Early pathological changes in the parkinsonian brain demonstrated by diffusion tensor MRI

Development and validation of cardiac diffusion weighted magnetic resonance imaging for the diagnosis of myocardial injury in small animal models

Cardiac diffusion weighted-magnetic resonance imaging (DWI) has slowly developed due to its technical difficulties. However, this limitation could be overcome by advanced techniques, including a stimulated echo technique and a gradient moment nulling technique. This study aimed to develop and validate a high-order DWI sequence, using echo-planar imaging (EPI) and second-order motion-compensated (M012) diffusion gradient applied to cardiac imaging in small-sized animals with fast heart and respiratory rates, and to investigate the feasibility of cardiac DWI, diagnosing acute myocardial injury in isoproterenol-induced myocardial injury rat models. The M012 diffusion gradient sequence was designed for diffusion tensor imaging of the rat myocardium and validated in the polyvinylpyrrolidone phantom. Following sequence optimization, 23 rats with isoproterenol-induced acute myocardial injury and five healthy control rats underwent cardiac MRI, including cine imaging, T1 mapping, and DWI. Diffusion gradient was applied using a 9.4-T MRI scanner (Bruker, BioSpec 94/20, gradient amplitude = 440 mT/m, maximum slew rate = 3440 T/m/s) with double gating (electrocardiogram and respiratory gating). Troponin I was used as a serum biomarker for myocardial injury. Histopathologic examination of the heart was subsequently performed. The developed DWI sequence using EPI and M012 provided the interpretable images of rat hearts. The apparent diffusion coefficient (ADC) values were significantly higher in rats with acute myocardial injury than in the control group (1.847 ± 0.326 * 10-3 mm2/s vs. 1.578 ± 0.144 * 10-3 mm2/s, P < 0.001). Troponin I levels were increased in the blood samples of rats with acute myocardial injury (P < 0.001). Histopathologic examinations detected myocardial damage and subendocardial fibrosis in rats with acute myocardial injury. The newly developed DWI technique has the ability to detect myocardial injury in small animal models, representing high ADC values on the myocardium with isoproterenol-induced injury.

Role of Diffusion Tensor Imaging in Early Diagnosis and Characterization of Movement Disorders

BACKGROUND

Symptoms of movement disorders in early stages are similar, which makes definite diagnosis difficult. Hence this study was conducted to explore the role of diffusion tensor imaging (DTI) in enhancing the early diagnosis and characterization of movement disorders.

METHODOLOGY

A cross-sectional study was conducted including 60 subjects. All of them were reviewed using conventional magnetic resonance imaging (MRI) and movement disorder DTI protocol. Commercially available software was used to produce fractional anisotropy (FA) maps. Post-processing 3D reconstruction was done to obtain tractograms. Both single and multiple regions of interest (ROIs) were selected for tractography in the pons, midbrain, substantia nigra (SN) and cerebellum. MRI and DTI images were interpreted and correlated with confirmatory diagnosis.

RESULTS

According to DTI diagnosis, out of the 30 cases, 28 had movement disorders. Among cases, 36.67% had Parkinson's disease (PD), 23.33% had progressive supranuclear palsy (PSP), 16.67% had essential tremor, 13.33% had multi-system atrophy (MSA) C, and 3.33% had MSA P. DTI correctly classified all cases with PD and PSP. All cases with long disease duration and 88.24% of cases with short disease duration were also correctly classified. A statistically significant difference was observed in the proportion of diagnosis between DTI and conventional MRI.

CONCLUSION

DTI has high sensitivity and specificity for the diagnosis of movement disorders. It is capable of early diagnosis of movement disorders and also differentiating and subcategorizing them.

Free water and iron content in the substantia nigra at different stages of Parkinson's disease

PURPOSE

Abnormalities in free water (FW) and susceptibility values exist in the substantia nigra (SN) of patients with Parkinson's disease (PD), but their role in characterizing the disease processes remains uncertain. This study investigated these values at various SN locations and stages of PD, and their relationship with clinical symptoms.

METHOD

FW and quantitative susceptibility mapping (QSM) values were evaluated in the anterior and posterior SN, along with swallow-tail-sign (STS) ratings, in patients with PD (early-stage: n = 39; middle-to-advanced-stage: n = 97) and healthy controls (n = 82). The correlation between these indices and motor and non-motor symptoms, and their capability to distinguish PD from healthy controls, were also examined.

RESULTS

Increased FW in the anterior and posterior SN and increased QSM values in the posterior SN were observed in both early-stage and middle-to-advanced-stage PD patients (p < 0.05). However, there was no significant difference in FW, QSM values, or STS ratings among patients at different stages. FW and QSM values correlated with motor symptoms in middle-to-advanced-stage patients (p < 0.05), while STS ratings were associated with non-motor symptoms (p < 0.05). Additionally, combining FW and QSM values in the posterior SN with STS ratings in logistic regression showed better performance in distinguishing PD (area under curve = 0.931) compared to using STS ratings alone (area under curve = 0.880).

CONCLUSIONS

Findings suggest elevated FW and iron content in PD at different stages, with dissociation in SN location between the two indices. Elevated signals are related to the motor symptom severity in middle-to-advanced-stage patients, and may have the potential for PD diagnosis and symptom assessment.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Diffusion tensor imaging techniques show that parkin gene S/N167 polymorphism is responsible for extensive brain white matter damage in patients with Parkinson's disease

Objective

To explore the influence of disease and genetic factors on the white matter microstructure in patients with PD. The white matter microstructural changes in the substantia nigra-striatum system were detected by diffusion tensor imaging (DTI) using the region of interest (ROI) and diffusion tensor tracer (DTT) methods.

Methods

Patients with primary Parkinson's disease (PD) without a family history of PD were selected and divided into PD-G/G and PD-G/A groups according to their parkin S/N167 polymorphism. Control groups matched for age, sex, and gene type (G/G and G/A) were also included. Three-dimensional brain volume imaging (3D-BRAVO) and DTI were performed. The microstructural changes in the substantia nigra-striatum system were evaluated by the ROI and DTT methods. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Hoehn-Yahr (H-Y) staging, and the third part of the Unified Parkinson's Disease Rating (UPDRS-III) scales evaluated the cognitive and motor function impairment in patients with PD. Independent samples t-test compared normally-distributed data, and the Wilcoxon rank sum test compared measurement or categorical non-normally distributed data. Multiple regression analysis was used to analyze the correlation between various DTI indicators and the MMSE, MoCA, UPDRS-III, and H-Y scores in the PD-G/G and PD-G/A groups. P < 0.05 was considered statistically significant.

Results

The white matter microstructural changes in the nigrostriatal pathway differed significantly between the PD or PD-G/A and the control group (P < 0.05)The ROI method showed that the left globus pallidus radial diffusivity (RD) value was negatively correlated with the MMSE score (r = -0.404, P = 0.040), and the left substantia nigra (LSN) fractional anisotropy (FA) value was positively correlated with the MoCA score (r = 0.405, P = 0.040) and negatively with the H-Y stage (r = -0.479, P = 0.013).The DTT method showed that the MMSE score was positively correlated with the right substantia nigra (RSN) FA value (r = 0.592, P = 0.001) and negatively with its RD value (r = -0.439, P = 0.025). The H-Y grade was negatively correlated with the number of fibers in the RSN (r = -0.406, P = 0.040). The UPDRS-Ⅲ score was positively correlated with the mean diffusivity (r = 0.420, P = 0.033) and RD (r = 0.396, P = 0.045) values of the LSN, and the AD value of the RSN (r = 0.439, P = 0.025).

