A divergent breakdown of neurocognitive networks in Parkinson's Disease mild cognitive impairment

Prediction of Cognitive Heterogeneity in Parkinson's Disease: A 4-Year Longitudinal Study Using Clinical, Neuroimaging, Biological and Electrophysiological Biomarkers

OBJECTIVE

Cognitive impairment in Parkinson's disease (PD) can show a very heterogeneous trajectory among patients. Here, we explored the mechanisms involved in the expression and prediction of different cognitive phenotypes over 4 years.

METHODS

In 2 independent cohorts (total n = 475), we performed a cluster analysis to identify trajectories of cognitive progression. Baseline and longitudinal level II neuropsychological assessments were conducted, and baseline structural magnetic resonance imaging, resting electroencephalogram and neurofilament light chain plasma quantification were carried out. Linear mixed-effects models were used to study longitudinal changes. Risk of mild cognitive impairment and dementia were estimated using multivariable hazard regression. Spectral power density from the electroencephalogram at baseline and source localization were computed.

RESULTS

Two cognitive trajectories were identified. Cluster 1 presented stability (PD-Stable) over time, whereas cluster 2 showed progressive cognitive decline (PD-Progressors). The PD-Progressors group showed an increased risk for evolving to PD mild cognitive impairment (HR 2.09; 95% CI 1.11-3.95) and a marked risk for dementia (HR 4.87; 95% CI 1.34-17.76), associated with progressive worsening in posterior-cortical-dependent cognitive processes. Both clusters showed equivalent clinical and sociodemographic characteristics, structural magnetic resonance imaging, and neurofilament light chain levels at baseline. Conversely, the PD-Progressors group showed a fronto-temporo-occipital and parietal slow-wave power density increase, that was in turn related to worsening at 2 and 4 years of follow-up in different cognitive measures.

INTERPRETATION

In the absence of differences in baseline cognitive function and typical markers of neurodegeneration, the further development of an aggressive cognitive decline in PD is associated with increased slow-wave power density and with a different profile of worsening in several posterior-cortical-dependent tasks. ANN NEUROL 2024.

TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.

Resting-state networks and their relationship with MoCA performance in PD patients

Although mild cognitive impairment is a common non-motor symptom experienced by individuals with Parkinson's Disease, the changes in intrinsic resting-state networks associated with its onset in Parkinson's remain underexamined. To address the issue, our study sought to examine resting-state network alterations and their association with total performance in the Montreal Cognitive Assessment and its cognitive domains in Parkinson's by means of functional magnetic resonance imaging of 29 Parkinson's patients with normal cognition, 25 Parkinson's patients with mild cognitive impairment, and 13 healthy controls. To contrast the Parkinson's groups with each other and the controls, the images were used to estimate the Z-score coefficient between the regions of interest from the default mode network, the salience network and the central executive network. Our first finding was that default mode and salience network connectivity decreased significantly in Parkinson's patients regardless of their cognitive status. Additionally, default mode network nodes had a negative and salience network nodes a positive correlation with the global assessment in Parkinson's with normal cognition; this inverse relationship of both networks to total score was not found in the group with cognitive impairment. Finally, a positive correlation was found between executive scores and anterior and posterior cortical network connectivity and, in the group with cognitive impairment, between language scores and salience network connectivity. Our results suggest that specific resting-state networks of Parkinson's patients with cognitive impairment differ from those of Parkinson's patients with normal cognition, supporting the evidence that cognitive impairment in Parkinson's Disease displays a differentiated neurodegenerative pattern.

Does the Montreal Cognitive Assessment (MoCA) identify cognitive impairment profiles in Parkinson's disease? An exploratory study

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

Cortical and subcortical functional connectivity and cognitive impairment in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson's Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We focus on canonical cortical networks linked to cognition, including the salience network (SAL), frontoparietal network (FPN), and default mode network (DMN), as well as a subcortical basal ganglia network (BGN). We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. In 82 PD patients, we found that lower MoCA scores were linked with lower intra-network connectivity of the FPN. We also found that lower MoCA scores were linked with lower inter-network connectivity between the SAL and the BGN, the SAL and the DMN, as well as the FPN and the DMN. These data elucidate the relationship of cortical and subcortical functional connectivity with cognitive impairments in PD.

Combined Metabolic Activators with Different NAD+ Precursors Improve Metabolic Functions in the Animal Models of Neurodegenerative Diseases

BACKGROUND

Mitochondrial dysfunction and metabolic abnormalities are acknowledged as significant factors in the onset of neurodegenerative disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD). Our research has demonstrated that the use of combined metabolic activators (CMA) may alleviate metabolic dysfunctions and stimulate mitochondrial metabolism. Therefore, the use of CMA could potentially be an effective therapeutic strategy to slow down or halt the progression of PD and AD. CMAs include substances such as the glutathione precursors (L-serine and N-acetyl cysteine), the NAD+ precursor (nicotinamide riboside), and L-carnitine tartrate.

METHODS

Here, we tested the effect of two different formulations, including CMA1 (nicotinamide riboside, L-serine, N-acetyl cysteine, L-carnitine tartrate), and CMA2 (nicotinamide, L-serine, N-acetyl cysteine, L-carnitine tartrate), as well as their individual components, on the animal models of AD and PD. We assessed the brain and liver tissues for pathological changes and immunohistochemical markers. Additionally, in the case of PD, we performed behavioral tests and measured responses to apomorphine-induced rotations.

