Alterations of Diffusion Kurtosis and Neurite Density Measures in Deep Grey Matter and White Matter in Parkinson's Disease

Histogram analysis of diffusion kurtosis imaging of deep brain nuclei in Parkinson's disease with different motor subtypes

AIM

To evaluate the diagnostic and differential efficacy of diffusion kurtosis imaging (DKI) histogram analysis for different motor subtypes of Parkinson's disease (PD).

MATERIALS AND METHODS

Seventy PD patients including 40 with postural instability and gait disorder (PIGD) and 30 with tremor-dominant (TD) and 36 healthy controls (HC) were enrolled prospectively and underwent MRI examinations. The regions of interest (ROI) in the deep brain nuclei were delineated and features were extracted on the map of mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr), respectively. The differences in histogram features between PD patients and HC and between patients with PIGD and TD were compared. The areas under the curve (AUCs) were calculated to evaluate the diagnostic efficacy of all histogram features. The correlations between histogram features and clinical indicators were evaluated.

RESULTS

Some DKI histogram features were significantly different between PD patients and HC, and also different between patients with PIGD and TD (all p<0.05). MK of the substantia nigra pars reticulate (SNprkurtosis), Ka of the substantia nigra pars compacta (SNpc) 50 percentile (SNpcP50), and Kr of SNpc 90th percentile showed the highest AUC for distinguishing patients with PIGD from HC. MK-SNpc 10th percentile, Ka-SNpc 25th percentile, and Kr of the head of the caudate nucleus (CN) 90th percentile had the highest AUC for distinguishing patients with TD from HC. MK of the putamen 10th percentile combined with Ka of the bilateral red nucleus RNkurtosis yielded the highest diagnostic performance with an AUC of 0.762 for distinguishing patients with PIGD from TD. Certain DKI histogram features were correlated with Hoehn-Yahr (H&Y) stage, Mini Mental State Examination (MMSE) score, tremor score, and PIGD score (all p<0.05).

CONCLUSION

DKI histogram analysis was useful to diagnose and discriminate different motor subtypes of PD. Certain DKI histogram features correlated with clinical indicators.

Fiber-specific white matter alterations in Parkinson's disease patients with freezing of gait

Freezing of gait (FOG) is a gait disorder that usually occurs in advanced stages of Parkinson's disease (PD). Understanding the underlying mechanism of FOG is important for treatment and prevention. Previous studies have investigated white matter (WM) structure to explore the pathology of FOG. However, the pathology is still unclear, possibly due to the methodological limitation in identifying specific fiber tracts. This study aimed to investigate tract-specific WM structural changes in FOG patients and their relationships with clinical characteristics. We enrolled 19 PD patients with FOG (PD-FOG), 19 without FOG (PD-woFOG) and 21 controls. Fixel-based analysis is a novel framework to avoid the effect of crossing fibers, which provides the metrics to assess WM morphology. By combining a method for segmenting fibers, we identified abnormalities in the specific fiber tracts. Compared to PD-woFOG, PD-FOG showed significant increased fiber-bundle cross-section (FC) in the corpus callosum (CC), fornix (FX), inferior longitudinal fasciculus (ILF), striato-premotor (ST_PREM), superior thalamic radiation (STR), thalamo-premotor (T_PREM), increased fiber density and cross-section (FDC) in the STR, and decreased fiber density (FD) in the CC and ILF. Additionally, the ILF was correlated with motor, cognition and memory, the CC was correlated with anxiety, and the T_PREM was also correlated with cognition. In conclusion, in addition to impairments of WM found in PD-FOG, we found enhancements in WM, which may imply compensatory mechanisms. Furthermore, multiple fiber tracts were correlated with clinical characteristics, especially the ILF, validating the involvement of transmission circuits of multiple distinct information in mechanisms of FOG.

Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T

BACKGROUND AND OBJECTIVE

Choosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics' variability with respect to the magnetic field on the adopted denoising methodology.

METHODS

For comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method.

RESULTS

Accounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin.

CONCLUSIONS

This study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.

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.

