Structural Brain Network Alteration and its Correlation With Structural Impairments in Patients With Depression in de novo and Drug-Naïve Parkinson's Disease

Risk of Parkinson's disease and depression severity in different populations: A two-sample Mendelian randomization analysis

BACKGROUND

Depression is widely recognized as a common non-motor symptom of Parkinson's disease (PD). Across different studies, the reported prevalence of depression in PD varies widely, ranging from 2.7% to 90%, but it is unclear whether this association is due to genetic or acquired factors. Whether there is a causal relationship remains unknown. The aim of this study was to use a two-sample Mendelian randomization (MR) approach to investigate the causal effect of PD on depression.

METHODS

Analyses were conducted separately for individuals of European and East Asian ancestry using publicly available summary data from genome-wide association studies. Depression was divided into two categories: ever depressed for a whole week and major depressive disorder (MDD). PD data were used as the exposure and were obtained from the International Parkinson's Disease Genomics Consortium and the BioBank Japan PheWeb, while depression data were used as the outcome and were obtained from the ntegrative Epidemiology Unit (IEU) Open GWAS Project(A public GWAS database） and the Psychiatric Genomics Consortium. The influence of PD on depression was assessed using inverse variance weighted (IVW), weighted median, MR-Egger, and weighted mode methods. Heterogeneity and pleiotropy were tested, and the results were validated using FinnGen GWAS data from version R9.

RESULTS

In individuals of European ancestry, there was a causal relationship between PD and ever depressed for a whole week (IVW method, odds ratio [OR] = 0.990; 95% CI, 0.984-0.996; p = .002), but no causal relationship was observed between PD and MDD (IVW method, OR = 0.974; 95% CI, 0.942-1.009; p = .141). In individuals of East Asian ancestry, no causal relationship was observed between PD and ever depressed for a whole week (IVW method, OR = 1.001; 95% CI, 0.829-1.209; p = .990) and between PD and MDD (IVW method, OR = 1.017; 95% CI, 0.982-1.052; p = .342). The results of the three additional analysis methods were similar to those of the IVW method, and there was no heterogeneity according to Cochran's Q-test. There was no evidence of pleiotropy based on MR-Egger intercept test and MR-PRESSO. The FinnGen validation dataset supported these findings. The results are stable and reliable.

CONCLUSION

The observed increase in depression among PD patients could potentially be attributed to modifiable acquired factors. Consequently, there is an urgent need to strengthen the management of PD patients in order to prevent the development of depression in the future.

Aberrant brain topological organization and granger causality connectivity in Parkinson's disease with impulse control disorders

Introduction

Impulse control disorders (ICDs) refer to the common neuropsychiatric complication of Parkinson's disease (PD). The white matter (WM) topological organization and its impact on brain networks remain to be established.

Methods

A total of 17 PD patients with ICD (PD-ICD), 17 without ICD (PD-NICD), and 18 healthy controls (HCs) were recruited. Graph theoretic analyses and Granger causality analyses were combined to investigate WM topological organization and the directional connection patterns of key regions.

Results

Compared to PD-NICD, ICD patients showed abnormal global properties, including decreased shortest path length (Lp) and increased global efficiency (Eg). Locally, the ICD group manifested abnormal nodal topological parameters predominantly in the left middle cingulate gyrus (MCG) and left superior cerebellum. Decreased directional connectivity from the left MCG to the right medial superior frontal gyrus was observed in the PD-ICD group. ICD severity was significantly correlated with Lp and Eg.

Discussion

Our findings reflected that ICD patients had excessively optimized WM topological organization, abnormally strengthened nodal structure connections within the reward network, and aberrant causal connectivity in specific cortical- limbic circuits. We hypothesized that the aberrant reward and motor inhibition circuit could play a crucial role in the emergence of ICDs.

White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model

Background

Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health.

Methods

This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model.

Results

In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively.