Conclusion

The DTI technique detected extensive white matter fiber damage in patients with PD, primarily in those with the G/A genotype, that led to motor and cognitivesymptoms.

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

Magnetic resonance imaging of the dopamine system in schizophrenia - A scoping review

For decades, aberrant dopamine transmission has been proposed to play a central role in schizophrenia pathophysiology. These theories are supported by human in vivo molecular imaging studies of dopamine transmission, particularly positron emission tomography. However, there are several downsides to such approaches, for example limited spatial resolution or restriction of the measurement to synaptic processes of dopaminergic neurons. To overcome these limitations and to measure complementary aspects of dopamine transmission, magnetic resonance imaging (MRI)-based approaches investigating the macrostructure, metabolism, and connectivity of dopaminergic nuclei, i.e., substantia nigra pars compacta and ventral tegmental area, can be employed. In this scoping review, we focus on four dopamine MRI methods that have been employed in patients with schizophrenia so far: neuromelanin MRI, which is thought to measure long-term dopamine function in dopaminergic nuclei; morphometric MRI, which is assumed to measure the volume of dopaminergic nuclei; diffusion MRI, which is assumed to measure fiber-based structural connectivity of dopaminergic nuclei; and resting-state blood-oxygenation-level-dependent functional MRI, which is thought to measure functional connectivity of dopaminergic nuclei based on correlated blood oxygenation fluctuations. For each method, we describe the underlying signal, outcome measures, and downsides. We present the current state of research in schizophrenia and compare it to other disorders with either similar (psychotic) symptoms, i.e., bipolar disorder and major depressive disorder, or dopaminergic abnormalities, i.e., substance use disorder and Parkinson's disease. Finally, we discuss overarching issues and outline future research questions.

Altered cerebral perfusion and microstructure in advanced Parkinson's disease and their associations with clinical features

OBJECTIVE

To explore the whole cerebral perfusion and microstructure alteration patterns in Parkinson's disease (PD) and the associations of these patterns with clinical features.

METHODS

Forty-one subjects [20 PD patients and 21 healthy controls (HCs)] underwent arterial spin labeling (ASL), diffusion tensor imaging (DTI) and 3D T1-weighted imaging (T1WI) MRI. The cerebral blood flow (CBF) of the whole brain and the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of subcortical and cerebellar regions were measured and compared between groups. Pearson's correlation was calculated between MRI measurements and clinical features [Unified Parkinson's Disease Rating Scale (UPDRS), UPDRS III, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and olfactory test scores].

RESULTS

Compared to HCs, PD patients showed lower CBF in the frontal, parietal and temporal lobes but higher CBF in bilateral hippocampi, red nuclei, right substantia nigra, thalamus and most cerebellar regions. The MD in the right thalamus and several regions in the cerebellum increased in PD compared to HCs. In PD patients, the total UPDRS, UPDRS III, MoCA, MMSE and olfactory test scores were related to FA or CBF in cerebellum. (all p < 0.05).

CONCLUSION

Hypoperfusion in cortical regions, together with hyperperfusion in subcortical and cerebellar regions may be the characteristic perfusion pattern in advanced PD patients. The microstructures of the right thalamus and cerebellum were changed in PD patients. The cognitive, motor and olfactory performance of PD patients is closely related to the perfusion and microstructure of the brain, especially the cerebellum.

Dopamine receptor density and white mater integrity: 18F-fallypride positron emission tomography and diffusion tensor imaging study in healthy and schizophrenia subjects

Dopaminergic dysfunction and changes in white matter integrity are among the most replicated findings in schizophrenia. A modulating role of dopamine in myelin formation has been proposed in animal models and healthy human brain, but has not yet been systematically explored in schizophrenia. We used diffusion tensor imaging and ¹⁸F-fallypride positron emission tomography in 19 healthy and 25 schizophrenia subjects to assess the relationship between gray matter dopamine D₂/D₃ receptor density and white matter fractional anisotropy in each diagnostic group. AFNI regions of interest were acquired for 42 cortical Brodmann areas and subcortical gray matter structures as well as stereotaxically placed in representative white matter areas implicated in schizophrenia neuroimaging literature. Welch's t-test with permutation-based p value adjustment was used to compare means of z-transformed correlations between fractional anisotropy and ¹⁸F-fallypride binding potentials in hypothesis-driven regions of interest in the diagnostic groups. Healthy subjects displayed an extensive pattern of predominantly negative correlations between ¹⁸F-fallypride binding across a range of cortical and subcortical gray matter regions and fractional anisotropy in rostral white matter regions (internal capsule, frontal lobe, anterior corpus callosum). These patterns were disrupted in subjects with schizophrenia, who displayed significantly weaker overall correlations as well as comparatively scant numbers of significant correlations with the internal capsule and frontal (but not temporal) white matter, especially for dopamine receptor density in thalamic nuclei. Dopamine D₂/D₃ receptor density and white matter integrity appear to be interrelated, and their decreases in schizophrenia may stem from hyperdopaminergia with dysregulation of dopaminergic impact on axonal myelination.

Utility of manual fractional anisotropy measurements in the management of patients with Parkinson disease: a feasibility study with a 1.5-T magnetic resonance imaging system

Background

Diffusion tensor imaging has emerged as a promising tool for quantitative analysis of neuronal damage in Parkinson disease, with potential value for diagnostic and prognostic evaluation.

Purpose

The aim of this study was to examine Parkinson disease-associated alterations in specific brain regions revealed by diffusion tensor imaging and how such alterations correlate with clinical variables.

Material and Methods

Diffusion tensor imaging was performed on 42 Parkinson disease patients and 20 healthy controls with a 1.5-T scanner. Manual fractional anisotropy measurements were performed for the ventral, intermediate, and dorsal portions of the substantia nigra, as well as for the cerebral peduncles, putamen, thalamus, and supplementary motor area. The correlation analysis between these measurements and the clinical variables was performed using χ² variance and multiple linear regression.

Results

Compared to healthy controls, Parkinson disease patients had significantly reduced fractional anisotropy values in the substantia nigra (P < .05). Some fractional anisotropy measurements in the substantia nigra correlated inversely with duration of Parkinson disease and Parkinson disease severity scores. Reduced fractional anisotropy values in the substantia nigra were also correlated inversely with age variable. fractional anisotropy values obtained for the right and left putamen varied significantly between males and females in both groups.

Conclusion

Manual fractional anisotropy measurements in the substantia nigra were confirmed to be feasible with a 1.5-T scanner. Diffusion tensor imaging data can be used as a reliable biomarker of Parkinson disease that can be used to support diagnosis, prognosis, and progression/treatment monitoring.

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.

NfL as a biomarker for neurodegeneration and survival in Parkinson disease

OBJECTIVE

To determine whether neurofilament light chain protein in CSF (cNfL), a sensitive biomarker of neuroaxonal damage, reflects disease severity or can predict survival in Parkinson disease (PD).

METHODS

We investigated whether disease severity, phenotype, or survival in patients with new-onset PD correlates with cNfL concentrations around the time of diagnosis in the population-based New Parkinsonism in Umeå (NYPUM) study cohort (n = 99). A second, larger new-onset PD cohort (n = 194) was used for independent validation. Association of brain pathology with the cNfL concentration was examined with striatal dopamine transporter imaging and repeated diffusion tensor imaging at baseline and 1 and 3 years.

RESULTS

Higher cNfL in the early phase of PD was associated with greater severity of all cardinal motor symptoms except tremor in both cohorts and with shorter survival and impaired olfaction. cNfL concentrations above the median of 903 ng/L conferred an overall 5.8 times increased hazard of death during follow-up. After adjustment for age and sex, higher cNfL correlated with striatal dopamine transporter uptake deficits and lower fractional anisotropy in diffusion tensor imaging of several axonal tracts.