The challenging quest of neuroimaging: From clinical to molecular-based subtyping of Parkinson disease and atypical parkinsonisms

The current framework of Parkinson disease (PD) focuses on phenotypic classification despite its considerable heterogeneity. We argue that this method of classification has restricted therapeutic advances and therefore limited our ability to develop disease-modifying interventions in PD. Advances in neuroimaging have identified several molecular mechanisms relevant to PD, variation within and between clinical phenotypes, and potential compensatory mechanisms with disease progression. Magnetic resonance imaging (MRI) techniques can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging have informed the neurotransmitter, metabolic, and inflammatory dysfunctions that could potentially distinguish disease phenotypes and predict response to therapy and clinical outcomes. However, rapid advancements in imaging techniques make it challenging to assess the significance of newer studies in the context of new theoretical frameworks. As such, there needs to not only be a standardization of practice criteria in molecular imaging but also a rethinking of target approaches. In order to harness precision medicine, a coordinated shift is needed toward divergent rather than convergent diagnostic approaches that account for interindividual differences rather than similarities within an affected population, and focus on predictive patterns rather than already lost neural activity.

Pedunculopontine Nucleus Dysconnectivity Correlates With Gait Impairment in Parkinson’s Disease: An Exploratory Study

Background

Gait impairment is a debilitating and progressive feature of Parkinson’s disease (PD). Increasing evidence suggests that gait control is partly mediated by cholinergic signaling from the pedunculopontine nucleus (PPN).

Objective

We investigated whether PPN structural connectivity correlated with quantitative gait measures in PD.

Methods

Twenty PD patients and 15 controls underwent diffusion tensor imaging to quantify structural connectivity of the PPN. Whole brain analysis using tract-based spatial statistics and probabilistic tractography were performed using the PPN as a seed region of interest for cortical and subcortical target structures. Gait metrics were recorded in subjects’ medication ON and OFF states, and were used to determine if specific features of gait dysfunction in PD were related to PPN structural connectivity.

Results

Tract-based spatial statistics revealed reduced structural connectivity involving the corpus callosum and right superior corona radiata, but did not correlate with gait measures. Abnormalities in PPN structural connectivity in PD were lateralized to the right hemisphere, with pathways involving the right caudate nucleus, amygdala, pre-supplementary motor area, and primary somatosensory cortex. Altered connectivity of the right PPN-caudate nucleus was associated with worsened cadence, stride time, and velocity while in the ON state; altered connectivity of the right PPN-amygdala was associated with reduced stride length in the OFF state.

Conclusion

Our exploratory analysis detects a potential correlation between gait dysfunction in PD and a characteristic pattern of connectivity deficits in the PPN network involving the right caudate nucleus and amygdala, which may be investigated in future larger studies.

Advanced Diffusion MRI of the Visual System in Glaucoma: From Experimental Animal Models to Humans

Simple Summary

This review summarizes current applications of advanced diffusion magnetic resonance imaging (MRI) throughout the glaucomatous visual system, focusing on the eye, optic nerve, optic tract, subcortical visual brain nuclei, optic radiations, and visual cortex. Glaucoma continues to be the leading cause of irreversible blindness worldwide and often remains undetected until later disease stages. The development of non-invasive methods for early detection of visual pathway integrity could pave the way for timely intervention and targeted treatment strategies. Principles of diffusion have been integrated with MRI protocols to produce a diffusion-weighted imaging modality for studying changes to tissue microstructures by quantifying the movement of water molecules in vivo. The development and applications of diffusion MRI in ophthalmology have allowed a better understanding of neural pathway changes in glaucoma. The feasibility of translating diffusion MRI techniques to assess both humans and experimental animal models of glaucoma and other optic neuropathies or neurodegenerative diseases is discussed. Recent research focuses on overcoming limitations in imaging quality, acquisition times, and biological interpretation suggest that diffusion MRI can provide an important tool for the non-invasive evaluation of glaucomatous changes in the visual system.

Abstract

Glaucoma is a group of ophthalmologic conditions characterized by progressive retinal ganglion cell death, optic nerve degeneration, and irreversible vision loss. While intraocular pressure is the only clinically modifiable risk factor, glaucoma may continue to progress at controlled intraocular pressure, indicating other major factors in contributing to the disease mechanisms. Recent studies demonstrated the feasibility of advanced diffusion magnetic resonance imaging (dMRI) in visualizing the microstructural integrity of the visual system, opening new possibilities for non-invasive characterization of glaucomatous brain changes for guiding earlier and targeted intervention besides intraocular pressure lowering. In this review, we discuss dMRI methods currently used in visual system investigations, focusing on the eye, optic nerve, optic tract, subcortical visual brain nuclei, optic radiations, and visual cortex. We evaluate how conventional diffusion tensor imaging, higher-order diffusion kurtosis imaging, and other extended dMRI techniques can assess the neuronal and glial integrity of the visual system in both humans and experimental animal models of glaucoma, among other optic neuropathies or neurodegenerative diseases. We also compare the pros and cons of these methods against other imaging modalities. A growing body of dMRI research indicates that this modality holds promise in characterizing early glaucomatous changes in the visual system, determining the disease severity, and identifying potential neurotherapeutic targets, offering more options to slow glaucoma progression and to reduce the prevalence of this world’s leading cause of irreversible but preventable blindness.

Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease

Objectives

The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson’s disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD.

Methods

A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results.

Results

An increase in mean diffusivity (MD) was found in the left substantia nigra (SN) in the PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn and Yahr (H&Y) scales. In the putamen (Put), FA values were positively correlated with the H&Y scales. It is worth noting that these findings were only observed with the deep learning method. There was neither a group difference nor a correlation with clinical assessments in the SN or striatum exceeding the significance level using the conventional model-fitting method.

Conclusions

The CNN-based method improves the robustness of DKI and can help to explore PD-associated imaging features.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40708-021-00139-z.

Utility of quantitative susceptibility mapping and diffusion kurtosis imaging in the diagnosis of early Parkinson's disease

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Putamen susceptibility value was higher in PD than controls one year into diagnosis.

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Putamen susceptibility value was associated with clinical motor scores in early PD.

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Mean diffusivity revealed greater cellular loss in the lateral substantial nigra.

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Putamen and caudate microstructural degradation were driven by radial diffusivity.

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A composite putamen-caudate DKI-QSM marker classified early PD from controls.

Objective

To investigate the utility of quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) as complementary tools in characterizing pathological changes in the deep grey nuclei in early Parkinson’s disease (PD) and their clinical correlates to aid in diagnosis of PD.

Method

Patients with a diagnosis of PD made within a year and age-matched healthy controls were recruited. All participants underwent clinical evaluation using the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) and Hoehn & Yahr stage (H&Y), and brain 3 T MRI including QSM and DKI. Regions-of-interest (ROIs) in the caudate nucleus, putamen, globus pallidus, and medial and lateral substantia nigra (SN) were manually drawn to compare the mean susceptibility (representing iron deposition) and DKI indices (representing restricted water diffusion) between PD patients and healthy controls and in correlation with MDS-UPDRS III and H&Y, focusing on susceptibility value, mean diffusivity (MD) and mean kurtosis (MK).

Results

There were forty-seven PD patients (aged 68.7 years, 51% male, disease duration 0.78 years) and 16 healthy controls (aged 67.4 years, 63% male). Susceptibility value was increased in PD in all ROIs except the caudate, and was significantly different after multiple comparison correction in the putamen (PD: 64.75 ppb, HC: 44.61 ppb, p = 0.004). MD was significantly higher in PD in the lateral SN, putamen and caudate, the regions with the lowest susceptibility value. In PD patients, we found significant association between the MDS-UPDRS III score and susceptibility value in the putamen after correcting for age and sex (β = 0.21, p = 0.003). A composite DKI-QSM diagnostic marker based on these findings successfully differentiated the groups (p < 0.0001) and had “good” classification performance (AUC = 0.88).

Conclusions

QSM and DKI are complementary tools allowing a better understanding of the complex contribution of iron deposition and microstructural changes in the pathophysiology of PD.

Progressive microstructural alterations in subcortical nuclei in Parkinson's disease: A diffusion magnetic resonance imaging study

OBJECTIVES

To explore the microstructural alterations in subcortical nuclei in Parkinson's disease (PD) at different stages with diffusion kurtosis imaging (DKI) and tensor imaging and to test the performance of diffusion metrics in identifying PD.

METHODS

108 PD patients (64 patients in early-stage PD group (EPD) and 44 patients in moderate-late-stage PD group (MLPD)) and 64 healthy controls (HC) were included. Tensor and kurtosis metrics in the subcortical nuclei were compared. Partial correlation was used to correlate the diffusion metrics and Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) score. Logistic regression and receiver operating characteristic analysis were applied to test the diagnostic performance of the diffusion metrics.