Conclusions

The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

Disrupted topologic efficiency of brain functional connectome in de novo Parkinson's disease with depression

Growing evidence supports that depression in Parkinson's disease (PD) depends on disruptions in specific neural networks rather than regional dysfunction. According to the resting-state functional magnetic resonance imaging data, the study attempted to decipher the alterations in the topological properties of brain networks in de novo depression in PD (DPD). The study also explored the neural network basis for depressive symptoms in PD. We recruited 20 DPD, 37 non-depressed PD and 41 healthy controls (HC). The Graph theory and network-based statistical methods helped analyse the topological properties of brain functional networks and anomalous subnetworks across these groups. The relationship between altered properties and depression severity was also investigated. DPD revealed significantly reduced nodal efficiency in the left superior temporal gyrus. Additionally, DPD decreased five hubs, primarily located in the temporal-occipital cortex, and increased seven hubs, mainly distributed in the limbic cortico-basal ganglia circuit. The betweenness centrality of the left Medio Ventral Occipital Cortex was positively associated with depressive scores in DPD. In contrast to HC, DPD had a multi-connected subnetwork with significantly lower connectivity, primarily distributed in the visual, somatomotor, dorsal attention and default networks. Regional topological disruptions in the temporal-occipital region are critical in the DPD neurological mechanism. It might suggest a potential network biomarker among newly diagnosed DPD patients.

Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis

Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.

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.

Tree representations of brain structural connectivity via persistent homology

The brain structural connectome is generated by a collection of white matter fiber bundles constructed from diffusion weighted MRI (dMRI), acting as highways for neural activity. There has been abundant interest in studying how the structural connectome varies across individuals in relation to their traits, ranging from age and gender to neuropsychiatric outcomes. After applying tractography to dMRI to get white matter fiber bundles, a key question is how to represent the brain connectome to facilitate statistical analyses relating connectomes to traits. The current standard divides the brain into regions of interest (ROIs), and then relies on an adjacency matrix (AM) representation. Each cell in the AM is a measure of connectivity, e.g., number of fiber curves, between a pair of ROIs. Although the AM representation is intuitive, a disadvantage is the high-dimensionality due to the large number of cells in the matrix. This article proposes a simpler tree representation of the brain connectome, which is motivated by ideas in computational topology and takes topological and biological information on the cortical surface into consideration. We demonstrate that our tree representation preserves useful information and interpretability, while reducing dimensionality to improve statistical and computational efficiency. Applications to data from the Human Connectome Project (HCP) are considered and code is provided for reproducing our analyses.

Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies

Diffusion tensor imaging (DTI) is gaining traction in neuroscience research as a tool for evaluating neural fibers. The technique can be used to assess white matter (WM) microstructure in neurodegenerative disorders, including Parkinson disease (PD). There is evidence that the uncinate fasciculus and the cingulum bundle are involved in the pathogenesis of PD. These fasciculus and bundle alterations correlate with the symptoms and stages of PD. PRISMA 2022 was used to search PubMed and Scopus for relevant articles. Our search revealed 759 articles. Following screening of titles and abstracts, a full-text review, and implementing the inclusion criteria, 62 papers were selected for synthesis. According to the review of selected studies, WM integrity in the uncinate fasciculus and cingulum bundles can vary according to symptoms and stages of Parkinson disease. This article provides structural insight into the heterogeneous PD subtypes according to their cingulate bundle and uncinate fasciculus changes. It also examines if there is any correlation between these brain structures' structural changes with cognitive impairment or depression scales like Geriatric Depression Scale-Short (GDS). The results showed significantly lower fractional anisotropy values in the cingulum bundle compared to healthy controls as well as significant correlations between FA and GDS scores for both left and right uncinate fasciculus regions suggesting that structural damage from disease progression may be linked to cognitive impairments seen in advanced PD patients. This review help in developing more targeted treatments for different types of Parkinson's disease, as well as providing a better understanding of how cognitive impairments may be related to these structural changes. Additionally, using DTI scans can provide clinicians with valuable information about white matter tracts which is useful for diagnosing and monitoring disease progression over time.