CONCLUSIONS

cNfL shows usefulness as a biomarker of disease severity and to predict survival in PD. The present results indicate that the cNfL concentration reflects the intensity of the neurodegenerative process, which could be important in future clinical trials.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with PD, cNfL concentrations are associated with more severe disease and shorter survival.

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.

Towards accurate and unbiased imaging-based differentiation of Parkinson's disease, progressive supranuclear palsy and corticobasal syndrome

The early and accurate differential diagnosis of parkinsonian disorders is still a significant challenge for clinicians. In recent years, a number of studies have used magnetic resonance imaging data combined with machine learning and statistical classifiers to successfully differentiate between different forms of Parkinsonism. However, several questions and methodological issues remain, to minimize bias and artefact-driven classification. In this study, we compared different approaches for feature selection, as well as different magnetic resonance imaging modalities, with well-matched patient groups and tightly controlling for data quality issues related to patient motion. Our sample was drawn from a cohort of 69 healthy controls, and patients with idiopathic Parkinson’s disease (n = 35), progressive supranuclear palsy Richardson’s syndrome (n = 52) and corticobasal syndrome (n = 36). Participants underwent standardized T1-weighted and diffusion-weighted magnetic resonance imaging. Strict data quality control and group matching reduced the control and patient numbers to 43, 32, 33 and 26, respectively. We compared two different methods for feature selection and dimensionality reduction: whole-brain principal components analysis, and an anatomical region-of-interest based approach. In both cases, support vector machines were used to construct a statistical model for pairwise classification of healthy controls and patients. The accuracy of each model was estimated using a leave-two-out cross-validation approach, as well as an independent validation using a different set of subjects. Our cross-validation results suggest that using principal components analysis for feature extraction provides higher classification accuracies when compared to a region-of-interest based approach. However, the differences between the two feature extraction methods were significantly reduced when an independent sample was used for validation, suggesting that the principal components analysis approach may be more vulnerable to overfitting with cross-validation. Both T1-weighted and diffusion magnetic resonance imaging data could be used to successfully differentiate between subject groups, with neither modality outperforming the other across all pairwise comparisons in the cross-validation analysis. However, features obtained from diffusion magnetic resonance imaging data resulted in significantly higher classification accuracies when an independent validation cohort was used. Overall, our results support the use of statistical classification approaches for differential diagnosis of parkinsonian disorders. However, classification accuracy can be affected by group size, age, sex and movement artefacts. With appropriate controls and out-of-sample cross validation, diagnostic biomarker evaluation including magnetic resonance imaging based classifiers may be an important adjunct to clinical evaluation.

A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson's disease at 3T: An efficient susceptibility map-weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet)

PURPOSE

Visibility of nigrosome 1 in the substantia nigra (SN) is used as an MR imaging biomarker for Parkinson's disease. Because of lower susceptibility induced tissue contrast and SNR visualization of the SN pars compacta (SNPC) using conventional imaging technique in the clinical field strength (≤3T) has been limited. Susceptibility map-weighted imaging (SMWI) has been proposed to visualize SNPC at 3T. To better visualize nigrosome 1 and SN areas using SMWI, accurate estimation of the quantitative susceptibility mapping (QSM) map is essential. In SMWI processing, however, QSM processing time using conventional algorithms is the most time-consuming step and may limit clinical use. In this study, we introduce an efficient SMWI processing approach using the deep neural network (QSMnet). To improve the processing speed of SMWI while maintaining similar image quality to that obtained with the conventional method, QSMnet was applied to generate a susceptibility mask for SMWI processing.

METHODS

To conduct a deep learning-based image to image operation modified QSMnet was utilized. The network was trained with in vivo MR data from 57 healthy controls. To validate SMWI results from QSMnet, four datasets from healthy controls were used as the test datasets. As a preliminary attempt to explore the clinical applicability, Parkinson's disease patient data were additionally tested. The SWMI images generated by QSMnet and conventional model-based QSM outputs were compared. To validate SMWI results, region of interest (ROI) analysis was performed. The mean signal values at the nigrosome 1 and the surrounding regions were measured to calculate contrast-to-noise ratio (CNR).

RESULTS

The experimental results confirmed that the proposed approach using QSMnet provided similar QSM and SMWI compared to that obtained with conventional iLSQR while the processing speed was much improved (5.4 times faster). The QSM results from QSMnet show similar tissue contrast with results from iLSQR. When compared, the absolute mean intensity difference between two methods near the nigrosome 1 was 0.015 ppm. SMWI results using susceptibility masks from QSMnet demonstrated signal distribution and tissue contrast that was comparable with those results from the conventional iLSQR method. The absolute difference maps of SMWI were calculated to show the similarity between the two methods. The overall mean absolute difference value in the presented ROIs obtained from healthy controls (n = 4) and a PD patient (n = 1) were 2.31 and 1.81, respectively. Mean CNR values (10 ROIs; n = 5; including both sides; 1.42 for QSMnet; 1.43 for iLSQR) between SN and nigrosome 1 in SMWI results obtained by ROI analysis were similar (P = 0.724).

CONCLUSIONS

In this study, we assessed an efficient approach for SMWI visualizing SN and nigrosome 1 on 3T. QSMnet provides a similar SMWI image to that obtained with the conventional iterative QSM algorithm and improves QSM processing speed by avoiding iterative computation. Since QSM is the most time-consuming step of SMWI processing, QSMnet can help to achieve a higher processing speed of SMWI. These results suggest that SMWI imaging with susceptibility masks using QSMnet is a more efficient approach.

Visualization of Nigrosome 1 from the Viewpoint of Anatomic Structure

BACKGROUND AND PURPOSE

Parkinson disease is related to neurodegeneration and iron deposition in the substantia nigra pars compacta and nigrosome 1. However, visualization of nigrosome 1 via MR imaging is poor owing to the bilateral asymmetry, regardless of whether it is healthy. We focused on the magic angle and susceptibility effect and evaluated the anatomic slant structure of nigrosome 1 by tilting subjects' heads in the B₀ direction.

MATERIALS AND METHODS

To investigate the effectiveness of the magic angle, we tilted the volunteers' heads to the right and left in the B₀ direction or not at all for evaluating correlations between the degree of head tilting and visualization of the right nigrosome 1 and left nigrosome 1 using 3D spoiled gradient-echo sequences with multiecho acquisitions. We evaluated the susceptibility of nigrosome 1 and the local field using quantitative susceptibility mapping to assess static magnetic field inhomogeneity.

RESULTS

The heads tilted to the right and left showed significantly higher contrasts of nigrosome 1 and the substantia nigra pars compacta than the nontilted heads. No significant differences were observed in the visualization and susceptibility between the right nigrosome 1 and left nigrosome 1 for each head tilt. The effect of the magic angle was remarkable in the nontilted heads. This finding was supported by quantitative susceptibility mapping because the anatomic slant structure of nigrosome 1 was coherent between the axis of nigrosome 1 and the magic angle.

CONCLUSIONS

The asymmetric visualization of nigrosome 1 is affected by the magic angle and susceptibility. The anatomic slant structure of nigrosome 1 causes these challenges in visualization.

Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity

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White matter disruption occurs in Parkinson's disease across several brain regions.

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DTI properties could identify clinically distinct subtypes of Parkinson's disease.

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Structural neural disruption can predict clinical outcomes in Parkinson's disease.

Objectives: Investigating biomarkers to demonstrate progression of Parkinson's disease (PD) is of high priority. We investigated the association of brain structural properties with progression of clinical outcomes and their ability to differentiate clinical subtypes of PD.

