Tracking Cortical Changes Throughout Cognitive Decline in Parkinson's Disease

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;96:981-993.

Disentangling gray matter atrophy and its neurotransmitter architecture in drug-naïve Parkinson's disease: an atlas-based correlation analysis

Parkinson's disease is characterized by multiple neurotransmitter systems beyond the traditional dopaminergic pathway, yet their influence on volumetric alterations is not well comprehended. We included 72 de novo, drug-naïve Parkinson's disease patients and 61 healthy controls. Voxel-wise gray matter volume was evaluated between Parkinson's disease and healthy controls, as well as among Parkinson's disease subgroups categorized by clinical manifestations. The Juspace toolbox was utilized to explore the spatial relationship between gray matter atrophy and neurotransmitter distribution. Parkinson's disease patients exhibited widespread GM atrophy in the cerebral and cerebellar regions, with spatial correlations with various neurotransmitter receptors (FDR-P < 0.05). Cognitively impaired Parkinson's disease patients showed gray matter atrophy in the left middle temporal atrophy, which is associated with serotoninergic, dopaminergic, cholinergic, and glutamatergic receptors (FDR-P < 0.05). Postural and gait disorder patients showed atrophy in the right precuneus, which is correlated with serotoninergic, dopaminergic, gamma-aminobutyric acid, and opioid receptors (FDR-P < 0.05). Patients with anxiety showed atrophy in the right superior orbital frontal region; those with depression showed atrophy in the left lingual and right inferior occipital regions. Both conditions were linked to serotoninergic and dopaminergic receptors (FDR-P < 0.05). Parkinson's disease patients exhibited regional gray matter atrophy with a significant distribution of specific neurotransmitters, which might provide insights into the underlying pathophysiology of clinical manifestations and develop targeted intervention strategies.

Stage-dependent differential impact of network communication on cognitive function across the continuum of cognitive decline in Parkinson's disease

OBJECTIVE

Our objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how the influences of individual-level network connections on cognition varied across clinical stages without assuming a constant relationship.

METHODS

108 PD patients with continuum of cognitive decline (PD-NC = 46, PD-MCI = 43, PDD = 19) and 34 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological tests. Independent component analysis (ICA) and graph theory analyses (GTA) were employed to explore RSN connection changes. Additionally, stage-dependent differential impact of network communication on cognitive performance were examined using sparse varying coefficient modeling.

RESULTS

Compared to HCs, the dorsal attention network (DAN) and dorsal sensorimotor network (dSMN) were central networks with decreased connections in PD-NC and PD-MCI stage, while the lateral visual network (LVN) emerged as a central network in patients with dementia. Additionally, connectivity of the cerebellum network (CBN) increased in the PD-NC and PD-MCI stages. GTA demonstrated decreased nodal metrics for DAN and dSMN, coupled with an increase for CBN. Moreover, the degree centrality (DC) values of DAN and dSMN exhibited a stage-dependent differential impact on cognitive performance across the continuum of cognitive decline.

CONCLUSION

Our findings suggest that across the progression of cognitive impairment, the LVN gradually transitions into a core node with reduced connectivity, while the enhancement of connections in CBN diminishes. Furthermore, the non-linear relationship between the DC values of RSNs and cognitive decline indicates the potential for tailored interventions targeting specific stages.

Mapping brain morphology to cognitive deficits: a study on PD-CRS scores in Parkinson's disease with mild cognitive impairment

Background

The Parkinson's Disease-Cognitive Rating Scale (PD-CRS) is a widely used tool for detecting mild cognitive impairment (MCI) in Parkinson's Disease (PD) patients, however, the neuroanatomical underpinnings of this test's outcomes require clarification. This study aims to: (a) investigate cortical volume (CVol) and cortical thickness (CTh) disparities between PD patients exhibiting mild cognitive impairment (PD-MCI) and those with preserved cognitive abilities (PD-IC); and (b) identify the structural correlates in magnetic resonance imaging (MRI) of overall PD-CRS performance, including its subtest scores, within a non-demented PD cohort.

Materials and methods

This study involved 51 PD patients with Hoehn & Yahr stages I-II, categorized into two groups: PD-IC (n = 36) and PD-MCI (n = 15). Cognitive screening evaluations utilized the PD-CRS and the Montreal Cognitive Assessment (MoCA). PD-MCI classification adhered to the Movement Disorder Society Task Force criteria, incorporating extensive neuropsychological assessments. The interrelation between brain morphology and cognitive performance was determined using FreeSurfer.

Results

Vertex-wise analysis of the entire brain demonstrated a notable reduction in CVol within a 2,934 mm2 cluster, encompassing parietal and temporal regions, in the PD-MCI group relative to the PD-IC group. Lower PD-CRS total scores correlated with decreased CVol in the middle frontal, superior temporal, inferior parietal, and cingulate cortices. The PD-CRS subtests for Sustained Attention and Clock Drawing were associated with cortical thinning in distinct regions: the Clock Drawing subtest correlated with changes in the parietal lobe, insula, and superior temporal cortex morphology; while the PD-CRS frontal-subcortical scores presented positive correlations with CTh in the transverse temporal, medial orbitofrontal, superior temporal, precuneus, fusiform, and supramarginal regions. Additionally, PD-CRS subtests for Semantic and Alternating verbal fluency were linked to CTh changes in orbitofrontal, temporal, fusiform, insula, and precentral regions.

Conclusion

PD-CRS performance mirrors neuroanatomical changes across extensive fronto-temporo-parietal areas, covering both lateral and medial cortical surfaces, in PD patients without dementia. The observed changes in CVol and CTh associated with this cognitive screening tool suggest their potential as surrogate markers for cognitive decline in PD. These findings warrant further exploration and validation in multicenter studies involving independent patient cohorts.

