Brain iron deposition fingerprints in Parkinson's disease and progressive supranuclear palsy

Iron deposition in subcortical nuclei of Parkinson's disease: A meta-analysis of quantitative iron-sensitive magnetic resonance imaging studies

BACKGROUND

Iron deposition plays a crucial role in the pathophysiology of Parkinson's disease (PD), yet the distribution pattern of iron deposition in the subcortical nuclei has been inconsistent across previous studies. We aimed to assess the difference patterns of iron deposition detected by quantitative iron-sensitive magnetic resonance imaging (MRI) between patients with PD and patients with atypical parkinsonian syndromes (APSs), and between patients with PD and healthy controls (HCs).

METHODS

A systematic literature search was conducted on PubMed, Embase, and Web of Science databases to identify studies investigating the iron content in PD patients using the iron-sensitive MRI techniques (R2* and quantitative susceptibility mapping [QSM]), up until May 1, 2023. The quality assessment of case-control and cohort studies was performed using the Newcastle-Ottawa Scale, whereas diagnostic studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Standardized mean differences and summary estimates of sensitivity, specificity, and area under the curve (AUC) were calculated for iron content, using a random effects model. We also conducted the subgroup-analysis based on the MRI sequence and meta-regression.

RESULTS

Seventy-seven studies with 3192 PD, 209 multiple system atrophy (MSA), 174 progressive supranuclear palsy (PSP), and 2447 HCs were included. Elevated iron content in substantia nigra (SN) pars reticulata (P <0.001) and compacta (P <0.001), SN (P <0.001), red nucleus (RN, P <0.001), globus pallidus (P <0.001), putamen (PUT, P = 0.009), and thalamus (P = 0.046) were found in PD patients compared with HCs. PD patients showed lower iron content in PUT (P <0.001), RN (P = 0.003), SN (P = 0.017), and caudate nucleus (P = 0.027) than MSA patients, and lower iron content in RN (P = 0.001), PUT (P <0.001), globus pallidus (P = 0.004), SN (P = 0.015), and caudate nucleus (P = 0.001) than PSP patients. The highest diagnostic accuracy distinguishing PD from HCs was observed in SN (AUC: 0.85), and that distinguishing PD from MSA was found in PUT (AUC: 0.90). In addition, the best diagnostic performance was achieved in the RN for distinguishing PD from PSP (AUC: 0.84).

CONCLUSION

Quantitative iron-sensitive MRI could quantitatively detect the iron content of subcortical nuclei in PD and APSs, while it may be insufficient to accurately diagnose PD. Future studies are needed to explore the role of multimodal MRI in the diagnosis of PD.

REGISTRISION

PROSPERO; CRD42022344413.

The Irony of Iron: The Element with Diverse Influence on Neurodegenerative Diseases

Iron accumulation in the brain is a common feature of many neurodegenerative diseases. Its involvement spans across the main proteinopathies involving tau, amyloid-beta, alpha-synuclein, and TDP-43. Accumulating evidence supports the contribution of iron in disease pathologies, but the delineation of its pathogenic role is yet challenged by the complex involvement of iron in multiple neurotoxicity mechanisms and evidence supporting a reciprocal influence between accumulation of iron and protein pathology. Here, we review the major proteinopathy-specific observations supporting four distinct hypotheses: (1) iron deposition is a consequence of protein pathology; (2) iron promotes protein pathology; (3) iron protects from or hinders protein pathology; and (4) deposition of iron and protein pathology contribute parallelly to pathogenesis. Iron is an essential element for physiological brain function, requiring a fine balance of its levels. Understanding of disease-related iron accumulation at a more intricate and systemic level is critical for advancements in iron chelation therapies.

Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data

Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data

OBJECTIVE

This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

MATERIAL AND METHODS

A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models' decision.

RESULTS

A comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders.

DISCUSSION

Our results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods.

CONCLUSION

The ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.

Whole-Brain Magnetic Resonance Spectroscopy Reveals Distinct Alterations in Neurometabolic Profile in Progressive Supranuclear Palsy

BACKGROUND

Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized by supranuclear gaze palsy, early postural instability, and a frontal dysexecutive syndrome. Contrary to normal brain magnetic resonance imaging in Parkinson's disease (PD), PSP shows specific cerebral atrophy patterns and alterations, but these findings are not present in every patient, and it is still unclear if these signs are also detectable in early disease stages.

OBJECTIVE

The aim of the present study was to analyze the metabolic profile of patients with clinically diagnosed PSP in comparison with matched healthy volunteers and PD patients using whole-brain magnetic resonance spectroscopic imaging (wbMRSI).

METHODS

Thirty-nine healthy controls (HCs), 29 PD, and 22 PSP patients underwent wbMRSI. PSP and PD patients were matched for age and handedness with HCs. Clinical characterization was performed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale, PSP rating scale, and DemTect (test for cognitive assessment).

RESULTS

In PSP patients a significant reduction in N-acetyl-aspartate (NAA) was detected in all brain lobes. Fractional volume of the cerebrospinal fluid significantly increased in PSP patients compared to PD and healthy volunteers.

CONCLUSIONS

In PSP much more neuronal degeneration and cerebral atrophy have been detected compared with PD. The most relevant alteration is the decrease in NAA in all lobes of the brain, which also showed a partial correlation with clinical symptoms. However, more studies are needed to confirm the additional value of wbMRSI in clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets

INTRODUCTION

Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets.

MATERIALS AND METHODS

A total of 2,041 T1-weighted MRI datasets from 13 different studies were collected, including 1,024 datasets from PD patients and 1,017 datasets from age- and sex-matched healthy controls (HC). The datasets were skull stripped, resampled to isotropic resolution, bias field corrected, and non-linearly registered to the MNI PD25 atlas. The Jacobian maps derived from the deformation fields together with basic clinical parameters were used to train a state-of-the-art convolutional neural network (CNN) to classify PD and HC subjects. Saliency maps were generated to display the brain regions contributing the most to the classification task as a means of explainable artificial intelligence.

