Quantitative susceptibility mapping as a biomarker for evaluating white matter alterations in Parkinson’s disease

Potential value of quantitative magnetic susceptibility mapping for the assessment of neonatal hyperbilirubinemic brain injury: A retrospective cohort study based on propensity score matching method

PURPOSE

The aim of the study was to explore the iron contend of patients with neonatal hyperbilirubinemia (NHB) and its correlation with clinical indicators.

METHODS

A total of 34 patients with NHB and 15 subjects in healthy controls (HC) group were included and underwent laboratory examinations and magnetic resonance imaging (MRI) for QSM. The propensity score matching (PSM) method was used to perform 1:1 no-return matching of subjects in the NHB and HC groups, and then the correlation analysis of the susceptibility values of each brain region with the clinical parameter was explored again, as well as the factors influencing the susceptibility values of each brain region were probed by multivariate linear regression analyses. The area under the curve (AUC) for each brain region was obtained via receiver operating characteristic (ROC) curve analysis.

RESULTS

The susceptibility values of the left putamen and left globus pallidum in NHB group were significantly greater than those in the HC group after PSM. And the susceptibility values of the left putamen were significantly positively correlated with serum bilirubin concentrations after PSM. The multiple linear regression analysis revealed that the susceptibility values of the left putamen were influenced by age and serum bilirubin concentrations. According to the ROC curves, the susceptibility values of the left putamen and left globus pallidus has the ability to diagnose NHB.

CONCLUSION

This study revealed that the initial brain damage manifestation in NHB patients may be abnormal iron content in the left putamen and left globus pallidus.

Evolving Landscape of Parkinson's Disease Research: Challenges and Perspectives

Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects movement. It occurs due to a gradual deficit of dopamine-producing brain cells, particularly in the substantia nigra. The precise etiology of PD is not fully understood, but it likely involves a combination of genetic and environmental factors. The therapies available at present alleviate symptoms but do not stop the disease's advancement. Research endeavors are currently directed at inventing disease-controlling therapies that aim at the inherent mechanisms of PD. PD biomarker breakthroughs hold enormous potential: earlier diagnosis, better monitoring, and targeted treatment based on individual response could significantly improve patient outcomes and ease the burden of this disease. PD research is an active and evolving field, focusing on understanding disease mechanisms, identifying biomarkers, developing new treatments, and improving care. In this report, we explore data from the CAS Content Collection to outline the research progress in PD. We analyze the publication landscape to offer perspective into the latest expertise advancements. Key emerging concepts are reviewed and strategies to fight disease evaluated. Pharmacological targets, genetic risk factors, as well as comorbid diseases are explored, and clinical usage of products against PD with their production pipelines and trials for drug repurposing are examined. This review aims to offer a comprehensive overview of the advancing landscape of the current understanding about PD, to define challenges, and to assess growth prospects to stimulate efforts in battling the disease.

A worldwide study of white matter microstructural alterations in people living with Parkinson's disease

The progression of Parkinson's disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20-89 years; 33% female) and 885 controls (age: 19-84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen's d effect sizes reached d = -1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder.

Quantitative susceptibility mapping for susceptibility source separation with adaptive relaxometric constant estimation (QSM-ARCS) from solely gradient-echo data

