Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

White Matter Abnormalities Track Disease Progression in Multiple System Atrophy

BACKGROUND

White matter (WM) abnormalities have been implicated in clinically relevant functional decline in multiple system atrophy (MSA).

OBJECTIVE

To identify the WM and gray matter (GM) abnormalities in MSA and assess the utility of longitudinal structural and diffusion changes as surrogate markers for tracking disease progression in MSA.

METHODS

Twenty-seven participants with early MSA [15 with clinically predominant cerebellar (MSA-C) and 12 with clinically predominant parkinsonian features (MSA-P)] and 14 controls were enrolled as a part of our prospective, longitudinal study of synucleinopathies. Using structural magnetic resonance imaging (MRI) and diffusion MRI (diffusion tensor and neurite orientation and dispersion density imaging), we analyzed whole and regional brain changes in these participants. We also evaluated temporal imaging trajectories based on up to three annual follow-up scans and assessed the impact of baseline diagnosis on these imaging biomarkers using mixed-effect models.

RESULTS

MSA patients exhibited more widespread WM changes than GM, particularly in the cerebellum and brainstem, with greater severity in MSA-C. Structural and diffusion measures in the cerebellum WM and brainstem deteriorated with disease progression. Rates of progression of these abnormalities were similar in both MSA subtypes, reflecting increasing overlap of clinical features over time.

CONCLUSION

WM abnormalities are core features of MSA disease progression and advance at similar rates in clinical MSA subtypes. Multimodal MRI imaging reveals novel insights into the distribution and pattern of brain abnormalities and their progression in MSA. Selected structural and diffusion measures may be useful for tracking disease progression in MSA clinical trials.

A Blinded Evaluation of Brain Morphometry for Differential Diagnosis of Atypical Parkinsonism

BACKGROUND

Advanced imaging techniques have been studied for differential diagnosis between PD, MSA, and PSP.

OBJECTIVES

This study aims to validate the utility of individual voxel-based morphometry techniques for atypical parkinsonism in a blinded fashion.

METHODS

Forty-eight healthy controls (HC) T1-WI were used to develop a referential dataset and fit a general linear model after segmentation into gray matter (GM) and white matter (WM) compartments. Segmented GM and WM with PD (n = 96), MSA (n = 18), and PSP (n = 20) were transformed into z-scores using the statistics of referential HC and individual voxel-based z-score maps were generated. An imaging diagnosis was assigned by two independent raters (trained and untrained) blinded to clinical information and final diagnosis. Furthermore, we developed an observer-independent index for ROI-based automated differentiation.

RESULTS

The diagnostic performance using voxel-based z-score maps by rater 1 and rater 2 for MSA yielded sensitivities: 0.89, 0.94 (95% CI: 0.74-1.00, 0.84-1.00), specificities: 0.94, 0.80 (0.90-0.98, 0.73-0.87); for PSP, sensitivities: 0.85, 0.90 (0.69-1.00, 0.77-1.00), specificities: 0.98, 0.94 (0.96-1.00, 0.90-0.98). Interrater agreement was good for MSA (Cohen's kappa: 0.61), and excellent for PSP (0.84). Receiver operating characteristic analysis using the ROI-based new index showed an area under the curve (AUC): 0.89 (0.77-1.00) for MSA, and 0.99 (0.98-1.00) for PSP.

CONCLUSIONS

These evaluations provide support for the utility of this imaging technique in the differential diagnosis of atypical parkinsonism demonstrating a remarkably high differentiation accuracy for PSP, suggesting potential use in clinical settings in the future.

Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping

Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.