RESULTS

The CNN model was trained using an 85%/5%/10% train/validation/test split stratified by diagnosis, sex, and study. The model achieved an accuracy of 79.3%, precision of 80.2%, specificity of 81.3%, sensitivity of 77.7%, and AUC-ROC of 0.87 on the test set while performing similarly on an independent test set. Saliency maps computed for the test set data highlighted frontotemporal regions, the orbital-frontal cortex, and multiple deep gray matter structures as most important.

CONCLUSION

The developed CNN model, trained on a large heterogenous database, was able to differentiate PD patients from HC subjects with high accuracy with clinically feasible classification explanations. Future research should aim to investigate the combination of multiple imaging modalities with deep learning and on validating these results in a prospective trial as a clinical decision support system.

The role of ferroptosis in neurodegenerative diseases

Ferroptosis is newly identified as a non-apoptotic form of programmed cell death. It is characterized by iron-dependent intracellular accumulation of lipid peroxides which ultimately leads to oxidative stress and cell death. Ferroptosis has been identified in several diseases, such as cancer, renal failure, liver injury, and ischemia-reperfusion injury. Besides, it has been reported to be involved in the pathological mechanism of neurodegenerative diseases (NDD). In addition, interventions targeting ferroptosis can influence the course of NDD, making it a potential therapeutic target for NDD. By summarizing the current research on ferroptosis and its impact on many neurological diseases, we hope to provide valuable strategies for the underlying mechanisms and treatment of these neurological diseases.

Diagnostic Performance of Putaminal Hypointensity on Susceptibility MRI in Distinguishing Parkinson Disease from Progressive Supranuclear Palsy: A Meta-Analysis

Background

Idiopathic Parkinson's disease (IPD) and progressive supranuclear palsy (PSP) have similar clinical signs and symptoms, making accurate clinical diagnosis difficult. T2* gradient echo (T2* GRE), susceptibility-weighted imaging (SWI), and quantitative susceptibility mapping (QSM) are susceptibility MR imaging sequences that provide more information about brain iron levels than other conventional MR imaging.

Objective

This study aimed to evaluate the diagnostic power of putaminal hypointensity on T2* GRE, SWI, and QSM in distinguishing PSP from IPD.

Methods

Eligible studies were identified via systematic searches of PubMed and Clarivate Analytics® Web of Science® Core Collection. Studies that satisfied the inclusion and exclusion criteria were reviewed. A meta-analysis was conducted using the hierarchical summary receiver operating characteristic curve approach.

Results

Our literature search of the two databases yielded 562 primary articles, 10 of which were deemed relevant and only six were eligible for further analyses. We performed a meta-analysis of putaminal hypointensity measurements: 438 patients with IPD and 109 patients with PSP were enrolled in the quantitative synthesis. The meta-analysis of six studies with 547 patients revealed a sensitivity of 69% (95% confidence interval (CI): 33%-90%) and specificity of 91% (95% CI: 80%-96%) for putaminal hypointensity on T2* GRE, SWI, or QSM distinguishing PSP from IPD.

Conclusions

Putaminal hypointensity on T2* GRE, SWI, or QSM is able to distinguish patients with PSP from those with IPD with high specificity. Further multicenter prospective studies on patients are needed to verify our results.

Quantitative susceptibility mapping and blood neurofilament light chain differentiate between parkinsonian disorders

Objectives

We employed quantitative susceptibility mapping (QSM) to assess iron deposition in parkinsonian disorders and explored whether combining QSM values and neurofilament light (NfL) chain levels can improve the accuracy of distinguishing Parkinson’s disease (PD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).

Materials and methods

Forty-seven patients with PD, 28 patients with MSA, 18 patients with PSP, and 28 healthy controls (HC) were enrolled, and QSM data were reconstructed. Susceptibility values in the bilateral globus pallidus (GP), putamen (PUT), caudate nucleus (CN), red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN) were obtained. Plasma NfL levels of 47 PD, 18 MSA, and 14 PSP patients and 22 HC were measured by ultrasensitive Simoa technology.

Results

The highest diagnostic accuracy distinguishing MSA from PD patients was observed with increased susceptibility values in CN (AUC: 0.740). The susceptibility values in RN yielded the highest diagnostic performance for distinguishing PSP from PD patients (AUC: 0.829). Plasma NfL levels were significantly higher in the MSA and PSP groups than in PD and HC groups. Combining the susceptibility values in the RN and plasma NfL levels improved the diagnostic performance for PSP vs. PD (AUC: 0.904), whereas plasma NfL levels had higher diagnostic accuracy for MSA vs. PD (AUC: 0.877).

Conclusion

The exploratory study indicates different patterns of iron accumulation in deep gray matter nuclei in Parkinsonian disorders. Combining QSM values with NfL levels may be a promising biomarker for distinguishing PSP from PD, whereas plasma NfL may be a reliable biomarker for differentiating MSA from PD. QSM and NfL measures appeared to have low accuracy for separating PD from controls.

A brief history of brain iron accumulation in Parkinson disease and related disorders

Iron has a long and storied history in Parkinson disease and related disorders. This essential micronutrient is critical for normal brain function, but abnormal brain iron accumulation has been associated with extrapyramidal disease for a century. Precisely why, how, and when iron is implicated in neuronal death remains the subject of investigation. In this article, we review the history of iron in movement disorders, from the first observations in the early twentieth century to recent efforts that view extrapyramidal iron as a novel therapeutic target and diagnostic indicator.

A brief history of brain iron accumulation in Parkinson disease and related disorders

Iron has a long and storied history in Parkinson disease and related disorders. This essential micronutrient is critical for normal brain function, but abnormal brain iron accumulation has been associated with extrapyramidal disease for a century. Precisely why, how, and when iron is implicated in neuronal death remains the subject of investigation. In this article, we review the history of iron in movement disorders, from the first observations in the early twentieth century to recent efforts that view extrapyramidal iron as a novel therapeutic target and diagnostic indicator.

Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning

Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects. Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods. Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone. Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.