FINDINGS

Histological analysis showed that the administration of both CMA1 and CMA2 formulations led to improvements in hyperemia, degeneration, and necrosis in neurons for both AD and PD models. Moreover, the administration of CMA2 showed a superior effect compared to CMA1. This was further corroborated by immunohistochemical data, which indicated a reduction in immunoreactivity in the neurons. Additionally, notable metabolic enhancements in liver tissues were observed using both formulations. In PD rat models, the administration of both formulations positively influenced the behavioral functions of the animals.

INTERPRETATION

Our findings suggest that the administration of both CMA1 and CMA2 markedly enhanced metabolic and behavioral outcomes, aligning with neuro-histological observations. These findings underscore the promise of CMA2 administration as an effective therapeutic strategy for enhancing metabolic parameters and cognitive function in AD and PD patients.

The Linguistic-Cognitive Profile in an Adult Population with Parkinson's Disease and Deep Brain Stimulation: A Comparative Study

Introduction. Individuals with Parkinson's disease (PD) exhibit general impairments, particularly non-motor symptoms that are related to language, communication, and cognition processes. People with this disease may undergo a surgical intervention for the placement of a deep brain stimulation device, which improves their motor symptoms. However, this type of intervention leads to a decline in their linguistic and cognitive abilities that becomes increasingly noticeable as the disease progresses. Objective. The objective of this research was to compare the performance and linguistic-cognitive profile of individuals with Parkinson's disease who underwent deep brain stimulation treatment based on the stage of the disease. Method. A total of 60 participants who were diagnosed with PD by their reference hospital were selected. These participants were divided into three groups based on the stage of the disease that they were in, forming three groups: a Stage I group (n = 20), a Stage II group (n = 20), and a Stage III group (n = 20). The linguistic-cognitive profile was assessed using the MoCA, ACE-III, and MetAphas tests. The design of this study was established as a quasi-experimental, cross-sectional investigation, and statistical analysis was performed using MANOVA to compare the scores between the study groups. Results. The results indicate that individuals in Stage I exhibit better linguistic and cognitive performance compared to the other groups of participants in Stage II and Stage III, with statistically significant differences (p < 0.05). Conclusion. In conclusion, the progression of PD leads to significant linguistic and cognitive decline in individuals with this disease who have a deep brain stimulation device, greatly limiting the autonomy and quality of life for people with PD.

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

INTRODUCTION

Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD.

METHODS

We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment.

RESULTS

Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD.

CONCLUSION

We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

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.

Optimization of structural connectomes and scaled patterns of structural-functional decoupling in Parkinson's disease

Parkinson's disease (PD) is manifested with disrupted topology of the structural connection network (SCN) and the functional connection network (FCN). However, the SCN and its interactions with the FCN remain to be further investigated. This multimodality study attempted to precisely characterize the SCN using diffusion kurtosis imaging (DKI) and further identify the neuropathological pattern of SCN-FCN decoupling, underscoring the neurodegeneration of PD. Diffusion-weighted imaging and resting-state functional imaging were available for network constructions among sixty-nine patients with PD and seventy demographically matched healthy control (HC) participants. The classification performance and topological prosperities of both the SCN and the FCN were analyzed, followed by quantification of the SCN-FCN couplings across scales. The SCN constructed by kurtosis metrics achieved optimal classification performance (area under the curve 0.89, accuracy 80.55 %, sensitivity 78.40 %, and specificity 80.65 %). Along with diverse alterations of structural and functional network topology, the PD group exhibited decoupling across scales including: reduced global coupling; increased nodal coupling within the sensorimotor network (SMN) and subcortical network (SN); higher intramodular coupling within the SMN and SN and lower intramodular coupling of the default mode network (DMN); decreased coupling between the modules of DMN-fronto-parietal network and DMN-visual network, but increased coupling between the SMN-SN module. Several associations between the coupling coefficient and topological properties of the SCN, as well as between network values and clinical scores, were observed. These findings validated the clinical implementation of DKI for structural network construction with better differentiation ability and characterized the SCN-FCN decoupling as supplementary insight into the pathological process underlying PD.

Salience network and cognitive impairment in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson's Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We tested the hypothesis that cognitive impairments in PD involve altered connectivity of the salience network (SN), a key cortical network that detects and integrates responses to salient stimuli. We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. We report two major results. First, in 82 PD patients we found significant relationships between lower intra-network connectivity of the frontoparietal network (FPN; comprising the dorsolateral prefrontal and posterior parietal cortices bilaterally) with lower MoCA scores. Second, we found significant relationships between lower inter-network connectivity between the SN and the basal ganglia network (BGN) and the default mode network (DMN) with lower MoCA scores. These data support our hypothesis about the SN and provide new insights into the brain networks contributing to cognitive impairments in PD.

Enhanced brain functional connectivity and activation after 12-week Tai Chi-based action observation training in patients with Parkinson's disease

Introduction

Motor-cognitive interactive interventions, such as action observation training (AOT), have shown great potential in restoring cognitive function and motor behaviors. It is expected that an advanced AOT incorporating specific Tai Chi movements with continuous and spiral characteristics can facilitate the shift from automatic to intentional actions and thus enhance motor control ability for early-stage PD. Nonetheless, the underlying neural mechanisms remain unclear. The study aimed to investigate changes in brain functional connectivity (FC) and clinical improvement after 12 weeks of Tai Chi-based action observation training (TC-AOT) compared to traditional physical therapy (TPT).