To separate the contributions of paramagnetic and diamagnetic sources within a voxel, a magnetic susceptibility source separation method based solely on gradient-echo data has been developed. To measure the opposing susceptibility sources more accurately, we propose a novel single-orientation quantitative susceptibility mapping method with adaptive relaxometric constant estimation (QSM-ARCS) for susceptibility source separation. Moreover, opposing susceptibilities and their anisotropic effects were determined in healthy volunteers in the white matter. Multiple spoiled gradient echo and diffusion tensor imaging of ten healthy volunteers was obtained using a 3 T magnetic resonance scanner. After the opposing susceptibility and fractional anisotropy (FA) maps had been reconstructed, the parametric maps were spatially normalized. To evaluate the agreements of QSM-ARCS against the susceptibility source separation method using R2 and R2* maps (χ-separation) by Bland-Altman plots, the opposing susceptibility values were measured using white and deep gray matter atlases. We then evaluated the relationships between the opposing susceptibilities and FAs in the white matter and used a field-to-fiber angle to assess the fiber orientation dependencies of the opposing susceptibilities. The susceptibility maps in QSM-ARCS were successfully reconstructed without large artifacts. In the Bland-Altman analyses, the opposing QSM-ARCS susceptibility values excellently agreed with the χ-separation maps. Significant inverse and proportional correlations were observed between FA and the negative and positive susceptibilities estimated by QSM-ARCS. The fiber orientation dependencies of the negative susceptibility represented a nonmonotonic feature. Conversely, the positive susceptibility increased linearly with the fiber angle with respect to the B0 field. The QSM-ARCS could accurately estimate the opposing susceptibilities, which were identical values of χ-separation, even using gradient echo alone. The opposing susceptibilities might offer direct biomarkers for assessment of the myelin and iron content in glial cells and, through the underlying magnetic sources, provide biologic insights toward clinical transition.

Magnetic Susceptibility in Progressive Supranuclear Palsy Variants, Parkinson's Disease, and Corticobasal Syndrome

BACKGROUND

Previous studies have shown that magnetic susceptibility is increased in several subcortical regions in progressive supranuclear palsy (PSP). However, it is still unclear how subcortical and cortical susceptibilities vary across different PSP variants, Parkinson's disease (PD), and corticobasal syndrome (CBS).

OBJECTIVE

This study aims to clarify the susceptibility profiles in the subcortical and cortical regions in different PSP variants, PD, and CBS.

METHODS

Sixty-four patients, 20 PSP-Richardson syndrome (PSP-RS), 9 PSP-parkinsonism (PSP-P), 7 PSP-progressive gait freezing, 4 PSP-postural instability, 11 PD, and 13 CBS, and 20 cognitively normal control subjects underwent a 3-Tesla magnetic resonance imaging scan to reconstruct quantitative susceptibility maps. Region-of-interest analysis was performed to obtain susceptibility in several subcortical and cortical regions. Bayesian linear mixed effect models were used to estimate susceptibility within group and differences between groups.

RESULTS

In the subcortical regions, patients with PSP-RS and PSP-P showed greater susceptibility than control subjects in the pallidum, substantia nigra, red nucleus, and cerebellar dentate (P < 0.05). Patients with PSP-RS also showed greater susceptibility than patients with PSP-progressive gait freezing, PD, and CBS in the red nucleus and cerebellar dentate, and patients with PSP-P showed greater susceptibility than PD in the red nucleus. Patients with PSP-postural instability and CBS showed greater susceptibility than control subjects in the pallidum and substantia nigra. No significant differences were observed in any cortical region.

CONCLUSIONS

The PSP variants and CBS had different patterns of magnetic susceptibility in the subcortical regions. The findings will contribute to our understanding about iron profiles and pathophysiology of PSP and may provide a potential biomarker to differentiate PSP variants, PD, and CBS. © 2023 International Parkinson and Movement Disorder Society.

White matter changes in Parkinson's disease

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

msQSM: Morphology-based Self-supervised Deep Learning for Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.

APART-QSM: an improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method

The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R₂^* with R₂^' in QSM. The magnitude decay kernel, describing the proportionality coefficient between R₂^' and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R₂^* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R₂^' relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.

Quantitative Evaluation of Oxygen Extraction Fraction Changes in the Monkey Brain during Acute Stroke by Using Quantitative Susceptibility Mapping

BACKGROUND

The oxygen extraction fraction (OEF) indicates the brain's oxygen consumption and can be estimated by using the quantitative susceptibility mapping (QSM) MRI technique. Recent studies have suggested that OEF alteration following stroke is associated with the viability of at-risk tissue. In the present study, the temporal evolution of OEF in the monkey brain during acute stroke was investigated using QSM.