RESULTS

Compared with HC, both EPD and MLPD patients showed higher fractional anisotropy and axial diffusivity, lower mean kurtosis (MK) and axial kurtosis in substantia nigra, lower MK and radial kurtosis (RK) in globus pallidus (GP) and thalamus (all p < 0.05). Compared with EPD, MLPD patients showed lower MK and RK in GP and thalamus (all p < 0.05). MK and RK in GP and thalamus were negatively correlated with UPDRS-III score (all p < 0.01). The logistic regression model combining kurtosis and tensor metrics showed the best performance in diagnosing PD, EPD, and MLPD (areas under curve were 0.817, 0.769, and 0.914, respectively).

CONCLUSIONS

PD has progressive microstructural alterations in the subcortical nuclei. DKI is sensitive to detect microstructural alterations in GP and thalamus during PD progression. Combining kurtosis and tensor metrics can achieve a good performance in diagnosing PD.

Subcortical microstructural diffusion changes correlate with gait impairment in Parkinson's disease

BACKGROUND

Gait impairments are common in Parkinson's Disease (PD) and are likely caused by degeneration in multiple brain circuits, including the basal ganglia, thalamus and mesencephalic locomotion centers (MLC). Diffusion tensor imaging (DTI) assesses fractional anisotropy (FA) and mean diffusivity (MD) that reflect the integrity of neuronal microstructure. We hypothesized that DTI changes in motor circuits correlate with gait changes in PD.

OBJECTIVE

We aimed to identify microstructural changes of brain locomotion control centers in PD via DTI and their correlations with clinical and quantitative measures of gait.

METHODS

Twenty-one PD patients reporting gait impairment and 15 controls were recruited. Quantitative gait and clinical tests were recorded in PD subjects' medication ON and OFF states. Region of Interest (ROI) analysis of the thalamus, basal ganglia and MLC was performed using ExploreDTI. Correlations between FA/MD with clinical gait parameters were examined.

RESULTS

Microstructural changes were seen in the thalamus, caudate and MLC in the PD compared to the control group. Thalamic microstructural changes significantly correlated with gait parameters in the pace domain including the Timed Up and Go in the ON state. Caudate changes correlated with cadence and stride time in the OFF state.

CONCLUSIONS

Our pilot study suggests that PD is associated with a characteristic regional pattern of microstructural degradation in the thalamus, caudate and MLC. The DTI changes may represent subcortical locomotion network failure. Overall, DTI ROI analyses might provide a useful tool for assessing PD for functional status and specific motor domains, such as gait, and potentially could serve as an imaging marker.

Neuroimaging Detectable Differences between Parkinson's Disease Motor Subtypes: A Systematic Review

BACKGROUND

The neuroanatomical substrates of Parkinson's disease (PD) with tremor-dominance (TD) and those with non-tremor dominance (nTD), postural instability and gait difficulty (PIGD), and akinetic-rigid (AR) are not fully differentiated. A better understanding of symptom specific pathoanatomical markers of PD subtypes may result in earlier diagnosis and more tailored treatment. Here, we aim to give an overview of the neuroimaging literature that compared PD motor subtypes.

METHODS

A systematic literature review on neuroimaging studies of PD subtypes was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Search terms submitted to the PubMed database included: "Parkinson's disease", "MRI" and "motor subtypes" (TD, nTD, PIGD, AR). The results are first discussed from macro to micro level of organization (i.e., (1) structural; (2) functional; and (3) molecular) and then by applied imaging methodology.

FINDINGS

Several neuroimaging methods including diffusion imaging and positron emission tomography (PET) distinguish specific PD motor subtypes well, although findings are mixed. Furthermore, our review demonstrates that nTD-PD patients have more severe neuroalterations compared to TD-PD patients. More specifically, nTD-PD patients have deficits within striato-thalamo-cortical (STC) circuitry and other thalamocortical projections related to cognitive and sensorimotor function, while TD-PD patients tend to have greater cerebello-thalamo-cortical (CTC) circuitry dysfunction.

CONCLUSIONS

Based on the literature, STC and CTC circuitry deficits seem to be the key features of PD and the subtypes. Future research should make greater use of multimodal neuroimaging and techniques that have higher sensitivity in delineating subcortical structures involved in motor diseases.