Neurobiology of depression in Parkinson's disease: Insights into epidemiology, molecular mechanisms and treatment strategies

Parkinson's disease (PD) is characterized mainly by motor dysfunctions due to the progressive loss of dopaminergic neurons. However, PD patients experience a multitude of debilitating non-motor symptoms, including depression, which may have deleteriously detrimental effects on life. Depression is multifactorial and exhibits a bimodal progression in PD, but its underlying molecular mechanisms are poorly understood. Studies demonstrating the pathophysiology of depression in PD and the specific treatment strategies for depression-like symptoms in PD patients are largely lacking, often underrated, under-recognized and, consequently, inadequately/under-treated. Nevertheless, reports suggest that the incidence of depression is approximately 20-30% of PD patients and may precede the onset of motor symptoms. Diagnosing depression in PD becomes difficult due to the clinical overlap in symptomatology between the two diseases, and the nigrostriatal dysfunction alone is insufficient to explain depressive symptoms in PD. Therefore, the current study provides an overview of the molecular mechanisms underlying the development of depression in PD and new insights into developing current antidepressant strategies to treat depression in PD. This review will identify and understand the molecular pathological mechanisms of depression in PD that will fundamentally help tailoring therapeutic interventions for depressive symptoms in PD.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Brain microstructural alterations of depression in Parkinson's disease: A systematic review of diffusion tensor imaging studies

Depression, a leading cause of disability worldwide, is also the most prevalent psychiatric problem among Parkinson disease patients. Both depression and Parkinson disease are associated with microstructural anomalies in the brain. Diffusion tensor imaging techniques have been developed to characterize the abnormalities in cerebral tissue. We included 11 studies investigating brain microstructural abnormalities in depressed Parkinson's disease patients. The included studies found alterations to essential brain structural networks, including impaired network integrity for specific cortical regions, such as the temporal and frontal cortices. Additionally, findings indicate that microstructural changes in specific limbic structures, such as the prefronto-temporal regions and connecting white matter pathways, are altered in depressed Parkinson's disease compared to non-depressed Parkinson's disease and healthy controls. There remain inconsistencies between studies reporting DTI measures and depression severity in Parkinson disease participants. Additional research evaluating underlying neurobiological relationships between major depression, depressed Parkinson's disease, and non-depressed Parkinson's disease is required to disentangle further mechanisms that underlie depression and related somatic symptoms, in Parkinson disease.

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

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

Alteration in Asymmetry of White Matter Network of Parkinson's Disease

Parkinson's disease (PD) is manifest clinically by an asymmetrical presentation of motor dysfunction. A large number of previous neuroimaging research studies have stated the alteration in the hemispheric asymmetry of morphological features in PD disease. Diffusion Magnetic Resonance Imaging (MRI), which is noninvasive, has been widely used to quantify the white matter network in the human brain of both healthy subjects and patients. Besides, graph theory analysis is widely used to quantify the topological architecture of the human brain network. Lately, researchers have discovered that the topological architecture of the white matter network significantly differs in PD compared with healthy controls (HC). Nevertheless, the asymmetry of the topological architecture of the white matter network for PD patients remains unclear. To clarify this, the diffusion-weighted images and tractography technique were used to reconstruct the hemispherical white matter networks for 22 bilateral PD patients and 18 HC subjects. Network-based statistical analysis and graph theory analysis approaches were employed to estimate the asymmetry at both the connectivity level and the hemispheric topological level for PD patients. We found that the PD group showed atypically right-higher-than-left asymmetry in hemispheric brain global and local efficiencies. The detected right-higher-than-left asymmetry was driven by the atypically topological changes in the left hemispheric brain in the PD group. Findings from these studies might provide new insights into the asymmetric features of hemispheric disconnectivity and emphasize that the topological asymmetry of the hemispheric brain could be used as a biomarker to identify PD individuals.

Individual-specific networks for prediction modelling – A scoping review of methods

Background

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

Methods

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase.

Results

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

Conclusion

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01544-6.

The effect of polygenic risk on white matter microstructural degeneration in Parkinson's disease: A longitudinal Diffusion Tensor Imaging study

BACKGROUND AND PURPOSE

This study was undertaken to investigate the effect of genetic risk on whole brain white matter (WM) integrity in patients with Parkinson disease (PD).

METHODS

Data were acquired from the Parkinson's Progression Markers Initiative (PPMI) database. Polygenic load was estimated by calculating weighted polygenic risk scores (PRS) using (i) all available 26 PD-risk single nucleotide polymorphisms (SNPs) (PRS1) and (ii) 23 SNPs with minor allele frequency (MAF) > 0.05 (PRS2). According to the PRS2, and combined with clinical and diffusion tensor imaging (DTI) data over 3-year follow-up, 60 PD patients were screened and assigned to the low-PRS group (n = 30) and high-PRS group (n = 30) to investigate intergroup differences in clinical profiles and WM microstructure measured by DTI cross-sectionally and longitudinally.