Methods: A comprehensive set of clinical features was evaluated at baseline and 4.5-year follow-up for 144 de-novo PD patients from the Parkinson's Progression Markers Initiative. We created a global composite outcome (GCO) by combining z-scores of non-motor and motor symptoms, motor signs, overall activities of daily living and global cognition, as a single numeric indicator of prognosis. We classified patients into three subtypes based on multi-domain clinical criteria: ‘mild motor-predominant’, ‘intermediate’ and ‘diffuse-malignant’. We analyzed diffusion-weighted scans at the early drug-naïve stage and extracted fractional anisotropy and mean diffusivity (MD) of basal ganglia and cortical sub-regions. Then, we employed graph theory to calculate network properties and used network-based statistic to investigate our primary hypothesis.

Results: Baseline MD of globus pallidus was associated with worsening of motor severity, cognition, and GCO after 4.5 years of follow-up. Connectivity disruption at baseline was correlated with decline in cognition, and increase in GCO. Baseline MD of nucleus accumbens, globus pallidus and basal-ganglia were linked to clinical subtypes at 4.5-year of follow-up. Disruption in sub-cortical networks associated with being subtyped as ‘diffuse-malignant’ versus ‘mild motor-predominant’ after 4.5 years.

Conclusions: Diffusion imaging analysis at the early de-novo stage of PD was able to differentiate clinical sub-types of PD after 4.5 years and was highly associated with future clinical outcomes of PD.

Diffusion Kurtosis Imaging as a Tool in Neurotoxicology

This commentary serves as an introduction to the magnetic resonance imaging (MRI) technique called diffusion kurtosis imaging (DKI) employed in the study by Arab et al. in the present issue of Neurotoxicology Research. In their study, DKI is employed for longitudinal investigation of a methamphetamine intoxication model of Parkinson's disease. The study employs an impressive number of animals and combines DKI with behavioral analysis at multiple time points. The commentary discusses some aspects of the study design especially the strength of combining behavioral analysis with MRI in an effort to provide as thorough a characterization and validity assessment of the animal model and cohort as possible. The potential clinical value of combining multiple MRI techniques (multimodal MRI) in PD is discussed as well as the benefit of multimodal MRI combined with behavioral analysis and subsequent histological analysis for in-depth characterization of animal models.

Free-Water Imaging in White and Gray Matter in Parkinson's Disease

This study aimed to discriminate between neuroinflammation and neuronal degeneration in the white matter (WM) and gray matter (GM) of patients with Parkinson’s disease (PD) using free-water (FW) imaging. Analysis using tract-based spatial statistics (TBSS) of 20 patients with PD and 20 healthy individuals revealed changes in FW imaging indices (i.e., reduced FW-corrected fractional anisotropy (FA_T), increased FW-corrected mean, axial, and radial diffusivities (MD_T, AD_T, and RD_T, respectively) and fractional volume of FW (FW) in somewhat more specific WM areas compared with the changes of DTI indices. The region-of-interest (ROI) analysis further supported these findings, whereby those with PD showed significantly lower FA_T and higher MD_T, AD_T, and RD_T (indices of neuronal degeneration) in anterior WM areas as well as higher FW (index of neuroinflammation) in posterior WM areas compared with the controls. Results of GM-based spatial statistics (GBSS) analysis revealed that patients with PD had significantly higher MD_T, AD_T, and FW than the controls, whereas ROI analysis showed significantly increased MD_T and FW and a trend toward increased AD_T in GM areas, corresponding to Braak stage IV. These findings support the hypothesis that neuroinflammation precedes neuronal degeneration in PD, whereas WM microstructural alterations precede changes in GM.

Combined Assessment of Diffusion Parameters and Cerebral Blood Flow Within Basal Ganglia in Early Parkinson's Disease

Diffusion tensor imaging (DTI) is a sensitive tool for detecting brain tissue microstructural alterations in Parkinson’s disease (PD). Abnormal cerebral perfusion patterns have also been reported in PD patients using arterial spin labeling (ASL) MRI. In this study we aimed to perform a combined DTI and ASL assessment in PD patients within the basal ganglia, in order to test the relationship between microstructural and perfusion alterations. Fifty-two subjects participated in this study. Specifically, 26 PD patients [mean age (SD) = 66.7 (8.9) years, 21 males, median (IQR) Modified Hoehn and Yahr = 1.5 (1–1.6)] and twenty-six healthy controls [HC, mean age (SD) = 65.2 (7.5), 15 males] were scanned with 1.5T MRI. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) maps were derived from diffusion-weighted images, while cerebral blood flow (CBF) maps were computed from ASL data. After registration to Montreal Neurological Institute standard space, FA, MD, AD, RD and CBF median values were extracted within specific regions of interest: substantia nigra, caudate, putamen, globus pallidus, thalamus, red nucleus and subthalamic nucleus. DTI measures and CBF were compared between the two groups. The relationship between diffusion parameters and CBF was tested with Spearman’s correlations. False discovery rate (FDR)-corrected p-values lower than 0.05 were considered significant, while uncorrected p-values <0.05 were considered a trend. No significant FA, MD and RD differences were observed. AD was significantly increased in PD patients compared with HC in the putamen (p = 0.005, p_FDR = 0.035). No significant CBF differences were found between PD patients and HC. Diffusion parameters were not significantly correlated with CBF in the HC group, while a significant correlation emerged for PD patients in the caudate nucleus, for all DTI measures (with FA: r = 0.543, p_FDR = 0.028; with MD: r = −0.661, p_FDR = 0.002; with AD: r = −0.628, p_FDR = 0.007; with RD: r = −0.635, p_FDR = 0.003). This study showed that DTI is a more sensitive technique than ASL to detect alterations in the basal ganglia in the early phase of PD. Our results suggest that, although DTI and ASL convey different information, a relationship between microstructural integrity and perfusion changes in the caudate may be present.

Is Diffusion Tensor Imaging a Good Biomarker for Early Parkinson's Disease?

Objectives: To assess white matter abnormalities in Parkinson's disease (PD).

Methods: A hundred and thirty-two patients with PD (mean age 60.93 years; average disease duration 7.8 years) and 137 healthy controls (HC; mean age 57.8 years) underwent the same MRI protocol. Patients were assessed by clinical scales and a complete neurological evaluation. We performed a TBSS analysis to compare patients and controls, and we divided patients into early PD, moderate PD, and severe PD and performed an ROI analysis using tractography.

Results: With TBSS we found lower FA in patients in corpus callosum, internal and external capsule, corona radiata, thalamic radiation, sagittal stratum, cingulum and superior longitudinal fasciculus. Increased AD was found in the corpus callosum, fornix, corticospinal tract, superior cerebellar peduncle, cerebral peduncle, internal and external capsules, corona radiata, thalamic radiation and sagittal stratum and increased RD were seen in the corpus callosum, internal and external capsules, corona radiata, sagittal stratum, fornix, and cingulum. Regarding the ROIs, a GLM analysis showed abnormalities in all tracts, mainly in the severe group, when compared to HC, mild PD and moderate PD.

Conclusions: Since major abnormalities were found in the severe PD group, we believe DTI analysis might not be the best tool to assess early alterations in PD, and probably, functional and other structural analysis might suit this purpose better. However it can be used to differentiate disease stages, and as a surrogate marker to assess disease progression, being an important measure that could be used in clinical trials.