Differentiation between Parkinson's Disease and the Parkinsonian Subtype of Multiple System Atrophy Using the Magnetic Resonance T1w/T2w Ratio in the Middle Cerebellar Peduncle

Multiple system atrophy with predominant parkinsonism (MSA-P) can hardly be distinguished from Parkinson's disease (PD) clinically in the early stages. This study investigated whether a standardized T1-weighted/T2-weighted ratio (sT1w/T2w ratio) can effectively detect degenerative changes in the middle cerebellar peduncle (MCP) associated with MSA-P and PD and evaluated its potential to distinguish between these two diseases. We included 35 patients with MSA-P, 32 patients with PD, and 17 controls. T1w and T2w scans were acquired using a 1.5-T MR system. The MCP sT1w/T2w ratio was analyzed via SPM12 using a region-of-interest approach in a normalized space. The diagnostic performance of the MCP sT1w/T2w ratio was compared between the MSA-P, PD, and controls. Patients with MSA-P had significantly lower MCP sT1w/T2w ratios than patients with PD and controls. Furthermore, MCP sT1w/T2w ratios were lower in patients with PD than in the controls. The MCP sT1w/T2w ratio showed excellent or good accuracy for differentiating MSA-P or PD from the control (area under the curve (AUC) = 0.919 and 0.814, respectively) and substantial power for differentiating MSA-P from PD (AUC = 0.724). Therefore, the MCP sT1w/T2w ratio is sensitive in detecting degenerative changes in the MCP associated with MSA-P and PD and is useful in distinguishing MSA-P from PD.

Cerebellar, Not Nigrostriatal Degeneration Impairs Dexterity in Multiple System Atrophy

BACKGROUND

Multiple system atrophy (MSA) clinically manifests with either predominant nigrostriatal or cerebellopontine degeneration. This corresponds to two different phenotypes, one with predominant Parkinson's symptoms (MSA-P [multiple system atrophy-parkinsonian subtype]) and one with predominant cerebellar deficits (MSA-C [multiple system atrophy-cerebellar subtype]). Both nigrostriatal and cerebellar degeneration can lead to impaired dexterity, which is a frequent cause of disability in MSA.

OBJECTIVE

The aim was to disentangle the contribution of nigrostriatal and cerebellar degeneration to impaired dexterity in both subtypes of MSA.

METHODS

We thus investigated nigrostriatal and cerebellopontine integrity using diffusion microstructure imaging in 47 patients with MSA-P and 17 patients with MSA-C compared to 31 healthy controls (HC). Dexterity was assessed using the 9-Hole Peg Board (9HPB) performance.

RESULTS

Nigrostriatal degeneration, represented by the loss of cells and neurites, leading to a larger free-fluid compartment, was present in MSA-P and MSA-C when compared to HCs. Whereas no intergroup differences were observed between the MSAs in the substantia nigra, MSA-P showed more pronounced putaminal degeneration than MSA-C. In contrast, a cerebellopontine axonal degeneration was observed in MSA-P and MSA-C, with stronger effects in MSA-C. Interestingly, the degeneration of cerebellopontine fibers is associated with impaired dexterity in both subtypes, whereas no association was observed with nigrostriatal degeneration.

CONCLUSION

Cerebellar dysfunction contributes to impaired dexterity not only in MSA-C but also in MSA-P and may be a promising biomarker for disease staging. In contrast, no significant association was observed with nigrostriatal dysfunction. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Planimetric and Volumetric Brainstem MRI Markers in Progressive Supranuclear Palsy, Multiple System Atrophy, and Corticobasal Syndrome. A Systematic Review and Meta-Analysis

BACKGROUND

Various MRI markers-including midbrain and pons areas (Marea, Parea) and volumes (Mvol, Pvol), ratios (M/Parea, M/Pvol), and composite markers (magnetic resonance imaging Parkinsonism Indices 1,2; MRPI 1,2)-have been proposed as imaging markers of Richardson's syndrome (RS) and multiple system atrophy-Parkinsonism (MSA-P). A systematic review/meta-analysis of relevant studies aiming to compare the diagnostic accuracy of these imaging markers is lacking.

METHODS

Pubmed and Scopus were searched for studies with >10 patients (RS, MSA-P or CBS) and >10 controls with data on Marea, Parea, Mvol, Pvol, M/Parea, M/Pvol, MRPI 1, and MRPI 2. Cohen's d, as a measure of effect size, was calculated for all markers in RS, MSA-P, and CBS.