Deferiprone Treatment in Aged Transgenic Tau Mice Improves Y-Maze Performance and Alters Tau Pathology

The accumulation of neurofibrillary tangles (NFTs), which is composed of abnormally hyperphosphorylated tau aggregates, is the classic neuropathology associated with cognitive dysfunction in tauopathies such as Alzheimer's disease (AD). However, there is an emerging theory suggesting that dysregulation in cerebral iron may contribute to NFT formation. Iron is speculated to bind to tau and induce conformational changes of the protein, potentially leading to subsequent aggregation and cognitive decline. Deferiprone (DFP) is a clinically available iron chelator, which has demonstrated potential therapeutic advantages of chelating iron in neurodegenerative disorders, and is currently in clinical trials for AD. However, its effect on tau pathology remains unclear. Here, we report the effects of short-term DFP treatment (4 weeks, 100 mg/kg/daily, via oral gavage) in a mixed-gender cohort of the rTg(tauP301L)4510 mouse model of tauopathy. Our results revealed that DFP improved Y-maze and open field performance, accompanied by a 28% decrease in brain iron levels, measured by inductively coupled plasma mass spectrometry (ICP-MS) and reduced AT8-labeled p-tau within the hippocampus in transgenic tau mice. This data supports the notion that iron may play a neurotoxic role in tauopathies and may be a potential therapeutic target for this class of disorders that can be modulated by the clinically available metal chelator DFP.

Multiparametric Quantitative MRI in Neurological Diseases

Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.

Iron Dysregulation and Inflammagens Related to Oral and Gut Health Are Central to the Development of Parkinson's Disease

Neuronal lesions in Parkinson's disease (PD) are commonly associated with α-synuclein (α-Syn)-induced cell damage that are present both in the central and peripheral nervous systems of patients, with the enteric nervous system also being especially vulnerable. Here, we bring together evidence that the development and presence of PD depends on specific sets of interlinking factors that include neuroinflammation, systemic inflammation, α-Syn-induced cell damage, vascular dysfunction, iron dysregulation, and gut and periodontal dysbiosis. We argue that there is significant evidence that bacterial inflammagens fuel this systemic inflammation, and might be central to the development of PD. We also discuss the processes whereby bacterial inflammagens may be involved in causing nucleation of proteins, including of α-Syn. Lastly, we review evidence that iron chelation, pre-and probiotics, as well as antibiotics and faecal transplant treatment might be valuable treatments in PD. A most important consideration, however, is that these therapeutic options need to be validated and tested in randomized controlled clinical trials. However, targeting underlying mechanisms of PD, including gut dysbiosis and iron toxicity, have potentially opened up possibilities of a wide variety of novel treatments, which may relieve the characteristic motor and nonmotor deficits of PD, and may even slow the progression and/or accompanying gut-related conditions of the disease.

The Role of Magnetic Resonance Imaging for the Diagnosis of Atypical Parkinsonism

The diagnosis of Parkinson's disease and atypical Parkinsonism remains clinically difficult, especially at the early stage of the disease, since there is a significant overlap of symptoms. Multimodal MRI has significantly improved diagnostic accuracy and understanding of the pathophysiology of Parkinsonian disorders. Structural and quantitative MRI sequences provide biomarkers sensitive to different tissue properties that detect abnormalities specific to each disease and contribute to the diagnosis. Machine learning techniques using these MRI biomarkers can effectively differentiate atypical Parkinsonian syndromes. Such approaches could be implemented in a clinical environment and improve the management of Parkinsonian patients. This review presents different structural and quantitative MRI techniques, their contribution to the differential diagnosis of atypical Parkinsonian disorders and their interest for individual-level diagnosis.

Altered neurometabolism in major depressive disorder: A whole brain 1H-magnetic resonance spectroscopic imaging study at 3T

INTRODUCTION

Major depressive disorder (MDD) is a severe mental disorder with a neurobiological basis that is poorly understood. Several studies demonstrated widespread, functional and neurometabolic alterations in MDD. However, little is known about whole brain neurometabolic alterations in MDD.

METHOD

Thirty-two patients with MDD and 32 paired on a one-to-one basis healthy controls (CTRL) underwent ¹H-whole brain spectroscopic (¹H-WBS) imaging. Lobar and cerebellar metabolite concentrations of brain N-acetylaspartate (NAA), total choline (tCho), total creatine (tCr), glutamine (Gln), glutamate (Glu), and myo-Inositol (mI) were assessed in patients and controls.

RESULTS

Decreased NAA, tCho, and tCr were found in the right frontal and right parietal lobe in MDD compared to CTRL, and to a lesser extent in the left frontal lobe. Furthermore, in MDD increased glutamine was observed in the right frontal lobe and bitemporal lobes, and increased glutamate in the cerebellum.

CONCLUSION

Altered global neurometabolism examined using ¹H-WBS imaging in MDD may be interpreted as signs of neuronal dysfunction, altered energy metabolism, and oligodendrocyte dysfunction. In particular, the parallel decrease in NAA, tCr and tCho in the same brain regions may be indicative of neuronal dysfunction that may be counterbalanced by an increase of the neuroprotective metabolite glutamine. Future prospective investigations are warranted to study the functional importance of these findings.

Binding of Metal-Ion-Induced Tau Oligomers to Lipid Surfaces Is Enhanced by GSK-3β-Mediated Phosphorylation

While fibrillar deposits of hyperphosphorylated protein tau are a key hallmark of several neurodegenerative diseases such as Alzheimer's disease, small oligomers have been speculated to be the key toxic aggregate species. Trivalent metal ions were shown to promote tau oligomer formation in vitro. However, little is known about potential intercellular spreading mechanisms or toxic modes of action of such oligomers. We investigated interactions of tau monomers and Fe³⁺/Al³⁺-induced oligomers with small unilamellar vesicles derived from 1-palmitoyl-2-oleoyl-phosphatidylcholine (neutral, liquid-crystalline phase) and dipalmitoyl-phosphatidylcholine (neutral, gel-phase). We further evaluated the influence of glycogen synthase kinase 3β (GSK-3β)-mediated tau phosphorylation applying the single-particle fluorescence spectroscopy techniques fluorescence correlation spectroscopy, fluorescence intensity distribution analysis, and scanning for intensely fluorescent targets. In these experiments, no binding to neutral lipid surfaces was observed for tau monomers. In contrast, metal-ion-induced tau oligomers showed a gain of function in binding to neutral lipid surfaces. Of note, tau phosphorylation by GSK-3β increased both oligomer formation and membrane affinity of the resulting oligomers. In conclusion, our data imply a pathological gain of function of metal-ion-induced oligomers of hyperphosphorylated tau, enabling membrane binding irrespective of surface charge even at nanomolar protein concentrations.

