Changes of brain structure in Parkinson’s disease patients with mild cognitive impairment analyzed via VBM technology

Neuroanatomical Signature of the Transition from Normal Cognition to MCI in Parkinson's Disease

The progression of Parkinson's disease (PD) is often accompanied by cognitive decline. We had previously developed a brain age estimation program utilizing structural MRI data of 949 healthy individuals from publicly available sources. Structural MRI data of 244 PD patients who were cognitively normal at baseline was acquired from the Parkinson Progression Markers Initiative (PPMI). 192 of these showed stable normal cognitive function from baseline out to 5 years (PD-SNC), and the remaining 52 had unstable normal cognition and developed mild cognitive impairment within 5 years (PD-UNC). 105 healthy controls were also included in the analysis as a reference. First, we examined if there were any baseline differences in regional brain structure between PD-UNC and PD-SNC cohorts utilizing the three most widely used atrophy estimation pipelines, i.e., voxel-based morphometry (VBM), deformation-based morphometry and cortical thickness analyses. We then investigated if accelerated brain age estimation with our multivariate regressive machine learning algorithm was different across these groups (HC, PD-SNC, and PD-UNC). As per the VBM analysis, PD-UNC patients demonstrated a noticeable increase in GM volume in the posterior and anterior lobes of the cerebellum, sub-lobar, extra-nuclear, thalamus, and pulvinar regions when compared to PD-SNC at baseline. PD-UNC patients were observed to have significantly older brain age compared to both PD-SNC patients (p=0.009) and healthy controls (p<0.009). The increase in GM volume in the PD-UNC group could potentially indicate an inflammatory or neuronal hypertrophy response, which could serve as a biomarker for future cognitive decline among this population.

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.

Predicting the occurrence of mild cognitive impairment in Parkinson's disease using structural MRI data

Introduction

Mild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson's disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early anatomical brain changes that precede the transition from healthy cognition to MCI in PD.

Methods

Structural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson's Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC, n = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, n = 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier.

Results

The average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain.

Discussion

Our machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.

Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease

BACKGROUND

Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.

OBJECTIVES

The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.

METHODS

Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models.

RESULTS

FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity.

CONCLUSIONS

Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Voxel-based morphometry of grey matter structures in Parkinson's Disease with wearing-off

Our study aimed to investigate the grey matter (GM) changes using voxel-based morphometry (VBM) in Parkinson's disease (PD) patients with wearing-off (WO). 3D-T1-weighted imaging was performed on 48 PD patients without wearing-off (PD-nWO), 39 PD patients with wearing-off (PD-WO) and 47 age and sex-matched healthy controls (HCs). 3D structural images were analyzed by VBM procedure with Statistical Parametric Mapping (SPM12) to detect grey matter volume. Widespread areas of grey matter changes were found in patients among three groups (in bilateral frontal, temporal lobes, lingual gyrus, inferior occipital gyrus, right precuneus, right superior parietal gyrus and right cerebellum). Grey matter reductions were found in frontal lobe (right middle frontal gyrus, superior frontal gyrus and precentral gyrus), right parietal lobe (precuneus, superior parietal gyrus, postcentral gyrus), right temporal lobe (superior temporal gyrus, middle temporal gyrus), bilateral lingual gyrus and inferior occipital gyrus in PD-WO group compared with the PD-nWO group. Our results suggesting that wearing-off may be associated with grey matter atrophy in the cortical areas. These findings may aid in a better understanding of the brain degeneration process in PD with wearing-off.