Brain White-Matter Degeneration Due to Aging and Parkinson Disease as Revealed by Double Diffusion Encoding

Microstructure imaging by means of multidimensional diffusion encoding is increasingly applied in clinical research, with expectations that it yields a parameter that better correlates with clinical disability than current methods based on single diffusion encoding. Under the assumption that diffusion within a voxel can be well described by a collection of diffusion tensors, several parameters of this diffusion tensor distribution can be derived, including mean size, variance of sizes, orientational dispersion, and microscopic anisotropy. The information provided by multidimensional diffusion encoding also enables us to decompose the sources of the conventional fractional anisotropy and mean kurtosis. In this study, we explored the utility of the diffusion tensor distribution approach for characterizing white-matter degeneration in aging and in Parkinson disease by using double diffusion encoding. Data from 23 healthy older subjects and 27 patients with Parkinson disease were analyzed. Advanced age was associated with greater mean size and size variances, as well as smaller microscopic anisotropy. By analyzing the parameters underlying diffusion kurtosis, we found that the reductions of kurtosis in aging and Parkinson disease reported in the literature are likely driven by the reduction in microscopic anisotropy. Furthermore, microscopic anisotropy correlated with the severity of motor impairment in the patients with Parkinson disease. The present results support the use of multidimensional diffusion encoding in clinical studies and are encouraging for its future clinical implementation.

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.

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.

Effects of Levodopa on Impairments to High-Level Vision in Parkinson's Disease

Studies have reported that Parkinson's disease (PD) is associated with impairments on cognitive visual tasks. However, the effects of dopamine on cognitive vision remain equivocal. The purpose of this study was to examine performance on cognitive vision tasks in persons with PD and the effects of levodopa on these tasks. Fourteen individuals with PD and 14 age- and sex-matched healthy older adults completed the study. Participants with PD completed the visual tasks following a 12-h withdrawal of dopaminergic medication and again 1 h after taking 1.5 times their normal dose of levodopa. Healthy older adults completed the visual tasks twice using the same session format. Five complex visual tasks were completed, including line discrimination, object discrimination, facial discrimination, visual working memory, and object rotation. The Unified Parkinson's Disease Rating Scale was also collected off and on medication. Participants with PD performed significantly worse than the healthy older adults across all five visual tasks. There were no significant differences in performance between the off and on medication state in persons with PD. This finding indicates either that dopamine deficiency may not be responsible for cognitive visual impairments in PD or that cognitive visual impairments in PD might simply be the result of deficits in more basic visual processing.

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.

The aging-liked alterations in Cushing's disease: A neurite orientation dispersion and density imaging (NODDI) study

PURPOSE

Glucocorticoid (GC) is probably related to biological aging, but the exact mechanism remains unknown. Cushing's disease (CD) could represent a unique human model for examining the effects of prolonged exposure to hypercortisolism and its relationship with aging. Thus, we studied the alterations of neurites in CD patients with Neurite orientation dispersion and density imaging (NODDI).

METHODS

CD patients (n = 15) and healthy control subjects (n = 15) were included in this study. Orientation dispersion index (Odi), neurite density index (Ndi), partial fraction of free water (f_iso), partial fraction of extracellular water (f_ec) were examined in a cross-sectional analysis.

RESULTS

Significant altered NODDI parameters were found in CD patients. Some of these alterations were correlated with current age. Additionally, increased dendritic density was found in cerebellar of CD patients.

CONCLUSION

Hypercortisolism relative reductions of the dendritic density were correlated with current age in several regions of CD patients. Our study enhances the understanding of the link between the aging and GC.

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.

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.

Advanced diffusion magnetic resonance imaging in patients with Alzheimer's and Parkinson's diseases

The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings; however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer’s and Parkinson’s diseases, the first and second most common neurodegenerative diseases, respectively.

Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis

Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and associated animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals with varied grades of disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T₂* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T₂* images were used to delineate focal demyelination/inflammation. Our results reveal a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. Relationships between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity were similar to what has been found in other hypomyelinating MS models, and in patients. However, the changes in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability.