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

PURPOSE OF THE REPORT

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Brain Connectivity Networks Constructed Using MRI for Predicting Patterns of Atrophy Progression in Parkinson Disease

Background Whether connectome mapping of structural and functional connectivity across the brain could be used to predict patterns of atrophy progression in patients with mild Parkinson disease (PD) has not been well studied. Purpose To assess the structural and functional connectivity of brain regions in healthy controls and its relationship with the spread of gray matter (GM) atrophy in patients with mild PD. Materials and Methods This prospective study included participants with mild PD and controls recruited from a single center between January 2012 and December 2023. Participants with PD underwent three-dimensional T1-weighted brain MRI, and the extent of regional GM atrophy was determined at baseline and every year for 3 years. The structural and functional brain connectome was constructed using diffusion tensor imaging and resting-state functional MRI in healthy controls. Disease exposure (DE) indexes-indexes of the pathology of each brain region-were defined as a function of the structural or functional connectivity of all the connected regions in the healthy connectome and the severity of atrophy of the connected regions in participants with PD. Partial correlations were tested between structural and functional DE indexes of each GM region at 1- or 2-year follow-up and atrophy progression at 2- or 3-year follow-up. Prediction models of atrophy at 2- or 3-year follow-up were constructed using exhaustive feature selection. Results A total of 86 participants with mild PD (mean age at MRI, 60 years ± 8 [SD]; 48 male) and 60 healthy controls (mean age at MRI, 62 years ± 9; 31 female) were included. DE indexes at 1 and 2 years were correlated with atrophy at 2 and 3 years (r range, 0.22-0.33; P value range, .002-.04). Models including DE indexes predicted GM atrophy accumulation over 3 years in the right caudate nucleus and some frontal, parietal, and temporal brain regions (R2 range, 0.40-0.61; all P < .001). Conclusion The structural and functional organization of the brain connectome plays a role in atrophy progression in the early stages of PD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yamada in this issue.

"Advanced" Parkinson's disease: A review

There is no consensus driven definition of "advanced" Parkinson's disease (APD) currently. APD has been described in terms of emergence of specific clinical features and clinical milestones of the disease e.g., motor fluctuations, time to increasing falls, emergence of cognitive decline, etc. The pathological burden of disease has been used to characterize various stages of the disease. Imaging markers have been associated with various motor and nonmotor symptoms of advancing disease. In this review, we present an overview of clinical, pathologic, and imaging markers of APD. We also propose a model of disease definition involving longitudinal assessments of these markers as well as quality of life metrics to better understand and predict disease progression in those with Parkinson's disease.

Altered functional-structural coupling may predict Parkinson's patient's depression

We aimed to elucidate the neurobiological basis of depression in Parkinson's disease and identify potential imaging markers for depression in patients with Parkinson's disease. We recruited 43 normal controls (NC), 46 depressed Parkinson's disease patients (DPD) and 56 non-depressed Parkinson's disease (NDPD). All participants underwent routine T2-weighted, T2Flair, and resting-state scans on the same 3.0 T magnetic resonance imaging (MRI) scanner at our hospital. Pre-processing includes calculating surface-based Regional Homogeneity (2DReHo) and cortical thickness. Then we defined the correlation coefficient between 2DReHo and cortical thickness as the functional-structural coupling index. Between-group comparisons were conducted on the Fisher's Z-transformed correlation coefficients. To identify specific regions of decoupling, the 2DReHo for each participant were divided by cortical thickness at each vertex, followed by threshold-free cluster enhancement (TFCE) multiple comparison correction. Binary logistic regression analysis was performed with DPD as the dependent variable, and significantly altered indicators as the independent variables. Receiver operating characteristic curves were constructed to compare the diagnostic performance of individual predictors and combinations using R and MedCalc software. DPD patients exhibited a significantly lower whole-brain functional-structural coupling index than NDPD patients and NC. Abnormal functional-structural coupling was primarily observed in the left inferior parietal lobule and right primary and early visual cortices in DPD patients. Receiver operating characteristic analysis revealed that the combination of cortical functional-structural coupling, surface-based ReHo, and thickness had the best diagnostic performance, achieving a sensitivity of 65% and specificity of 77.7%. This is the first study to explore the relationship between functional and structural changes in DPD patients and evaluate the diagnostic performance of these altered correlations to predict depression in Parkinson's disease patients. We posit that these changes in functional-structural relationships may serve as imaging biomarkers for depression in Parkinson's disease patients, potentially aiding in the classification and diagnosis of Parkinson's disease. Additionally, our findings provide functional and structural imaging evidence for exploring the neurobiological basis of depression in Parkinson's disease.

Deep brain stimulation of the subthalamic nucleus restores spatial reversal learning in patients with Parkinson's disease

Spatial learning and navigation are supported by distinct memory systems in the human brain such as the hippocampus-based navigational system and the striatum-cortex-based system involved in motor sequence, habit and reversal learning. Here, we studied the role of subthalamic circuits in hippocampus-associated spatial memory and striatal-associated spatial reversal learning formation in patients with Parkinson's disease, who underwent a deep brain stimulation of the subthalamic nucleus. Deep brain stimulation patients (Parkinson's disease-subthalamic nucleus: n = 26) and healthy subjects (n = 15) were tested in a novel experimental spatial memory task based on the Morris water maze that assesses both hippocampal place memory as well as spatial reversal learning. All subjects were trained to navigate to a distinct spatial location hidden within the virtual environment during 16 learning trials in a subthalamic nucleus Stim-On condition. Patients were then randomized into two groups with either a deep brain stimulation On or Off condition. Four hours later, subjects were retested in a delayed recall and reversal learning condition. The reversal learning was realized with a new hidden location that should be memorized during six consecutive trials. The performance was measured by means of an index indicating the improvement during the reversal learning. In the delayed recall condition, neither patients, healthy subjects nor the deep brain stimulation On- versus Off groups showed a difference in place memory performance of the former trained location. In the reversal learning condition, healthy subjects (reversal index 2.0) and patients in the deep brain stimulation On condition (reversal index 1.6) showed a significant improvement. However, patients in the deep brain stimulation Off condition (reversal index 1.1) performed significantly worse and did not improve. There were no differences between all groups in a final visual guided navigation task with a visible target. These results suggest that deep brain stimulation of subthalamic nucleus restores spatial reversal learning in a virtual navigation task in patients with Parkinson's disease and gives insight into the neuromodulation effects on cognition of subthalamic circuits in Parkinson's disease.