White matter changes in Parkinson's disease

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

Magnetic Resonance T1w/T2w Ratio in the Putamen and Cerebellum as a Marker of Cognitive Impairment in MSA: a Longitudinal Study

The exact pathophysiology of cognitive impairment in multiple system atrophy (MSA) is unclear. In our longitudinal study, we aimed to analyze (I) the relationships between cognitive functions and some subcortical structures, such as putamen and cerebellum assessed by voxel-based morphometry (VBM) and T1-weighted/T2-weighted (T1w/T2w) ratio, and (II) the neuroimaging predictors of the progression of cognitive deficits. Twenty-six patients with MSA underwent a comprehensive neuropsychological battery, motor examination, and brain MRI at baseline (T0) and 1-year follow-up (T1). Patients were then divided according to cognitive status into MSA with normal cognition (MSA-NC) and MSA with mild cognitive impairment (MCI). At T1, we divided the sample according to worsening/non worsening of cognitive status compared to baseline evaluation. Logistic regression analysis showed that age (β =  - 9.45, p = .02) and T1w/T2w value in the left putamen (β = 230.64, p = .01) were significant predictors of global cognitive status at T0, explaining 65% of the variance. Logistic regression analysis showed that ∆-values of WM density in the cerebellum/brainstem (β = 2188.70, p = .02) significantly predicted cognitive worsening at T1, explaining 64% of the variance. Our results suggest a role for the putamen and cerebellum in the cognitive changes of MSA, probably due to their connections with the cortex. The putaminal T1w/T2w ratio may deserve further studies as a marker of cognitive impairment in MSA.

The Influence of Healthy Habits on Cognitive Functions in a Group of Hemodialysis Patients

(1) Background: Cognitive impairment (CI) is more prevalent in hemodialysis (HD) patients than in the general population. The purpose of this study was to examine if behavioral, clinical, and vascular variables are linked with CI in individuals with HD. (2) Methods: Initially, 47 individuals with chronic HD volunteered to participate in the trial, but only 27 patients ultimately completed the Montreal Cognitive Assessment (MoCA) and the Computerized Cognitive Assessment Tool (CompBased-CAT). We collected information on smoking, mental activities, physical activity (Rapid Assessment of Physical Activity, RAPA), and comorbidity. The oxygen saturation (rSO2) and pulse wave velocity (PWV; IEM Mobil-O-Graph) of the frontal lobes were measured. (3) Results: Significant associations were discovered between MoCA and rSO2 (r = 0.44, p = 0.02 and r = 0.62, p = 0.001, right/left, respectively), PWV (r = -0.69, p = 0.0001), CCI (r = 0.59, p = 0.001), and RAPA (r = 0.72, p = 0.0001). Those who actively occupied their time during dialysis and non-smokers achieved higher cognitive exam results. A multivariate regression study demonstrated that physical activity (RAPA) and PWV had separate effects on cognitive performance. (4) Conclusions: Cognitive skills are related to inter-dialysis healthy habits (physical activity, smoking) and intra-dialysis activities (tasks and mind games). Arterial stiffness, oxygenation of the frontal lobes, and CCI were linked with CI.

GBA1 Gene Mutations in α-Synucleinopathies-Molecular Mechanisms Underlying Pathology and Their Clinical Significance

α-Synucleinopathies comprise a group of neurodegenerative diseases characterized by altered accumulation of a protein called α-synuclein inside neurons and glial cells. This aggregation leads to the formation of intraneuronal inclusions, Lewy bodies, that constitute the hallmark of α-synuclein pathology. The most prevalent α-synucleinopathies are Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). To date, only symptomatic treatment is available for these disorders, hence new approaches to their therapy are needed. It has been observed that GBA1 mutations are one of the most impactful risk factors for developing α-synucleinopathies such as PD and DLB. Mutations in the GBA1 gene, which encodes a lysosomal hydrolase β-glucocerebrosidase (GCase), cause a reduction in GCase activity and impaired α-synuclein metabolism. The most abundant GBA1 gene mutations are N370S or N409S, L444P/L483P and E326K/E365K. The mechanisms by which GCase impacts α-synuclein aggregation are poorly understood and need to be further investigated. Here, we discuss some of the potential interactions between α-synuclein and GCase and show how GBA1 mutations may impact the course of the most prevalent α-synucleinopathies.

Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease

Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

The structural changes of gray matter in Parkinson disease patients with mild cognitive impairments

Objectives

Parkinson disease (PD) is associated with cognitive impairments. However, the underlying neural mechanism of cognitive impairments in PD is still not clear. This study aimed to investigate the anatomic alternations of gray matter in PD patients with mild cognitive impairment (MCI) and their associations with neurocognitive measurements.