Methods

Thirty early-stage PD patients were recruited and randomly assigned to the TC-AOT group (N = 15) or TPT group (N = 15). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans before and after 12 weeks of training and clinical assessments. The FCs were evaluated by seed-based correlation analysis based on the default mode network (DMN). The rehabilitation effects of the two training methods were compared while the correlations between significant FC changes and clinical improvement were investigated.

Results

The results showed that the TC-AOT group exhibited significantly increased FCs between the dorsal medial prefrontal cortex and cerebellum crus I, between the posterior inferior parietal lobe and supramarginal gyrus, and between the temporal parietal junction and clusters of middle occipital gyrus and superior temporal. Moreover, these FC changes had a positive relationship with patients' improved motor and cognitive performance.

Discussion

The finding supported that the TC-AOT promotes early-stage PD rehabilitation outcomes by promoting brain neuroplasticity where the FCs involved in the integration of sensorimotor processing and motor learning were strengthened.

Combined metabolic activators improve metabolic functions in the animal models of neurodegenerative diseases

BACKGROUND

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are associated with metabolic abnormalities. Integrative analysis of human clinical data and animal studies have contributed to a better understanding of the molecular and cellular pathways involved in the progression of NDDs. Previously, we have reported that the combined metabolic activators (CMA), which include the precursors of nicotinamide adenine dinucleotide and glutathione can be utilized to alleviate metabolic disorders by activating mitochondrial metabolism.

METHODS

We first analysed the brain transcriptomics data from AD patients and controls using a brain-specific genome-scale metabolic model (GEM). Then, we investigated the effect of CMA administration in animal models of AD and PD. We evaluated pathological and immunohistochemical findings of brain and liver tissues. Moreover, PD rats were tested for locomotor activity and apomorphine-induced rotation.

FINDINGS

Analysis of transcriptomics data with GEM revealed that mitochondrial dysfunction is involved in the underlying molecular pathways of AD. In animal models of AD and PD, we showed significant damage in the high-fat diet groups' brain and liver tissues compared to the chow diet. The histological analyses revealed that hyperemia, degeneration and necrosis in neurons were improved by CMA administration in both AD and PD animal models. These findings were supported by immunohistochemical evidence of decreased immunoreactivity in neurons. In parallel to the improvement in the brain, we also observed dramatic metabolic improvement in the liver tissue. CMA administration also showed a beneficial effect on behavioural functions in PD rats.

INTERPRETATION

Overall, we showed that CMA administration significantly improved behavioural scores in parallel with the neurohistological outcomes in the AD and PD animal models and is a promising treatment for improving the metabolic parameters and brain functions in NDDs.

Cortical macro and microstructural correlates of cognitive and neuropsychiatric symptoms in Parkinson's disease

BACKGROUND

Cognitive and neuropsychiatric disturbances in Parkinson's disease are as common and as disabling as its well-known motor symptoms. Even though several neural substrates for these symptoms have been suggested, to which extent these symptoms reflect cortical neurodegeneration in Parkinson's disease remains to be fully elucidated.

METHODS

In a representative sample of 44 Parkinson's disease patients, the data about the following symptoms was recorded: cognitive performance, apathy, depression and anxiety. Surface-based vertexwise multiple regression analyses were performed to investigate the cortical macro (cortical thinning) and microstructural (increased intracortical diffusivity) correlates of each symptom. A group of 18 healthy controls with similar sociodemographics was also included to assess the disease specificity of the neuroimaging results.

RESULTS

Compared to healthy controls, Parkinson's disease patients showed significantly increased scores in all the considered non-motor scales (p < 0.01). Within the Parkinson's disease group, increased scores in these scales were associated with cortical macro- and microstructural neurodegeneration (p < 0.05 corrected). Each of the considered non-motor scales was associated with a specific pattern of cortical degeneration. When observing both neuroimaging techniques, intracortical diffusivity revealed similar but extensive patterns of cortical compromise than cortical thickness for each symptom, with the exception of anxiety.

CONCLUSIONS

Cognitive and neuropsychiatric symptoms in Parkinson's disease reflect cortical degeneration. Increases in intracortical diffusivity were able to detect symptom-specific cortical microstructural damage in the absence of cortical thinning. A better understanding of this association may contribute to characterize the brain circuitry and the neurotransmitter pathways underlying these highly prevalent and debilitating symptoms in Parkinson's disease.

Alterations in Large-Scale Intrinsic Connectivity Networks in the Parkinson's Disease-Associated Cognitive Impairment Continuum: A Systematic Review

Introduction

Cognitive impairment is common in the course of Parkinson's disease (PD) and displays a continuum from subjective cognitive impairment to dementia. Illuminating the pathophysiological processes associated with the continuum may help create follow-up and new treatment approaches. In this context, large-scale intrinsic connectivity networks are widely investigated to elucidate the neural processes underlying PD and are promising as non-invasive biomarkers. This systematic review aims to examine the alterations in large-scale intrinsic connectivity networks in the continuum of PD-associated cognitive impairment.

Method

ScienceDirect, Web of Science, and PubMed databases were searched with the specified keywords. The studies obtained as a result of this review were investigated by the PRISMA criteria, which were taken as a basis for the systematic review and writing of meta-analyses.

Results

A total of 974 studies were obtained from three databases. Twenty studies were included in the systematic review based on predetermined eligibility criteria. Among the large-scale connectivity networks examined in these studies, it was found that sensory-motor networks decreased their connectivity in the continuum of PD-associated cognitive impairment, and there were conflicting results in terms of cognitive networks.