A multiple hits hypothesis for memory dysfunction in Parkinson disease

Cognitive disorders are increasingly recognized in Parkinson disease (PD), even in early disease stages, and memory is one of the most affected cognitive domains. Classically, hippocampal cholinergic system dysfunction was associated with memory disorders, whereas nigrostriatal dopaminergic system impairment was considered responsible for executive deficits. Evidence from PD studies now supports involvement of the amygdala, which modulates emotional attribution to experiences. Here, we propose a tripartite model including the hippocampus, striatum and amygdala as key structures for cognitive disorders in PD. First, the anatomo-functional relationships of these structures are explored and experimental evidence supporting their role in cognitive dysfunction in PD is summarized. We then discuss the potential role of α-synuclein, a pathological hallmark of PD, in the tripartite memory system as a key mechanism in the pathogenesis of memory disorders in the disease.

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.

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson's disease

BACKGROUND

Early detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease.

OBJECTIVES

To investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods.

METHODS

We included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance.

RESULTS

A total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62-0.68; ACC range: 0.63-0.77; SEN range: 0.45-0.66; SPE range: 0.64-0.84). Models trained with structural connectivity (AUC range, 0.81-0.84; ACC range, 0.75-0.86; SEN range, 0.77-0.91; SPE range, 0.71-0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81-0.85; ACC range, 0.74-0.85; SEN range, 0.79-0.91; SPE range, 0.70-0.89).

CONCLUSIONS

Baseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

Basal Forebrain Atrophy, Cortical Thinning, and Amyloid-β Status in Parkinson's disease-Related Cognitive Decline

BACKGROUND

Degeneration of the cortically-projecting cholinergic basal forebrain (cBF) is a well-established pathologic correlate of cognitive decline in Parkinson's disease (PD). In Alzheimer's disease (AD) the effect of cBF degeneration on cognitive decline was found to be mediated by parallel atrophy of denervated cortical areas.

OBJECTIVES

To examine whether the association between cBF degeneration and cognitive decline in PD is mediated by parallel atrophy of cortical areas and whether these associations depend on the presence of comorbid AD pathology.

METHODS

We studied 162 de novo PD patients who underwent serial 3 T magnetic resonance imaging scanning (follow-up: 2.33 ± 1.46 years) within the Parkinson's Progression Markers Initiative. cBF volume and regional cortical thickness were automatically calculated using established procedures. Individual slopes of structural brain changes and cognitive decline were estimated using linear-mixed models. Associations between longitudinal cBF degeneration, regional cortical thinning, and cognitive decline were assessed using regression analyses and mediation effects were assessed using nonparametric bootstrap. Complementary analyses assessed the effect of amyloid-β biomarker positivity on these associations.

RESULTS

After controlling for global brain atrophy, longitudinal cBF degeneration was highly correlated with faster cortical thinning (PFDR  < 0.05), and thinning in cBF-associated cortical areas mediated the association between cBF degeneration and cognitive decline (rcBF-MoCA  = 0.30, P < 0.001). Interestingly, both longitudinal cBF degeneration and its association with cortical thinning were largely independent of amyloid-β status.

CONCLUSIONS

cBF degeneration in PD is linked to parallel thinning of cortical target areas, which mediate the effect on cognitive decline. These associations are independent of amyloid-β status, indicating that they reflect proper features of PD pathophysiology. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Segmental disturbance of white matter microstructure in predicting mild cognitive impairment in idiopathic Parkinson's disease: An individualized study based on automated fiber quantification tractography

PURPOSE

The neurobiological mechanisms and an early identification of MCI in idiopathic Parkinson's disease (IPD) remain unclear. To investigate the abnormalities of types of white matter (WM) fiber tracts segmentally and establish reliable indicator in IPD-MCI.

METHODS

Forty IPD with normal cognition (IPD-NCI), thirty IPD-MCI, and thirty healthy controls were included. Automated fiber quantification was applied to extract the fractional anisotropy (FA) and mean diffusivity (MD) values at 100 locations along the major fibers. Partial correlation was performed between diffusion values and cognitive performance. Furthermore, machine learning analyses were conducted to determine the imaging biomarker of MCI. Permutation tests were performed to evaluate the pointwise differences under the FWE correction.

RESULTS

IPD-MCI had similar but more severe and widespread WM degeneration in the association, projection, and commissural fibers compared with IPD-NCI. Meanwhile, IPD-MCI showed distinct degeneration pattern in the association fibers. The FA of the anterior segment of right inferior fronto-occipital fasciculus (IFOF) was positively correlated with MoCA (P < 0.05) and executive function (P < 0.05). The MD of the middle and posterior segment of left superior longitudinal fasciculus (SLF) was negatively correlated with MoCA P < 0.05), executive (P < 0.05), visuospatial function (P < 0.05). Furthermore, the AUC of support vector machine model was 0.80 in the validation dataset. The FA of anterior segment of right IFOF contribute the most.