Methods

T1-weighted magnetic resonance imaging (MRI) data were acquired from 23 PD patients with MCI, 23 PD patients without MCI, and 23 matched healthy controls. The MRI data were analyzed using voxel-based morphometry (VBM) and surfaced-based morphometry (SBM) methods to assess the structural changes in gray matter volume and cortical thickness respectively. Receiver operating characteristic (ROC) analysis was used to examine the diagnostic accuracies of the indexes of interest. The correlations between the structural metrics and neurocognitive assessments (e.g., Montreal cognitive assessment, MOCA; Mini-mental state examination, MMSE) were further examined.

Results

PD patients with MCI showed reduced gray matter volume (GMV) in the frontal cortex (e.g., right inferior frontal gyrus and middle frontal gyrus) and extended to insula as well as cerebellum compared with the healthy controls and PD patients without MIC. Thinner of cortical thickens in the temporal lobe (e.g., left middle temporal gyrus and right superior temporal gyrus) extending to parietal cortex (e.g., precuneus) were found in the PD patients with MCI relative to the healthy controls and PD patients without MCI.ROC analysis indicated that the area under the ROC curve (AUC) values in the frontal, temporal, and subcortical structures (e.g., insula and cerebellum) could differentiate the PD patients with MCI and without MCI and healthy controls. Furthermore, GMV of the right middle frontal gyrus and cortical thickness of the right superior temporal gyrus were correlated with neurocognitive dysfunctions (e.g., MOCA and MMSE) in PD patients with MCI.

Conclusion

This study provided further evidence that PD with MCI was associated with structural alternations of brain. Morphometric analysis focusing on the cortical and subcortical regions could be biomarkers of cognitive impairments in PD patients.

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning

Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.

The effect of the PARK16 rs11240572 variant on brain structure in Parkinson's disease

Increasing evidence suggests that genetic factors play a key role in the development of Parkinson's disease (PD). The variant rs11240572 in the PARK16 gene locus is strongly associated with PD. However, its effect on the pathogenesis of PD is yet to be clarified. The objective of the study was to explore the effect of the PARK16 rs11240572 variant on brain structure in PD patients. A total of 51 PD patients were enrolled in the study and genotyped for the rs11240572 variant. Clinical assessments and MRI scans were conducted across all participants. Voxel-based morphometry (VBM) was used to investigate gray matter volume (GMV) of the whole brain between these two groups. Correlation analysis was performed to identify the relationships between GMV and clinical features. There were 17 rs11240572-A variant carriers and 34 non-carriers, with no significant demographic differences between these two groups. Compared with non-carriers, rs11240572-A carriers showed increased GMV in the left caudate nucleus and putamen, but decreased GMV in the left superior temporal gyrus and supramarginal gyrus. In non-carriers, left basal ganglia GMV was positively correlated with UPDRS III (r = 0.365, p = 0.034) and bradykinesia (r = 0.352, p = 0.042), but negatively correlated with MMSE (r =  - 0.344, p = 0.047), while in carriers negative correlation between basal ganglia GMV and MMSE was also observed (r =  - 0.666, p = 0.004). Moreover, the GMV of left temporoparietal cortex was positively associated with cognitive function in both groups (carriers, r = 0.692, p = 0.002; non-carriers, r = 0.879, p < 0.001). When reducing the sample size of non-carriers to the level of the carrier sample, similar correlations were observed in both groups. Our study showed that the PARK16 rs11240572 variant affects the brain structure of patients with PD, especially in the basal ganglia and temporoparietal cortex. This indicated that this variant might play an important role in the pathogenesis of PD.