RESULTS

PRS were associated with younger age at onset in patients with PD (PRS1, Spearman ρ = -0.190, p = 0.003; PRS2, Spearman ρ = -0.189, p = 0.003). The high-PRS group showed more extensive WM microstructural degeneration compared with the low-PRS group, mainly involving the anterior thalamic radiation (AThR) and inferior fronto-occipital fasciculus (IFOF) (p < 0.05). Furthermore, WM microstructural changes in AThR correlated with declining cognitive function (r = -0.401, p = 0.028) and increasing dopaminergic deficits in caudate (r = -0.405, p = 0.030).

CONCLUSIONS

These findings suggest that PD-associated polygenic load aggravates the WM microstructural degeneration and these changes may lead to poor cognition with continuous dopamine depletion. This study provides advanced evidence that combined with a cumulative PRS and DTI methods may predict disease progression in PD patients.

Impaired white matter microstructure associated with severe depressive symptoms in patients with PD

Depression is a common occurrence in patients with Parkinson's disease (PD); however, its pathophysiology is still unclear. This study assessed the association between the integrity of white matter and depressive symptoms in patients with PD. 67 patients with PD were divided into a non-depressed PD group (ndPD, n = 30) and a depressed PD group (dPD, n = 37). The dPD group was further subdivided into a mild-moderately depressed PD (mdPD, n = 22) and a severely depressed PD group (sdPD, n = 15). Tract-Based Spatial Statistics was used to compare fractional anisotropy (FA) between groups. Region-of-interest analysis was used to explore changes in diffusivity indices in the regions showing FA abnormalities. The sdPD patients exhibited significantly reduced FA in the left superior longitudinal fasciculus, uncinate fasciculus, anterior corona radiata, corticospinal tract, and bilateral inferior fronto-occipital fasciculus when compared with the ndPD patients, but the decreased FA was within a smaller area when compared with the mdPD patients. No significant difference in FA was found between the mdPD and ndPD groups. Among the dPD patients, FA values in the left superior longitudinal fasciculus negatively correlated with BDI scores. Impaired white matter integrity in the prefronto-limbic/temporal circuitry, mainly in the left hemisphere, is associated with severe, but not mild-moderate depressive symptoms in patients with PD.

Altered brain structural topological properties and its correlations with clinical characteristics in episodic migraine without aura

PURPOSE

To investigate the topological alterations of the whole-brain white matter structural networks in episodic migraine (EM) without aura.

METHODS

Forty-five EM patients without aura and 35 age- and sex-matched healthy controls were registered, and underwent diffusion tensor MRI acquisition at interictal. Graph theory-based analyses were then performed for the characterization of brain structural network properties. Pearson correlation analysis was performed on each network metric between the EM patients and healthy controls.

RESULTS

The EM patients exhibited abnormal global network properties and local network topology that were characterized by more strongly integrated, more efficient, and faster information transferring. These network differences were widely located in the occipital, temporal, and parietal regions. Additionally, the local efficient of global parameters showed positive correlation with visual analogue scale, and along with prolonging disease duration, the nodal efficiency would be reduced, and the nodal shortest path length would be increased. Headache Impact Test version 6 scores have negative correlation with the nodal shortest path length, and positive correlations with the nodal efficiency.

CONCLUSION

The results indicate that EM patients had aberrant topological structure and make a better understanding of structural connectivity in EM; it may provide imaging evidence for clinical study of migraine pathogenesis.

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

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

Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis

INTRODUCTION

The current diagnosis of Parkinson's disease (PD) comorbidity with depression (DPD) largely depends on clinical evaluation. However, the modality may tend to lack precision in detecting PD with depression. A radiomic approach that combines functional connectivity and activity with clinical scores has the potential to achieve accurate and differential diagnosis between PD and DPD.

METHODS

In this study, we aimed to employ the radiomic approach to extract large-scale features of functional connectivity and activity for differentiating among DPD, PD with no depression (NDPD), and healthy controls (HC). We extracted 6,557 features of five types from all subjects including clinical characteristics, resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC). Lasso, random forest, and support vector machine (SVM) were implemented for feature selection and dimension reduction based on the training sets, and the prediction performance for different methods in the testing sets was compared.