CONCLUSION

This study demonstrated that regional tract-specific microstructural degeneration, especially the association fibers, can be used to predict MCI in IPD. Especially, the right IFOF may be a significant imaging biomarker in predicting IPD with MCI.

Sleep Traits Causally Affect the Brain Cortical Structure: A Mendelian Randomization Study

Background: Brain imaging results in sleep deprived patients showed structural changes in the cerebral cortex; however, the reasons for this phenomenon need to be further explored. Methods: This MR study evaluated causal associations between morningness, ease of getting up, insomnia, long sleep, short sleep, and the cortex structure. Results: At the functional level, morningness increased the surface area (SA) of cuneus with global weighted (beta(b) (95% CI): 32.63 (10.35, 54.90), p = 0.004). Short sleep increased SA of the lateral occipital with global weighted (b (95% CI): 394.37(107.89, 680.85), p = 0.007. Short sleep reduced cortical thickness (TH) of paracentral with global weighted (OR (95% CI): -0.11 (-0.19, -0.03), p = 0.006). Short sleep reduced TH of parahippocampal with global weighted (b (95% CI): -0.25 (-0.42, -0.07), p = 0.006). No pleiotropy was detected. However, none of the Bonferroni-corrected p values of the causal relationship between cortical structure and the five types of sleep traits met the threshold. Conclusions: Our results potentially show evidence of a higher risk association between neuropsychiatric disorders and not only paracentral and parahippocampal brain areas atrophy, but also an increase in the middle temporal zone. Our findings shed light on the associations of cortical structure with the occurrence of five types of sleep traits.

Evolution of brain network dynamics in early Parkinson's disease with mild cognitive impairment

How mild cognitive impairment (MCI) is instantiated in dynamically interacting and spatially distributed functional brain networks remains an unexplored mystery in early Parkinson's disease (PD). We applied a machine-learning technology based on personalized sliding-window algorithm to track continuously time-varying and overlapping subnetworks under the functional brain networks calculated form resting state electroencephalogram data within a sample of 33 early PD patients (13 early PD patients with MCI and 20 early PD patients without MCI). We decoded a set of subnetworks that captured surprisingly dynamically varying and integrated interactions among certain brain lobes. We observed that the master expressed subnetworks were particularly transient, and flexibly switching between high and low expression during integration into a dynamic brain network. This transience was particularly salient in a subnetwork predominantly linking temporal-parietal-occipital lobes, which decreases in both expression and flexibility in early PD patients with MCI and expresses their degree of cognitive impairment. Moreover, MCI induced a regularly interrupted, slow evolution of subnetworks in functional brain network dynamics in early PD at the individual level, and the dynamic expression characteristics of subnetworks also reflected the degree of cognitive impairment in patients with early PD. Collectively, these results provide novel and deeper insights regarding MCI-induced abnormal dynamical interaction and large-scale changes in functional brain network of early PD.

NEAT1 enhances MPP+ -induced pyroptosis in a cell model of Parkinson's disease via targeting miR-5047/YAF2 signaling

PURPOSE

Parkinson's disease (PD) is the second most frequent neurodegenerative disease. The aim of our study is to explore the role and the regulatory mechanism of long noncoding RNA (lncRNA) NEAT1 in MPP+ -induced pyroptosis in a cell model of PD.

MATERIALS AND METHODS

MPP+ -treated SH-SY5Y cells were used as an in vitro model of dopaminergic neurons for PD. Expression levels of miR-5047 and YAF2 mRNA were determined through qRT-PCR. TUNEL staining was carried out to analyze neuronal apoptosis. Luciferase activity assay was accomplished to analyze the combination of miR-5047 with NEAT1 or YAF2 3'-UTR region. Besides, concentrations of IL-1β and IL-18 in supernatant samples were analyzed by using ELISA assay. Expression level of proteins were examined through Western blot.

RESULTS

NEAT1 and YAF2 expression were increased, while miR-5047 expression was declined in the SH-SY5Y cells treated with MPP+ . NEAT1 was a positively regulator to SH-SY5Y cells pyroptosis induced by MPP+ . In addition, YAF2 was a downstream target of miR-5047. NEAT1 promoted YAF2 expression via inhibiting miR-5047. Importantly, the promotion of NEAT1 to SH-SY5Y cells pyroptosis induced by MPP+ was rescued by miR-5047 mimic transfection or YAF2 downregulation.

CONCLUSION

In conclusion, NEAT1 was increased in MPP+ -induced SH-SY5Y cells, and it promoted MPP+ -induced pyroptosis through facilitating YAF2 expression by sponging miR-5047.

Cerebral cortical thickness and cognitive decline in Parkinson's disease

In Parkinson's disease (PD), reduced cerebral cortical thickness may reflect network-based degeneration. This study performed cognitive assessment and brain MRI in 30 PD participants and 41 controls at baseline and 18 months later. We hypothesized that cerebral cortical thickness and volume, as well as change in these metrics, would differ between PD participants who remained cognitively stable and those who experienced cognitive decline. Dividing the participant sample into PD-stable, PD-decline, and control-stable groups, we compared mean cortical thickness and volume within segments that comprise the prefrontal cognitive-control, memory, dorsal spatial, and ventral object-based networks at baseline. We then compared the rate of change in cortical thickness and volume between the same groups using a vertex-wise approach. We found that the PD-decline group had lower cortical thickness within all 4 cognitive networks in comparison with controls, as well as lower cortical thickness within the prefrontal and medial temporal networks in comparison with the PD-stable group. The PD-decline group also experienced a greater rate of volume loss in the lateral temporal cortices in comparison with the control group. This study suggests that lower thickness and volume in prefrontal, medial, and lateral temporal regions may portend cognitive decline in PD.