Diffusion Tensor MRI to Distinguish Progressive Supranuclear Palsy from α-Synucleinopathies

Background The differential diagnosis of progressive supranuclear palsy (PSP) and Lewy body disorders, which include Parkinson disease and dementia with Lewy bodies, is often challenging due to the overlapping symptoms. Purpose To develop a diagnostic tool based on diffusion tensor imaging (DTI) to distinguish between PSP and Lewy body disorders at the individual-subject level. Materials and Methods In this retrospective study, skeletonized DTI metrics were extracted from two independent data sets: the discovery cohort from the Swedish BioFINDER study and the validation cohort from the Penn Frontotemporal Degeneration Center (data collected between 2010 and 2018). Based on previous neuroimaging studies and neuropathologic evidence, a combination of regions hypothesized to be sensitive to pathologic features of PSP were identified (ie, the superior cerebellar peduncle and frontal white matter) and fractional anisotropy (FA) was used to compute an FA score for each individual. Classification performances were assessed by using logistic regression and receiver operating characteristic analysis. Results In the discovery cohort, 16 patients with PSP (mean age ± standard deviation, 73 years ± 5; eight women, eight men), 34 patients with Lewy body disorders (mean age, 71 years ± 6; 14 women, 20 men), and 44 healthy control participants (mean age, 66 years ± 8; 26 women, 18 men) were evaluated. The FA score distinguished between clinical PSP and Lewy body disorders with an area under the curve of 0.97 ± 0.04, a specificity of 91% (31 of 34), and a sensitivity of 94% (15 of 16). In the validation cohort, 34 patients with PSP (69 years ± 7; 22 women, 12 men), 25 patients with Lewy body disorders (70 years ± 7; nine women, 16 men), and 32 healthy control participants (64 years ± 7; 22 women, 10 men) were evaluated. The accuracy of the FA score was confirmed (area under the curve, 0.96 ± 0.04; specificity, 96% [24 of 25]; and sensitivity, 85% [29 of 34]). Conclusion These cross-validated findings lay the foundation for a clinical test to distinguish progressive supranuclear palsy from Lewy body disorders. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Shah in this issue.

Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ("stick-like") diffusion. Second, the "density" of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with "b-tensor encoding" and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b-tensor data is associated with myelinated axons but not with dendrites. Furthermore, b-tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data-driven estimates of compartment-specific T2 values. Finally, the "stick" fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment-specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained "index" parameters could be valid within limited domains that should be delineated by future studies.

Brain imaging of locomotion in neurological conditions

Impaired locomotion is a frequent and major source of disability in patients with neurological conditions. Different neuroimaging methods have been used to understand the brain substrates of locomotion in various neurological diseases (mainly in Parkinson's disease) during actual walking, and while resting (using mental imagery of gait, or brain-behavior correlation analyses). These studies, using structural (i.e., MRI) or functional (i.e., functional MRI or functional near infra-red spectroscopy) brain imaging, electrophysiology (i.e., EEG), non-invasive brain stimulation (i.e., transcranial magnetic stimulation, or transcranial direct current stimulation) or molecular imaging methods (i.e., PET, or SPECT) reveal extended brain networks involving both grey and white matters in key cortical (i.e., prefrontal cortex) and subcortical (basal ganglia and cerebellum) regions associated with locomotion. However, the specific roles of the various pathophysiological mechanisms encountered in each neurological condition on the phenotype of gait disorders still remains unclear. After reviewing the results of individual brain imaging techniques across the common neurological conditions, such as Parkinson's disease, dementia, stroke, or multiple sclerosis, we will discuss how the development of new imaging techniques and computational analyses that integrate multivariate correlations in "large enough datasets" might help to understand how individual pathophysiological mechanisms express clinically as an abnormal gait. Finally, we will explore how these new analytic methods could drive our rehabilitative strategies.

Divergent topological networks in Alzheimer's disease: a diffusion kurtosis imaging analysis

Background

Brain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer’s disease (AD).

Methods

Magnetic resonance DKI scanning was performed on 21 AD patients and 19 controls. Brain networks were constructed by the correlation matrices of 90 regions and analyzed through graph theoretical approaches.

Results

We found small world characteristics of DKI networks not only in the normal subjects but also in the AD patients; Grey matter networks of AD patients tended to be a less optimized network. Moreover, the divergent small world network features were shown in the AD white matter networks, which demonstrated increased shortest paths and decreased global efficiency with fiber tractography but decreased shortest paths and increased global efficiency with other DKI metrics. In addition, AD patients showed reduced nodal centrality predominantly in the default mode network areas. Finally, the DKI networks were more closely associated with cognitive impairment than the DTI networks.

Conclusions

Our results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.

Neurite orientation dispersion and density imaging of the nigrostriatal pathway in Parkinson's disease: Retrograde degeneration observed by tract-profile analysis

INTRODUCTION

Parkinson's disease (PD) is marked by the degeneration of dopaminergic neurons in the nigrostriatal pathway (NSP). We aimed to identify the microstructural changes in the NSP of PD patients using neurite orientation dispersion and density imaging (NODDI).