Conclusion

Well-designed longitudinal studies are needed to clarify the alterations in the intrinsic connectivity networks in the PD cognitive impairment continuum. In these studies, it is necessary to define the cognitive disorder groups well, to control the connectivity changes that may occur due to dopaminergic treatment, and to evaluate Parkinson's patients with subjective cognitive impairment and dementia within the continuum.

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

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

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.

Locus coeruleus degeneration is associated with disorganized functional topology in Parkinson's disease

Degeneration of the locus coeruleus (LC) is recognized as a critical hallmark of Parkinson's disease (PD). Recent studies have reported that noradrenaline produced from the LC has critical effects on brain functional organization. However, it is unknown if LC degeneration in PD contributes to cognitive/motor manifestations through modulating brain functional organization. This study enrolled 94 PD patients and 68 healthy controls, and LC integrity was measured using the contrast-to-noise ratio of the LC (CNR_LC) calculated from T1-weighted magnetic resonance imaging. We used graph-theory-based network analysis to characterize brain functional organization. The relationships among LC degeneration, network disruption, and cognitive/motor manifestations in PD were assessed. Whether network disruption was a mediator between LC degeneration and cognitive/motor impairments was assessed further. In addition, an independent PD subgroup (n = 35) having functional magnetic resonance scanning before and after levodopa administration was enrolled to evaluate whether LC degeneration-related network deficiencies were independent of dopamine deficiency. We demonstrated that PD patients have significant LC degeneration compared to healthy controls. CNR_LC was positively correlated with Montreal Cognitive Assessment score and the nodal efficiency (NE) of several cognitive-related regions. Lower NE of the superior temporal gyrus was a mediator between LC degeneration and cognitive impairment in PD. However, levodopa treatment could not normalize the reduced NE of the superior temporal gyrus (mediator). In conclusion, we provided evidence for the relationship between LC degeneration and extensive network disruption in PD, and highlight the role of network disorganization in LC degeneration-related cognitive impairment.

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.

The impact of dopaminergic treatment over cognitive networks in Parkinson's disease: Stemming the tide?

Dopamine‐replacing therapies are an effective treatment for the motor aspects of Parkinson's disease. However, its precise effect over the cognitive resting‐state networks is not clear; whether dopaminergic treatment normalizes their functional connectivity‐as in other networks‐ and the links with cognitive decline are presently unknown. We recruited 35 nondemented PD patients and 16 age‐matched controls. Clinical and neuropsychological assessments were performed at baseline, and conversion to dementia was assessed in a 10 year follow‐up. Structural and functional brain imaging were acquired in both the ON and practical OFF conditions. We assessed functional connectivity in both medication states compared to healthy controls, connectivity differences within participants related to the ON/OFF condition, and baseline connectivity of PD participants that converted to dementia compared to those who did not convert. PD participants showed and increased frontoparietal connectivity compared to controls: a pattern of higher connectivity between salience (SN) and default‐mode (DMN) networks both in the ON and OFF states. Within PD patients, this higher SN‐DMN connectivity characterized the participants in the ON state, while within‐DMN connectivity prevailed in the OFF state. Interestingly, participants who converted to dementia also showed higher SN‐DMN connectivity in their baseline ON scans compared to nonconverters. To conclude, PD patients showed higher frontoparietal connectivity in cognitive networks compared to healthy controls, irrespective of medication status, but dopaminergic treatment specifically promoted SN‐DM hyperconnectivity.

Structure and Dynamics of Large-Scale Cognitive Networks in Huntington's Disease

BACKGROUND

Huntington's disease is a neurodegenerative disorder characterized by clinical alterations in the motor, behavioral, and cognitive domains. However, the structure and disruptions to large-scale brain cognitive networks have not yet been established.

OBJECTIVE

We aimed to profile changes in large-scale cognitive networks in premanifest and symptomatic patients with Huntington's disease.

METHODS

We prospectively recruited premanifest and symptomatic Huntington's disease mutation carriers as well as healthy controls. Clinical and sociodemographic data were obtained from all participants, and resting-state functional connectivity data, using both time-averaged and dynamic functional connectivity, was acquired from whole-brain and cognitively oriented brain parcellations.

RESULTS

A total of 64 gene mutation carriers and 23 healthy controls were included; 21 patients with Huntington's disease were classified as premanifest and 43 as symptomatic Huntington's disease. Compared with healthy controls, patients with Huntington's disease showed decreased network connectivity within the posterior hubs of the default-mode network and the medial prefrontal cortex, changes that correlated with cognitive (t = 2.25, P = 0.01) and disease burden scores (t = -2.42, P = 0.009). The salience network showed decreased functional connectivity between insular and supramarginal cortices and also correlated with cognitive (t = 2.11, P = 0.02) and disease burden scores (t = -2.35, P = 0.01). Dynamic analyses showed that network variability was decreased for default-central executive networks, a feature already present in premanifest mutation carriers (dynamic factor 8, P = 0.02).

CONCLUSIONS

Huntington's disease shows an early and widespread disruption of large-scale cognitive networks. Importantly, these changes are related to cognitive and disease burden scores, and novel dynamic functional analyses uncovered subtler network changes even in the premanifest stages.