Morphological changes in Parkinson's disease based on magnetic resonance imaging: A mini-review of subcortical structures segmentation and shape analysis

Parkinson's disease (PD) is a neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, resulting in clinical symptoms, including bradykinesia, resting tremor, rigidity, and postural instability. The pathophysiological changes in PD are inextricably linked to the subcortical structures. Shape analysis is a method for quantifying the volume or surface morphology of structures using magnetic resonance imaging. In this review, we discuss the recent advances in morphological analysis techniques for studying the subcortical structures in PD in vivo. This approach includes available pipelines for volume and shape analysis, focusing on the morphological features of volume and surface area.

Integrated Clinical Features with Plasma and Multi-modal Neuroimaging Biomarkers to Diagnose Mild Cognitive Impairment in Early Drug-Naive Parkinson's Disease

The pathogenesis of cognitive impairment in Parkinson's disease (PD) patients remains unclear, and there is no ideal diagnostic tool available at present. We assessed integrated clinical features with plasma and multi-modal neuroimaging biomarkers to identify mild cognitive impairment (MCI) in early drug-naive PD patients. 49 early drug-naive PD patients, including 26 with MCI (PD-MCI) and 23 with normal cognition (PD-NC), and 20 controls were recruited. Plasma markers [α-synuclein, beta-amyloid 1-40 (Aβ40), beta-amyloid 1-42 (Aβ42), and phosphorylated Tau 181 (p-Tau181) levels], functional connectivity (FC) of the default mode network, and cortical thickness (CTh) were evaluated to identify PD-MCI. The PD-MCI group had significantly higher plasma p-Tau181 levels and p-Tau181/Aβ42 ratio and lower Aβ42/Aβ40 ratio compared to the PD-NC group. Compared to PD-NC, the PD-MCI group showed increased FC between left posterior cingulate cortex (pCC) and the left parahippocampal gyrus (PHG), and between the right hippocampal formation and the left anterior cingulate and paracingulate gyri, and the right middle temporal gyrus. Additionally, the PD-MCI group had thinner cortex thickness in the right lateral occipital and frontal pole compared to the PD-NC group. The final model combining clinical characteristics and several variables (age, sex, plasma p-Tau181 level, Aβ42/Aβ40 ratio, the right lateral occipital CTh, and the FC value between the left pCC and left PHG) had the highest diagnostic accuracy for PD-MCI (AUC = 0.987, 95% CI 0.903-1.000; p = 0.001 compared to age and sex alone). The combination of clinical features, plasma biomarkers, and multi-modal neuroimaging biomarkers can identify early cognitive decline in PD patients.

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.

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.

Tracing embodied word production in persons with Parkinson's disease in distinct motor conditions

Embodied cognition theories posit direct interactions between sensorimotor and mental processing. Various clinical observations have been interpreted in this controversial framework, amongst others, low verb generation in word production tasks performed by persons with Parkinson's disease (PD). If this were the consequence of reduced motor simulation of prevalent action semantics in this word class, reduced PD pathophysiology should result in increased verb production and a general shift of lexical contents towards particular movement-related meanings. 17 persons with PD and bilateral deep brain stimulation (DBS) of the subhtalamic nucleus (STN) and 17 healthy control persons engaged in a semantically unconstrained, phonemic verbal fluency task, the former in both DBS-off and DBS-on states. The analysis referred to the number of words produced, verb use, and the occurrence of different dimensions of movement-related semantics in the lexical output. Persons with PD produced fewer words than controls. In the DBS-off, but not in the DBS-on condition, the proportion of verbs within this reduced output was lower than in controls. Lowered verb production went in parallel with a semantic shift: in persons with PD in the DBS-off, but not the DBS-on condition, the relatedness of produced words to own body-movement was lower than in controls. In persons with PD, DBS induced-changes of the motor condition appear to go along with formal and semantic shifts in word production. The results are compatible with the idea of some impact of motor system states on lexical processing.

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

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

Association of Structural Measurements of Brain Reserve With Motor Progression in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

To investigate the relationship between baseline structural measurements of brain reserve and clinical progression in Parkinson disease (PD). To further provide a possible underlying mechanism for structural measurements of brain reserve in PD, we combined functional and transcriptional data and investigated their relationship with progression-associated patterns derived from structural measurements and longitudinal clinical scores.

METHODS

This longitudinal study collected data from June 2010 to March 2019 from 2 datasets. The Parkinson's Progression Markers Initiative (PPMI) included controls and patients with newly diagnosed PD from 24 participating sites worldwide. Results were confirmed using data from the Huashan dataset (Shanghai, China), which included controls and patients with PD. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores and Schwab & England activities of daily living (ADL). Both datasets were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine whether changes in clinical scores over time differed as a function of brain structural measurements at baseline.

RESULTS

A total of 389 patients with PD (n = 346, age 61.3 ± 10.03, 35% female, PPMI dataset; n = 43, age 59.4 ± 7.3, 38.7% female, Huashan dataset) with T1-MRI and follow-up clinical assessments were included in this study. Results of LME models revealed significant interactions between baseline structural measurements of subcortical regions and time on longitudinal deterioration of clinical scores (MDS-UPDRS Part II, absolute β > 0.27; total MDS-UPDRS scores, absolute β > 1.05; postural instability-gait difficulty (PIGD) score, absolute β > 0.03; Schwab & England ADL, absolute β > 0.59; all p < 0.05, false discovery rate corrected). The interaction of baseline structural measurements of subcortical regions and time on longitudinal deterioration of the PIGD score was replicated using data from Huashan Hospital. Furthermore, the β-coefficients of these interactions recapitulated the spatial distribution of dopaminergic, metabolic, and structural changes between patients with PD and normal controls and the spatial distribution of expression of the α-synuclein gene (SNCA).