METHODS

Ischemic stroke was induced in adult rhesus monkeys (n = 8) with permanent middle cerebral artery occlusion (pMCAO) by using an interventional approach. Diffusion-, T2-, and T2*-weighted images were conducted on day 0, day 2, and day 4 post-stroke using a clinical 3T scanner. Progressive changes in magnetic susceptibility and OEF, along with their correlations with the transverse relaxation rates and diffusion indices, were examined.

RESULTS

The magnetic susceptibility and OEF in injured gray matter of the brain significantly increased during the hyperacute phase, and then decreased significantly on day 2 and day 4. Moreover, the temporal changes of OEF in gray matter were moderately correlated with mean diffusivity (MD) (r = 0.52; p = 0.046) from day 0 to day 4. Magnetic susceptibility in white matter progressively increased (from negative values to near zero) during acute stroke, and significant increases were seen on day 2 (p = 0.08) and day 4 (p = 0.003) when white matter was significantly degenerated. However, significant reduction of OEF in white matter was not seen until day 4 post-stroke.

CONCLUSION

The preliminary results demonstrate that QSM-derived OEF is a robust approach to examine the progressive changes of gray matter in the ischemic brain from the hyperacute phase to the subacute phase of stroke. The changes of OEF in gray matter were more prominent than those in white matter following stroke insult. The findings suggest that QSM-derived OEF may provide complementary information for understanding the neuropathology of the brain tissue following stroke and predicting stroke outcomes.

Spatial patterns of elevated magnetic susceptibility in progressive apraxia of speech

PURPOSE

Progressive apraxia of speech (PAOS) is a neurodegenerative disorder affecting the planning or programming of speech. Little is known about its magnetic susceptibility profiles indicative of biological processes such as iron deposition and demyelination. This study aims to clarify (1) the pattern of susceptibility in PAOS patients, (2) the susceptibility differences between the phonetic (characterized by predominance of distorted sound substitutions and additions) and prosodic (characterized by predominance of slow speech rate and segmentation) subtypes of PAOS, and (3) the relationships between susceptibility and symptom severity.

METHODS

Twenty patients with PAOS (nine phonetic and eleven prosodic subtypes) were prospectively recruited and underwent a 3 Tesla MRI scan. They also underwent detailed speech, language, and neurological evaluations. Quantitative susceptibility maps (QSM) were reconstructed from multi-echo gradient echo MRI images. Region of interest analysis was conducted to estimate susceptibility coefficients in several subcortical and frontal regions. We compared susceptibility values between PAOS and an age-matched control group and performed a correlation analysis between susceptibilities and an apraxia of speech rating scale (ASRS) phonetic and prosodic feature ratings.

RESULTS

The magnetic susceptibility of PAOS was statistically greater than that of controls in subcortical regions (left putamen, left red nucleus, and right dentate nucleus) (p < 0.01, also survived FDR correction) and in the left white-matter precentral gyrus (p < 0.05, but not survived FDR correction). The prosodic patients showed greater susceptibilities than controls in these subcortical and precentral regions. The susceptibility in the left red nucleus and in the left precentral gyrus correlated with the prosodic sub-score of the ASRS.

CONCLUSION

Magnetic susceptibility in PAOS patients was greater than controls mainly in the subcortical regions. While larger samples are needed before QSM is considered ready for clinical differential diagnosis, the present study contributes to our understanding of magnetic susceptibility changes and the pathophysiology of PAOS.

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.

Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates

Recent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter-scale subcortical brain structures in humans. However, the simultaneous visualization of cortical, subcortical, and white matter structure remains challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortex and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first applied QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analysis methods allowed a similar accurate delineation of subcortical structures in humans. However, the QSM contrast of white and cortical gray matter was not sufficient for appropriate segmentation. Applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of subcortical brain structures as compared to the single T1 contrast by maintaining an excellent white to cortical gray matter contrast. Furthermore, we validated our dual-contrast fusion approach in humans and similarly demonstrated improvements in automated segmentation of the cortex and subcortical structures. We believe the proposed contrast will facilitate translational studies in nonhuman primates to investigate the pathophysiology of neurodegenerative diseases that affect subcortical structures such as the basal ganglia in humans.

