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

Brain white matter changes and their associations with non-motor dysfunction in orthostatic hypotension in α-synucleinopathy: A NODDI study

BACKGROUND

The specific non-motor symptoms associated with α-synucleinopathies, including orthostatic hypotension (OH), cognitive impairment, and emotional abnormalities, have been a subject of ongoing controversy over the mechanisms underlying the development of a vicious cycle among them. The distinct structural alterations in white matter (WM) in patients with α-synucleinopathies experiencing OH, alongside their association with other non-motor symptoms, remain unexplored. This study employs axial diffusivity and density imaging (NODDI) to investigate WM damage specific to α-synucleinopathies with concurrent OH, delivering fresh evidence to supplement our understanding of the pathogenic mechanisms and pathological rationales behind the occurrence of a spectrum of non-motor functional impairments in α-synucleinopathies.

METHODS

This study recruited 49 individuals diagnosed with α-synucleinopathies, stratified into an α-OH group (n = 24) and an α-NOH group (without OH, n = 25). Additionally, 17 healthy controls were included for supine and standing blood pressure data collection, as well as neuropsychological assessments. Magnetic resonance imaging (MRI) was utilized for the calculation of NODDI parameters, and tract-based spatial statistics (TBSS) were employed to explore differential clusters. The fibers covered by these clusters were defined as regions of interest (ROI) for the extraction of NODDI parameter values and the analysis of their correlation with neuropsychological scores.

RESULTS

The TBSS analysis unveiled specific cerebral regions exhibiting disparities within the α-OH group as compared to both the α-NOH group and the healthy controls. These differences were evident in clusters that indicated a decrease in the acquisition of the neurite density index (NDI), a reduction in the orientation dispersion index (ODI), and an increase in the isotropic volume fraction (FISO) (p < 0.05). The extracted values from these ROIs demonstrated significant correlations with clinically assessed differences in supine and standing blood pressure, overall cognitive scores, and anxiety-depression ratings (p < 0.05).

CONCLUSION

Patients with α-synucleinopathies experiencing OH exhibit distinctive patterns of microstructural damage in the WM as revealed by the NODDI model, and there is a correlation with the onset and progression of non-motor functional impairments.

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.

Neurofilament light predicts worse nonmotor symptoms and depression in Parkinson's disease

BACKGROUND

The identification of biomarkers that reflect worse progression of nonmotor symptoms (NMS) in Parkinson's disease (PD) is currently an unmet need. The main aim of this study was to investigate whether cerebrospinal fluid (CSF) and serum neurofilament light (NfL), measured at baseline or longitudinally, can be used to predict the progression of NMS in patients with PD.

METHODS

Baseline and longitudinal NfL levels were measured in the CSF and serum in 392 PD patients and 184 healthy controls from the Parkinson's Progression Marker Initiative. NMS were assessed using several scales, including, but not restricted to, the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part I, the Geriatric Depression Scale (GDS) and the State-Trait Anxiety Inventory (STAI). The relationship between baseline and longitudinal NfL levels with changes in NMS was assessed using linear mixed effects models (LME) in PD patients. In addition, we compared CSF and serum NfL levels between groups and assessed the relationship between NfL biomarkers with baseline NMS. Finally, to assess the specificity of our findings we ran the previous LME models using other biomarkers such as CSF amyloid-β1-42, total tau, phosphorylated tau181 and total α-synuclein and we also ran the models in healthy controls.

RESULTS

Baseline levels and longitudinal changes in serum and CSF NfL predicted worse longitudinal MDS-UPDRS-I and depression scores over time in PD (p < 0.01). This relationship remained significant only for CSF NfL when controlling for motor and cognitive status. Furthermore, longitudinal changes in serum and CSF NfL were associated with worse anxiety over time in PD patients (p < 0.05). In contrast to CSF NfL, serum NfL levels were slightly higher at baseline (p = 0.043) and showed significant longitudinal increases (p < 0.001) in PD patients compared to controls. There were no significant correlations between NfL levels (CSF or serum) with other NMS scales, baseline NMS variables, other biomarkers or in healthy controls.

CONCLUSIONS

Our findings indicate that both serum and CSF NfL are associated with worse longitudinal NMS burden, particularly in relation to the progression of depression and anxiety. Serum NfL showed stronger associations with NMS suggesting it could potentially be used as a non-invasive marker of NMS progression for PD.

Neuroinflammation and Mitochondrial Dysfunction in Parkinson's Disease: Connecting Neuroimaging with Pathophysiology

There is a pressing need for disease-modifying therapies in patients suffering from neurodegenerative diseases, including Parkinson's disease (PD). However, these disorders face unique challenges in clinical trial designs to assess the neuroprotective properties of potential drug candidates. One of these challenges relates to the often unknown individual disease mechanisms that would, however, be relevant for targeted treatment strategies. Neuroinflammation and mitochondrial dysfunction are two proposed pathophysiological hallmarks and are considered to be highly interconnected in PD. Innovative neuroimaging methods can potentially help to gain deeper insights into one's predominant disease mechanisms, can facilitate patient stratification in clinical trials, and could potentially map treatment responses. This review aims to highlight the role of neuroinflammation and mitochondrial dysfunction in patients with PD (PwPD). We will specifically introduce different neuroimaging modalities, their respective technical hurdles and challenges, and their implementation into clinical practice. We will gather preliminary evidence for their potential use in PD research and discuss opportunities for future clinical trials.