DISCUSSION

Patients with PD with greater brain resources (that is, higher deformation-based morphometry values) had greater compensatory capacity, which was associated with slower rates of clinical progression. This knowledge could be used to stratify and monitor patients for clinical trials.

The Intranigral Infusion of Human-Alpha Synuclein Oligomers Induces a Cognitive Impairment in Rats Associated with Changes in Neuronal Firing and Neuroinflammation in the Anterior Cingulate Cortex

Parkinson’s disease (PD) is a complex pathology causing a plethora of non-motor symptoms besides classical motor impairments, including cognitive disturbances. Recent studies in the PD human brain have reported microgliosis in limbic and neocortical structures, suggesting a role for neuroinflammation in the development of cognitive decline. Yet, the mechanism underlying the cognitive pathology is under investigated, mainly for the lack of a valid preclinical neuropathological model reproducing the disease’s motor and non-motor aspects. Here, we show that the bilateral intracerebral infusion of pre-formed human alpha synuclein oligomers (H-αSynOs) within the substantia nigra pars compacta (SNpc) offers a valid model for studying the cognitive symptoms of PD, which adds to the classical motor aspects previously described in the same model. Indeed, H-αSynOs-infused rats displayed memory deficits in the two-trial recognition task in a Y maze and the novel object recognition (NOR) test performed three months after the oligomer infusion. In the anterior cingulate cortex (ACC) of H-αSynOs-infused rats the in vivo electrophysiological activity was altered and the expression of the neuron-specific immediate early gene (IEG) Npas4 (Neuronal PAS domain protein 4) and the AMPA receptor subunit GluR1 were decreased. The histological analysis of the brain of cognitively impaired rats showed a neuroinflammatory response in cognition-related regions such as the ACC and discrete subareas of the hippocampus, in the absence of any evident neuronal loss, supporting a role of neuroinflammation in cognitive decline. We found an increased GFAP reactivity and the acquisition of a proinflammatory phenotype by microglia, as indicated by the increased levels of microglial Tumor Necrosis Factor alpha (TNF-α) as compared to vehicle-infused rats. Moreover, diffused deposits of phospho-alpha synuclein (p-αSyn) and Lewy neurite-like aggregates were found in the SNpc and striatum, suggesting the spreading of toxic protein within anatomically interconnected areas. Altogether, we present a neuropathological rat model of PD that is relevant for the study of cognitive dysfunction featuring the disease. The intranigral infusion of toxic oligomeric species of alpha-synuclein (α-Syn) induced spreading and neuroinflammation in distant cognition-relevant regions, which may drive the altered neuronal activity underlying cognitive deficits.

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.

Impaired Ocular Tracking and Cortical Atrophy in Idiopathic Rapid Eye Movement Sleep Behavior Disorder

BACKGROUND

Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies. Patients with synucleinopathies frequently display eye movement abnormalities. However, whether patients with iRBD have eye movement abnormalities remains unknown.

OBJECTIVE

The aim of this study was to assess eye movement abnormalities and related gray matter alterations and explore whether such abnormalities can serve as biomarkers to indicate phenoconversion to synucleinopathies in iRBD.

METHODS

Forty patients with iRBD with early disease progression and 35 healthy control subjects participated in a 15-minute ocular-tracking task that evaluated their control of eye movement abilities. They also underwent clinical assessments for olfactory function, nonmotor symptoms, and autonomic symptoms, all of which are biomarkers to predict phenoconversion to synucleinopathies in iRBD. A subgroup of the participants (20 patients with iRBD and 20 healthy control subjects) also participated in structural magnetic resonance imaging.

RESULTS

The ocular-tracking ability in patients with iRBD was inferior to that of healthy control subjects in two aspects: pursuit initiation and steady-state tracking. Cortical thinning in the right visual area V4 in patients with iRBD is coupled with impaired pursuit initiation. Furthermore, prolonged pursuit initiation in patients with iRBD exhibits a trend of correlation with olfactory loss, the earliest biomarker that develops prior to other prodromal biomarkers.

CONCLUSIONS

We found ocular-tracking abnormalities in patients with iRBD even early in their disease progression that have not been reported before. These abnormalities are coupled with atrophy of brain areas involved in the perception of object motion and might indicate phenoconversion to synucleinopathies in iRBD. © 2022 International Parkinson and Movement Disorder Society.

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.

Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease

Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.

Kidney damage causally affects the brain cortical structure: A Mendelian randomization study

BACKGROUND

Alterations in the brain cortical structures of patients with chronic kidney disease (CKD) have been reported; however, the cause has not been determined yet. Herein, we used Mendelian randomization (MR) to reveal the causal effect of kidney damage on brain cortical structure.

METHODS

Genome-wide association studies summary data of estimated glomerular filtration rate (eGFR) in 480,698 participants from the CKDGen Consortium were used to identify genetically predicted eGFR. Data from 567,460 individuals from the CKDGen Consortium were used to assess genetically determined CKD; 302,687 participants from the UK Biobank were used to evaluate genetically predicted albuminuria. Further, data from 51,665 patients from the ENIGMA Consortium were used to assess the relationship between genetic predisposition and reduced eGFR, CKD, and progressive albuminuria with alterations in cortical thickness (TH) or surficial area (SA) of the brain. Magnetic resonance imaging was used to measure the SA and TH globally and in 34 functional regions. Inverse-variance weighted was used as the primary estimate whereas MR Pleiotropy RESidual Sum and Outlier, MR-Egger and weighted median were used to detect heterogeneity and pleiotropy.