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

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

Thalamic volume mediates associations between cardiorespiratory fitness (VO2max) and cognition in Parkinson's disease

INTRODUCTION

Cognitive deficits occur in Parkinson's disease (PD). Cardiorespiratory fitness (CRF) is associated with better cognitive performance in aging especially in executive function (EF) and memory. The association between CRF and cognitive performance is understudied in people with PD. Brain structures underlying associations also remains unknown. This cross-sectional study examined the associations between CRF and cognitive performance in PD. We also examined associations between CRF and brain structures impacted in PD. Mediation analysis were conducted to examine whether brain structures impacted in PD mediate putative associations between CRF and cognitive performance.

METHODS

Individuals with PD (N = 33) underwent magnetic resonance imaging (MRI), CRF evaluation (estimated VO₂max), and neuropsychological assessment. Composite cognitive scores of episodic memory, EF, attention, language, and visuospatial functioning were generated. Structural equation models were constructed to examine whether MRI volume estimates (thalamus and pallidum) mediated associations between CRF and cognitive performance (adjusting for age, education, PD disease duration, sex, MDS-UPDRS motor score, and total intracranial volume).

RESULTS

Higher CRF was associated with better episodic memory (Standardized β = 0.391; p = 0.008), EF (Standardized β = 0.324; p = 0.025), and visuospatial performance (Standardized β = 0.570; p = 0.005). Higher CRF was associated with larger thalamic (Standardized β = 0.722; p = 0.004) and pallidum (Standardized β = 0.635; p = 0.004) volumes. Thalamic volume mediated the association between higher CRF and better EF (Indirect effect = 0.309) and episodic memory (Indirect effect = 0.209) performance (p < 0.05). The pallidum did not significantly mediate associations between CRF and cognitive outcomes.

CONCLUSION

The thalamus plays an important role in the association between CRF and both EF and episodic memory in PD.

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

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

Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson's Disease Patients With Mild Cognitive Impairment

Recent studies have demonstrated structural and functional alterations in Parkinson's disease (PD) with mild cognitive impairment (MCI). However, the topological patterns of functional brain networks in newly diagnosed PD patients with MCI are unclear so far. In this study, we used functional magnetic resonance imaging (fMRI) and graph theory approaches to explore the functional brain network in 45 PD patients with MCI (PD-MCI), 22 PD patients without MCI (PD-nMCI), and 18 healthy controls (HC). We found that the PD-MCI, PD-nMCI, and HC groups exhibited a small-world architecture in the functional brain network. However, early-stage PD-MCI patients had decreased clustering coefficient, increased characteristic path length, and changed nodal centrality in the default mode network (DMN), control network (CN), somatomotor network (SMN), and visual network (VN), which might contribute to factors for MCI symptoms in PD patients. Our results demonstrated that PD-MCI patients were associated with disrupted topological organization in the functional network, thus providing a topological network insight into the role of information exchange in the underlying development of MCI symptoms in PD patients.

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

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

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

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

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

Nondisplaceable Binding Is a Potential Confounding Factor in 11C-PBR28 Translocator Protein PET Studies

The PET ligand ¹¹C-PBR28 (N-((2-(methoxy-¹¹C)-phenyl)methyl)-N-(6-phenoxy-3-pyridinyl)acetamide) binds to the 18-kDa translocator protein (TSPO), a biomarker of glia. In clinical studies of TSPO, the ligand total distribution volume, V_T, is frequently the reported outcome measure. Since V_T is the sum of the ligand-specific distribution volume (V_S) and the nondisplaceable-binding distribution volume (V_ND), differences in V_ND across subjects and groups will have an impact on V_T Methods: Here, we used a recently developed method for simultaneous estimation of V_ND (SIME) to disentangle contributions from V_ND and V_S Data from 4 previously published ¹¹C-PBR28 PET studies were included: before and after a lipopolysaccharide challenge (8 subjects), in alcohol use disorder (14 patients, 15 controls), in first-episode psychosis (16 patients, 16 controls), and in Parkinson disease (16 patients, 16 controls). In each dataset, regional V_T estimates were obtained with a standard 2-tissue-compartment model, and brain-wide V_ND was estimated with SIME. V_S was then calculated as V_T - V_ND V_ND and V_S were then compared across groups, within each dataset. Results: A lower V_ND was found for individuals with alcohol-use disorder (34%, P = 0.00084) and Parkinson disease (34%, P = 0.0032) than in their corresponding controls. We found no difference in V_ND between first-episode psychosis patients and their controls, and the administration of lipopolysaccharide did not change V_ND Conclusion: Our findings suggest that in TSPO PET studies, nondisplaceable binding can differ between patient groups and conditions and should therefore be considered.

