research was performed using the PubMed database, and the most pertinent articles were read and are discussed in this paper. The diagnostic criteria for DLB have recently been updated, with the addition of indicative and supportive biomarker information. The time interval of dementia onset relative to parkinsonism remains the major distinction between DLB and PDD, underpinning controversy about whether they are the same illness in a different spectrum of the disease or two separate neurodegenerative disorders. The treatment for LBD is only symptomatic, but the expected progression and prognosis differ between the two entities. Diagnosis in prodromal stages should be of the utmost importance, because implementing early treatment might change the course of the illness if disease-modifying therapies are developed in the future. Thus, the identification of novel biomarkers constitutes an area of active research, with a special focus on α-synuclein markers.

Impaired Structural Connectivity in Parkinson's Disease Patients with Mild Cognitive Impairment: A Study Based on Probabilistic Tractography

Background: Probabilistic tractography, in combination with graph theory, has been used to reconstruct the structural whole-brain connectome. Threshold-free network-based statistics (TFNBS) is a useful technique to study structural connectivity in neurodegenerative disorders; however, there are no previous studies using TFNBS in Parkinson's disease (PD) with and without mild cognitive impairment (MCI). Materials and Methods: Sixty-two PD patients, 27 of whom classified as PD-MCI, and 51 healthy controls (HC) underwent diffusion-weighted 3T magnetic resonance imaging. Probabilistic tractography, using FMRIB Software Library (FSL), was used to compute the number of streamlines (NOS) between regions. NOS matrices were used to find group differences with TFNBS, and to calculate global and local measures of network integrity using graph theory. A binominal logistic regression was then used to assess the discrimination between PD with and without MCI using non-overlapping significant tracts. Tract-based spatial statistics were also performed with FSL to study changes in fractional anisotropy (FA) and mean diffusivity. Results: PD-MCI showed 37 white matter connections with reduced connectivity strength compared with HC, mainly involving temporal/occipital regions. These were able to differentiate PD-MCI from PD without MCI with an area under the curve of 83-85%. PD without MCI showed disrupted connectivity in 18 connections involving frontal/temporal regions. No significant differences were found in graph measures. Only PD-MCI showed reduced FA compared with HC. Discussion: TFNBS based on whole-brain probabilistic tractography can detect structural connectivity alterations in PD with and without MCI. Reduced structural connectivity in fronto-striatal and posterior cortico-cortical connections is associated with PD-MCI.

Structural connectivity networks in Alzheimer's disease and Lewy body disease

OBJECTIVE

We evaluated disruption of the white matter (WM) network related with Alzheimer's disease (AD) and Lewy body disease (LBD), which includes Parkinson's disease and dementia with Lewy bodies.

METHODS

We consecutively recruited 37 controls and 77 patients with AD-related cognitive impairment (ADCI) and/or LBD-related cognitive impairment (LBCI). Diagnoses of ADCI and LBCI were supported by amyloid PET and dopamine transporter PET, respectively. There were 22 patients with ADCI, 19 patients with LBCI, and 36 patients with mixed ADCI/LBCI. We investigated the relationship between ADCI, LBCI, graph theory-based network measures on diffusion tensor images, and cognitive dysfunction using general linear models after controlling for age, sex, education, deep WM hyperintensities (WMH), periventricular WMH, and intracranial volume.

RESULTS

LBCI, especially mixed with ADCI, was associated with increased normalized path length and decreased normalized global efficiency. LBCI was related to the decreased nodal degree of left caudate, which was further associated with broad cognitive dysfunction. Decreased left caudate nodal degree was associated with decreased fractional anisotropy (FA) in the brain regions vulnerable to LBD. Compared with the control group, the LBCI group had an increased betweenness centrality in the occipital nodes, which was associated with decreased FA in the WM adjacent to the striatum and visuospatial dysfunction.

