Spatial correlation and segregation of multimodal MRI abnormalities in multiple system atrophy

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.

Diffusion Magnetic Resonance Imaging Microstructural Abnormalities in Multiple System Atrophy: A Comprehensive Review

Multiple system atrophy (MSA) is a neurodegenerative disease characterized by autonomic failure, ataxia, and/or parkinsonism. Its prominent pathological alterations can be investigated using diffusion magnetic resonance imaging (dMRI), a technique that exploits the characteristics of water random motion inside brain tissue. The aim of this report was to review currently available literature on the application of dMRI in MSA and to describe microstructural abnormalities, diagnostic applications, and pathophysiological correlates. Sixty-four published studies involving microstructural investigation using dMRI in MSA were included. Widespread microstructural abnormalities of white matter were described, especially in the middle cerebellar peduncle, corticospinal tract, and hemispheric fibers. Gray matter degeneration was identified as well, with diffuse involvement of subcortical structures, especially in the putamina. Diagnostic applications of dMRI were mostly explored for the differential diagnosis between MSA parkinsonism and Parkinson's disease. Recently, machine learning algorithms for image processing and disease classification have demonstrated high diagnostic accuracy, showing potential for translation into clinical practice. To a lesser extent, clinical correlates of microstructural abnormalities have also been investigated, and abnormalities related to motor, ocular, and cognitive impairments were described. dMRI in MSA has contributed to in vivo identification of known pathological abnormalities. Translation into clinical practice of the latest advancements for the differential diagnosis between MSA and other forms of parkinsonism seems feasible. Current limitations involve the possibility of correctly diagnosing MSA in the very early stages, when the clinical diagnosis is most uncertain. Furthermore, pathophysiological correlates of microstructural abnormalities remain understudied. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

FDG PET in the differential diagnosis of degenerative parkinsonian disorders: usefulness of voxel-based analysis in clinical practice

Background

The early differential diagnosis among neurodegenerative parkinsonian disorders becomes essential to set up the correct clinical-therapeutic approach. The increased utilization of [18F] fluoro-deoxy-glucose positron emission tomography (FDG PET) and the pressure for cost-effectiveness request a systematic evaluation and a validation of its utility in clinical practice. This retrospective study aims to consider the contribution, in terms of increasing accuracy and increasing diagnostic confidence, of voxel-based FDG PET analyses in the differential diagnosis of these disorders, including Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, and cortico-basal syndrome.

Method

Eighty-three subjects with a clinically confirmed diagnosis of degenerative parkinsonian disorders who underwent FDG brain PET/CT were selected. A voxel-based analysis was set up using statistical parametric mapping (SPM) on MATLAB to produce maps of brain hypometabolism and relative hypermetabolism. Four nuclear physicians (two expert and two not expert), blinded to the patients’ symptoms, other physicians’ evaluations, and final clinical diagnosis, independently evaluated all data by visual assessment and by adopting metabolic maps.

Results

In not-expert evaluators, the support of both hypometabolism and hypermetabolism maps results in a significant increase in diagnostic accuracy as well as clinical confidence. In expert evaluators, the increase in accuracy and in diagnostic confidence is mainly supported by hypometabolism maps alone.

Conclusions

In this study, we demonstrated the additional value of combining voxel-based analyses with qualitative assessment of brain PET images. Moreover, maps of relative hypermetabolism can also make their contribution in clinical practice, particularly for less experienced evaluators.

Quantitative Validation of a Visual Rating Scale for Defining High-Iron Putamen in Patients With Multiple System Atrophy

Objectives: To validate a visual rating scale reflecting sub-regional patterns of putaminal hypointensity in susceptibility-weighted imaging of patients with multiple system atrophy (MSA).

Methods: Using a visual rating scale (from G0 to G3), 2 examiners independently rated putaminal hypointensities of 37 MSA patients and 21 control subjects. To investigate the correlation with the scales, R2^* values and the volume of the entire putamen were measured.

Results: MSA patients with parkinsonian variant had significantly higher scores than those with cerebellar variant. Visual rating scores in MSA were correlated with R2^* values [General estimating equation (GEE), Wald chi-square = 25.89, corrected p < 0.001] and volume (Wald chi-square = 75.44, corrected p < 0.001). They correlated with UPDRS motor scores. Binary logistic regression analyses revealed that the visual rating scale was a significant predictor for discriminating MSA patients from controls [multivariate model adjusted for age and sex, odds ratio 52.722 (corrected p = 0.009)]. Pairwise comparison between areas under the curve (AUCs) revealed that the visual rating scale demonstrated higher accuracy than R2^* values [difference between AUCs; univariate model = 0.247 (corrected p < 0.001); multivariate model = 0.186 (corrected p = 0.003)]. There were no significant differences in clinical characteristics between the high-iron group, defined as putamen with visual rating scale ≥ G2 and R2^* values ≥ third quartile, and the remaining patients.

Conclusion: The visual rating scale, which reflects quantitative iron content and atrophy of the putamen as well as motor severities, could be useful for the discrimination and evaluation of patients with MSA.

A totally data-driven whole-brain multimodal pipeline for the discrimination of Parkinson's disease, multiple system atrophy and healthy control

Parkinson's Disease (PD) and Multiple System Atrophy (MSA) are two parkinsonian syndromes that share many symptoms, albeit having very different prognosis. Although previous studies have proposed multimodal MRI protocols combined with multivariate analysis to discriminate between these two populations and healthy controls, studies combining all MRI indexes relevant for these disorders (i.e. grey matter volume, fractional anisotropy, mean diffusivity, iron deposition, brain activity at rest and brain connectivity) with a completely data-driven voxelwise analysis for discrimination are still lacking. In this study, we used such a complete MRI protocol and adapted a fully-data driven analysis pipeline to discriminate between these populations and a healthy controls (HC) group. The pipeline combined several feature selection and reduction steps to obtain interpretable models with a low number of discriminant features that can shed light onto the brain pathology of PD and MSA. Using this pipeline, we could discriminate between PD and HC (best accuracy = 0.78), MSA and HC (best accuracy = 0.94) and PD and MSA (best accuracy = 0.88). Moreover, we showed that indexes derived from resting-state fMRI alone could discriminate between PD and HC, while mean diffusivity in the cerebellum and the putamen alone could discriminate between MSA and HC. On the other hand, a more diverse set of indexes derived by multiple modalities was needed to discriminate between the two disorders. We showed that our pipeline was able to discriminate between distinct pathological populations while delivering sparse model that could be used to better understand the neural underpinning of the pathologies.

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Structuro-functional MRI can discriminate between parkinsonian syndromes

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Discriminant MRI modalities vary as a function of the discrimination task

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fMRI is crucial in discriminating between Parkinson's disease patients and controls

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Structural MRI discriminate between Multiple System Atrophy patients and controls

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.