HIGHLIGHTS

DTI is not the best tool to identify early PD

DTI can differentiate disease stages

DTI analysis may be a useful marker for disease progression

White matter degeneration profile in the cognitive cortico-subcortical tracts in Parkinson's disease

BACKGROUND

In Parkinson's disease cognitive impairment is an early nonmotor feature, but it is still unclear why some patients are able to maintain their cognitive performance at normal levels, as quantified by neuropsychological tests, whereas others cannot. The objectives of this study were to perform a cross-sectional study and analyze the white matter changes in the cognitive and motor bundles in patients with Parkinson's disease.

METHODS

Sixteen Parkinson's disease patients with normal cognitive performance, 19 with mild cognitive impairment (based on their performance of 1.5 standard deviations below the healthy population mean), and 16 healthy controls were compared with respect to their tractography patterns between the cortical cognitive / motor regions and subcortical structures, using high angular resolution diffusion imaging and constrained spherical deconvolution computation.

RESULTS

Motor bundles showed decreased apparent fiber density in both PD groups, associated with a significant increase in diffusivity metrics, number of reconstructed streamlines, and track volumes, compared with healthy controls. By contrast, in the cognitive bundles, decreased fiber density in both Parkinson's groups was compounded by the absence of changes in diffusivity in patients with normal cognition, whereas patients with cognitive impairment had increased diffusivity metrics, lower numbers of reconstructed streamlines, and lower track volumes.

CONCLUSIONS

Both PD groups showed similar patterns of white matter neurodegeneration in the motor bundles, whereas cognitive bundles showed a distinct pattern: Parkinson's patients with normal cognition had white matter diffusivity metrics similar to healthy controls, whereas in patients with cognitive impairment white matter showed a neurodegeneration pattern. © 2018 International Parkinson and Movement Disorder Society.

Principles of diffusion kurtosis imaging and its role in early diagnosis of neurodegenerative disorders

Pathology of neurodegenerative diseases can be correlated with intra-neuronal as well as extracellular changes which lead to neuronal degeneration. The central nervous system (CNS) is a complex structure comprising of many biological barriers. These microstructural barriers might be affected by a variety of pathological processes. Specifically, changes in the brain tissue's microstructure affect the diffusion of water which can be assessed non-invasively by diffusion weighted (DW) magnetic resonance imaging (MRI) techniques. Diffusion tensor imaging (DTI) is a diffusion MRI technique that considers diffusivity as a Gaussian process, i.e. does not account for any diffusion hindrance. However, environment of the brain tissues is characterized by a non-Gaussian diffusion. Therefore, diffusion kurtosis imaging (DKI) was developed as an extension of DTI method in order to quantify the non-Gaussian distribution of water diffusion. This technique represents a promising approach for early diagnosis of neurodegenerative diseases when the neurodegenerative process starts. Hence, the purpose of this article is to summarize the ongoing clinical and preclinical research on Parkinson's, Alzheimer's and Huntington diseases, using DKI and to discuss the role of this technique as an early stage biomarker of neurodegenerative conditions.

Role of the combination of FA and T2* parameters as a new diagnostic method in therapeutic evaluation of parkinson's disease

BACKGROUND

Simple diffusion delivery (SDD) has attained good effects with only tiny amounts of drugs. Fractional anisotropy (FA) and relaxation time T2* that indicate the integrity of fiber tracts and iron concentration within brain tissue were used to evaluate the therapeutic effect of SDD.

PURPOSE

To evaluate therapeutic effect of SDD in the Parkinson's disease (PD) rat model with FA and T2* parameters.

STUDY TYPE

Prospective case-control animal study.

POPULATION

Thirty-two male Sprague Dawley rats (eight normal, eight PD, eight SDD, and eight subcutaneous injection rats).

FIELD STRENGTH/SEQUENCE

Single-shot spin echo echo-planar imaging and fast low-angle shot T₂ WI sequences at 3.0T.

ASSESSMENT

Parameters of FA and T2* on the treated side of the substantia nigra were measured to evaluate the therapeutic effect of SDD in a PD rat model.

STATISTICAL TESTS

The effects of time on FA and T2* values were analyzed by repeated measurement tests. A one-way analysis of variance was conducted, followed by individual comparisons of the mean FA and T2* values at different timepoints.

RESULTS

The FA values on the treated side of the substantia nigra in the SDD treatment group and subcutaneous injection treatment group were significantly higher at week 1 and lower at week 6 than that of the PD control group (SDD vs. PD, week 1, adjusted P = 0.012; subcutaneous vs. PD, week 1, adjusted P < 0.001; SDD vs. PD, week 6, adjusted P = 0.004; subcutaneous vs. PD, week 6, adjusted P = 0.024). The T2* parameter in the SDD treatment group and subcutaneous injection treatment group was significantly higher than that in the PD control group at week 6 (SDD vs. PD, adjusted P = 0.032; subcutaneous vs. PD, adjusted P < 0.001).

DATA CONCLUSION

The combination of FA and T2* parameters can potentially serve as a new effective evaluation method of the therapeutic effect of SDD.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2017.

Overview of neuroradiology

Neuroradiology with computed tomography (CT) and magnetic resonance imaging (MRI) is essential for the initial evaluation of patients with a clinical suspicion of brain and spine disorders. Morphologic imaging is required to obtain a probable diagnosis to support the treatment decisions in pre- and perinatal disorders, vascular diseases, traumatic injuries, metabolic disorders, epilepsy, infection/inflammation, neurodegenerative disorders, degenerative spinal disease, and tumors of the central nervous system. Different postprocessing tools are increasingly used for three-dimensional visualization and quantification of lesions. Additional information is provided by angiographic methods and physiologic CT and MRI techniques, such as diffusion MRI, perfusion CT/MRI, MR spectroscopy, functional MRI, tractography, and nuclear medicine imaging methods. Positron emission tomography (PET) is now integrated with CT (PET/CT), and PET/MR scanners have recently also been introduced. These hybrid techniques facilitate the co-registration of lesions with different modalities, and give new possibilites for functional imaging. Repeated imaging is increasingly performed for treatment monitoring. The improved imaging techniques together with the neuropathologic diagnosis after biopsy or surgery allow more personalized treatment of the patient. Neuroradiology also includes endovascular treatment of aneurysms and arteriovenous malformations as well as thrombectomy in acute stroke. This catheter-based treatment has replaced invasive neurosurgery in many cases.

Free water improves detection of changes in the substantia nigra in parkinsonism: A multisite study

BACKGROUND

Imaging markers that are sensitive to parkinsonism across multiple sites are critically needed for clinical trials. The objective of this study was to evaluate changes in the substantia nigra using single- and bi-tensor models of diffusion magnetic resonance imaging in PD, MSA, and PSP.

METHODS

The study cohort (n = 425) included 107 healthy controls and 184 PD, 63 MSA, and 71 PSP patients from 3 movement disorder centers. Bi-tensor free water, free-water-corrected fractional anisotropy, free-water-corrected mean diffusivity, single-tensor fractional anisotropy, and single-tensor mean diffusivity were computed for the anterior and posterior substantia nigra. Correlations were computed between diffusion MRI measures and clinical measures.

RESULTS

In the posterior substantia nigra, free water was greater for PSP than MSA and PD patients and controls. PD and MSA both had greater free water than controls. Free-water-corrected fractional anisotropy values were greater for PSP patents than for controls and PD patients. PSP and MSA patient single-tensor mean diffusivity values were greater than controls, and single-tensor fractional anisotropy values were lower for PSP patients than for healthy controls. The parkinsonism effect size for free water was 0.145 in the posterior substantia nigra and 0.072 for single-tensor mean diffusivity. The direction of correlations between single-tensor mean diffusivity and free-water values and clinical scores was similar at each site.

CONCLUSIONS

Free-water values in the posterior substantia nigra provide a consistent pattern of findings across patients with PD, MSA, and PSP in a large cohort across 3 sites. Free water in the posterior substantia nigra relates to clinical measures of motor and cognitive symptoms in a large cohort of parkinsonism. © 2017 International Parkinson and Movement Disorder Society.

Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

BACKGROUND

Neuroimaging studies help us better understand the pathophysiology and symptoms of Parkinson's disease (PD). In several of these studies, diffusion tensor imaging (DTI) was used to investigate structural changes in cerebral tissue. Although data have been provided as regards to specific brain areas, a whole brain meta-analysis is still missing.

METHODS

We compiled 39 studies in this meta-analysis: 14 used fractional anisotropy (FA), 1 used mean diffusivity (MD), and 24 used both indicators. These studies comprised 1855 individuals, 1087 with PD and 768 healthy controls. Regions of interest were classified anatomically (subcortical structures; white matter; cortical areas; cerebellum). Our statistical analysis considered the disease effect size (DES) as the main variable; the heterogeneity index (I2) and Pearson's correlations between the DES and co-variables (demographic, clinical and MRI parameters) were also calculated.

RESULTS

Our results showed that FA-DES and MD-DES were able to distinguish between patients and healthy controls. Significant differences, indicating degenerations, were observed within the substantia nigra, the corpus callosum, and the cingulate and temporal cortices. Moreover, some findings (particularly in the corticospinal tract) suggested opposite brain changes associated with PD. In addition, our results demonstrated that MD-DES was particularly sensitive to clinical and MRI parameters, such as the number of DTI directions and the echo time within white matter.

CONCLUSIONS

Despite some limitations, DTI appears as a sensitive method to study PD pathophysiology and severity. The association of DTI with other MRI methods should also be considered and could benefit the study of brain degenerations in PD.

Welding-related brain and functional changes in welders with chronic and low-level exposure

Although an essential nutrient, manganese (Mn) can be toxic at high doses. There is, however, uncertainty regarding the effects of chronic low-level Mn-exposure. This review provides an overview of Mn-related brain and functional changes based on studies of a cohort of asymptomatic welders who had lower Mn-exposure than in most previous work. In welders with low-level Mn-exposure, we found: 1) Mn may accumulate in the brain in a non-linear fashion: MRI R1 (1/T1) signals significantly increased only after a critical level of exposure was reached (e.g., ≥300 welding hours in the past 90days prior to MRI). Moreover, R1 may be a more sensitive marker to capture short-term dynamic changes in Mn accumulation than the pallidal index [T1-weighted intensity ratio of the globus pallidus vs. frontal white matter], a traditional marker for Mn accumulation; 2) Chronic Mn-exposure may lead to microstructural changes as indicated by lower diffusion tensor fractional anisotropy values in the basal ganglia (BG), especially when welding years exceeded more than 30 years; 3) Mn-related subtle motor dysfunctions can be captured sensitively by synergy metrics (indices for movement stability), whereas traditional fine motor tasks failed to detect any significant differences; and 4) Iron (Fe) also may play a role in welding-related neurotoxicity, especially at low-level Mn-exposure, evidenced by higher R2* values (an estimate for brain Fe accumulation) in the BG. Moreover, higher R2* values were associated with lower phonemic fluency performance. These findings may guide future studies and the development of occupation- and public health-related polices involving Mn-exposure.

Application of high-field magnetic resonance imaging in Parkinson's disease

The present study aimed to observe the structural changes of the extracorticospinal tract in Parkinson's disease (PD) using susceptibility-weighted imaging (SWI) and diffusion tensor imaging (DTI) magnetic resonance (MR) scans. The association of DTI parameters and brain-iron accumulation with PD was examined and imaging signs useful in the diagnosis of PD were explored. The study included 30 patients with PD and 30 age- and gender-matched healthy controls who underwent routine MR, SWI and DTI scans. The corrected phase (CP) values of the substantia nigra (SN), red nucleus (RN), globus pallidus (GP) and putamen (PUT) were measured, and fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were obtained. Significant differences were found in the CP values between the PD and control groups in the SN, RN and PUT, but there were no differences in other regions of interest (ROIs). The FA values of the SN and PUT in the PD group were significantly decreased compared with those of the control group, but there was no significant difference in the FA values of the GP. Furthermore, there was no significant inter-group difference in the ADC values of any ROIs. In conclusion, SWI is a method useful for evaluating brain-iron deposition in PD. Increasing iron storage levels have previously been shown to be associated with PD pathogenesis but not with the degree of PD severity. FA values may be useful for diagnosing PD, and DTI may offer some insight into PD pathomechanisms and clinical diagnosis.

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.

Axonal damage and loss of connectivity in nigrostriatal and mesolimbic dopamine pathways in early Parkinson's disease

A progressive loss of dopamine neurons in the substantia nigra (SN) is considered the main feature of idiopathic Parkinson's disease (PD). Recent neuropathological evidence however suggests that the axons of the nigrostriatal dopaminergic system are the earliest target of α-synuclein accumulation in PD, thus the principal site for vulnerability. Whether this applies to in vivo PD, and also to the mesolimbic system has not been investigated yet.

We used [¹¹C]FeCIT PET to measure presynaptic dopamine transporter (DAT) activity in both nigrostriatal and mesolimbic systems, in 36 early PD patients (mean disease duration in months ± SD 21.8 ± 10.7) and 14 healthy controls similar for age. We also performed anatomically-driven partial correlation analysis to evaluate possible changes in the connectivity within both the dopamine networks at an early clinical phase.

In the nigrostriatal system, we found a severe DAT reduction in the afferents to the dorsal putamen (DPU) (η² = 0.84), whereas the SN was the less affected region (η² = 0.31). DAT activity in the ventral tegmental area (VTA) and the ventral striatum (VST) were also reduced in the patient group, but to a lesser degree (VST η² = 0.71 and VTA η² = 0.31). In the PD patients compared to the controls, there was a marked decrease in dopamine network connectivity between SN and DPU nodes, supporting the significant derangement in the nigrostriatal pathway.

These results suggest that neurodegeneration in the dopamine pathways is initially more prominent in the afferent axons and more severe in the nigrostriatal system. Considering PD as a disconnection syndrome starting from the axons, it would justify neuroprotective interventions even if patients have already manifested clinical symptoms.

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In vivo study of mesolimbic and nigrostriatal dopamine systems in early iPD

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Evidence for a severe axonal damage with relative sparing of SN

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Evidence for a moderate damage of the mesolimbic pathway in early iPD

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Significant reduction of molecular connectivity between nigrostriatal nodes

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Justification for neuroprotective interventions in early-iPD phase

Diffusion kurtosis imaging for diagnosis of Parkinson's disease: A novel software tool proposal

In order to diagnose Parkinson disease (PD) at an early stage, it is important to develop a sensitive method for detecting structural changes in the substantia nigra (SN). Diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) have become important tools in supporting diagnosis of PD, with findings based on increased apparent diffusion coefficients (ADCs) in basal ganglia and decreased fractional anisotropy (FA) in SN. Based on the hypothesis that a diffusion kurtosis imaging (DKI) theory is a valuable method for PD diagnosis based on the non-Gaussian diffusion of water in biologic systems, the purpose of this study is to develop an image processing scheme (software) based on Image-J for the facilitating the application of DKI to assist PD diagnosis. Using the new DKI software enables to estimate the diffusional kurtosis and diffusion coefficients, which reflect the structural differences between regions of interest. The experimental results of applying the new software showed that diffusional kurtosis was highly sensitive to microstructural tissue changes, which were not noticeable in the diffusion coefficient values. Thus, the study results may suggest that applying the new image processing software can be useful for assessing tissue structural abnormalities, monitoring and following disease progression.