CONCLUSION

Concomitant ADCI and LBCI are associated with the accentuation of LBCI-related WM network disruption centered in the left caudate nucleus. The increase of occipital betweenness centrality could be a characteristic biologic change associated with visuospatial dysfunction in LBCI.

Visual Dysfunction Predicts Cognitive Impairment and White Matter Degeneration in Parkinson's Disease

BACKGROUND

Visual dysfunction predicts dementia in Parkinson's disease (PD), but whether this translates to structural change is not known. The objectives of this study were to identify longitudinal white matter changes in patients with Parkinson's disease and low visual function and also in those who developed mild cognitive impairment.

METHODS

We used fixel-based analysis to examine longitudinal white matter change in PD. Diffusion MRI and clinical assessments were performed in 77 patients at baseline (22 low visual function/55 intact vision and 13 PD-mild cognitive impairment/51 normal cognition) and 25 controls and again after 18 months. We compared microstructural changes in fiber density, macrostructural changes in fiber bundle cross-section and combined fiber density and cross-section, across white matter, adjusting for age, sex, and intracranial volume.

RESULTS

Patients with PD and visual dysfunction showed worse cognitive performance at follow-up and were more likely to develop mild cognitive impairment compared with those with normal vision (P = 0.008). Parkinson's with poor visual function showed diffuse microstructural and macrostructural changes at baseline, whereas those with mild cognitive impairment showed fewer baseline changes. At follow-up, Parkinson's with low visual function showed widespread macrostructural changes, involving the fronto-occipital fasciculi, external capsules, and middle cerebellar peduncles bilaterally. No longitudinal change was seen in those with mild cognitive impairment at baseline or converters, even when the 2 groups were combined.

CONCLUSION

Parkinson's patients with poor visual function show increased white matter damage over time, providing further evidence for visual function as a marker of imminent cognitive decline. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Meta-Analysis of Cognition in Parkinson's Disease Mild Cognitive Impairment and Dementia Progression

Mild cognitive changes, including executive dysfunction, are seen in Parkinson's Disease (PD). Approximately 30% of individuals with PD develop Parkinson's disease dementia (PDD). Mild cognitive impairment (MCI) has been identified as a transitional state between normal cognition and dementia. Although PD-MCI and its cognitive correlates have been increasingly studied as a risk indicator for development of PDD, investigations into the PD-MCI construct have yielded heterogeneous findings. Thus, a typical PD-MCI cognitive profile remains undefined. The present meta-analysis examined published cross-sectional studies of PD-MCI and cognitively normal PD (PD-CN) groups to provide aggregated effect sizes of group test performance by cognitive domain. Subsequently, longitudinal studies examining PD-MCI to PDD progression were meta-analyzed. Ninety-two cross-sectional articles of PD-MCI vs. PD-CN were included; 5 longitudinal studies of PD-MCI conversion to PDD were included. Random effects meta-analytic models were constructed resulting in effect sizes (Hedges' g) for cognitive domains. Overall performance across all measures produced a large effect size (g = 0.83, 95% CI [0.79, 0.86], t² = 0.18) in cross-sectional analyses, with cognitive screeners producing the largest effect (g = 1.09, 95% CI [1.00, 1.17], t² = 0.19). Longitudinally, overall measures produced a moderate effect (g = 0.47, 95% CI [0.40, 0.53], t² = 0.01), with measures of executive functioning exhibiting the largest effect (g = 0.70, 95% CI [0.51, 0.89], t² = 0.01). Longitudinal effects were made more robust by low heterogeneity. This report provides the first comprehensive meta-analysis of PD-MCI cognitive outcomes and predictors in PD-MCI conversion to PDD. Limitations include heterogeneity of cross-sectional effect sizes and the potential impact of small-study effects. Areas for continued research include visuospatial skills and visual memory in PD-MCI and longitudinal examination of executive dysfunction in PD-MCI.