Topologically convergent and divergent morphological gray matter networks in early-stage Parkinson's disease with and without mild cognitive impairment

Patients with Parkinson's disease with mild cognitive impairment (PD-M) progress to dementia more frequently than those with normal cognition (PD-N), but the underlying neurobiology remains unclear. This study aimed to define the specific morphological brain network alterations in PD-M, and explore their potential diagnostic value. Twenty-four PD-M patients, 17 PD-N patients, and 29 healthy controls (HC) underwent a structural MRI scan. Similarity between interregional gray matter volume distributions was used to construct individual morphological brain networks. These were analyzed using graph theory and network-based statistics (NBS), and their relationship to neuropsychological tests was assessed. Support vector machine (SVM) was used to perform individual classification. Globally, compared with HC, PD-M showed increased local efficiency (p = .001) in their morphological networks, while PD-N showed decreased normalized path length (p = .008). Locally, similar nodal deficits were found in the rectus and lingual gyrus, and cerebellum of both PD groups relative to HC; additionally in PD-M nodal deficits involved several frontal and parietal regions, correlated with cognitive scores. NBS found that similar connections were involved in the default mode and cerebellar networks of both PD groups (to a greater extent in PD-M), while PD-M, but not PD-N, showed altered connections involving the frontoparietal network. Using connections identified by NBS, SVM allowed discrimination with high accuracy between PD-N and HC (90%), PD-M and HC (85%), and between the two PD groups (65%). These results suggest that default mode and cerebellar disruption characterizes PD, more so in PD-M, whereas frontoparietal disruption has diagnostic potential.

Tipping the scales: how clinical assessment shapes the neural correlates of Parkinson's disease mild cognitive impairment

Mild cognitive impairment in Parkinson's disease (PD-MCI) is associated with consistent structural and functional brain changes. Whether different approaches for diagnosing PD-MCI are equivalent in their neural correlates is presently unknown. We aimed to profile the neuroimaging changes associated with the two endorsed methods of diagnosing PD-MCI. We recruited 53 consecutive non-demented PD patients and classified them as PD-MCI according to comprehensive neuropsychological examination as operationalized by the Movement Disorders Task Force. Voxel-based morphometry, cortical thickness, functional connectivity and graph theoretical measures were obtained on a 3-Tesla MRI scanner. 18 patients (32%) were classified as PD-MCI with Level-II criteria, 19 (33%) with the Parkinson's disease Cognitive Rating Scale (PD-CRS) and 32 (60%) with the Montreal Cognitive Assessment (MoCA) scale. Though regions of atrophy differed across classifications, reduced gray matter in the precuneus was found using both Level-II and PD-CRS classifications in PD-MCI patients. Patients diagnosed with the PD-CRS also showed extensive changes in cortical thickness, concurring with the MoCA in regions of the cingulate cortex, and again with Level-II regarding cortical thinning in the precuneus. Functional connectivity analysis found higher coherence within salience network regions of interest, and decreased anticorrelations between salience/central executive and default-mode networks in the PD-CRS classification for PD-MCI patients. Graph theoretical metrics showed a widespread decrease in node degree for the three classifications in PD-MCI, whereas betweenness centrality was increased in select nodes of the default mode network (DMN). Clinical and neuroimaging commonalities between the endorsed methods of cognitive assessment suggest a corresponding set of neural correlates in PD-MCI: loss of structural integrity in DMN structures, mainly the precuneus, and a loss of weighted connections in the salience network that might be counterbalanced by increased centrality in the DMN. Furthermore, the similarity of the results between exhaustive Level-II and screening Level-I tools might have practical implications in the search for neuroimaging biomarkers of cognitive impairment in Parkinson's disease.

Disturbance of functional and effective connectivity of the salience network involved in attention deficits in right temporal lobe epilepsy

The salience network (SN) acts as a switch that generates transient control signals to regulate the executive control network (ECN) and the default mode network (DMN) and has been implicated in cognitive processes. Temporal lobe epilepsy (TLE) is usually accompanied by different types of cognitive deficits, but whether it is associated with dysfunctional connectivity of the SN remains unknown. To address this, thirty-six patients with right TLE (rTLE) and thirty-six healthy controls (HCs) were recruited for the present study. All of the participants were subjected to attention network test (ANT) and resting-state functional resonance imaging (rs-fMRI) scanning. The patient group showed deficits in attention performance. Moreover, the functional connectivity (FC) and effective connectivity (EC) were analyzed based on key SN hubs (the anterior cingulate cortex (ACC) and the bilateral anterior insula (AI)). When compared with those in the HC group, the ACC showed increased FC with the left middle frontal gyrus and the left precentral gyrus, and the right AI showed decreased FC with the right precuneus and the right superior occipital gyrus in the patient group. The EC analysis revealed an increased inflow of information from the left middle temporal gyrus to the ACC and the right AI and an increased outflow of information from the bilateral AI to the left middle frontal gyrus. Furthermore, in the correlation analysis, the abnormal EC from the right AI to the left middle temporal gyrus was positively correlated with the executive control effect. These findings demonstrated aberrant modulation of the SN in rTLE, which was particularly characterized by dysfunctional connectivity between the SN and key brain regions in the DMN and ECN. Elucidation of this effect may further contribute to the comprehensive understanding of the neural mechanisms of the SN in regard to attention deficits in patients with TLE.