Magnetic resonance imaging as a potential biomarker for Parkinson's disease

Although a magnetic resonance imaging (MRI) biomarker for Parkinson's disease (PD) remains an unfulfilled objective, there have been numerous developments in MRI methodology and some of these have shown promise for PD. With funding from the National Institutes of Health and the Michael J Fox Foundation there will be further validation of structural, diffusion-based, and iron-focused MRI methods as possible biomarkers for PD. In this review, these methods and other strategies such as neurochemical and metabolic MRI have been covered. One of the challenges in establishing a biomarker is in the selection of individuals as PD is a heterogeneous disease with varying clinical features, different etiologies, and a range of pathologic changes. Additionally, longitudinal studies are needed of individuals with clinically diagnosed PD and cohorts of individuals who are at great risk for developing PD to validate methods. Ultimately an MRI biomarker will be useful in the diagnosis of PD, predicting the course of PD, providing a means to track its course, and provide an approach to select and monitor treatments.

Editor's Highlight: Lower Fractional Anisotropy in the Globus Pallidus of Asymptomatic Welders, a Marker for Long-Term Welding Exposure

INTRODUCTION

Welding fumes contain several metals including manganese (Mn), iron (Fe), and copper (Cu) that at high exposure may co-influence welding-related neurotoxicity. The relationship between brain accumulation of these metals and neuropathology, especially in welders with subclinical exposure levels, is unclear. This study examined the microstructural integrity of basal ganglia (BG) regions in asymptomatic welders using diffusion tensor imaging (DTI).

METHODS

Subjects with (n = 43) and without (age- and gender-matched controls; n = 31) history of welding were studied. Occupational questionnaires estimated short-term (HrsW; welding hours and E90; cumulative exposure, past 90 days) and long-term (YrsW; total years welding and ELT; cumulative exposure, lifetime) exposure. Whole blood metal levels (Mn, Fe, and Cu) were obtained. Brain MRI pallidal index (PI), R1 (1/T1), and R2* (1/T2*) were measured to estimate Mn and Fe accumulation in BG [caudate, putamen, and globus pallidus (GP)]. DTI was used to assess BG microstructural differences, and related with exposure measurements.

RESULTS

When compared with controls, welders had significantly lower fractional anisotropy (FA) in the GP. In welders, GP FA values showed non-linear relationships to YrsW, blood Mn, and PI. GP FA decreased after a critical level of YrsW or Mn was reached, whereas it decreased with increasing PI values until plateauing at the highest PI values. GP FA, however, did not show any relationship with short-term exposure measurements (HrsW, E90), blood Cu and Fe, or R(2)* values.

CONCLUSION

GP FA captured microstructural changes associated with chronic low-level Mn exposure, and may serve as a biomarker for neurotoxicity in asymptomatic welders.

Influence of User-Defined Parameters on Diffusion Tensor Tractography of the Corticospinal Tract

This study discusses the influence of user-defined parameters on fiber tracking results obtained from a standard deterministic streamline tractography algorithm. Diffusion tensor imaging with fiber tractography was performed in five healthy volunteers. A region of interest was highlighted in the ventral part of the pons at the level of the middle cerebellar peduncle. The parameters studied were angle threshold, fractional anisotropy threshold, step length and number of seed samples per voxel. Changes in fiber tracts were described for increasing values per parameter. Increasing the angle threshold resulted in more and longer fibers. A higher fractional anisotropy threshold resulted in decreased length and fiber tracts that were not representative. Increasing the step length decreased the fiber continuity and altered its position. A higher number of seed samples per voxel resulted in a higher fiber tract density. When interpreting diffusion tensor images, the reader should understand the influence of user-defined settings on the results, and should be aware of the inter-dependency of fiber tracking parameters.

Non-neoplastic diseases of the fallopian tube: MR imaging with emphasis on diffusion-weighted imaging

OBJECTIVE

We illustrate the magnetic resonance imaging (MRI) features of non-neoplastic tubaric conditions.

BACKGROUND

A variety of pathologic non-neoplastic conditions may affect the fallopian tubes. Knowledge of their imaging appearance is important for correct diagnosis. With recent advances in MRI, along with conventional MR sequences, diffusion-weighted imaging (DWI) sequences are available and may improve lesion characterization by discriminating the nature of the content of the dilated tube. Tubal fluid with low signal intensity on T1-weighted images, high signal intensity on T2-weighted images and no restricted diffusion on DWI is indicative of hydrosalpinx. Content with high signal intensity on T1-weighted images and restricted diffusion on DWI is suggestive of hematosalpinx associated with endometriosis or tubal pregnancy. A dilated tube with variable or heterogeneous signal intensity content on conventional MR sequences and restricted diffusion on DWI may suggest a pyosalpinx or tubo-ovarian abscess. We describe morphological characteristics, MR signal intensity features, enhancement behaviour and possible differential diagnosis of each lesion.

CONCLUSION

MRI is the method of choice to study adnexal pelvic masses. Qualitative and quantitative functional imaging with DWI can be of help in characterization of tubaric diseases, provided that findings are interpreted in conjunction with those obtained with conventional MRI sequences.

TEACHING POINTS

• Nondilated fallopian tubes are not usually seen on MR images. • MRI is the method of choice to characterize and localize utero-adnexal masses. • MRI allows characterization of lesions through evaluation of the fluid content's signal intensity. • DWI in conjunction with conventional MRI sequences may improve tissue characterization. • Pelvic inflammatory disease is the most common tubal pathology.

Diffusion-Weighted Magnetic Resonance Imaging for Therapy Response Monitoring and Early Treatment Prediction of Photothermal Therapy

Photothermal therapy (PTT) as a relatively new cancer treatment method has attracted worldwide attention. Previous research on PTT has focused on its therapy efficiency and selectivity. The early prognosis of PTT, which is pivotal for the assessment of the treatment and the therapy stratification, however, has been rarely studied. In the present study, we investigated diffusion-weighted magnetic resonance imaging (DW-MRI) as a tool for therapy monitoring and early prognosis of PTT. To this end, we injected PEGylated graphene oxide (GO-PEG) or iron oxide deposited graphene oxide (GO-IONP-PEG) to 4T1 tumor models and irradiated the tumors at different drug-light intervals to induce PTT. For GO-IONP-PEG injected animals, we also included therapy arms where an external magnetic field was applied to the tumors to improve the delivery of the nanoparticle transducers. DW-MRI was performed at different time points after PTT and the tumor apparent diffusion coefficients (ADCs) were analyzed and compared. Our studies show that photothermal agents, magnetic guidance, and drug-light intervals can all affect PTT treatment efficacy. Impressively, ADC value changes at early time points after PTT (less than 48 h) were found to be well-correlated with tumor growth suppression that was apparent days or weeks later. The changes were most sensitive to conditions that can extend the survival for more than 4 weeks, in which cases the 48 h ADC values were increased by more than 80%. These studies demonstrate for the first time that DW-MRI can be an accurate prognosis tool for PTT, suggesting an important role it can play in the future PTT evaluation and clinical translation of the modality.

Using Tractography to Distinguish SWEDD from Parkinson's Disease Patients Based on Connectivity

Background. It is critical to distinguish between Parkinson's disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches. Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography. Methods. Diffusion magnetic resonance images of SWEDD (n = 37) and PD (n = 40) were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences. Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections. Conclusions. The connections and regions identified represent candidates for future research investigations.