Altered Neurometabolic Profile in Early Parkinson's Disease: A Study With Short Echo-Time Whole Brain MR Spectroscopic Imaging

Objective: To estimate alterations in neurometabolic profile of patients with early stage Parkinson's disease (PD) by using a short echo-time whole brain magnetic resonance spectroscopic imaging (wbMRSI) as possible biomarker for early diagnosis and monitoring of PD.

Methods: 20 PD patients in early stage (H&Y ≤ 2) without evidence of severe other diseases and 20 age and sex matched healthy controls underwent wbMRSI. In each subject brain regional concentrations of metabolites N-acetyl-aspartate (NAA), choline (Cho), total creatine (tCr), glutamine (Gln), glutamate (Glu), and myo-inositol (mIns) were obtained in atlas-defined lobar structures including subcortical basal ganglia structures (the left and right frontal lobes, temporal lobes, parietal lobes, occipital lobes, and the cerebellum) and compared between patients and matched healthy controls. Clinical characteristics of the PD patients were correlated with spectroscopic findings.

Results: In comparison to controls the PD patients revealed altered lobar metabolite levels in all brain lobes contralateral to dominantly affected body side, i.e., decreases of temporal NAA, Cho, and tCr, parietal NAA and tCr, and frontal as well as occipital NAA. The frontal NAA correlated negatively with the MDS-UPDRS II (R = 22120.585, p = 0.008), MDS-UPDRS IV (R = −0.458, p = 0.048) and total MDS-UPDRS scores (R = −0.679, p = 0.001).

Conclusion: In early PD stages metabolic alterations are evident in all contralateral brain lobes demonstrating that the neurodegenerative process affects not only local areas by dopaminergic denervation, but also the functional network within different brain regions. The wbMRSI-detectable brain metabolic alterations reveal the potential to serve as biomarkers for early PD.

Brain Iron Accumulation in Atypical Parkinsonian Syndromes: in vivo MRI Evidences for Distinctive Patterns

Recent data suggest mechanistic links among perturbed iron homeostasis, oxidative stress, and misfolded protein aggregation in neurodegenerative diseases. Iron overload and toxicity toward dopaminergic neurons have been established as playing a role in the pathogenesis of Parkinson's disease (PD). Brain iron accumulation has also been documented in atypical parkinsonian syndromes (APS), mainly comprising multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). Iron-sensitive magnetic resonance imaging (MRI) has been applied to identify iron-related signal changes for the diagnosis and differentiation of these disorders. Topographic patterns of widespread iron deposition in deep brain nuclei have been described as differing between patients with MSA and PSP and those with PD. A disease-specific increase of iron occurs in the brain regions mainly affected by underlying disease pathologies. However, whether iron changes are a primary pathogenic factor or an epiphenomenon of neuronal degeneration has not been fully elucidated. Moreover, the clinical implications of iron-related pathology in APS remain unclear. In this review study, we collected data from qualitative and quantitative MRI studies on brain iron accumulation in APS to identify disease-related patterns and the potential role of iron-sensitive MRI.

Regional Metabolite Concentrations in Aging Human Brain: Comparison of Short-TE Whole Brain MR Spectroscopic Imaging and Single Voxel Spectroscopy at 3T

PURPOSE

The aim of this study was to compare a recently established whole brain MR spectroscopic imaging (wbMRSI) technique using spin-echo planar spectroscopic imaging (EPSI) acquisition and the Metabolic Imaging and Data Analysis System (MIDAS) software package with single voxel spectroscopy (SVS) technique and LCModel analysis for determination of relative metabolite concentrations in aging human brain.

METHODS

A total of 59 healthy subjects aged 20-70 years (n ≥ 5 per age decade for each gender) underwent a wbEPSI scan and 3 SVS scans of a 4 ml voxel volume located in the right basal ganglia, occipital grey matter and parietal white matter. Concentration ratios to total creatine (tCr) for N‑acetylaspartate (NAA/tCr), total choline (tCho/tCr), glutamine (Gln/tCr), glutamate (Glu/tCr) and myoinositol (mI/tCr) were obtained both from EPSI and SVS acquisitions with either LCModel or MIDAS. In addition, an aqueous phantom containing known metabolite concentrations was also measured.

RESULTS

Metabolite concentrations obtained with wbMRSI and SVS were comparable and consistent with those reported previously. Decreases of NAA/tCr and increases of line width with age were found with both techniques, while the results obtained from EPSI acquisition revealed generally narrower line widths and smaller Cramer-Rao lower bounds than those from SVS data.

CONCLUSION

The wbMRSI could be used to estimate metabolites in vivo and in vitro with the same reliability as using SVS, with the main advantage being the ability to determine metabolite concentrations in multiple brain structure simultaneously in vivo. It is expected to be widely used in clinical diagnostics and neuroscience.

Widespread diffusion changes differentiate Parkinson's disease and progressive supranuclear palsy

BACKGROUND

Parkinson's disease (PD) and progressive supranuclear palsy - Richardson's syndrome (PSP-RS) are often represented by similar clinical symptoms, which may challenge diagnostic accuracy. The objective of this study was to investigate and compare regional cerebral diffusion properties in PD and PSP-RS subjects and evaluate the use of these metrics for an automatic classification framework.

MATERIAL AND METHODS

Diffusion-tensor MRI datasets from 52 PD and 21 PSP-RS subjects were employed for this study. Using an atlas-based approach, regional median values of mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were measured and employed for feature selection using RELIEFF and subsequent classification using a support vector machine.

RESULTS

According to RELIEFF, the top 17 diffusion values consisting of deep gray matter structures, the brainstem, and frontal cortex were found to be especially informative for an automatic classification. A MANCOVA analysis performed on these diffusion values as dependent variables revealed that PSP-RS and PD subjects differ significantly (p < .001). Generally, PSP-RS subjects exhibit reduced FA, and increased MD, RD, and AD values in nearly all brain structures analyzed compared to PD subjects. The leave-one-out cross-validation of the support vector machine classifier revealed that the classifier can differentiate PD and PSP-RS subjects with an accuracy of 87.7%. More precisely, six PD subjects were wrongly classified as PSP-RS and three PSP-RS subjects were wrongly classified as PD.