Qualitative Deficits in Verbal Fluency in Parkinson's Disease with Mild Cognitive Impairment: A Clinical and Neuroimaging Study

BACKGROUND

Mild cognitive impairment (MCI) in Parkinson's disease (PD) is considered a risk factor for PD with dementia (PDD). Verbal fluency tasks are widely used to assess executive function in PDD. However, in cases of PD with MCI (PD-MCI), the relative diagnostic accuracy of different qualitative verbal fluency measures and their related neural mechanisms remain unknown.

OBJECTIVE

This study aimed to investigate the relative diagnostic accuracy of qualitative (clustering and switching) verbal fluency strategies and their correlates with functional imaging in PD-MCI.

METHODS

Forty-five patients with PD (26 with MCI and 19 without MCI) and 25 healthy controls underwent comprehensive neurocognitive testing and resting-state functional magnetic resonance imaging. MCI in patients with PD was diagnosed according to established clinical criteria. The diagnostic accuracy of verbal fluency measures was determined via receiver operating characteristic analysis. Changes in brain functional connectivity between groups and across clinical measures were assessed using seed-to-voxel analyses.

RESULTS

Patients with PD-MCI generated fewer words and switched less frequently in semantic and phonemic fluency tasks compared to other groups. Switching in semantic fluency showed high diagnostic accuracy for PD-MCI and was associated with reduced functional connectivity in the salience network.

CONCLUSION

Our results indicate that reduced switching in semantic fluency tasks is a sensitive and specific marker for PD-MCI. Qualitative verbal fluency deficits and salience network dysfunction represent early clinical changes observed in PD-MCI.

The influence of white matter hyperintensity on cognitive impairment in Parkinson's disease

The aim of this meta‐analysis was to review systematically and to identify the relationship between the severity and location of white matter hyperintensities (WMHs) and the degree of cognitive decline in patients with Parkinson’s disease (PD). We searched the PubMed, EMBASE, Web of Science, Ovid, and Cochrane Library databases for clinical trials of the severity and location of WMHs on the degree of cognitive impairment in PD through October 2020. We conducted the survey to compare the association of WMH burden in patients with PD with mild cognitive impairment (PD‐MCI) versus those with normal cognition (PD‐NC) and in patients with PD with dementia (PDD) versus those with PD without dementia (PD‐ND). Nine studies with PD‐MCI versus PD‐NC and 10 studies with PDD versus PD‐ND comparisons were included. The WMH burden in PD‐MCI patients was significantly different compared to that in PD‐NC patients (standard mean difference, SMD = 0.39, 95% CI: 0.12 to 0.66, p = 0.005), while there was no correlation shown in the age‐matched subgroup of the comparison. In addition, PDD patients had a significantly higher burden of WMHs (SMD = 0.8, 95% CI: 0.44 to 1.71, p < 0.0001), especially deep white matter hyperintensities (SMD = 0.54, 95% CI: 0.36 to 0.73, p < 0.00001) and periventricular hyperintensities (SMD = 0.70, 95% CI: 0.36 to 1.04, p < 0.0001), than PD‐NC patients, regardless of the adjustment of age. WMHs might be imaging markers for cognitive impairment in PDD but not in PD‐MCI, regardless of age, vascular risk factors, or race. Further prospective studies are needed to validate the conclusions.

Spatial Covariance of Cholinergic Muscarinic M1 /M4 Receptors in Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is associated with cholinergic dysfunction, although the role of M1 and M4 receptors remains unclear.

OBJECTIVE

To investigate spatial covariance patterns of cholinergic muscarinic M₁ /M₄ receptors in PD and their relationship with cognition and motor symptoms.

METHODS

Some 19 PD and 24 older adult controls underwent ¹²³ I-iodo-quinuclidinyl-benzilate (QNB) (M₁ /M₄ receptor) and ^99m Tc-exametazime (perfusion) single-photon emission computed tomography (SPECT) scanning. We implemented voxel principal components analysis, producing a series of images representing patterns of intercorrelated voxels across individuals. Linear regression analyses derived specific M₁ /M₄ spatial covariance patterns associated with PD.

RESULTS

A cholinergic M₁ /M₄ pattern that converged onto key hubs of the default, auditory-visual, salience, and sensorimotor networks fully discriminated PD patients from controls (F_1,41  = 135.4, P < 0.001). In PD, we derived M₁ /M₄ patterns that correlated with global cognition (r = -0.62, P = 0.008) and motor severity (r = 0.53, P = 0.02). Both patterns emerged with a shared topography implicating the basal forebrain as well as visual, frontal executive, and salience circuits. Further, we found a M₁ /M₄ pattern that predicted global cognitive decline (r = 0.46, P = 0.04) comprising relative decreased binding within default and frontal executive networks.

CONCLUSIONS

Cholinergic muscarinic M₁ /M₄ modulation within key brain networks were apparent in PD. Cognition and motor severity were associated with a similar topography, inferring both phenotypes possibly rely on related cholinergic mechanisms. Relative decreased M₁ /M₄ binding within default and frontal executive networks could be an indicator of future cognitive decline. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Functional Connectome in Parkinson's Disease and Parkinsonism

PURPOSE OF REVIEW

There has been an exponential growth in functional connectomics research in neurodegenerative disorders. This review summarizes the recent findings and limitations of the field in Parkinson's disease (PD) and atypical parkinsonian syndromes.