METHODS

NSPs of 29 PD patients, who were retrospectively selected from patients previously admitted to our institution, and 29 age- and gender-matched healthy controls were isolated via deterministic tractography. The NODDI indices, intracellular volume fraction (Vic), orientation dispersion index (OD), and isotropic volume fraction (Viso) were compared between the two groups. The significant results were assessed with a tract-profile analysis. The correlation between indices and disease duration or motor symptom severity was evaluated with the Pearson's correlation test.

RESULTS

The contralateral distal Vic (p = 0.00028) of the nigrostriatal pathway was significantly lower in PD patients than in healthy controls. No correlations were detected between any of the indices and disease duration or motor symptom severity.

CONCLUSIONS

NODDI can be used to identify retrograde degeneration of the NSP in PD patients and might be useful for monitoring the disease progression of PD.

Alteration of putaminal fractional anisotropy in Parkinson's disease: a longitudinal diffusion kurtosis imaging study

Purpose

In Parkinson’s disease (PD), pathological microstructural changes occur that may be detected using diffusion magnetic resonance imaging (dMRI). However, there are few longitudinal studies that explore the effect of disease progression on diffusion indices.

Methods

We prospectively included 76 patients with PD and 38 healthy controls (HC), all of whom underwent diffusion kurtosis imaging (DKI) as part of the prospective Swedish BioFINDER study at baseline and 2 years later. Annualized rates of change in DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK), were estimated in the gray matter (GM) by placing regions of interest (ROIs) in the basal ganglia and the thalamus, and in the white matter (WM) by tract-based spatial statistics (TBSS) analysis.

Results

When adjusting for potential confounding factors (age, gender, baseline-follow-up interval, and software upgrade of MRI scanner), only a decrease in FA in the putamen of PD patients (β = − 0.248, P < .01) over 2 years was significantly different from the changes observed in HC over the same time period. This 2-year decrease in FA in the putamen in PD correlated with higher l-dopa equivalent dose at baseline (Spearman’s rho = .399, P < .0001).

Conclusion

The study indicates that in PD microstructural changes in the putamen occur selectively over a 2-year period and can be detected with DKI.

Electronic supplementary material

The online version of this article (10.1007/s00234-017-1971-3) contains supplementary material, which is available to authorized users.

Differential White Matter Regional Alterations in Motor Subtypes of Early Drug-Naive Parkinson's Disease Patients

BACKGROUND

Parkinson's disease (PD) can be classified into tremor dominant (TD) and postural instability and gait difficulty (PIGD) subtypes with TD considered as the benign subtype. The neural alterations of the 2 subtypes in the early stages before administration of medications remain elusive.

OBJECTIVE

This study assessed the subtype-related white matter (WM) microstructural features in newly diagnosed and drug-naive PD patients from the Parkinson's Progression Markers Initiative (PPMI).

METHODS

Sixty-five early PDs with stable subtypes (52 TD and 13 PIGD patients) and 61 controls underwent diffusion tensor imaging (DTI) scanning and clinical assessment. Tract-based special statistics (TBSS), graph-theoretical and network-based analyses were used to compare WM regional and network features between groups.

RESULTS

No differences in disease stages and duration were found between the 2 patient groups. TD patients showed increased fractional anisotropy (FA), but decreased radial and axial diffusivities (RD and AD) in several projection, association, and commissural tracts, compared with PIGD patients and controls. Motor severity had mild-to-moderate correlations with FA and RD of the corpus callosum (genu) in TD, but strong correlations with FA and RD of multiple association tracts in PIGD. Conversely, no significant network changes were noted.

CONCLUSIONS

TD patients showed regionally increased FA but decreased diffusivities, implying neural reorganization to compensate PD pathology in early stages. PIGD patients, despite having similar disease stages and duration, exhibited more WM degradation. These results demonstrate differential WM regional features between the 2 subtypes in early PD and support the notion of TD being a benign subtype.