RESULTS

The results showed that nineteen features were selected for the group of DPD versus HC, 34 features were selected for the group of NDPD versus HC, and 17 features were retained for the group of DPD versus NDPD. In the testing sets, Lasso prediction achieved the accuracies of 0.95, 0.96, and 0.85 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Random forest achieved the accuracies of 0.90, 0.82, and 0.90 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively, while SVM yielded the accuracies of 1, 0.86 and 0.65 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively.

CONCLUSIONS

By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomic approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.

Topological changes in white matter connectivity network in patients with Parkinson's disease and depression

Depression is the most common non-motor symptom accompanying Parkinson's disease (PD) with high prevalence but unclear pathophysiological mechanism. Relatively little is known about the topological patterns of white matter structural networks in depressed patients with PD. In this study, we used diffusion-tensor imaging (DTI) and graph theory approaches to explore the brain structural connectome in non-depressed patients with PD (n = 47), depressed patients with PD (n = 20) and healthy controls (n = 46). All three groups exhibited small-world topology. Compared with healthy controls, non-depressed patients with PD and depressed patients with PD showed a significant reduction of network efficiency in the cortico-subcortical circuits. Moreover, depressed patients with PD exhibited higher network efficiency in fronto-limbic system, compared to non-depressed patients with PD. To sum up, our data indicated a disrupted integrity in the large-scale brain systems in depressed patients with PD patients. The structural connectome provided a basis for functional alterations in depressed patients with PD that may advance our current understanding of pathophysiological mechanism underlying Parkinson's disease.

Aberrant brain structural network and altered topological organization in minimal hepatic encephalopathy

PURPOSE

We aimed to investigate the multilevel impairments of brain structural network in patients with minimal hepatic encephalopathy (MHE).

METHODS

Twenty-two patients with MHE and 22 well-matched healthy controls (HC) underwent structural magnetic resonance imaging (MRI) brain scans and neuropsychological evaluations. Individual brain structural networks were constructed using diffusion tensor imaging. Comparing with HC, we investigated the possible impairments of brain structural network in MHE, by applying graph-theory approaches to analyze the topological organization at global, modular, and local levels. The correlations between altered brain structural network and neuropsychological tests scores and venous ammonia levels were also examined in MHE patients.

RESULTS

In the MHE group, small-worldness showed significant decrease and normalized characteristic path length showed increase at the global level. In the modular section, six modules were identified. The inter-modular connective strengths showed significant increase between modules 2 and 4 and between modules 4 and 5. The results of node analysis showed similar hub distributions in the MHE and HC groups except for the right postcentral gyrus, which was only found in the MHE group. No significant differences were found in connective strength of edges between MHE and HC groups using network-based statistics.

CONCLUSION

The altered brain structural networks with reduced network integration and module segregation were demonstrated in patients with MHE. The dysconnectivity of brain structural network could provide an explanation for the brain dysfunctions of MHE.

Alterations in intrinsic functional networks in Parkinson's disease patients with depression: A resting-state functional magnetic resonance imaging study

Aims

The aim of this research was to investigate the alterations in functional brain networks and to assess the relationship between depressive impairment and topological network changes in Parkinson's disease (PD) patients with depression (DPD).

Methods

Twenty‐two DPD patients, 23 PD patients without depression (NDPD), and 25 matched healthy controls (HCs) were enrolled. All participants were examined by resting‐state functional magnetic resonance imaging scans. Graph theoretical analysis and network‐based statistic methods were used to analyze brain network topological properties and abnormal subnetworks, respectively.

Results

The DPD group showed significantly decreased local efficiency compared with the HC group (P = .008, FDR corrected). In nodal metrics analyses, the degree of the right inferior occipital gyrus (P = .0001, FDR corrected) was positively correlated with the Hamilton Depression Rating Scale scores in the DPD group. Meanwhile, the temporal visual cortex, including the bilateral middle temporal gyri and right inferior temporal gyrus in the HC and NDPD groups and the left posterior cingulate gyrus in the NDPD group, was defined as hub region, but not in the DPD group. Compared with the HC group, the DPD group had extensive weakening of connections between the temporal‐occipital visual cortex and the prefrontal‐limbic network.