FINDINGS

At the global level, albuminuria decreased TH (β = -0.07 mm, 95% CI: -0.12 mm to -0.02 mm, P = 0.004); at the functional level, albuminuria reduced TH of pars opercularis gyrus without global weighted (β = -0.11 mm, 95% CI: -0.16 mm to -0.07 mm, P = 3.74×10^-6). No pleiotropy was detected.

INTERPRETATION

Kidney damage causally influences the cortex structure which suggests the existence of a kidney-brain axis.

FUNDING

This study was supported by the Science and Technology Planning Project of Guangdong Province (Grant No. 2020A1515111119 and 2017B020227007), the National Key Research and Development Program of China (Grant No. 2018YFA0902803), the National Natural Science Foundation of China (Grant No. 81825016, 81961128027, 81772719, 81772728), the Key Areas Research and Development Program of Guangdong (Grant No. 2018B010109006), Guangdong Special Support Program (2017TX04R246), Grant KLB09001 from the Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes, and Grants from the Guangdong Science and Technology Department (2020B1212060018).

An individualized prediction of time to cognitive impairment in Parkinson's disease: A combined multi-predictor study

BACKGROUND

Cognitive impairment (CI) is important for the prognosis of Parkinson's disease (PD). Early prediction whether and when cognitive decline from normal cognition (NC) will occur is crucial for preventing or delaying the progression timely. The current study aimed to provide a personalized risk assessment of CI by using baseline information and establishing a multi-predictor nomogram.

METHODS

108 patients with PD were collected from the Parkinson's Progression Markers Initiative (PPMI), of whom 58 had progressed to CI and 50 remained NC during 5-year follow up. Radiomics signatures were obtained by using least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. Clinical factors and laboratory biomarkers were selected by multivariate Cox regression analysis. The combined model of radiomics signatures and clinical risk factors was developed by a multivariate Cox proportional hazard model. A multi-predictor nomogram derived from the combined model was established for individualized estimation of time to progress (TTP) of CI. We analyzed the risk of two subgroups of the combined model by Kaplan-Meier (KM) analysis.

RESULTS

The combined model showed the best performance with a C-index of 0.988 and 0.926 in the training and validation datasets. KM analysis verified significant TTP of CI (P＜0.05) between two subgroups stratified by the cutoff value (-0.058).

CONCLUSION

The combined model and its multi-predictor nomogram can be used to perfectly and individually predict the TTP of CI for patients with PD. Stratification of PD will benefit its timely clinical intervention and the delay and prevention of CI.

Longitudinal clinical, cognitive, and neuroanatomical changes over 5 years in GBA-positive Parkinson's disease patients

OBJECTIVE

To study the longitudinal disease course of Parkinson's disease (PD) patients with glucocerebrosidase (GBA) mutation (GBA-positive) compared to PD non-carriers (GBA-negative) along a 5-year follow-up, evaluating changes in clinical and cognitive outcomes, cortical thickness, and gray-matter (GM) volumes.

METHODS

Ten GBA-positive and 20 GBA-negative PD patients underwent clinical, neuropsychological, and MRI assessments (cortical thickness and subcortical, hippocampal, and amygdala volumes) at study entry and once a year for 5 years. At baseline and at the last visit, each group of patients was compared with 22 age-matched healthy controls. Clinical, cognitive, and MRI features were compared between groups at baseline and over time.

RESULTS

At baseline, GBA-positive and GBA-negative PD patients had similar clinical and cognitive profiles. Compared to GBA-negative and controls, GBA-positive patients showed cortical thinning of left temporal, parietal, and occipital gyri. Over time, compared to GBA-negative, GBA-positive PD patients progressed significantly in motor and cognitive symptoms, and showed a greater pattern of cortical thinning of posterior regions, and frontal and orbito-frontal cortices. After 5 years, compared to controls, GBA-negative PD patients showed a pattern of cortical thinning similar to that showed by GBA-positive cases at baseline. The two groups of patients showed similar patterns of subcortical, hippocampal, and amygdala volume loss over time.

CONCLUSIONS

Compared to GBA-negative PD, GBA-positive patients experienced a more rapid motor and cognitive decline together with a greater, earlier and faster cortical thinning. Cortical thickness measures may be a useful tool for monitoring and predicting PD progression in accordance with the genetic background.

Imaging the Substantia Nigra in Parkinson Disease and Other Parkinsonian Syndromes

Parkinson disease is characterized by dopaminergic cell loss in the substantia nigra of the midbrain. There are various imaging markers for Parkinson disease. Recent advances in MRI have enabled elucidation of the underlying pathophysiologic changes in the nigral structure. This has contributed to accurate and early diagnosis and has improved disease progression monitoring. This article aims to review recent developments in nigral imaging for Parkinson disease and other parkinsonian syndromes, including nigrosome imaging, neuromelanin imaging, quantitative iron mapping, and diffusion-tensor imaging. In particular, this article examines nigrosome imaging using 7-T MRI and 3-T susceptibility-weighted imaging. Finally, this article discusses volumetry and its clinical importance related to symptom manifestation. This review will improve understanding of recent advancements in nigral imaging of Parkinson disease. Published under a CC BY 4.0 license.

Hippocampal correlates of episodic memory in Parkinson's disease: A systematic review of magnetic resonance imaging studies

The present review asks whether magnetic resonance imaging (MRI) studies are able to define neural correlates of episodic memory within the hippocampus in Parkinson's disease (PD). Systematic searches were performed in PubMed, Web of Science, Medline, CINAHL, and EMBASE using search terms related to structural and functional MRI (fMRI), the hippocampus, episodic memory, and PD. Risk of bias was assessed for each study using the Newtown-Ottawa Scale. Thirty-nine studies met inclusion criteria; eight fMRI, seven diffusion MRI (dMRI), and 24 structural MRI (14 exploring whole hippocampus and 10 exploring hippocampal subfields). Critical analysis of the literature revealed mixed evidence from functional and dMRI, but stronger evidence from sMRI of the hippocampus as a biomarker for episodic memory impairment in PD. Hippocampal subfield studies most often implicated CA1, CA3/4, and subiculum volume in episodic memory and cognitive decline in PD. Despite differences in imaging methodology, study design, and sample characteristics, MRI studies have helped elucidate an important neural correlate of episodic memory impairment in PD with both clinical and theoretical implications. Natural progression of this work encourages future research on hippocampal subfield function as a potential biomarker of, or therapeutic target for, episodic memory dysfunction in PD.