Characterization of a pathogenic variant in GBA for Parkinson's disease with mild cognitive impairment patients

Parkinson's disease (PD) is the second most common neurodegenerative disease, and mild cognitive impairment (MCI) is a well-established risk factor for the development of dementia in PD. A growing body of evidence suggests that low expression of glucocerebrosidase (GBA) promotes the transmission of α-synuclein (α-Syn) interpolymers and the progression of PD. However, how GBA mutations affect the pathogenesis of PD via abnormal aggregation of α-Syn is unclear, and no clinically valid PD-MCI genetic markers have been identified. Here, we first located a GBA eQTL, rs12411216, by analysing DHS, eQTL SNP, and transcription factor binding site data using the UCSC database. Subsequently, we found that rs12411216 was significantly associated with PD-MCI (P < 0.05) in 306 PD patients by genotyping. In exploring the relationship between rs12411216 and GBA expression, the SNP was found to be associated with GBA expression in 50 PD patients through qPCR verification. In a further CRISPR/Cas9-mediated genome editing module, the SNP was identified to cause a decrease in GBA expression, weaken enzymatic activity and enhance the abnormal aggregation of α-Syn in SH-SY5Y cells. Additionally, using an electrophoretic mobility shift assay, we confirmed that the binding efficiency of transcription factor E2F4 was affected by the rs12411216 SNP. In conclusion, our results showed that rs12411216 regulated GBA expression, supporting its potential role as a PD-MCI genetic biomarker and highlighting novel mechanisms underlying Parkinson's disease.

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Longitudinal gray matter changes of the pain matrix in patients with carbon monoxide intoxication: A voxel-based morphometry study

PURPOSE

Carbon monoxide (CO) intoxication causes gray matter (GM) changes and headache symptom in patients with CO intoxication, but the headache-associated GM changes are not well understood. The purpose of this study was to perform a voxel-based morphometry (VBM) analysis to investigate longitudinal GM changes of brain pain matrix in patients with CO intoxication.

METHODS

This prospective study enrolled 24 patients with CO intoxication and 20 healthy controls. Whole brain high-resolution T1-weighted images were acquired in both groups and were repeated in patients at 1 week, and 1, 3, and 9 months after CO exposure. VBM was performed to detect global GM changes in patients with CO intoxication, and the automated anatomical labeling template was utilized to estimate the distribution of significant GM clusters in the brain.

RESULTS

GM volumes were significantly decreased mainly in the frontal and occipital lobes, including several pain-matrix regions 1 week after CO intoxication. The regions with significant GM changes further involved the central GM structures and the periaqueductal gray (pain-modulating center) at 1 and 3 months after CO intoxication, but the alterations were partially normalized in the frontal lobe and cerebellum 9 months after CO intoxication. Significant negative correlations were revealed between GM volume and duration of coma in the pain matrix regions. Moreover, five patients exhibited delayed neuropsychiatric sequelae (DNS) and had greater GM volume changes than non-DNS patients.

CONCLUSION

VBM analysis is helpful to understand the longitudinal GM changes of the pain matrix in patients with CO intoxication.