Mood disturbances in Parkinson's disease: From prodromal origins to application of animal models

Parkinson's disease (PD) is a complex illness with a constellation of environmental insults and genetic vulnerabilities being implicated. Strikingly, many studies only focus on the cardinal motor symptoms of the disease and fail to appreciate the major non-motor features which typically occur early in the disease process and are debilitating. Common comorbid psychiatric features, notably clinical depression, as well as anxiety and sleep disorders are thought to emerge before the onset of prominent motor deficits. In this review, we will delve into the prodromal stage of PD and how early neuropsychiatric pathology might unfold, followed by later motor disturbances. It is also of interest to discuss how animal models of PD capture the complexity of the illness, including depressive-like characteristics along with motor impairment. It remains to be determined how the underlying PD disease processes contributes to such comorbidity. But some of the environmental toxicants and microbial pathogens implicated in PD might instigate pro-inflammatory effects favoring α-synuclein accumulation and damage to brainstem neurons fueling the evolution of mood disturbances. We posit that comprehensive animal-based research approaches are needed to capture the complexity and time-dependent nature of the primary and co-morbid symptoms. This will allow for the possibility of early intervention with more novel and targeted treatments that fit with not only individual patient variability, but also with changes that occur over time with the evolution of the disease.

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.

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

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

Multimodal magnetic resonance imaging quantification of gray matter alterations in relapsing-remitting multiple sclerosis and neuromyelitis optica spectrum disorder

Herein, we combined neurite orientation dispersion and density imaging (NODDI) and synthetic magnetic resonance imaging (SyMRI) to evaluate the spatial distribution and extent of gray matter (GM) microstructural alterations in patients with relapsing-remitting multiple sclerosis (RRMS) and neuromyelitis optica spectrum disorder (NMOSD). The NODDI (neurite density index [NDI], orientation dispersion index [ODI], and isotropic volume fraction [ISOVF]) and SyMRI (myelin volume fraction [MVF]) measures were compared between age- and sex-matched groups of 30 patients with RRMS (6 males and 24 females; mean age, 51.43 ± 8.02 years), 18 patients with anti-aquaporin-4 antibody-positive NMOSD (2 males and 16 females; mean age, 52.67 ± 16.07 years), and 19 healthy controls (6 males and 13 females; mean age, 51.47 ± 9.25 years) using GM-based spatial statistical analysis. Patients with RRMS showed reduced NDI and MVF and increased ODI and ISOVF, predominantly in the limbic and paralimbic regions, when compared with healthy controls, while only increases in ODI and ISOVF were observed when compared with NMOSD. Compared to NDI and MVF, the changes in ODI and ISOVF were observed more widely, including in the cerebellar cortex. These abnormalities were associated with disease progression and disability. In contrast, patients with NMOSD only showed reduced NDI mainly in the cerebellar, limbic, and paralimbic cortices when compared with healthy controls and patients with RRMS. Taken together, our study supports the notion that GM pathologies in RRMS are distinct from those of NMOSD. However, owing to the limitations of the study, the results should be cautiously interpreted.

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

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

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

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

Background

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

Materials and methods

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

Results

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

Conclusion

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

Mapping Motor Pathways in Parkinson’s Disease Patients with Subthalamic Deep Brain Stimulator: A Diffusion MRI Tractography Study

Introduction

This study assessed the safety of postoperative diffusion tensor imaging (DTI) with on-state deep brain stimulation (DBS) and the feasibility of reconstruction of the white matter tracts in the vicinity of the stimulation site of the subthalamic nucleus (STN). The association between the impact of DBS on the nigrostriatal pathway (NSP) and the treatment effect on motor symptoms in Parkinson’s disease (PD) was then evaluated.

Methods

Thirty-one PD patients implanted with STN-DBS (mean age: 66 years; 25 male) were scanned on a 1.5-T magnetic resonance imaging (MRI) scanner using the DTI sequence with DBS on. Twenty-three of them were scanned a second time with DBS off. The NSP, dentato-rubro-thalamic tract (DRTT), and hyperdirect pathway (HDP) were generated using both deterministic and probabilistic tractography methods. The DBS-on-state and off-state tractography results were validated and compared. Afterward, the relationships between the characteristics of the reconstructed white matter tracts and the clinical assessment of PD symptoms and the DBS effect were further examined.