Levodopa responsiveness and white matter alterations in Parkinson's disease: A DTI-based study and brain network analysis: A cross-sectional study

BACKGROUND

Patients with Parkinson's disease (PD) present various responsiveness to levodopa, but the cause of such differences in levodopa responsiveness is unclear. Previous studies related the damage of brain white matter (WM) to levodopa responsiveness in PD patients, but no study investigated the relationship between the structural brain network change in PD patients and their levodopa responsiveness.

METHODS

PD patients were recruited and evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS). Each patient received a diffusion tensor imaging (DTI) scan and an acute levodopa challenge test. The improvement rate of UPDRS-III was calculated. PD patients were grouped into irresponsive group (improvement rate < 30%) and responsive group (improvement rate ≥ 30%). Tract-based spatial statistics (TBSS), deterministic tracing (DT), region of interest (ROI) analysis, and automatic fiber identification (AFQ) analyses were performed. The structural brain network was also constructed and the topological parameters were calculated.

RESULTS

Fifty-four PD patients were included. TBSS identified significant differences in fractional anisotropy (FA) values in the corpus callosum and other regions of the brain. DT and ROI analysis of the corpus callosum found a significant difference in FA between the two groups. Graph theory analysis showed statistical differences in global efficiency, local efficiency, and characteristic path length.

CONCLUSION

PD patients with poor responsiveness to levodopa had WM damage in multiple brain areas, especially the corpus callosum, which might cause disruption of information integration of the structural brain network.

Combination of Quantitative Susceptibility Mapping and Diffusion Kurtosis Imaging Provides Potential Biomarkers for Early-Stage Parkinson's Disease

Purpose: This study aimed to detect changes in iron deposition and neural microstructure in the substantia nigra (SN), red nucleus (RN), and basal ganglia of Parkinson's disease (PD) patients at different stages using quantitative susceptibility mapping and diffusion kurtosis imaging to identify potential indicators of early-stage PD. Methods: We enrolled 20 early-stage and 15 late-stage PD patients, as well as 20 age- and sex-matched controls. All participants underwent quantitative susceptibility mapping and diffusion kurtosis imaging to determine magnetic susceptibility (MS), fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) in several brain regions. Results: Compared with the control group, MS and MK values in the SN were significantly increased in the early- and late-stage PD group, whereas MS values in the red nucleus (RN), globus pallidus (GP), and caudate nucleus (CN), FA value in the CN and GP, and MK value in the CN and putamen (PU) were significantly increased in the late-stage PD group. There were positive correlations between MS and MK values in the CN and MS and FA values in the GP. Furthermore, the combination of MS and MK values in the SN provided high accuracy for distinguishing early-stage PD patients from controls. Conclusions: This study identified MS and MK in the SN as potential indicators of early-stage PD.

Diagnostic accuracy of quantitative susceptibility mapping in multiple system atrophy: The impact of echo time and the potential of histogram analysis

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We performed histogram analysis on χ maps at different TEs on MSA patients and HC.

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We found altered χ distribution in Pu, SN, GP, CN for MSAp and in SN, DN for MSAc.

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QSM diagnostic accuracy is TE-dependent and is enhanced at short TEs.

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Short TEs capture rapidly-decaying contributions of high χ sources.

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Histogram features detect χ spatial heterogeneities improving diagnostic accuracy.

The non-invasive quantification of iron stores via Quantitative Susceptibility Mapping (QSM) could play an important role in the diagnosis and the differential diagnosis of atypical Parkinsonisms. However, the susceptibility (χ) values measured via QSM depend on echo time (TE). This effect relates to the microstructural organization within the voxel, whose composition can be altered by the disease. Moreover, pathological iron deposition in a brain area may not be spatially uniform, and conventional Region of Interest (ROI)-based analysis may fail in detecting alterations. Therefore, in this work we evaluated the impact of echo time on the diagnostic accuracy of QSM on a population of patients with Multiple System Atrophy (MSA) of either Parkinsonian (MSAp) or cerebellar (MSAc) phenotypes. In addition, we tested the potential of histogram analysis to improve QSM classification accuracy.