Regional gray matter reductions associated with mild cognitive impairment in Parkinson's disease: A meta-analysis of voxel-based morphometry studies

Mild cognitive impairment (MCI) is inconclusively associated with regional gray matter (GM) abnormalities in Parkinson's disease (PD). We aimed to quantitatively evaluate whole-brain voxel-based morphometry (VBM) studies that have investigated brain GM changes in PD patients with MCI (PD-MCI). Seed-based d Mapping, a well-validated coordinate-based meta-analytic approach, was utilized. We included 20 VBM studies that reported 22 datasets containing 504 patients with PD-MCI and 554 PD patients without MCI (PD-NCI). The most reliable finding identified in this meta-analysis was that patients with PD-MCI exhibited greater GM atrophy in the left anterior insula than those with PD-NCI. Our findings further suggest that several moderators (age, gender, educational level, disease stage, severity of motor disability, and the severity of cognitive impairments) in PD-MCI individuals, as well as scanner field-strength, may drive heterogeneous GM changes across studies. GM abnormalities in the anterior insula, an important cognitive hub involved in switching between neural networks, contribute to understanding the neural substrates of MCI in PD, which may serve as a biomarker of PD-MCI.

Advances in the Research of Risk Factors and Prodromal Biomarkers of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. With the advent of an aging population and improving life expectancy worldwide, the number of PD patients is expected to increase, which may lead to an urgent need for effective preventive and diagnostic strategies for PD. Although there is increasing research regarding the pathogenesis of PD, there is limited knowledge regarding the prevention of PD. Moreover, the diagnosis of PD depends on clinical criteria, which require the occurrence of bradykinesia and at least one symptom of rest tremor or rigidity. However, converging evidence from clinical, genetic, neuropathological, and imaging studies suggests the initiation of PD-specific pathology prior to the initial presentation of these classical motor clinical features by years or decades. This latent stage of neurodegeneration in PD is a particularly important stage for effective neuroprotective therapies, which might retard the progression or prevent the onset of PD. Therefore, the exploration of risk factors and premotor biomarkers is not only crucial to the early diagnosis of PD but is also helpful in the development of effective neuroprotection and health care strategies for appropriate populations at risk for PD. In this review, we searched and summarized ∼249 researches and 31 reviews focusing on the risk factors and prodromal biomarkers of PD and published in MEDLINE.

Cingulate cortex in Parkinson's disease

Once a diagnosis of Parkinson's disease (PD) has been made, even in its earliest prodromal form of subjective memory impairment, cognitive impairment has begun and involves anterior cingulate cortex (ACC). While the Braak staging scheme showed mid- to later-stage PD progression from cingulate allocortex adjacent to the corpus callosum and progressing into its neocortical moieties, the last decade has produced substantial information on the role of cingulate cortex in multiple symptoms, not just global measures of cognition. Voxel-based morphometry has been used in many studies of mild cognitive impairment (MCI) in PD to show reduced thickness in ACC and posterior cingulate cortex (PCC). Regional cerebral blood flow is altered in association with verbal IQ in all the PCC and anterior midcingulate cortex and executive impairments in ACC. Diffusion tensor imaging shows reduced fractional anisotropy throughout the entire cingulum bundle. Amnestic MCI is associated with reduced dopamine-2 receptor binding in ACC and, even in cognitively normal PD cases, dopaminergic pathways in ACC are impaired early in association with executive and language functions. The cholinergic system also has substantial changes in nicotinic and muscarinic receptor binding, and therapy with donepezil improves Mini-Mental State Exam scores and metabolism in pACC and dPCC. Cingulate cortex is also engaged in two critical symptoms: apathy and visual hallucinations. Finally, one can be optimistic that cingulate cortex will play an important role in developing new biomarkers of early PD. These methods have already been shown to be useful in cingulate cortex and include magnetic resonance spectroscopy, next-generation gene expression, and the new α-synuclein proximity ligation assay that specifically recognizes α-synuclein oligomers. Thus the future is bright for developing multivariate, multimodal biomarkers that include cingulate cortex.

Decreased Gray Matter Volume of Right Inferior Parietal Lobule Is Associated With Severity of Mental Disorientation in Patients With Mild Cognitive Impairment

Background: Mental disorientation in time, space, and with respect to people is common in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Recently, a high-resolution functional MRI (fMRI) study revealed that the inferior parietal lobule (IPL) and precuneus are important regions related to mental orientation in healthy individuals. We hypothesized that the IPL and/or precuneus are crucial regions for mental disorientation in patients with amnestic MCI (aMCI). Therefore, our aim was to assess our hypothesis in these patients using voxel-based morphometry (VBM).