Diffusion-weighted tractography in the common marmoset monkey at 9.4T

The common marmoset (Callithrix jacchus) is a small New World primate that is becoming increasingly popular in the neurosciences as an animal model of preclinical human disease. With several major disorders characterized by alterations in neural white matter (e.g., multiple sclerosis, Alzheimer's disease, schizophrenia), proposed to be transgenically modeled using marmosets, the ability to isolate and characterize reliably major white matter fiber tracts with MRI will be of use for evaluating structural brain changes related to disease processes and symptomatology. Here, we propose protocols for isolating major white matter fiber tracts in the common marmoset using in vivo ultrahigh-field MRI (9.4T) diffusion-weighted imaging (DWI) data. With the use of a high angular-resolution DWI (256 diffusion-encoding directions) sequence, collected on four anesthetized marmosets, we provide guidelines for manually drawing fiber-tracking regions of interest, based on easily identified anatomical landmarks in DWI native space. These fiber-tract isolation protocols are expected to be experimentally useful for visualization and quantification of individual white matter fiber tracts in both control and experimental groups of marmosets (e.g., transgenic models). As disease models in the marmoset advance, the determination of how macroscopic white matter anatomy is altered as a function of disease state will be relevant in bridging the existing translational gap between preclinical rodent models and human patients.NEW & NOTEWORTHY Although significant progress has been made in mapping white matter connections in the marmoset brain using ex vivo tracing techniques, the application of in vivo virtual dissection of major white matter fiber tracts has been established by few studies in the marmoset literature. Here, we demonstrate the feasibility of whole-brain diffusion-weighted tractography in anesthetized marmosets at ultrahigh-field MRI (9.4T) and propose protocols for isolating nine major white matter fiber tracts in the marmoset brain.

Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging

Mapping gray matter (GM) pathology in Parkinson's disease (PD) with conventional MRI is challenging, and the need for more sensitive brain imaging techniques is essential to facilitate early diagnosis and assessment of disease severity. GM microstructure was assessed with GM-based spatial statistics applied to diffusion kurtosis imaging (DKI) and neurite orientation dispersion imaging (NODDI) in 30 participants with PD and 28 age- and gender-matched controls. These were compared with currently used assessment methods such as diffusion tensor imaging (DTI), voxel-based morphometry (VBM), and surface-based cortical thickness analysis. Linear discriminant analysis (LDA) was also used to test whether subject diagnosis could be predicted based on a linear combination of regional diffusion metrics. Significant differences in GM microstructure were observed in the striatum and the frontal, temporal, limbic, and paralimbic areas in PD patients using DKI and NODDI. Significant correlations between motor deficits and GM microstructure were also noted in these areas. Traditional VBM and surface-based cortical thickness analyses failed to detect any GM differences. LDA indicated that mean kurtosis (MK) and intra cellular volume fraction (ICVF) were the most accurate predictors of diagnostic status. In conclusion, DKI and NODDI can detect cerebral GM abnormalities in PD in a more sensitive manner when compared with conventional methods. Hence, these methods may be useful for the diagnosis of PD and assessment of motor deficits. Hum Brain Mapp 38:3704-3722, 2017. © 2017 Wiley Periodicals, Inc.

Early and progressive microstructural brain changes in mice overexpressing human α-Synuclein detected by diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is sensitive in detecting α-Synuclein (α-Syn) accumulation-associated microstructural changes at late stages of the pathology in α-Syn overexpressing TNWT-61 mice. The aim of this study was to perform DKI in young TNWT-61 mice when α-Syn starts to accumulate and to compare the imaging results with an analysis of motor and memory impairment and α-Syn levels. Three-month-old (3mo) and six-month-old (6mo) mice underwent DKI scanning using the Bruker Avance 9.4T magnetic resonance imaging system. Region of interest (ROI) analyses were performed in the gray matter; tract-based spatial statistics (TBSS) analyses were performed in the white matter. In the same mice, α-Syn expression was evaluated using quantitative immunofluorescence. Mean kurtosis (MK) was the best differentiator between TNWT-61 mice and wildtype (WT) mice. We found increases in MK in 3mo TNWT-61 mice in the striatum and thalamus but not in the substantia nigra (SN), hippocampus, or sensorimotor cortex, even though the immunoreactivity of human α-Syn was similar or even higher in the latter regions. Increases in MK in the SN were detected in 6mo mice. These findings indicate that α-Syn accumulation-associated changes may start in areas with a high density of dopaminergic nerve terminals. We also found TBSS changes in white matter only at 6mo, suggesting α-Syn accumulation-associated changes start in the gray matter and later progress to the white matter. MK is able to detect microstructural changes induced by α-Syn overexpression in TNWT-61 mice and could be a useful clinical tool for detecting early-stage Parkinson's disease in human patients.