RECENT FINDINGS

Increasingly more sophisticated methods ranging from seed-based to network and whole-brain dynamic functional connectivity have been used. Results regarding the disruption in the functional connectome vary considerably based on disease severity and phenotypes, and treatment status in PD. Non-motor symptoms of PD also link to the dysfunction in heterogeneous networks. Studies in atypical parkinsonian syndromes are relatively scarce. An important clinical goal of functional connectomics in neurodegenerative disorders is to establish the presence of pathology, track disease progression, predict outcomes, and monitor treatment response. The obstacles of reliability and reproducibility in the field need to be addressed to improve the potential of the functional connectome as a biomarker for these purposes in PD and atypical parkinsonian syndromes.

Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures

INTRODUCTION

Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonance imaging, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), to predict an individual's current and future severity over up to 4 years and to elucidate the most prognostic brain regions.

METHODS

ReHo and fALFF are measured for 82 Parkinson's Disease subjects and used to train machine learning predictors of baseline clinical and future severity at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Predictive performance is measured with nested cross-validation, validated on an external dataset, and again validated through leave-one-site-out cross-validation. Important predictive features are identified.

RESULTS

The models explain up to 30.4% of the variance in current MDS-UPDRS scores, 55.8% of the variance in year 1 scores, and 47.1% of the variance in year 2 scores (p < 0.0001). For distinguishing high and low-severity individuals at each timepoint (MDS-UPDRS score above or below the median, respectively), the models achieve positive predictive values up to 79% and negative predictive values up to 80%. Higher ReHo and fALFF in several regions, including components of the default motor network, predicted lower severity across current and future timepoints.

CONCLUSION

These results identify an accurate prognostic neuroimaging biomarker which may be used to better inform enrollment in trials of neuroprotective treatments and enable physicians to counsel their patients.

Differential functional connectivity of insular subdivisions in de novo Parkinson's disease with mild cognitive impairment

The insula, consisting of functionally diverse subdivisions, plays a significant role in Parkinson's disease (PD)-related cognitive disorders. However, the functional connectivity (FC) patterns of insular subdivisions in PD remain unclear. Our aim is to investigate the changes in FC patterns of insular subdivisions and their relationships with cognitive domains. Three groups of participants were recruited in this study, including PD patients with mild cognitive impairment (PD-MCI, n = 25), PD patients with normal cognition (PD-NC, n = 13), and healthy controls (HCs, n = 17). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in insular subdivisions of the three groups. Moreover, all participants underwent a neuropsychological battery to assess cognition so that the relationship between altered FC and cognitive performance could be elucidated. Compared with the PD-NC group, the PD-MCI group exhibited increased FC between the left dorsal anterior insular (dAI) and the right superior parietal gyrus (SPG), and altered FC was negatively correlated with memory and executive function. Compared with the HC group, the PD-MCI group showed significantly increased FC between the right dAI and the right median cingulate and paracingulate gyri (DCG), and altered FC was positively related to attention/working memory, visuospatial function, and language. Our findings highlighted the different abnormal FC patterns of insular subdivisions in PD patients with different cognitive abilities. Furthermore, dysfunction of the dAI may partly contribute to the decline in executive function and memory in early drug-naïve PD patients.

Different functional connectivity modes of the right fronto-insular cortex in akinetic-rigid and tremor-dominant Parkinson's disease

BACKGROUND

Patients with akinetic-rigid Parkinson's disease (AR-PD) are more prone to cognitive decline and depressive symptoms than tremor-dominant PD (TD-PD) patients. The right fronto-insular cortex (rFIC), as a key node of salience network, plays a critical role in the switching between central executive network and default mode network. In this study, we explored the functional connectivity mode of rFIC with triple-brain networks, namely default mode network, salience network, and central executive network, in two motor subtypes of PD.

METHODS

We recruited 44 PD patients (including the TD-PD group and AR-PD group) and 18 age-matched healthy controls (HCs). We performed functional connectivity (FC) analysis of resting-state functional MRI.

RESULTS

Compared with TD-PD, decreased FC were found in the right insular cortex and bilateral anterior cingulate gyrus in AR-PD. Compared with HCs, decreased FC in the bilateral insula, the anterior cingulate gyrus, the precentral gyrus, and the right medial frontal gyrus were found; therein, the FC value of rFIC-precentral gyrus was positively correlated with the Unified Parkinson's Disease Rating Scale-II score in AR-PD (p = 0.0482, r = 0.4162). While TD-PD showed decreased FC in the left insula as well as bilateral anterior cingulate gyrus when compared with HCs, and the FC value of the rFIC-left insula was positively correlated with its Hamilton Depression Rating Scale score (p = 0.02, r = 0.50).

CONCLUSION

The functional connectivity mode of rFIC in AR-PD differed from that in TD-PD. The decreased rFIC FC with the other nodes of salience network might be a potential indicator for AR-PD patients prone to develop cognitive decline and depressive symptoms.

Extrastriatal SPECT-DAT uptake correlates with clinical and biological features of de novo Parkinson's disease

Striatal dopamine transporter (DAT) uptake assessment through I¹²³-Ioflupane Single-Pphoton Emission Computed Tomography (SPECT) provides valuable information about the dopaminergic denervation occurring in Parkinson's disease (PD). However, little is known about the clinical or biological relevance of extrastriatal DAT uptake in PD. Here, from the Parkinson's Progression Markers Initiative, we studied 623 participants (431 PD and 192 healthy controls) with available SPECT data. Even though striatal denervation was undoubtedly the imaging hallmark of PD, extrastriatal DAT uptake was also reduced in patients with PD. Topographically, widespread frontal but also temporal and posterior cortical regions showed lower DAT uptake in PD patients with respect to healthy controls. Importantly, a longitudinal voxelwise analysis confirmed an active one-year loss of extrastriatal DAT uptake within the PD group. Extrastriatal DAT uptake also correlated with the severity of motor symptoms, cognitive performance, and cerebrospinal fluid α-synuclein levels. In addition, we found an association between the Catechol-O-methyltransferase val¹⁵⁸met genotype and extrastriatal DAT uptake. These results highlight the clinical and biological relevance of extrastriatal SPECT-DAT uptake in PD.