Methods: Fifteen patients with aMCI participated. The Neurobehavioral Cognitive Status Examination (COGNISTAT) as well as the Mini-Mental State Examination (MMSE) were used to evaluate mental disorientation. Subsequently, we used VBM analysis to identify brain regions that exhibited gray matter (GM) volume loss associated with mental disorientation. Based on our hypothesis, four brain regions (bilateral IPLs and precuneus) were selected as regions of interest (ROIs).

Results: We found a significant decreased GM volume in the right IPL, which was correlated with lower orientation scores on the COGNISTAT. In contrast, GM volume in other ROIs did not show a significant positive correlation with mental disorientation. Regarding the MMSE, no significant reduction in GM associated with decline in orientation were observed in any ROI.

Conclusion: We found the significant relationship between low GM volume in the right IPL and severity of mental disorientation. Therefore, the right IPL is responsible for mental disorientation in aMCI.

Structural MRI in Idiopathic Parkinson's Disease

Among modern neuroimaging modalities, magnetic resonance imaging (MRI) is a widely available, non-invasive, and cost-effective method to detect structural and functional abnormalities related to neurodegenerative disorders. In the last decades, MRI have been widely implemented to support PD diagnosis as well as to provide further insights into motor and non-motor symptoms pathophysiology, complications and treatment-related effects. Different aspects of the brain morphology and function may be derived from a single scan, by applying different analytic approaches. Biomarkers of neurodegeneration as well as tissue microstructural changes may be extracted from structural MRI techniques. In this chapter, we analyze the role of structural imaging to differentiate PD patients from controls and to define neural substrates of motor and non-motor PD symptoms. Evidence collected in the premotor PD phase will be also critically discussed. White matter as well as gray matter integrity imaging studies has been reviewed, aiming to highlight points of strength and limits to their potential application in clinical settings.

Can neuroimaging predict dementia in Parkinson's disease?

Dementia in Parkinson's disease affects 50% of patients within 10 years of diagnosis but there is wide variation in severity and timing. Thus, robust neuroimaging prediction of cognitive involvement in Parkinson's disease is important: (i) to identify at-risk individuals for clinical trials of potential new treatments; (ii) to provide reliable prognostic information for individuals and populations; and (iii) to shed light on the pathophysiological processes underpinning Parkinson's disease dementia. To date, neuroimaging has not made major contributions to predicting cognitive involvement in Parkinson's disease. This is perhaps unsurprising considering conventional methods rely on macroscopic measures of topographically distributed neurodegeneration, a relatively late event in Parkinson's dementia. However, new technologies are now emerging that could provide important insights through detection of other potentially relevant processes. For example, novel MRI approaches can quantify magnetic susceptibility as a surrogate for tissue iron content, and increasingly powerful mathematical approaches can characterize the topology of brain networks at the systems level. Here, we present an up-to-date overview of the growing role of neuroimaging in predicting dementia in Parkinson's disease. We discuss the most relevant findings to date, and consider the potential of emerging technologies to detect the earliest signs of cognitive involvement in Parkinson's disease.

Readability of the 100 Most-Cited Neuroimaging Papers Assessed by Common Readability Formulae

Background: From time to time, neuroimaging research findings receive press coverage and attention by the general public. Scientific articles therefore should be written in a readable manner to facilitate knowledge translation and dissemination. However, no published readability report on neuroimaging articles like those published in education, medical and marketing journals is available. As a start, this study therefore aimed to evaluate the readability of the most-cited neuroimaging articles.