White Matter Hyperintensities, Dopamine Loss, and Motor Deficits in De Novo Parkinson's Disease

BACKGROUND

White matter hyperintensities, prevalent in patients with Parkinson's disease (PD), significantly affect parkinsonian motor symptoms. The objective of this study was to investigate the relationship between white matter hyperintensities and nigrostriatal dopamine depletion and their interaction or mediating effects on motor symptoms in patients with drug-naive early-stage PD.

METHODS

This cross-sectional study enrolled 501 patients with de novo PD who initially underwent [¹⁸ F] N-(3-fluoropropyl)-2β-carbonethoxy-3β-(4-iodophenyl) nortropane positron emission tomography and brain magnetic resonance imaging scans between April 2009 and September 2015 in a tertiary-care university hospital. We quantified dopamine transporter availability in each striatal subregion and assessed the severity of periventricular and lobar white matter hyperintensities using the Scheltens scale. The relationship between white matter hyperintensities, dopamine transporter availability in the posterior putamen, and Unified Parkinson's Disease Rating Scale (UPDRS) motor scores was assessed using multivariate linear regression and mediation analyses.

RESULTS

Periventricular and frontal white matter hyperintensities were generally associated with dopamine transporter availability in striatal subregions after adjusting for age at symptom onset, sex, disease duration, and vascular risk factors. There was an interaction effect between periventricular white matter hyperintensities and dopamine transporter availability in the posterior putamen for the axial motor score. The effect of white matter hyperintensities on UPDRS total score and bradykinesia subscore was indirectly mediated by dopamine transporter availability in the posterior putamen, whereas the axial sub-score was directly affected by white matter hyperintensities.

CONCLUSIONS

This study suggests that the detrimental effect of white matter hyperintensities on parkinsonian motor symptoms is more relevant and independent for axial motor impairments in the status of mildly decreased striatal dopamine transporter availability. © 2021 International Parkinson and Movement Disorder Society.

White Matter Abnormalities in Patients With Parkinson's Disease: A Meta-Analysis of Diffusion Tensor Imaging Using Tract-Based Spatial Statistics

Background: Tract-based spatial statistics (TBSS) studies based on diffusion tensor imaging (DTI) have revealed extensive abnormalities in white matter (WM) fibers of Parkinson's disease (PD); however, the results were inconsistent. Therefore, a meta-analytical approach was used in this study to find the most prominent and replicable WM abnormalities of PD. Methods: Online databases were systematically searched for all TBSS studies comparing fractional anisotropy (FA) between patients with PD and controls. Subsequently, we performed the meta-analysis using a coordinate-based meta-analytic software called seed-based d mapping. Meanwhile, meta-regression was performed to explore the potential correlation between the alteration of FA and the clinical characteristics of PD. Results: Out of a total of 1,701 studies that were identified, 23 studies were included. Thirty datasets, including 915 patients (543 men) with PD and 836 healthy controls (449 men), were included in the current study. FA reduction was identified in the body of the corpus callosum (CC; 245 voxels; z = -1.739; p < 0.001) and the left inferior fronto-occipital fasciculus (IFOF) 118 voxels; z = -1.182; p < 0.001). Both CC and IFOF maintained significance in the sensitivity analysis. No increase in FA was identified, but the percentage of male patients with PD was positively associated with the value of FA in the body of the CC. Conclusions: Although some limitations exist, DTI is regarded as a valid way to identify the pathophysiology of PD. It could be more beneficial to integrate DTI parameters with other MRI techniques to explore brain degeneration in PD.

Altered Functional Network Associated With Cognitive Performance in Early Parkinson Disease Measured by Eigenvector Centrality Mapping

Objective: To investigate relationships between whole-brain functional changes and the performance of multiple cognitive functions in early Parkinson’s disease (PD).

Methods: In the current study, we evaluated resting-state functional MRI (rsfMRI) data and neuropsychological assessments for various cognitive functions in a cohort with 84 early PD patients from the Parkinson’s Progression Markers Initiative (PPMI). Eigenvector centrality (EC) mapping based on rsfMRI was used to identify the functional connectivity of brain areas correlated with different neuropsychological scores at a whole-brain level.