Conclusions

These results suggest that PD depression is associated with disruptions in the topological organization of functional brain networks, mainly involved the temporal‐occipital visual cortex and the posterior cingulate gyrus and may advance our current understanding of the pathophysiological mechanisms underlying DPD.

Abnormal white matter microstructures in Parkinson's disease and comorbid depression: A whole-brain diffusion tensor imaging study

OBJECTIVE

Depressive symptoms are the most common non-motor symptom accompanying Parkinson's disease (PD); however, the neural basis of depression in PD remains unclear. The aim of this study was to characterize possible depression-related white matter microstructural differences in patients with PD and comorbid depression compared with PD patients and healthy controls (HC) without depression.

METHODS

We used diffusion tensor imaging (DTI) to analyze white matter integrity in depressed PD patients (n = 30), non-depressed PD patients (n = 43), and HC (n = 91). Five MRI-derived indices were estimated: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and white matter volume (WMV).

RESULTS

Compared with HC and non-depressed PD, depressed PD patients showed significantly increased AD values in the body of corpus callosum, right anterior corona radiate, and left hippocampal part of the cingulum, as well as increased MD values in the left hippocampal part of the cingulum.

CONCLUSIONS

Our results show that frontal and limbic white matter integrity is impaired in depressed PD patients. These findings can be used to better understand potential mechanisms of depression in PD.

Clinically relevant connectivity features define three subtypes of Parkinson's disease patients

Parkinson's disease (PD) is characterized by complex clinical symptoms, including classic motor and nonmotor disturbances. Patients with PD vary in clinical manifestations and prognosis, which point to the existence of subtypes. This study aimed to find the fiber connectivity correlations with several crucial clinical symptoms and identify PD subtypes using unsupervised clustering analysis. One hundred and thirty-four PD patients and 77 normal controls were enrolled. Canonical correlation analysis (CCA) was performed to define the clinically relevant connectivity features, which were then used in the hierarchical clustering analysis to identify the distinct subtypes of PD patients. Multimodal neuroimaging analyses were further used to explore the neurophysiological basis of these subtypes. The methodology was validated in an independent data set. CCA revealed two significant clinically relevant patterns (motor-related pattern and depression-related pattern; r = .94, p < .001 and r = .926, p = .001, respectively) among PD patients, and hierarchical clustering analysis identified three neurophysiological subtypes ("mild" subtype, "severe depression-dominant" subtype and "severe motor-dominant" subtype). Multimodal neuroimaging analyses suggested that the patients in the "severe depression-dominant" subtype exhibited widespread disruptions both in function and structure, while the other two subtypes exhibited relatively mild abnormalities in brain function. In the independent validation, three similar subtypes were identified. In conclusion, we revealed heterogeneous subtypes of PD patients according to their distinct clinically relevant connectivity features. Importantly, depression symptoms have a considerable impact on brain damage in patients with PD.

Assessing White Matter Pathology in Early-Stage Parkinson Disease Using Diffusion MRI: A Systematic Review

Structural brain white matter (WM) changes such as axonal caliber, density, myelination, and orientation, along with WM-dependent structural connectivity, may be impacted early in Parkinson disease (PD). Diffusion magnetic resonance imaging (dMRI) has been used extensively to understand such pathological WM changes, and the focus of this systematic review is to understand both the methods utilized and their corresponding results in the context of early-stage PD. Diffusion tensor imaging (DTI) is the most commonly utilized method to probe WM pathological changes. Previous studies have suggested that DTI metrics are sensitive in capturing early disease-associated WM changes in preclinical symptomatic regions such as olfactory regions and the substantia nigra, which is considered to be a hallmark of PD pathology and progression. Postprocessing analytic approaches include region of interest-based analysis, voxel-based analysis, skeletonized approaches, and connectome analysis, each with unique advantages and challenges. While DTI has been used extensively to study WM disorganization in early-stage PD, it has several limitations, including an inability to resolve multiple fiber orientations within each voxel and sensitivity to partial volume effects. Given the subtle changes associated with early-stage PD, these limitations result in inaccuracies that severely impact the reliability of DTI-based metrics as potential biomarkers. To overcome these limitations, advanced dMRI acquisition and analysis methods have been employed, including diffusion kurtosis imaging and q-space diffeomorphic reconstruction. The combination of improved acquisition and analysis in DTI may yield novel and accurate information related to WM-associated changes in early-stage PD. In the current article, we present a systematic and critical review of dMRI studies in early-stage PD, with a focus on recent advances in DTI methodology. Yielding novel metrics, these advanced methods have been shown to detect diffuse WM changes in early-stage PD. These findings support the notion of early axonal damage in PD and suggest that WM pathology may go unrecognized until symptoms appear. Finally, the advantages and disadvantages of different dMRI techniques, analysis methods, and software employed are discussed in the context of PD-related pathology.