Methods: The 100 most-cited articles in neuroimaging identified in a recent study by Kim et al. (2016) were evaluated. Headings, mathematical equations, tables, figures, footnotes, appendices, and reference lists were trimmed from the articles. The rest was processed for number of characters, words and sentences. Five readability indices that indicate the school grade appropriate for that reading difficulty (Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Gunning Fog index and Simple Measure of Gobbledygook index) were computed. An average reading grade level (AGL) was calculated by taking the mean of these five indices. The Flesch Reading Ease (FRE) score was also computed. The readability of the trimmed abstracts and full texts was evaluated against number of authors, country of corresponding author, total citation count, normalized citation count, article type, publication year, impact factor of the year published and type of journal.

Results: Mean AGL ± standard deviation (SD) of the trimmed abstracts and full texts were 17.15 ± 2.81 (college graduate level) and 14.22 ± 1.66 (college level) respectively. Mean FRE score ± SD of the abstracts and full texts were 15.70 ± 14.11 (college graduate level) and 32.11 ± 8.56 (college level) respectively. Both items indicated that the full texts were significantly more readable than the abstracts (p < 0.001). Abstract readability was not associated with any factors under investigation. ANCOVAs showed that review/meta-analysis (mean AGL ± SD: 16.0 ± 1.4) and higher impact factor significantly associated with lower readability of the trimmed full texts surveyed.

Conclusion: Concerning the 100 most-cited articles in neuroimaging, the full text appears to be more readable than the abstracts. Experimental articles and methodology papers were more readable than reviews/meta-analyses. Articles published in journals with higher impact factors were less readable.

Brain MRI morphometric analysis in Parkinson's disease patients with sleep disturbances

Background

Sleep disturbances are common in patients with advanced Parkinson disease (PD). The aim of this study was to evaluate a possible association of cortical thickness, cortical and subcortical volume with sleep disturbances in PD patients.

Methods

Twenty-eight PD patients (14 men and 14 women, median age 58 years) were evaluated for sleep disturbances with PDSS and underwent brain MRI. Control group consisted of 28 healthy volunteers who were matched by age and gender. Automated voxel based image analysis was performed with the FreeSurfer software.

Results

PD patients when compared to controls had larger ventricles, smaller volumes of hippocampus and superior cerebellar peduncle, smaller grey matter thickness in the left fusiform, parahipocampal and precentral gyruses, and right caudal anterior cingulate, parahipocampal and precentral hemisphere gyruses, as well as smaller volume of left rostral middle frontal and frontal pole areas, and right entorhinal and transverse temporal areas. According to the Parkinson’s disease Sleep Scale (PDSS), 15 (53.58%) patients had severely disturbed sleep. The most frequent complaints were difficulties staying asleep during the night and nocturia. The least frequent sleep disturbances were distressing hallucinations and urine incontinence due to off symptoms. Patients who fidgeted during the night had thicker white matter in the left caudal middle frontal area and lesser global left hemisphere cortical surface, especially in the lateral orbitofrontal and lateral occipital area, and right hemisphere medial orbitofrontal area. Patients with frequent distressful dreams had white matter reduction in cingulate area, and cortical surface reduction in left paracentral area, inferior frontal gyrus and right postcentral and superior frontal areas. Nocturnal hallucinations were associated with volume reduction in the basal ganglia, nucleus accumbens and putamen bilaterally. Patients with disturbing nocturia had reduction of cortical surface on the left pre- and postcentral areas, total white matter volume decrease bilaterally as well in the pons.

Conclusions

PD patients with nocturnal hallucinations had prominent basal ganglia volume reduction. Distressful dreams were associated with limbic system and frontal white matter changes, meanwhile nocturia was mostly associated with global white matter reduction and surface reduction of cortical surface on the left hemisphere pre- and postcentral areas.

Structural and Functional Brain Patterns of Non-Motor Syndromes in Parkinson's Disease

Parkinson’s disease (PD) is a common, progressive and multisystem neurodegenerative disorder characterized by motor and non-motor symptoms. Advanced magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging can render the view toward understanding the neural basis of these non-motor syndromes, as they help to understand the underlying pathophysiological abnormalities. This review provides an up-to-date description of structural and functional brain alterations in patients with PD with cognitive deficits, visual hallucinations, fatigue, impulsive behavior disorders, sleep disorders, and pain.