We enrolled 32 patients (19 MSAp and 13 MSAc) and 16 healthy controls, who underwent a 7T MRI session including a gradient-recalled multi-echo sequence for χ mapping. Nine histogram features were extracted from the χ maps computed for each TE in atlas-based ROIs covering deep brain nuclei, and compared among groups.

Alterations of susceptibility distribution were found in the Putamen, Substantia Nigra, Globus Pallidus and Caudate Nucleus for MSAp and in the Substantia Nigra and Dentate Nucleus for MSAc. Increased iron deposition was observed in a larger number of ROIs for the two shortest TEs and the standard deviation, the 75^th and the 90^th percentile were the most informative features yielding excellent diagnostic accuracy with area under the ROC curve > 0.9.

In conclusion, short TEs may enhance QSM diagnostic performances, as they can capture variations in rapidly-decaying contributions of high χ sources. The analysis of histogram features allowed to reveal fine heterogeneities in the spatial distribution of susceptibility alteration, otherwise undetected by a simple evaluation of ROI χ mean values.

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.

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

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

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

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

Background

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

Materials and methods

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

Results

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

Conclusion

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

White Matter Free Water is a Composite Marker of Cerebral Small Vessel Degeneration

To investigate the association between white matter free water (FW) and common imaging markers of cerebral small vessel diseases (CSVD) in two groups of subjects with different clinical status. One hundred and forty-four community subjects (mean age 60.5) and 84 CSVD subjects (mean age 61.2) were retrospectively included in the present study. All subjects received multi-modal magnetic resonance imaging and clinical assessments. The association between white matter FW and common CSVD imaging markers, including white matter hyperintensities (WMH), dilated perivascular space (PVS), lacunes, and microbleeds, were assessed using simple and multiple regression analysis. The association between FW and cognitive scores were also investigated. White matter FW was positively associated with WMH volume (β = 0.270, p = 0.001), PVS volume (β = 0.290, p < 0.001), number of microbleeds (β = 0.148, p = 0.043), and age (β = 0.170, p = 0.036) in the community cohort. In the CSVD cohort, FW was positively associated with WMH volume (β = 0.648, p < 0.001), PVS score (β = 0.224, p < 0.001), number of lacunes (β = 0.140, p = 0.046), and sex (β = 0.125, p = 0.036). The associations between FW and cognitive scores were stronger than conventional CSVD markers in both datasets. White matter FW is a potential composite marker that can sensitively detect cerebral small vessel degeneration and also reflect cognitive impairments.

Decompose quantitative susceptibility mapping (QSM) to sub-voxel diamagnetic and paramagnetic components based on gradient-echo MRI data

PURPOSE

A method named DECOMPOSE-QSM is developed to decompose bulk susceptibility measured with QSM into sub-voxel paramagnetic and diamagnetic components based on a three-pool complex signal model.

METHODS

Multi-echo gradient echo signal is modeled as a summation of three weighted exponentials corresponding to three types of susceptibility sources: reference susceptibility, diamagnetic and paramagnetic susceptibility relative to the reference. Paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS) maps are constructed to represent the sub-voxel compartments by solving for linear and nonlinear parameters in the model.

RESULTS

Numerical forward simulation and phantom validation confirmed the ability of DECOMPOSE-QSM to separate the mixture of paramagnetic and diamagnetic components. The PCS obtained from temperature-variant brainstem imaging follows the Curie's Law, which further validated the model and the solver. Initial in vivo investigation of human brain images showed the ability to extract sub-voxel PCS and DCS sources that produce visually enhanced contrast between brain structures comparing to threshold QSM.