Microstructural White Matter Characteristics in Parkinson's Disease With Depression: A Diffusion Tensor Imaging Replication Study

Background: Clarifying the neuropathology of depression as a symptom of Parkinson's disease (PD) has been the goal of recent neuroimaging studies; however, results have been conflicting and lack replication. The purpose of the current study was to replicate recent methods that have used diffusion tensor imaging (DTI) to compare individuals with PD with and without depression and to extend previous findings to allow for a better understanding of the results. Methods: Thirty-seven participants with de novo PD were retrieved from the Parkinson's Progression Marker's Initiative (PPMI) and were separated into a depressed PD group (dPD) or a non-depressed PD group (ndPD). Groups were determined based on scores on the Geriatric Depression Scale Short Form (GDS-15). Initially, a replicated cut off score of ≥ 5 for dPD and <5 for ndPD was applied. To better understand the results, we secondarily applied a more extreme group analysis with ≥ 9 for dPD and 0 for ndPD. White matter integrity between groups was compared between groups using tract-based spatial statistics. Results and Conclusion: The current study did not reveal significant differences in white matter microstructure between dPD and ndPD groups at the whole brain level or in specific regions of interest. The extreme group results were consistent. These findings did not replicate previous work that found reduced white matter integrity in limbic prefrontal regions in dPD relative to ndPD. The current study highlights the need for more replications of neuroimaging research.

Selegiline Recovers Synaptic Plasticity in the Medial Prefrontal Cortex and Improves Corresponding Depression-Like Behavior in a Mouse Model of Parkinson's Disease

In patients with Parkinson’s disease (PD), non-motor symptoms (NMS) including depression and anxiety are often recognized before motor symptoms develop. Monoamine oxidase (MAO)-B inhibitors are therapeutically effective for motor symptoms; however, their effects on NMS in PD are yet to be fully assessed. Here, we aimed to explore the antidepressant-like effects of propargyl MAO-B inhibitors, selegiline and rasagiline, in mice treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) as a PD model, and to elucidate the mechanisms underlying these effects. Four repeated intraperitoneal injections of MPTP at 17.5 mg/kg to C57BL/6 mice led to a partial reduction in the number of nigrostriatal tyrosine hydroxylase-positive neurons and to the extension of immobility time during the tail suspension test (TST), without any obvious induction of motor deficits. A single subcutaneous administration of selegiline at 10 mg/kg shortened the extended immobility time of MPTP mice in the TST, without any increase in motor activities, suggesting that selegiline exerts antidepressant-like effects. In this test, rasagiline did not produce antidepressant-like effects, although the inhibitory effect of 3 mg/kg rasagiline on brain MAO activity was comparable to that of 10 mg/kg selegiline. The shortened immobility time in the TST correlated with reduced cortical dopamine (DA) turnover rates in MPTP mice treated with selegiline, but not in MPTP mice treated with rasagiline. These results suggest that MAO inhibition does not entirely account for the antidepressant-like effects of selegiline. Administration of selegiline (10 mg/kg), but not rasagiline (1 mg/kg), to MPTP mice restored the impaired long-term potentiation induced by high-frequency stimulation in the medial prefrontal cortex (mPFC), and normalized the reduced phosphorylation of Ca²⁺/calmodulin-dependent protein kinase IIα, which is known to be involved in neuroplasticity, in the frontal cortex. In MPTP mice, the antiparkinsonian drug pramipexole (0.3 mg/kg), a DA D₂ and D₃ receptor agonist, that has been shown to be effective in treating depression in PD, ameliorated depression-like behavior and synaptic dysfunction in the mPFC. Taken together, the antidepressant-like effects of selegiline in MPTP mice are attributable to the restoration of impaired synaptic plasticity in the mPFC, suggesting its potential for treating depression in early PD.