CONCLUSION

The results of this study demonstrate that PSP-RS subjects exhibit widespread and more severe diffusion alterations compared to PD patients, which appears valuable for an automatic computer-aided diagnosis approach.

Improved Automatic Morphology-Based Classification of Parkinson's Disease and Progressive Supranuclear Palsy

OBJECTIVES

The overlapping symptoms of Parkinson's disease (PD) and progressive supranuclear palsy-Richardson's syndrome (PSP-RS) often make a correct clinical diagnosis difficult. The volume of subcortical brain structures derived from high-resolution T1-weighted magnetic resonance imaging (MRI) datasets is frequently used for individual level classification of PD and PSP-RS patients. The aim of this study was to evaluate the benefit of including additional morphological features beyond the simple regional volume, as well as clinical features, and morphological features of cortical structures for an automatic classification of PD and PSP-RS patients.

MATERIAL AND METHODS

A total of 98 high-resolution T1-weighted MRI datasets from 76 PD patients, and 22 PSP-RS patients were available for this study. Using an atlas-based approach, the volume, surface area, and surface-area-to-volume ratio (SA:V) of 21 subcortical and 48 cortical brain regions were calculated and used as features for a support vector machine classification after application of a RELIEF feature selection method.

RESULTS

The comparison of the classification results suggests that including all three morphological parameters (volume, surface area and SA:V) can considerably improve classification accuracy compared to using volume or surface area alone. Likewise, including clinical patient features in addition to morphological parameters also considerably increases the classification accuracy. In contrast to this, integrating morphological features of other cortical structures did not lead to improved classification accuracy. Using this optimal set-up, an accuracy of 98% was achieved with only one falsely classified PD and one falsely classified PSP-RS patient.

CONCLUSION

The results of this study suggest that clinical features as well as more advanced morphological features should be used for future computer-aided diagnosis systems to differentiate PD and PSP-RS patients based on morphological parameters.

Iron deposition quantification: Applications in the brain and liver

Iron has long been implicated in many neurological and other organ diseases. It is known that over and above the normal increases in iron with age, in certain diseases there is an excessive iron accumulation in the brain and liver. MRI is a noninvasive means by which to image the various structures in the brain in three dimensions and quantify iron over the volume of the object of interest. The quantification of iron can provide information about the severity of iron-related diseases as well as quantify changes in iron for patient follow-up and treatment monitoring. This article provides an overview of current MRI-based methods for iron quantification, specifically for the brain and liver, including: signal intensity ratio, R₂ , R2*, R2', phase, susceptibility weighted imaging and quantitative susceptibility mapping (QSM). Although there are numerous approaches to measuring iron, R₂ and R2* are currently preferred methods in imaging the liver and QSM has become the preferred approach for imaging iron in the brain.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018. J. MAGN. RESON. IMAGING 2018;48:301-317.

Iron in neurodegenerative disorders: being in the wrong place at the wrong time?

Brain iron deposits have been reported consistently in imaging and histologic examinations of patients with neurodegenerative disorders. While the origins of this finding have not been clarified yet, it is speculated that impaired iron homeostasis or deficient transport mechanisms result in the accumulation of this highly toxic metal ultimately leading to formation of reactive oxygen species and cell death. On the other hand, there are also those who support that iron is just an incidental finding, a by product of neuronal loss. A literature review has been performed in order to present the key findings in support of the iron hypothesis of neurodegeneration, as well as to identify conditions causing or resulting from iron overload and compare and contrast their features with the most prominent neurodegenerative disorders. There is an abundance of experimental and observational findings in support of the hypothesis in question; however, as neurodegeneration is a rare incident of commonly encountered iron-associated disorders of the nervous system, and this metal is found in non-neurodegenerative disorders as well, it is possible that iron is the result or even an incidental finding in neurodegeneration. Understanding the underlying processes of iron metabolism in the brain and particularly its release during cell damage is expected to provide a deeper understanding of the origins of neurodegeneration in the years to come.

Brain regional iron contents in progressive supranuclear palsy

INTRODUCTION

To determine motor-related brain regions in which iron contents correlate with the degree of motor deficits of progressive supranuclear palsy (PSP).

METHODS

Twenty-four patients with probable PSP and 20 controls were included. Using a 3.0T magnetic resonance imaging scanner, R2* values were measured in the putamen, globus pallidus (GP), substantia nigra (SN), subthalamic nucleus, and dentate nucleus. After adjustment for disease duration and age at examination, correlations between regional brain R2* values and Unified Parkinson Disease Rating Scale (UPDRS) total motor scores or subscores for bradykinesia, rigidity, tremor, or axial motor deficits were investigated.

RESULTS

Compared to controls, patients with PSP had significantly higher R2* values in all of the five brain regions. UPDRS total motor scores and subscores for bradykinesia and axial motor deficits did not correlate with R2* values of the five brain regions. However, UPDRS subscores for unilateral rigidity were correlated with R2* values of the contralateral putamen and GP. In addition, unilateral UPDRS subscores for tremor were associated with R2* values of the ipsilateral dentate nucleus, contralateral putamen, GP, and SN.

CONCLUSION

In PSP, excessive iron accumulation occurs in motor-related subcortical regions. Iron-related PSP pathologies in the lenticular nucleus are associated with rigidity severity, while those in the nigro-striato-pallidal unit and dentate nucleus are associated with tremor severity. Bradykinesia and axial motor deficits of PSP seem to be associated with widespread pathologies in the cerebrum, brainstem, cerebellum, as well as the basal ganglia.

[Degenerative cerebellar diseases and differential diagnoses]

CLINICAL/METHODICAL ISSUE

Cerebellar syndromes result in distinct clinical symptoms, such as ataxia, dysarthria, dysmetria, intention tremor and eye movement disorders.

STANDARD RADIOLOGICAL METHODS

In addition to the medical history and clinical examination, imaging is particularly important to differentiate other diseases, such as hydrocephalus and multi-infarct dementia from degenerative cerebellar diseases. Degenerative diseases with cerebellar involvement include Parkinson's disease, multiple system atrophy as well as other diseases including spinocerebellar ataxia.