Levodopa responsiveness in Parkinson's disease patients and white matter alterations in diffusion tensor imaging: a cross-sectional tract-based spatial statistics study

This study aimed to investigate the relationship between levodopa responsiveness and white matter alterations in Parkinson's disease patients using diffusion tensor imaging (DTI). Twenty-six recruited Parkinson's disease patients were evaluated using the Mini-Mental State Examination, Hoehn and Yahr scale (H&Y) and Unified Parkinson's Disease Rating Scale (UPDRS). Each patient underwent a DTI scan and an acute levodopa challenge test. The improvement rate of UPDRS-III was calculated, Parkinson's disease patients were grouped into a responsive group (improvement rate ≥30%) and a nonresponsive group (improvement rate <30%). The differences in fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity between the two groups were measured using tract-based spatial statistics. There was no difference in demographic features or baseline evaluations between groups. The UPDRS-III score after the challenge was higher in the nonresponsive group than that in the responsive group. Compared to the responsive group, patients in the nonresponsive group exhibited decreased fractional anisotropy in the corpus callosum; cingulum; left corona radiata; left internal capsule; left middle frontal gyrus; left superior longitudinal fasciculus and right somatosensory cortex. Mean diffusivity and radial diffusivity were increased in wide-ranging areas in the nonresponsive group. No difference was observed in axial diffusivity. White matter alterations in the abovementioned areas may affect the function of the dopaminergic network and thus may be associated with the levodopa response in Parkinson's disease patients. Further studies are needed to analyze the specific mechanism and pathological changes underlying these effects.

Systematic Review: Quantitative Susceptibility Mapping (QSM) of Brain Iron Profile in Neurodegenerative Diseases

Iron has been increasingly implicated in the pathology of neurodegenerative diseases. In the past decade, development of the new magnetic resonance imaging technique, quantitative susceptibility mapping (QSM), has enabled for the more comprehensive investigation of iron distribution in the brain. The aim of this systematic review was to provide a synthesis of the findings from existing QSM studies in neurodegenerative diseases. We identified 80 records by searching MEDLINE, Embase, Scopus, and PsycInfo databases. The disorders investigated in these studies included Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Wilson's disease, Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, Fabry disease, myotonic dystrophy, pantothenate-kinase-associated neurodegeneration, and mitochondrial membrane protein-associated neurodegeneration. As a general pattern, QSM revealed increased magnetic susceptibility (suggestive of increased iron content) in the brain regions associated with the pathology of each disorder, such as the amygdala and caudate nucleus in Alzheimer's disease, the substantia nigra in Parkinson's disease, motor cortex in amyotrophic lateral sclerosis, basal ganglia in Huntington's disease, and cerebellar dentate nucleus in Friedreich's ataxia. Furthermore, the increased magnetic susceptibility correlated with disease duration and severity of clinical features in some disorders. Although the number of studies is still limited in most of the neurodegenerative diseases, the existing evidence suggests that QSM can be a promising tool in the investigation of neurodegeneration.

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

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

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.

PPARδ and its ligand erucic acid may act anti-tumoral, neuroprotective, and myelin protective in neuroblastoma, glioblastoma, and Parkinson's disease

In this review study, we focus on potential benefits of the transcription factor PPARδ and its ligand erucic acid (EA) in management of neuroectodermal tumors and Parkinson's Disease. PPARδ is a nuclear receptor and transcription factor that induces myelination, promotes oligodendroglial and neuronal differentiation, and possess anti-neuroinflammatory properties. While both pro-tumorigenic and anti-tumorigenic effects have been described for PPARδ, we propose that PPARδ may perform a predominantly anticancer role in tumors originating from the neuroectoderm. PPARδ ligand-activation via oleic acid and GW501516, or overexpression of PPARδ, elicits profound antitumor actions in neuroblastoma and melanoma. In glioblastomas, there is evidence indicating a differentiation failure of O2A (oligodendroglial-astrocytic biprogenitor) cells and it has been shown that EA reduced DNA synthesis in C6 rat glioblastoma spheroid cultures in clinically achievable concentrations. EA is a ω9 fatty acid which is being used in the treatment of adrenoleukodystrophy. EA is widely consumed in Asian countries via ingestion of cruciferous vegetables including mustard and rapeseed oil. EA also exerts antioxidant and anti-inflammatory activities. Recent studies of Parkinson's Disease (PD) have implicated demyelination, white matter pathology, oligodendroglial injury, and neural inflammation in the underlying pathophysiology. In the rotenone PD model in rats, PPARδ ligand GW501516 saves dopaminergic neurons during injury induced by chemical toxins and improves behavioral functioning in PD via alleviation of endoplasmic reticulum stress. PPARδ agonists also reduce the NLRP3 inflammasome-associated neural inflammation in the MPTP PD model in mice. Herein, we propose that PPARδ and its ligand EA highly deserve to be studied in animal models of neuroblastoma, glioblastoma, and PD.