Results: Our study demonstrated that in the early PD patients, scores of Letter Number Sequencing (LNS) were positively correlated with EC in the left inferior occipital gyrus (IOG) and lingual gyrus. The immediate recall scores of Hopkins Verbal Learning Test-Revised (HVLT-R) were positively correlated with EC in the left superior frontal gyrus. No correlation was found between the EC and other cognitive performance scores.

Conclusions: Functional alternations in the left occipital lobe (inferior occipital and lingual gyrus) and left superior frontal gyrus may account for the performance of working memory and immediate recall memory, respectively in early PD. These results may broaden the understanding of the potential mechanism of cognitive impairments in early PD.

Dementia risk in Parkinson's disease is associated with interhemispheric connectivity loss and determined by regional gene expression

Parkinson's dementia is a common and devastating part of Parkinson's disease. Whilst timing and severity vary, dementia in Parkinson's is often preceded by visual dysfunction. White matter changes, representing axonal loss, occur early in the disease process. Clarifying which white matter connections are affected in Parkinson's with visual dysfunction and why specific connections are vulnerable will provide important mechanistic insights. Here, we use diffusion tractography in 100 Parkinson's patients (33 low visual performers) and 34 controls to identify patterns of connectivity loss in Parkinson's with visual dysfunction. We examine the relationship between regional transcription and connectivity loss, using the Allen Institute for Brain Science transcriptome atlas. We show that interhemispheric connections are preferentially affected in Parkinson's low visual performers. Interhemispheric connection loss was associated with downweighted genes related to the smoothened signalling pathway (enriched in glutamatergic neurons) and upweighted metabolic genes. Risk genes for Parkinson's but not Alzheimer's or Dementia with Lewy bodies were over-represented in upweighted genes associated with interhemispheric connection loss. Our findings suggest selective vulnerability in Parkinson's patients at highest risk of dementia (those with visual dysfunction), where differences in gene expression and metabolic dysfunction, affecting longer connections with higher metabolic burden, drive connectivity loss.

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.

Altered white matter microarchitecture in Parkinson's disease: a voxel-based meta-analysis of diffusion tensor imaging studies

This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson's disease (PD) reflected by fractional anisotropy (FA), addressing clinical profiles and methodology-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls (HC) using the anisotropic effect size-signed differential mapping. A total of 808 patients with PD and 760 HC coming from 27 databases were finally included. Subgroup analyses were conducted considering heterogeneity with respect to medication status, disease stage, analysis methods, and the number of diffusion directions in acquisition. Compared with HC, patients with PD had decreased FA in the left middle cerebellar peduncle, corpus callosum (CC), left inferior fronto-occipital fasciculus, and right inferior longitudinal fasciculus. Most of the main results remained unchanged in subgroup meta-analyses of medicated patients, early stage patients, voxel-based analysis, and acquisition with 30 diffusion directions. The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex. The cerebellum and CC, associated with typical motor impairment, showed the most consistent FA decreases in PD. Medication status, analysis approaches, and the number of diffusion directions have an important impact on the findings, needing careful evaluation in future meta-analyses.

Voxel-wise meta-analysis of structural changes in gray matter of Parkinson's disease patients with mild cognitive impairment