Results

No adverse events related to DTI were identified in either the DBS-on-state or off-state. Overall, the NSP was best reconstructed, followed by the DRTT and HDP, using the probabilistic tractography method. The connection probability of the left NSP was significantly lower than that of the right side (p < 0.05), and a negative correlation (r = −0.39, p = 0.042) was identified between the preoperative symptom severity in the medication-on state and the connection probability of the left NSP in the DBS-on-state images. Furthermore, the distance from the estimated left-side volume of tissue activated (VTA) by STN-DBS to the ipsilateral NSP was significantly shorter in the DBS-responsive group compared to the DBS-non-responsive group (p = 0.046).

Conclusions

DTI scanning is safe and delineation of white matter pathway is feasible for PD patients implanted with the DBS device. Postoperative DTI is a useful technique to strengthen our current understanding of the therapeutic effect of DBS for PD and has the potential to refine target selection strategies for brain stimulation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40120-022-00331-1.

For some more seriously affected Parkinson’s disease (PD) patients, drugs are no longer effective in treating their symptoms. An alternate treatment is to use deep brain stimulation (DBS), a commonly used neurosurgical therapy for PD patients. For those DBS treatments targeting the subthalamic nucleus (STN), the electrical stimulation used may impact nearby white matter tracts and alter the effectiveness of the DBS treatment. The nigrostriatal pathway (NSP), dentato-rubro-thalamic tract, and hyperdirect pathway are three white matter tracts near the STN. They are all relevant to motor symptoms in PD. This study examined whether imaging these tracts using magnetic resonance imaging (MRI) is safe and feasible in the presence of DBS leads. The relationships between the fiber-tracking characteristics and distance to the DBS leads were then evaluated. For this purpose, 31 PD patients with stimulation-on were scanned on a 1.5 T MRI scanner using a diffusion tensor imaging sequence. A total of 23 subjects underwent another scan using the same sequence with stimulation-off. No adverse events related to diffusion tensor imaging were found. Among the white matter tracts near the STN, the NSP was best delineated, followed by the dentato-rubro-thalamic tract and the hyperdirect pathway. The connection probability of the left NSP was significantly lower than that of the right side as were the subject’s motor symptoms. The closer the distance between the NSP and the stimulation location, the better the DBS outcome. These findings indicate that imaging white matter tracts with DBS on is safe and useful in mapping DBS outcomes.

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

The role of brain perivascular space burden in early-stage Parkinson's disease

Perivascular space (PVS) is associated with neurodegenerative diseases, while its effect on Parkinson's disease (PD) remains unclear. We aimed to investigate the clinical and neuroimaging significance of PVS in basal ganglia (BG) and midbrain in early-stage PD. We recruited 40 early-stage PD patients and 41 healthy controls (HCs). Both PVS number and volume were calculated to evaluate PVS burden on 7 T magnetic resonance imaging images. We compared PVS burden between PD and HC, and conducted partial correlation analysis between PVS burden and clinical and imaging features. PD patients had a significantly more serious PVS burden in BG and midbrain, and the PVS number in BG was significantly correlated to the PD disease severity and L-dopa equivalent dosage. The fractional anisotropy and mean diffusivity values of certain subcortical nuclei and white matter fibers within or nearby the BG and midbrain were significantly correlated with the ipsilateral PVS burden indexes. Regarding to the midbrain, the difference between bilateral PVS burden was, respectively, correlated to the difference between fiber counts of white fiber tract passing through bilateral substantia nigra in PD. Our study suggests that PVS burden indexes in BG are candidate biomarkers to evaluate PD motor symptom severity and aid in predicting medication dosage. And our findings also highlight the potential correlations between PVS burden and both grey and white matter microstructures.

Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation

Purpose

To investigate whether Parkinson’s disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)–based structural connectome matrices calculated from diffusion-weighted MRI.

Methods

In this prospective study, 115 PD patients and 115 healthy controls were enrolled. NOS-based and parameter-weighted connectome matrices were calculated from MRI images obtained with a 3-T MRI unit. With 5-fold cross-validation, diagnostic performance of convolutional neural network (CNN) models using those connectome matrices in differentiating patients with PD from healthy controls was evaluated. To identify the important brain connections for diagnosing PD, gradient-weighted class activation mapping (Grad-CAM) was applied to the trained CNN models.

Results

CNN models based on some parameter-weighted structural matrices (diffusion kurtosis imaging (DKI)–weighted, neurite orientation dispersion and density imaging (NODDI)–weighted, and g-ratio-weighted connectome matrices) showed moderate performance (areas under the receiver operating characteristic curve (AUCs) = 0.895, 0.801, and 0.836, respectively) in discriminating PD patients from healthy controls. The DKI-weighted connectome matrix performed significantly better than the conventional NOS-based matrix (AUC = 0.761) (DeLong’s test, p < 0.0001). Alterations of neural connections between the basal ganglia and cerebellum were indicated by applying Grad-CAM to the NODDI- and g-ratio-weighted matrices.

Conclusion

Patients with PD can be differentiated from healthy controls by applying the deep learning technique to the parameter-weighted connectome matrices, and neural circuit disorders including those between the basal ganglia on one side and the cerebellum on the contralateral side were visualized.

NODDI in clinical research

Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.

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

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