ACHIEVEMENTS

In addition to magnetic resonance imaging (MRI), nuclear medicine imaging investigations are also helpful for the differentiation.

PRACTICAL RECOMMENDATIONS

Axial fluid-attenuated inversion recovery (FLAIR) and T2-weighted sequences can sometimes show a signal increase in the pons as a sign of degeneration of pontine neurons and transverse fibers in the basilar part of the pons. The imaging is particularly necessary to exclude other diseases, such as normal pressure hydrocephalus (NPH), multi-infarct dementia and cerebellar lesions.

Quantitative susceptibility mapping differentiates between parkinsonian disorders

INTRODUCTION

It is often challenging to clinically distinguish between Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Quantitative susceptibility mapping (QSM) is an accurate indirect method for estimating brain iron levels in vivo. This method has yet to be applied in atypical parkinsonism. We aimed to investigate differences in brain iron accumulation parkinsonian disorders and healthy controls using QSM.

METHODS

15 patients with PSP, 11 patients with MSA, 62 patients with PD and 14 healthy controls were included in the study and their phase and magnitude data from susceptibility-weighted magnetic resonance imaging were retrospectively analyzed with an in-house pipeline to create susceptibility maps. Two-way ANCOVA were used to assess group differences. Pairwise comparisons within the ANCOVA were corrected for multiple comparisons.

RESULTS

Red nucleus susceptibility was higher in PSP compared with PD (p < 0.001), MSA (p < 0.001) and controls (p < 0.001), which separated PSP from these groups with areas under receiver operating characteristic curve of 0.97, 0.75 and 0.98 respectively. PSP showed higher globus pallidus susceptibility compared with PD (p < 0.001), MSA (p = 0.006) and controls (p < 0.001). Putamen susceptibility was higher in MSA than in PD (p = 0.022) and controls (p = 0.026). Substantia nigra susceptibility was increased in PD compared to controls (p = 0.030).

CONCLUSION

We show that all studied parkinsonian disorders have increased susceptibility subcortically, reflecting distinct topographical patterns of abnormal brain iron accumulation. QSM, particularly of the red nucleus, is a promising biomarker in differentiating parkinsonian disorders, and would be interesting to study longitudinally for monitoring disease progression and treatment effects.

Radiological biomarkers for diagnosis in PSP: Where are we and where do we need to be?

PSP is a pathologically defined neurodegenerative tauopathy with a variety of clinical presentations including typical Richardson's syndrome and other variant PSP syndromes. A large body of neuroimaging research has been conducted over the past two decades, with many studies proposing different structural MRI and molecular PET/SPECT biomarkers for PSP. These include measures of brainstem, cortical and striatal atrophy, diffusion weighted and diffusion tensor imaging abnormalities, [18F] fluorodeoxyglucose PET hypometabolism, reductions in striatal dopamine imaging and, most recently, PET imaging with ligands that bind to tau. Our aim was to critically evaluate the degree to which structural and molecular neuroimaging metrics fulfill criteria for diagnostic biomarkers of PSP. We queried the PubMed, Cochrane, Medline, and PSYCInfo databases for original research articles published in English over the past 20 years using postmortem diagnosis or the NINDS-SPSP criteria as the diagnostic standard from 1996 to 2016. We define a five-level theoretical construct for the utility of neuroimaging biomarkers in PSP, with level 1 representing group-level findings, level 2 representing biomarkers with demonstrable individual-level diagnostic utility, level 3 representing biomarkers for early disease, level 4 representing surrogate biomarkers of PSP pathology, and level 5 representing definitive PSP biomarkers of PSP pathology. We discuss the degree to which each of the currently available biomarkers fit into this theoretical construct, consider the role of biomarkers in the diagnosis of Richardson's syndrome, variant PSP syndromes and autopsy confirmed PSP, and emphasize current shortfalls in the field. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Combined Diffusion Tensor Imaging and Apparent Transverse Relaxation Rate Differentiate Parkinson Disease and Atypical Parkinsonism

BACKGROUND AND PURPOSE

Both diffusion tensor imaging and the apparent transverse relaxation rate have shown promise in differentiating Parkinson disease from atypical parkinsonism (particularly multiple system atrophy and progressive supranuclear palsy). The objective of the study was to assess the ability of DTI, the apparent transverse relaxation rate, and their combination for differentiating Parkinson disease, multiple system atrophy, progressive supranuclear palsy, and controls.

MATERIALS AND METHODS

A total of 106 subjects (36 controls, 35 patients with Parkinson disease, 16 with multiple system atrophy, and 19 with progressive supranuclear palsy) were included. DTI and the apparent transverse relaxation rate measures from the striatal, midbrain, limbic, and cerebellar regions were obtained and compared among groups. The discrimination performance of DTI and the apparent transverse relaxation rate among groups was assessed by using Elastic-Net machine learning and receiver operating characteristic curve analysis.

RESULTS

Compared with controls, patients with Parkinson disease showed significant apparent transverse relaxation rate differences in the red nucleus. Compared to those with Parkinson disease, patients with both multiple system atrophy and progressive supranuclear palsy showed more widespread changes, extending from the midbrain to striatal and cerebellar structures. The pattern of changes, however, was different between the 2 groups. For instance, patients with multiple system atrophy showed decreased fractional anisotropy and an increased apparent transverse relaxation rate in the subthalamic nucleus, whereas patients with progressive supranuclear palsy showed an increased mean diffusivity in the hippocampus. Combined, DTI and the apparent transverse relaxation rate were significantly better than DTI or the apparent transverse relaxation rate alone in separating controls from those with Parkinson disease/multiple system atrophy/progressive supranuclear palsy; controls from those with Parkinson disease; those with Parkinson disease from those with multiple system atrophy/progressive supranuclear palsy; and those with Parkinson disease from those with multiple system atrophy; but not those with Parkinson disease from those with progressive supranuclear palsy, or those with multiple system atrophy from those with progressive supranuclear palsy.

CONCLUSIONS

DTI and the apparent transverse relaxation rate provide different but complementary information for different parkinsonisms. Combined DTI and apparent transverse relaxation rate may be a superior marker for the differential diagnosis of parkinsonisms.