Evidence from previous voxel-based morphometry (VBM) studies indicates that widespread brain regions are involved in Parkinson’s disease with mild cognitive impairment (PD-MCI). However, the spatial localization reported for gray matter (GM) abnormalities is heterogeneous. The aim of the present study was to quantitatively integrate studies on GM abnormalities observed in PD-MCI in order to determine whether a pattern exists. Eligible whole-brain VBM studies were identified by a systematic search of articles in PubMed and EMBASE databases spanning from 1995 to January 1, 2019. A meta-analysis was performed to investigate regional GM abnormalities in PD-MCI. The anisotropic effect size version of seed-based d mapping (AES-SDM) meta-analysis was conducted to explore the GMV differences of PD-MCI compared with PD patients with normal cognitive function (PD-NC). A total of 12 studies comprising 243 PD-MCI patients and 326 PD-NC were included in the meta-analysis. PD-MCI patients showed a robust GM decrease in the left insula and left superior temporal gyrus. Moreover, meta-regression analysis demonstrated that age, PD duration and stage, and Unified Parkinson’s Disease Rating Scale III and Mini-Mental State Examination scores might be partly correlated with the GM abnormalities observed in PD-MCI patients. The convergent findings of this quantitative meta-analysis revealed a characteristic neuroanatomical pattern in PD-MCI. The findings provide some evidence that MCI in PD may result in the breakdown of the insula and temporal gyrus, which may serve as specific regions of interest for further investigations.

Increased CSF NFL in Non-demented Parkinson's Disease Subjects Reflects Early White Matter Damage

Parkinson's disease (PD) is a chronic neurodegenerative disorder with various underlying pathological processes. Until now, no fluid biomarkers have been established for PD. Given recent biochemical and neuroimaging evidence for the presence of white matter damage in PD, which may even precede neuronal loss, we investigated whether neurofilament light (NFL) was increased in the cerebrospinal fluid (CSF) of PD patients in comparison to controls. NFL is located mainly in large myelinated axons, and increased CSF levels of this protein reflect axonal injury. CSF levels of NFL in 58 early PD patients and 28 controls were quantified by ELISA (Uman Diagnostics). Measures of PD severity included disease duration, UPDRS-III, and Hoehn-Yahr stage. Statistically significant differences in CSF NFL levels were found between PD patients and controls [median with interquartile range 524.82 (393.28-678.34) vs. 271.84 (198.09-335.24) ng/l; p < 0.05)]. In PD patients, there were no correlations between CSF NFL level and the measures of disease severity. The CSF NFL turned out to have a high discriminatory value (AUC 0.850) for differentiating between PD subjects and healthy controls, with 84% sensitivity and 85.2% specificity. The study indirectly demonstrates that axonal damage is present in early PD in addition to neuronal loss. Interestingly, white matter damage was observed in non-demented PD patients. In the light of the results of recent MRI studies which confirm early white matter damage in PD, our data may turn out to be potentially useful in the diagnosis of early, or even preclinical, stages of the disease.

Fiber-specific white matter reductions in Parkinson hallucinations and visual dysfunction

OBJECTIVE

To investigate the microstructural and macrostructural white matter changes that accompany visual hallucinations and low visual performance in Parkinson disease, a risk factor for Parkinson dementia.

METHODS

We performed fixel-based analysis, a novel technique that provides metrics of specific fiber-bundle populations within a voxel (or fixel). Diffusion MRI data were acquired from patients with Parkinson disease (n = 105, of whom 34 were low visual performers and 19 were hallucinators) and age-matched controls (n = 35). We used whole-brain fixel-based analysis to compare microstructural differences in fiber density (FD), macrostructural differences in fiber bundle cross section (FC), and the combined FD and FC (FDC) metric across all white matter fixels. We then performed a tract-of-interest analysis comparing the most sensitive FDC metric across 11 tracts within the visual system.

RESULTS

Patients with Parkinson disease hallucinations exhibited macrostructural changes (reduced FC) within the splenium of the corpus callosum and the left posterior thalamic radiation compared to patients without hallucinations. While there were no significant changes in FD, we found large reductions in the combined FDC metric in Parkinson hallucinators within the splenium (>50% reduction compared to nonhallucinators). Patients with Parkinson disease and low visual performance showed widespread microstructural and macrostructural changes within the genu and splenium of the corpus callosum, bilateral posterior thalamic radiations, and left inferior fronto-occipital fasciculus.

CONCLUSIONS

We demonstrate specific white matter tract degeneration affecting posterior thalamic tracts in patients with Parkinson disease with hallucinations and low visual performance, providing direct mechanistic support for attentional models of visual hallucinations.