Altered white matter microarchitecture in Parkinson's disease: a voxel-based meta-analysis of diffusion tensor imaging studies

This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson's disease (PD) reflected by fractional anisotropy (FA), addressing clinical profiles and methodology-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls (HC) using the anisotropic effect size-signed differential mapping. A total of 808 patients with PD and 760 HC coming from 27 databases were finally included. Subgroup analyses were conducted considering heterogeneity with respect to medication status, disease stage, analysis methods, and the number of diffusion directions in acquisition. Compared with HC, patients with PD had decreased FA in the left middle cerebellar peduncle, corpus callosum (CC), left inferior fronto-occipital fasciculus, and right inferior longitudinal fasciculus. Most of the main results remained unchanged in subgroup meta-analyses of medicated patients, early stage patients, voxel-based analysis, and acquisition with 30 diffusion directions. The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex. The cerebellum and CC, associated with typical motor impairment, showed the most consistent FA decreases in PD. Medication status, analysis approaches, and the number of diffusion directions have an important impact on the findings, needing careful evaluation in future meta-analyses.

Simultaneous voxel-based magnetic susceptibility and morphometry analysis using magnetization-prepared spoiled turbo multiple gradient echo

This study aimed to develop and test a simultaneous acquisition and analysis pipeline for voxel-based magnetic susceptibility and morphometry (VBMSM) on a single dataset using young volunteers, elderly healthy volunteers, and an Alzheimer's disease (AD) group. 3D T₁ -weighted and multi-echo phase images for VBM and quantitative susceptibility mapping (QSM) were simultaneously acquired using a magnetization-prepared spoiled turbo multiple gradient echo sequence with inversion pulse for QSM (MP-QSM). The magnitude image was split into gray matter (GM) and white matter (WM) and was spatially normalized. The susceptibility map was reconstructed from the phase images. The segmented image and susceptibility map were compared with those obtained from conventional multiple spoiled gradient echo (mGRE) and MP-spoiled gradient echo (MP-GRE) in healthy volunteers to validate the availability of MP-QSM by numerical measurements. To assess the feasibility of the VBMSM analysis pipeline, voxel-based comparisons of susceptibility and morphometry in MP-QSM were conducted in volunteers with a bimodal age distribution, and in elderly volunteers and the AD group, using spatially normalized GM and WM volume images and a susceptibility map. GM/WM contrasts in MP-QSM, MP-GRE, and mGRE were 0.14 ± 0.011, 0.17 ± 0.015, and 0.045 ± 0.010, respectively. Segmented GM and WM volumes in the MP-QSM closely coincided with those in the MP-GRE. Region of interest analyses indicated that the mean susceptibility values in MP-QSM were completely in agreement with those in mGRE. In an evaluation of the aging effect, a significant increase and decrease in susceptibility and volume were found by VBMSM in deep GM and WM, respectively. Between the elderly volunteers and the AD group, the characteristic susceptibility and volume changes in GM and WM were observed. The proposed MP-QSM sequence makes it possible to acquire acceptable-quality images for simultaneous analysis and determine brain atrophy and susceptibility distribution without image registration by using voxel-based analyses.

Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a slow progress. The clinical manifestations of PD in patients are highly heterogeneous. Thus, PD diagnosis process is complex and mainly depends on the professional knowledge and experience of the physician. Magnetic resonance imaging (MRI) could detect the small changes in the brain of PD patients, and quantitative analysis of brain MRI may improve the clinical diagnosis efficiency. However, due to the complexity of clinical courses in PD and the high dimensionality in multimodal MRI data, traditional mathematical analysis could not effectively extract the huge information in them. Up to now, the accuracy of PD diagnosis in large sample size is still unsatisfying. As artificial intelligence (AI) is becoming more mature, varieties of statistical models and machine learning (ML) algorithms have been used for quantitative imaging data analysis to explore a diagnostic result. This review aims to state an overview of existing research recently that used statistical ML/AI methods to perform quantitative analysis of MR image data for the study of PD diagnosis. First we review the recent research in three subareas: diagnosis, differential diagnosis, and subtyping of PD. Then we described the overall workflow from MR image to classification result. Finally, we summarized a critical assessment of the current research and provide some recommendations for likely future research developments and trends.

Iron deposition in Parkinson's disease by quantitative susceptibility mapping

Background

Patients with Parkinson’s disease (PD) have elevated levels of brain iron, especially in the nigrostriatal dopaminergic system. The purpose of this study was to evaluate the iron deposition in the substantia nigra (SN) and other deep gray matter nuclei of PD patients using quantitative susceptibility mapping (QSM) and its clinical relationship, and to explore whether there is a gradient of iron deposition pattern in globus pallidus (GP)–fascicula nigrale (FN)–SN pathway.

Methods

Thirty-three PD patients and 26 age- and sex-matched healthy volunteers (HVs) were included in this study. Subjects underwent brain MRI and constructed QSM data. The differences in iron accumulation in the deep gray matter nuclei of the subjects were compared, including the PD group and the control group, the early-stage PD (EPD) group and the late-stage PD (LPD) group. The iron deposition pattern of the GP–FN–SN pathway was analyzed.

Results

The PD group showed increased susceptibility values in the FN, substantia nigra pars compacta (SNc), internal globus pallidus (GPi), red nucleus (RN), putamen and caudate nucleus compared with the HV group (P < 0.05). In both PD and HV group, iron deposition along the GP–FN–SN pathway did not show an increasing gradient pattern. The SNc, substantia nigra pars reticulata (SNr) and RN showed significantly increased susceptibility values in the LPD patients compared with the EPD patients.

Conclusion

PD is closely related to iron deposition in the SNc. The condition of PD patients is related to the SNc and the SNr. There is not an increasing iron deposition gradient along the GP–FN–SN pathway. The source and mechanism of iron deposition in the SN need to be further explored, as does the relationship between the iron deposition in the RN and PD.

Background field removal for susceptibility mapping of human brain with large susceptibility variations

PURPOSE

In quantitative susceptibility mapping (QSM) of human brain, the background field induced by air-tissue interface varies significantly with respect to the rotation angle between the head and the static field, which may result in substantial error in the estimated magnetic susceptibility values. The goal of this study was to develop a strategy to better remove such orientation dependent background field.

METHODS

An improved background field removal method is proposed based on the sophisticated harmonic artifact reduction for phase data using a region adaptive kernel (R-SHARP), named iRSHARP. It uses a spatially weighted spherical Gaussian kernel exploiting the amplitude, gradient, and wrap count of the phase map. The method was validated using both numerical simulations and in vivo human brain data at multiple head orientations. Performance was compared with the variable kernel (V-SHARP) and R-SHARP methods.

RESULTS

The proposed iRSHARP method showed improved background removal over R-SHARP while cutting the computational time in half. As compared to V-SHARP and R-SHARP, the iRSHARP generated local field and susceptibility maps showed fewer artifacts in regions of large susceptibility variations, and for the in vivo human brain, the susceptibilities of the deep gray matter nuclei were consistent with the in vivo gold-standard "Calculation of Susceptibility through Multiple Orientation Sampling" (COSMOS) values.

CONCLUSION

iRSHARP can remove the orientation dependent background field effectively. Using iRSHARP, the paranasal sinus regions can be preserved in the brain mask and the brain integrity was conserved, which may facilitate further data analysis and clinical application.