Neurodegeneration Induced by Metals in Caenorhabditis elegans

Metals are a component of a variety of ecosystems and organisms. They can generally be divided into essential and nonessential metals. The essential metals are involved in physiological processes once the deficiency of these metals has been associated with diseases. Although iron, manganese, copper, and zinc are important for life, it has been evidenced that they are also involved in neuronal damage in many neurodegenerative disorders. Nonessential metals, which are metals without physiological functions, are present in trace or higher levels in living organisms. Occupational, environmental, or deliberate exposures to lead, mercury, aluminum, and cadmium are clearly correlated with the increase of toxicity and varied kinds of pathological situations. Actually, the field of neurotoxicology needs to satisfy two opposing demands: the testing of a growing list of chemicals and resource limitations and ethical concerns associated with testing using traditional mammalian species. Toxicological assays using alternative animal models may relieve some of this pressure by allowing testing of more compounds while reducing expenses and using fewer mammals. The nervous system is by far the more complex system in C. elegans. Almost a third of their cells are neurons (302 neurons versus 959 cells in adult hermaphrodite). It initially underwent extensive development as a model organism in order to study the nervous system, and its neuronal lineage and the complete wiring diagram of its nervous system are stereotyped and fully described. The neurotransmission systems are phylogenetically conserved from nematodes to vertebrates, which allows for findings from C. elegans to be extrapolated and further confirmed in vertebrate systems. Different strains of C. elegans offer a new perspective on neurodegenerative processes. Some genes have been found to be related to neurodegeneration induced by metals. Studying these interactions may be an effective tool to slow neuronal loss and deterioration.

Utility of susceptibility-weighted imaging in Parkinson's disease and atypical Parkinsonian disorders

In the clinic, the diagnosis of Parkinson's disease (PD) largely depends on clinicians' experience. When the diagnosis is made, approximately 80% of dopaminergic cells in the substantia nigra (SN) have been lost. Additionally, it is rather challenging to differentiate PD from atypical parkinsonian disorders (APD). Clinially-available 3T conventional MRI contributes little to solve these problems. The pathologic alterations of parkinsonism show abnormal brain iron deposition, and therefore susceptibility-weighted imaging (SWI), which is sensitive to iron concentration, has been applied to find iron-related lesions for the diagnosis and differentiation of PD in recent decades. Until now, the majority of research has revealed that in SWI the signal intensity changes in deep brain nuclei, such as the SN, the putamen (PUT), the globus pallidus (GP), the thalamus (TH), the red nucleus (RN) and the caudate nucleus (CN), thereby raising the possibility of early diagnosis and differentiation. Furthermore, the signal changes in SN, PUT and TH sub-regions may settle the issues with higher accuracy. In this article, we review the brain iron deposition of PD, MSA-P and PSP in SWI in the hope of exhibiting a profile of SWI features in PD, MSA and PSP and its clinical values.

Brain MR Contribution to the Differential Diagnosis of Parkinsonian Syndromes: An Update

Brain magnetic resonance (MR) represents a useful and feasible tool for the differential diagnosis of Parkinson's disease. Conventional MR may reveal secondary forms of parkinsonism and may show peculiar brain alterations of atypical parkinsonian syndromes. Furthermore, advanced MR techniques, such as morphometric-volumetric analyses, diffusion-weighted imaging, diffusion tensor imaging, tractography, proton MR spectroscopy, and iron-content sensitive imaging, have been used to obtain quantitative parameters useful to increase the diagnostic accuracy. Currently, many MR studies have provided both qualitative and quantitative findings, reflecting the underlying neuropathological pattern of the different degenerative parkinsonian syndromes. Although the variability in the methods and results across the studies limits the conclusion about which technique is the best, specific radiologic phenotypes may be identified. Qualitative/quantitative MR changes in the substantia nigra do not discriminate between different parkinsonisms. In the absence of extranigral abnormalities, the diagnosis of PD is more probable, whereas basal ganglia changes (mainly in the putamen) suggest the diagnosis of an atypical parkinsonian syndrome. In this context, changes in pons, middle cerebellar peduncles, and cerebellum suggest the diagnosis of MSA, in midbrain and superior cerebellar peduncles the diagnosis of PSP, and in whole cerebral hemispheres (mainly in frontoparietal cortex with asymmetric distribution) the diagnosis of Corticobasal Syndrome.

Parkinson's Disease: The Mitochondria-Iron Link

Mitochondrial dysfunction, iron accumulation, and oxidative damage are conditions often found in damaged brain areas of Parkinson's disease. We propose that a causal link exists between these three events. Mitochondrial dysfunction results not only in increased reactive oxygen species production but also in decreased iron-sulfur cluster synthesis and unorthodox activation of Iron Regulatory Protein 1 (IRP1), a key regulator of cell iron homeostasis. In turn, IRP1 activation results in iron accumulation and hydroxyl radical-mediated damage. These three occurrences-mitochondrial dysfunction, iron accumulation, and oxidative damage-generate a positive feedback loop of increased iron accumulation and oxidative stress. Here, we review the evidence that points to a link between mitochondrial dysfunction and iron accumulation as early events in the development of sporadic and genetic cases of Parkinson's disease. Finally, an attempt is done to contextualize the possible relationship between mitochondria dysfunction and iron dyshomeostasis. Based on published evidence, we propose that iron chelation-by decreasing iron-associated oxidative damage and by inducing cell survival and cell-rescue pathways-is a viable therapy for retarding this cycle.

Role of iron in neurodegenerative diseases

Currently, we still lack effective measures to modify disease progression in neurodegenerative diseases. Iron-containing proteins play an essential role in many fundamental biological processes in the central nervous system. In addition, iron is a redox-active ion and can induce oxidative stress in the cell. Although the causes and pathology hallmarks of different neurodegenerative diseases vary, iron dyshomeostasis, oxidative stress and mitochondrial injury constitute a common pathway to cell death in several neurodegenerative diseases. MRI is capable of depicting iron content in the brain, and serves as a potential biomarker for early and differential diagnosis, tracking disease progression and evaluating the effectiveness of neuroprotective therapy. Iron chelators have shown their efficacy against neurodegeneration in a series of animal models, and been applied in several clinical trials. In this review, we summarize recent developments on iron dyshomeostasis in Parkinson's disease, Alzheimer's disease, Friedreich ataxia, and Huntington's disease.