Brain iron deposition is linked with cognitive severity in Parkinson's disease

BACKGROUND

Dementia is common in Parkinson's disease (PD) but measures that track cognitive change in PD are lacking. Brain tissue iron accumulates with age and co-localises with pathological proteins linked to PD dementia such as amyloid. We used quantitative susceptibility mapping (QSM) to detect changes related to cognitive change in PD.

METHODS

We assessed 100 patients with early-stage to mid-stage PD, and 37 age-matched controls using the Montreal Cognitive Assessment (MoCA), a validated clinical algorithm for risk of cognitive decline in PD, measures of visuoperceptual function and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part 3 (UPDRS-III). We investigated the association between these measures and QSM, an MRI technique sensitive to brain tissue iron content.

RESULTS

We found QSM increases (consistent with higher brain tissue iron content) in PD compared with controls in prefrontal cortex and putamen (p<0.05 corrected for multiple comparisons). Whole brain regression analyses within the PD group identified QSM increases covarying: (1) with lower MoCA scores in the hippocampus and thalamus, (2) with poorer visual function and with higher dementia risk scores in parietal, frontal and medial occipital cortices, (3) with higher UPDRS-III scores in the putamen (all p<0.05 corrected for multiple comparisons). In contrast, atrophy, measured using voxel-based morphometry, showed no differences between groups, or in association with clinical measures.

CONCLUSIONS

Brain tissue iron, measured using QSM, can track cognitive involvement in PD. This may be useful to detect signs of early cognitive change to stratify groups for clinical trials and monitor disease progression.

Fish Intake May Affect Brain Structure and Improve Cognitive Ability in Healthy People

As the population ages worldwide, the prevalence of cognitive disorders including mild cognitive impairment (MCI) is increasing. MCI appears in 10–20% of adults aged 65 years and older and is generally referred to as an intermediate stage between normal cognitive aging and dementia. To develop timely prevention and early treatment strategies by identifying biological factors, we investigated the relationship between dietary consumption of fish, brain structure, and MCI in cognitively normal subjects. The brain structure was assessed using neuroimaging-derived measures including the “gray-matter brain healthcare quotient (GM-BHQ)” and “fractional-anisotropy brain healthcare quotient (FA-BHQ),” which are approved as the international standard (H.861.1) by the International Telecommunication Union Telecommunication Standardization Sector. Dietary consumption of fish was calculated using the brief self-administered diet history questionnaire (BDHQ), and MCI was assessed using the Memory Performance Index (MPI) of MCI screening method (MCI Screen). This study showed that fish intake was positively associated with both FA-BHQ and MPI, and FA-BHQ was more strongly associated with MPI than fish intake. Our findings are in line with those in previous studies, but our study further indicates that the condition of the whole brain integrity measured by the FA-BHQ may mediate the relationship between fish intake and MCI prevention in healthy people. In other words, FA-BHQ may be used to identify people at high risk of MCI to provide the appropriate intervention.

Neurocognitive and psychiatric disorders-related axonal degeneration in Parkinson's disease

Neurocognitive and psychiatric disorders have significant consequences for quality of life in patients with Parkinson's disease (PD). In the current study, we evaluated microstructural white matter (WM) alterations associated with neurocognitive and psychiatric disorders in PD using neurite orientation dispersion and density imaging (NODDI) and linked independent component analysis (LICA). The indices of NODDI were compared between 20 and 19 patients with PD with and without neurocognitive and psychiatric disorders, respectively, and 25 healthy controls using tract‐based spatial statistics and tract‐of‐interest analyses. LICA was applied to model inter‐subject variability across measures. A widespread reduction in axonal density (indexed by intracellular volume fraction [ICVF]) was demonstrated in PD patients with and without neurocognitive and psychiatric disorders, as compared with healthy controls. Compared with patients without neurocognitive and psychiatric disorders, patients with neurocognitive and psychiatric disorders exhibited more extensive (posterior predominant) decreases in axonal density. Using LICA, ICVF demonstrated the highest contribution (59% weight) to the main effects of diagnosis that reflected widespread decreases in axonal density. These findings suggest that axonal loss is a major factor underlying WM pathology related to neurocognitive and psychiatric disorders in PD, whereas patients with neurocognitive and psychiatric disorders had broader axonal pathology, as compared with those without. LICA suggested that the ICVF can be used as a useful biomarker of microstructural changes in the WM related to neurocognitive and psychiatric disorders in PD.