Serum neurofilament light chain levels reflect cortical neurodegeneration in de novo Parkinson's disease

INTRODUCTION

Cognitive impairment and dementia are highly prevalent non-motor complications in Parkinson's disease (PD) with deleterious consequences for patients and caregivers. With no treatment currently available, finding and validating minimally-invasive biomarkers of neurodegeneration in this population represents an urgent need for clinical trials targeting its prevention or delay. Recently, serum neurofilament light chain (NfL) levels have been identified as a promising biomarker of neural loss, but whether they reflect cortical neurodegeneration in early PD stages has not been addressed.

METHODS

From the Parkinson's Progression Markers Initiative (PPMI), we selected 133 de novo PD patients and 56 healthy controls (HC) with available structural neuroimaging and serum NfL data. We then studied whether NfL levels were abnormal in the PD group with respect to HC, and whether they correlated with cognitive indicators and cortical macro (cortical thinning) and microstructural (increased intracortical mean diffusivity) degeneration.

RESULTS

Serum NfL levels were significantly increased in the PD group (p = 0.010), and were also related to worse cognitive performance and a cortical macro and microstructural compromise (p < 0.05 corrected). These associations were observed both cross-sectionally and longitudinally within a one-year follow-up period. Topographically, NfL levels reflected posterior-cortical deterioration rather than frontal damage. Importantly, NfL levels were not associated with striatal SPECT-DAT uptake or β-amyloid burden.

DISCUSSION

Our results show that serum NfL levels reflect cortical neurodegeneration from the very early stages of PD. Moreover, its brain structural correlates and its lack of relationship with dopaminergic depletion or amyloidosis suggests that NfL could track the underlying pathological process leading to PD dementia.

Periventricular white matter hyperintensity burden and cognitive impairment in early Parkinson's disease

BACKGROUND AND PURPOSE

This study quantified the total brain and periventricular white matter hyperintensity (WMH) burdens in patients with early Parkinson's disease (PD) and explored their associations with cardiovascular risk factors and cognitive performance.

METHODS

A total of 175 non-demented patients with early PD who had undergone baseline brain magnetic resonance imaging were included. Comprehensive neurocognitive testing was conducted to identify PD with mild cognitive impairment (PD-MCI) and to evaluate performances in individual cognitive domains. Cardiovascular risk was expressed as a modified Framingham 10-year cardiovascular risk score (mFRS).

RESULTS

A total of 53.7% of this early PD cohort fulfilled the diagnostic criteria for PD-MCI. An increase in mFRS was significantly associated with increases in the total brain WMH (P = 0.015) and periventricular WMH (P = 0.040) burden, independent of age and gender. The periventricular WMH burden was significantly associated with PD-MCI (P = 0.046) in early PD, independent of cardiovascular risk factors. Patients in the 5th quintile of periventricular WMH burden were 8.6 times more likely to have PD-MCI compared with patients in the 1st quintile of periventricular WMH burden (P = 0.004). However, total brain WMH burden was not associated with PD-MCI (P = 0.158). In individual cognitive domains, heavier periventricular WMH burden was associated with worse executive function and visuospatial function independent of cardiovascular risk factors.

CONCLUSION

Periventricular WMHs are a useful imaging biomarker for cognitive impairment in early PD. Cardiovascular risk factors, although associated with periventricular WMHs, were unable to fully explain the association between periventricular WMHs and cognitive impairment in early PD.

A divergent breakdown of neurocognitive networks in Parkinson's Disease mild cognitive impairment

Cognitive decline is a major disabling feature in Parkinson's disease (PD). Multimodal imaging studies have shown functional disruption in neurocognitive networks related to cognitive impairment. However, it remains unknown whether these changes are related to gray matter loss, or whether they outline network vulnerability in the early stages of cognitive impairment. In this work, we intended to assess functional connectivity and graph theoretical measures and their relation to gray matter loss in Parkinson's disease with mild cognitive impairment (PD-MCI). We recruited 53 Parkinson's disease patients and classified them for cognitive impairment using Level-1 Movement Disorders Society-Task Force Criteria. Voxel-based morphometry, functional connectivity and graph theoretical measures were obtained on a 3-Tesla MRI scanner. Loss of gray matter was observed in the default mode network (bilateral precuneus), without a corresponding disruption of functional or graph theoretical properties. However, functional and graph theoretical changes appeared in salience network nodes, without evidence of gray matter loss. Global cognition and executive scores showed a correlation with node degree in the right anterior insula. We also found a correlation between visuospatial scores and right supramarginal gyrus node degree. Our findings highlight the loss of functional connectivity and topological features without structural damage in salience network regions in PD-MCI. They also underline the importance of multimodal hubs in the transition to mild cognitive impairment. This functional disruption in the absence of gray matter atrophy suggests that the salience network is a key vulnerable system at the onset of mild cognitive impairment in PD.