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.

Iron and Neurodegeneration: Is Ferritinophagy the Link?

Mounting evidence indicates that the lysosome-autophagy pathway plays a critical role in iron release from ferritin, the main iron storage cellular protein, hence in the distribution of iron to the cells. The recent identification of nuclear receptor co-activator 4 as the receptor for ferritin delivery to selective autophagy sheds further light on the understanding of the mechanisms underlying this pathway. The emerging view is that iron release from ferritin through the lysosomes is a general mechanism in normal and tumour cells of different tissue origins, but it has not yet been investigated in brain cells. Defects in the lysosome-autophagy pathway are often involved in the pathogenesis of neurodegenerative disorders, and brain iron homeostasis disruption is a hallmark of many of these diseases. However, in most cases, it has not been established whether iron dysregulation is directly involved in the pathogenesis of the diseases or if it is a secondary effect derived from other pathogenic mechanisms. The recent evidence of the crucial involvement of autophagy in cellular iron handling offers new perspectives about the role of iron in neurodegeneration, suggesting that autophagy dysregulation could cause iron dyshomeostasis. In this review, we recapitulate our current knowledge on the routes through which iron is released from ferritin, focusing on the most recent advances. We summarise the current evidence concerning lysosome-autophagy pathway dysfunctions and those of iron metabolism and discuss their potential interconnections in several neurodegenerative disorders, such as Alzheimer's, Parkinson's and Huntington's diseases; amyotrophic lateral sclerosis; and frontotemporal lobar dementia.

Putaminal hypointensity on T2*-weighted MR imaging is the most practically useful sign in diagnosing multiple system atrophy: A preliminary study

OBJECTIVE

To identify useful MRI abnormalities in the putamen for diagnosing multiple system atrophy.

METHODS

Patients with multiple system atrophy (n=15), Parkinson's disease (n=16), or progressive supranuclear palsy (n=9) and healthy controls (n=10) were enrolled. Using a visual analog scale, 4 examiners independently rated high-intensity signals along the lateral putamen on T2-weighted and T2*-weighted images, low-intensity signals within the putamen on T2-weighted and T2*-weighted images, and putaminal atrophy. Receiver operating characteristic analyses were performed, and the area under the receiver operating characteristic curve was calculated.

RESULTS

For differentiating multiple system atrophy from progressive supranuclear palsy, Parkinson's disease, and healthy controls, the mean area under the curve values was the highest for low-intensity signals within the putamen on T2*-weighted images (0.797, 0.867, 0.896, respectively). Variations in the area under the curve values among the 4 examiners were the smallest in low-intensity signals within the putamen on T2*-weighted images. Good inter-rater reliability was achieved for low-intensity signals within the putamen on T2*-weighted images and high-intensity signals along the lateral putamen on T2*-weighted images.

CONCLUSION

Low-intensity signals within the putamen on T2*-weighted images is the most useful MRI abnormality for diagnosing multiple system atrophy.

The role of iron in brain ageing and neurodegenerative disorders

SUMMARY

In the CNS, iron in several proteins is involved in many important processes such as oxygen transportation, oxidative phosphorylation, myelin production, and the synthesis and metabolism of neurotransmitters. Abnormal iron homoeostasis can induce cellular damage through hydroxyl radical production, which can cause the oxidation and modification of lipids, proteins, carbohydrates, and DNA. During ageing, different iron complexes accumulate in brain regions associated with motor and cognitive impairment. In various neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, changes in iron homoeostasis result in altered cellular iron distribution and accumulation. MRI can often identify these changes, thus providing a potential diagnostic biomarker of neurodegenerative diseases. An important avenue to reduce iron accumulation is the use of iron chelators that are able to cross the blood-brain barrier, penetrate cells, and reduce excessive iron accumulation, thereby affording neuroprotection.

Eryptosis as a marker of Parkinson's disease

A major trend in recent Parkinson's disease (PD) research is the investigation of biological markers that could help in identifying at-risk individuals or to track disease progression and response to therapies. Central to this is the knowledge that inflammation is a known hallmark of PD and of many other degenerative diseases. In the current work, we focus on inflammatory signalling in PD, using a systems approach that allows us to look at the disease in a more holistic way. We discuss cyclooxygenases, prostaglandins, thromboxanes and also iron in PD. These particular signalling molecules are involved in PD pathophysiology, but are also very important in an aberrant coagulation/hematology system. We present and discuss a hypothesis regarding the possible interaction of these aberrant signalling molecules implicated in PD, and suggest that these molecules may affect the erythrocytes of PD patients. This would be observable as changes in the morphology of the RBCs and of PD patients relative to healthy controls. We then show that the RBCs of PD patients are indeed rather dramatically deranged in their morphology, exhibiting eryptosis (a kind of programmed cell death). This morphological indicator may have useful diagnostic and prognostic significance.

A review of the use of magnetic resonance imaging in Parkinson's disease

To date, the most frequently used Parkinson's disease (PD) biomarkers are the brain imaging measures of dopaminergic dysfunction using positron emission tomography and single photon emission computed tomography. However, major advances have occurred in the development of magnetic resonance imaging (MRI) biomarkers for PD in the past decade. Although conventional structural imaging remains normal in PD, advanced techniques have shown changes in the substantia nigra and the cortex. The most well-developed MRI markers in PD include diffusion imaging and iron load using T2/T2* relaxometry techniques. Other quantitative biomarkers such as susceptibility-weighted imaging for iron load, magnetization transfer and ultra-high-field MRI have shown great potential. More sophisticated techniques such as tractography and resting state functional connectivity give access to anatomical and functional connectivity changes in the brain, respectively. Brain perfusion can be assessed using non-contrast-agent techniques such as arterial spin labelling and spectroscopy gives access to metabolites concentrations. However, to date these techniques are not yet fully validated and standardized quantitative metrics for PD are still lacking. This review presents an overview of new structural, perfusion, metabolic and anatomo-functional connectivity biomarkers, their use in PD and their potential applications to improve the clinical diagnosis of Parkinsonian syndromes and the quality of clinical trials.