Imaging Mild Cognitive Impairment and Dementia in Parkinson's Disease

Cognitive dysfunction is a significant non-motor feature of Parkinson's disease, with the risk of dementia increasing with prolonged disease duration. Multiple cognitive domains are affected, and the pathophysiology cannot be explained by dopaminergic loss alone. Sophisticated neuroimaging techniques can detect the nature and extent of extra-nigral involvement by targeting neurotransmitters, abnormal protein aggregates and tissue metabolism. This review identifies the functional and anatomical imaging characteristics that predict cognitive impairment in PD, the limitations that challenge this process, and the avenues of potential research.

Amnestic mild cognitive impairment in Parkinson's disease: White matter structural changes and mechanisms

Mild cognitive impairment (MCI) is a heterogeneous cognitive disorder that is often comorbid with Parkinson's diseases (PD). The amnestic subtype of PD-MCI (PD-aMCI) has a higher risk to develop dementia. However, there is a lack of studies on the white matter (WM) structural changes of PD-aMCI. We characterized the WM structural changes of PD-aMCI (n = 17) with cognitively normal PD (PD-CN, n = 19) and normal controls (n = 20), using voxel-based and tract-based spatial statistics (TBSS) analyses on fractional anisotropy (FA) axial diffusivity (AD), and radial diffusivity (RD). By excluding and then including the motor performance as a covariate in the comparison analysis between PD-aMCI and PD-CN, we attempted to discern the influences of two neuropathological mechanisms on the WM structural changes of PD-aMCI. The correlation analyses between memory and voxel-based WM measures in all PD patients were also performed (n = 36). The results showed that PD-aMCI had smaller FA values than PD-CN in the diffuse WM areas, and PD-CN had higher AD and RD values than normal controls in the right caudate. Most FA difference between PD-aMCI and PD-CN could be weakened by the motor adjustment. The FA differences between PD-aMCI and PD-CN were largely spatially overlapped with the memory-correlated FA values. Our findings demonstrated that the WM structural differences between PD-aMCI and PD-CN were mainly memory-related, and the influence of motor adjustment might indicate a common mechanism underlying both motor and memory impairment in PD-aMCI, possibly reflecting a predominant influence of dopaminergic neuropathology.

Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology

Accurate quantification of fractional anisotropy (FA) and mean diffusivity (MD) in MR diffusion tensor imaging (DTI) requires adequate signal-to-noise ratio (SNR) especially in low FA areas of the brain, which necessitates clinically impractical long image acquisition times. We explored a SNR enhancement strategy using region-of-interest (ROI)-based diffusion tensor for quantification. DTI scans from a healthy male were acquired 15 times and combined into sets with different number of signal averages (NSA = 1–4, 15) at one 1.5-T Philips and three 3-T (Philips, Siemens and GE) scanners. Equivalence test was performed to determine NSA thresholds for bias-free FA and MD quantifications by comparison with reference values derived from images with NSA = 15. We examined brain areas with low FA values including caudate nucleus, globus pallidus, putamen, superior temporal gyrus, and substructures within thalamus (lateral dorsal, ventral anterior and posterior nuclei), where bias-free FA is difficult to obtain using a conventional approach. Our results showed that bias-free FA can be obtained with NSA = 2 or 3 in some cases using ROI-based analysis. ROI-based analysis allows reliable FA and MD quantifications in various brain structures previously difficult to study with clinically feasible data acquisition schemes.