Computer-based magnetic resonance imaging as a tool in clinical diagnosis in neurodegenerative diseases

Magnetic resonance imaging (MRI) is one of the core elements within the differential diagnostic work-up of patients with neurodegenerative diseases such as dementia syndromes, Parkinsonian syndromes, and motor neuron diseases. Currently, computerized MRI analyses are not routinely used for individual diagnosis; however, they have improved the anatomical understanding of pathomorphological alterations in various neurodegenerative diseases by quantitative comparisons between patients and controls at the group level. For multiparametric MRI protocols, including T1-weighted MRI, diffusion-weighted imaging, and intrinsic functional connectivity MRI, the potential as a surrogate marker is a subject of investigation. The additional value of MRI with respect to diagnosis at the individual level and for future disease-modifying multicentre trials remains to be defined. Here, we give an overview of recent applications of multiparametric MRI to patients with various neurodegenerative diseases. Starting from applications at the group level, continuous progress of a transfer to individual diagnostic classification is ongoing.

Diffusion tensor imaging of the extracorticospinal network in the brains of patients with Wilson disease

OBJECTIVE

To evaluate damage to the extracorticospinal tract in Wilson disease (WD) patients using diffusion tensor imaging (DTI).

METHODS

70 patients with WD, including 50 with cerebral type and 20 with hepatic type, and 20 age-matched healthy controls were enrolled. Neurological symptoms were scored using the modified Young Scale. Patients with cerebral type WD were divided into four subgroups: those with (1) hypokinesia, (2) parkinsonism, (3) mouth and throat dystonia, and (4) psychiatric symptoms. All study subjects underwent DTI of the brain. Five regions of interest (ROIs) were chosen. Fractional anisotropy (FA) and fiber volumes between ROIs were determined, and the relationships between DTI metrics and clinical status were evaluated.

RESULTS

FA values and fiber volumes between subcortical nuclei were lower in WD patients. Fiber volumes between the putamen (PU) and the globus pallidus (GP), substantia nigra (SN), and thalamus (TH); between the head of the caudate nucleus (CA) and the GP and TH; and between the TH and cerebellum were lower in group 1 than in the other groups of WD patients. Fiber volumes between the GP and the SN and TH were lower in group 2, and fiber volumes between the SN and TH were lower in group 3. DTI metrics differed between patients with the cerebral and hepatic types of WD.

CONCLUSIONS

DTI can reconstruct the network of the extracorticospinal tract. Fiber projection between subcortical nuclei was abnormal in WD patients. Damage to fiber connections may correlate with neurological symptoms in WD patients.

Potential of Diffusion Tensor Imaging and Relaxometry for the Detection of Specific Pathological Alterations in Parkinson's Disease (PD)

The purpose of the present study was to evaluate the potential of multimodal MR imaging including mean diffusivity (MD), fractional anisotropy (FA), relaxation rates R2 and R2* to detect disease specific alterations in Parkinson's Disease (PD). We enrolled 82 PD patients (PD-all) with varying disease durations (≤5 years: PD≤5, n = 43; >5 years: PD>5, n = 39) and 38 matched healthy controls (HC), receiving diffusion tensor imaging as well as R2 and R2* relaxometry calculated from multi-echo T2*-weighted and dual-echo TSE imaging, respectively. ROIs were drawn to delineate caudate nucleus (CN), putamen (PU), globus pallidus (GP) and substantia nigra (SN) on the co-registered maps. The SN was divided in 3 descending levels (SL 1–3). The most significant parameters were used for a flexible discrimination analysis (FDA) in a training collective consisting of 25 randomized subjects from each group in order to predict the classification of remaining subjects. PD-all showed significant increases in MD, R2 and R2* within SN and its subregions as well as in MD and R2* within different basal ganglia regions. Compared to the HC group, the PD≤5 and the PD>5 group showed significant MD increases within the SN and its lower two subregions, while the PD≤5 group exhibited significant increases in R2 and R2* within SN and its subregions, and tended to elevation within the basal ganglia. The PD>5 group had significantly increased MD in PU and GP, whereas the PD≤5 group presented normal MD within the basal ganglia. FDA achieved right classification in 84% of study participants. Micro-structural damage affects primarily the SN of PD patients and in later disease stages the basal ganglia. Iron contents of PU, GP and SN are increased at early disease stages of PD.

Free-water imaging in Parkinson's disease and atypical parkinsonism

Conventional single tensor diffusion analysis models have provided mixed findings in the substantia nigra of Parkinson's disease, but recent work using a bi-tensor analysis model has shown more promising results. Using a bi-tensor model, free-water values were found to be increased in the posterior substantia nigra of Parkinson's disease compared with controls at a single site and in a multi-site cohort. Further, free-water increased longitudinally over 1 year in the posterior substantia nigra of Parkinson's disease. Here, we test the hypothesis that other parkinsonian disorders such as multiple system atrophy and progressive supranuclear palsy have elevated free-water in the substantia nigra. Equally important, however, is whether the bi-tensor diffusion model is able to detect alterations in other brain regions beyond the substantia nigra in Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy and to accurately distinguish between these diseases. Free-water and free-water-corrected fractional anisotropy maps were compared across 72 individuals in the basal ganglia, midbrain, thalamus, dentate nucleus, cerebellar peduncles, cerebellar vermis and lobules V and VI, and corpus callosum. Compared with controls, free-water was increased in the anterior and posterior substantia nigra of Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. Despite no other changes in Parkinson's disease, we observed elevated free-water in all regions except the dentate nucleus, subthalamic nucleus, and corpus callosum of multiple system atrophy, and in all regions examined for progressive supranuclear palsy. Compared with controls, free-water-corrected fractional anisotropy values were increased for multiple system atrophy in the putamen and caudate, and increased for progressive supranuclear palsy in the putamen, caudate, thalamus, and vermis, and decreased in the superior cerebellar peduncle and corpus callosum. For all disease group comparisons, the support vector machine 10-fold cross-validation area under the curve was between 0.93-1.00 and there was high sensitivity and specificity. The regions and diffusion measures selected by the model varied across comparisons and are consistent with pathological studies. In conclusion, the current study used a novel bi-tensor diffusion analysis model to indicate that all forms of parkinsonism had elevated free-water in the substantia nigra. Beyond the substantia nigra, both multiple system atrophy and progressive supranuclear palsy, but not Parkinson's disease, showed a broad network of elevated free-water and altered free-water corrected fractional anisotropy that included the basal ganglia, thalamus, and cerebellum. These findings may be helpful in the differential diagnosis of parkinsonian disorders, and thereby facilitate the development and assessment of targeted therapies.

Diffusion Kurtosis Imaging of Substantia Nigra Is a Sensitive Method for Early Diagnosis and Disease Evaluation in Parkinson's Disease

Background. To diagnose Parkinson disease (PD) in an early stage and accurately evaluate severity, it is important to develop a sensitive method for detecting structural changes in the substantia nigra (SN). Method. Seventy-two untreated patients with early PD and 72 healthy controls underwent diffusion tensor and diffusion kurtosis imaging. Regions of interest were drawn in the rostral, middle, and caudal SN by two blinded and independent raters. Mean kurtosis (MK) and fractional anisotropy in the SN were compared between the groups. Receiver operating characteristic (ROC) and Spearman correlation analyses were used to compare the diagnostic accuracy and correlate imaging findings with Hoehn-Yahr (H-Y) staging and part III of the Unified Parkinson's Disease Rating Scale (UPDRS-III). Result. MK in the SN was increased significantly in PD patients compared with healthy controls. The area under the ROC curve was 0.976 for MK in the SN (sensitivity, 0.944; specificity, 0.917). MK in the SN had a positive correlation with H-Y staging and UPDRS-III scores. Conclusion. Diffusion kurtosis imaging is a sensitive method for PD diagnosis and severity evaluation. MK in the SN is a potential biomarker for imaging studies of early PD that can be widely used in clinic.