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

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

Comparing the Clinical and Neuropsychological Characteristics of Parkinson's Disease With and Without Freezing of Gait

Introduction

Freezing of gait (FOG) is one of the most common walking problems in Parkinson’s disease (PD). Impaired cognitive function is believed to play an important role in developing and aggravating FOG in PD. But some evidence suggests that motor function discrepancy may affect testing results. Therefore, we think it is necessary for PD-FOG(+) and PD-FOG(−) patients to complete neuropsychological tests under similar motor conditions.

Methods

This study recruited 44 idiopathic PD patients [PD-FOG(+) n = 22, PD-FOG(−) n = 22] and 20 age-matched healthy controls (HC). PD-FOG(+) and PD-FOG(−) patients were matched for age, year of education, and Hoehn and Yahr score (H&Y). All participants underwent a comprehensive battery of neuropsychological assessment, and demographical and clinical information was also collected.

Results

PD patients showed poorer cognitive function, higher risks of depression and anxiety, and more neuropsychiatric symptoms compared with HC. When controlling for age, years of education, and H&Y, there were no statistical differences in cognitive function between PD-FOG(+) and PD-FOG(−) patients. But PD-FOG(+) patients had worse motor and non-motor symptoms than PD-FOG(−) patients. PD patients whose motor symptoms initiated with rigidity and initiated unilaterally were more likely to experience FOG.

Conclusion

Traditional neuropsychological testing may not be sensitive enough to detect cognitive impairment in PD. Motor symptoms initiated with rigidity and initiated unilaterally might be an important predictor of FOG.

A selective NLRP3 inflammasome inhibitor attenuates behavioral deficits and neuroinflammation in a mouse model of Parkinson's disease

Nod-like receptor pyrin containing (NLRP)3 inflammasome-mediated neuroinflammation is involved in the pathology of Parkinson's disease (PD), but the roles of other inflammasomes in PD remain unclear. The NLRP3 inhibitor MCC950 exerts neuroprotective effects in several neurological diseases. Using a 1-methyl-4-phenyl-1,2,3,6-tetrahydro pyridine (MPTP)-induced mouse model with or without intraperitoneal MCC950 administration, we assessed whether specifically the NLRP3 inflammasome is activated in the nigrostriatal system and whether MCC950 has therapeutic potential in this PD model. Western blots were used to determine the nigrostriatal expression of inflammasome-specific proteins, including NLRP1, NLRP2, NLRP3, nod-like receptor CARD containing 4 (NLRC4), and absent in melanoma 2 (AIM2). The pole, hanging, and swimming tests were used to assess functional deficits, western blots and immunostainings were used to analyze dopaminergic neuronal degeneration, as well as activation of glial cells and the NLRP3 inflammasome. NLRP3 expression in the nigrostriatal system of MPTP-induced mice was significantly increased compared to control, whereas NLRP1, NLRP2, NLRC4, and AIM2 expression in the nigrostriatal system, as well as NLRP3 expression in the cerebral cortex and hippocampus, were similar in the two groups. Furthermore, MPTP-induced mice exhibited behavioral dysfunctions, dopaminergic neuronal degeneration, and activation of glial cells and the NLRP3 inflammasome. MCC950 treatment of MPTP-induced mice improved behavioral dysfunctions, reduced dopaminergic neuronal degeneration, and inhibited the activation of glial cells and the NLRP3 inflammasome. In conclusion, these findings indicated that NLRP3, not NLRP1, NLRP2, NLRC4, and AIM2, may be the key inflammasome that promotes MPTP-induced pathogenesis. MCC950 protects against MPTP-induced nigrostriatal damage and may be a novel promising therapeutic approach in treating MPTP-induced PD.

The default mode network and cognition in Parkinson's disease: A multimodal resting-state network approach

Involvement of the default mode network (DMN) in cognitive symptoms of Parkinson's disease (PD) has been reported by resting-state functional MRI (rsfMRI) studies. However, the relation to metabolic measures obtained by [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) is largely unknown. We applied multimodal resting-state network analysis to clarify the association between intrinsic metabolic and functional connectivity abnormalities within the DMN and their significance for cognitive symptoms in PD. PD patients were classified into normal cognition (n = 36) and mild cognitive impairment (MCI; n = 12). The DMN was identified by applying an independent component analysis to FDG-PET and rsfMRI data of a matched subset (16 controls and 16 PD patients) of the total cohort. Besides metabolic activity, metabolic and functional connectivity within the DMN were compared between the patients' groups and healthy controls (n = 16). Glucose metabolism was significantly reduced in all DMN nodes in both patient groups compared to controls, with the lowest uptake in PD-MCI (p < .05). Increased metabolic and functional connectivity along fronto-parietal connections was identified in PD-MCI patients compared to controls and unimpaired patients. Functional connectivity negatively correlated with cognitive composite z-scores in patients (r = -.43, p = .005). The current study clarifies the commonalities of metabolic and hemodynamic measures of brain network activity and their individual significance for cognitive symptoms in PD, highlighting the added value of multimodal resting-state network approaches for identifying prospective biomarkers.