RESULTS

Twenty-five studies on RS, five studies on MSA-P, and four studies on CBS were included. Midbrain area provided the greatest effect size for differentiating RS from controls (Cohen's d = -3.10; p < 0.001), followed by M/Parea and MRPI 1. MSA-P had decreased midbrain and pontine areas. Included studies exhibited high heterogeneity, whereas publication bias was low.

CONCLUSIONS

Midbrain area is the optimal MRI marker for RS, and pons area is optimal for MSA-P. M/Parea and MRPIs produce smaller effect sizes for differentiating RS from controls.

Surface-Based Neuroimaging Pattern of Multiple System Atrophy

RATIONALE AND OBJECTIVES

Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect.

MATERIALS AND METHODS

We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model.

RESULTS

Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P < .001) and normalized pontine volume significantly decreased in patients with MSA-C (P < .001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%.

CONCLUSION

The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis.

Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects

Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.

Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy

OBJECTIVES

The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability to delineate the putamen in multiple system atrophy (MSA), Parkinson's disease (PD), and healthy controls.

METHODS

We retrospectively included patients with MSA and PD as well as healthy controls. A DNP was trained on manual segmentations of the putamen as ground truth. For this, the cohort was randomly split into a training (N = 131) and test set (N = 120). The DNP's performance was compared with putaminal segmentations as derived by Automatic Anatomic Labelling, Freesurfer and Fastsurfer. For validation, we assessed the diagnostic accuracy of the resulting segmentations in the delineation of MSA vs. PD and healthy controls.

RESULTS

A total of 251 subjects (61 patients with MSA, 158 patients with PD, and 32 healthy controls; mean age of 61.5 ± 8.8 years) were included. Compared to the dice-coefficient of the DNP (0.96), we noted significantly weaker performance for AAL3 (0.72; p < .001), Freesurfer (0.82; p < .001), and Fastsurfer (0.84, p < .001). This was corroborated by the superior diagnostic performance of MSA vs. PD and HC of the DNP (AUC 0.93) versus the AUC of 0.88 for AAL3 (p = 0.02), 0.86 for Freesurfer (p = 0.048), and 0.85 for Fastsurfer (p = 0.04).

CONCLUSION

By utilization of a DNP, accurate segmentations of the putamen can be obtained even if substantial atrophy is present. This allows for more precise extraction of imaging parameters or shape features from the putamen in relevant patient cohorts.

CLINICAL RELEVANCE STATEMENT

Deep learning-based segmentation of the putamen was superior to currently available algorithms and is beneficial for the diagnosis of multiple system atrophy.

KEY POINTS

• A Deep Neural Patchwork precisely delineates the putamen and performs equal to human labeling in multiple system atrophy, even when pronounced putaminal volume loss is present. • The Deep Neural Patchwork-based segmentation was more capable to differentiate between multiple system atrophy and Parkinson's disease than the AAL3 atlas, Freesurfer, or Fastsurfer.

Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design

Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.

Multidimensional biomarkers for multiple system atrophy: an update and future directions

Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.

Diagnostic utility of movement disorder society criteria for multiple system atrophy

Background

The 2008 criteria for the diagnosis of multiple system atrophy (MSA) has been widely used for more than 10 years, but the sensitivity is low, particularly for patients in the early stage. Recently, a new MSA diagnostic criteria was developed.

Objective

The objective of the study was to assess and compare the diagnostic utility of the new movement disorder society (MDS) MSA criteria with the 2008 MSA criteria.

Methods

This study included patients diagnosed with MSA between January 2016 and October 2021. All patients underwent regular face-to-face or telephonic follow-ups every year until October 2022. A total of 587 patients (309 males and 278 females) were retrospectively reviewed to compare the diagnostic accuracy of the MDS MSA criteria to that of the 2008 MSA criteria (determined by the proportion of patients categorized as established or probable MSA). Autopsy is the gold standard diagnosis of MSA, which is not available in clinical practice. Thus, we applied the 2008 MSA criteria at the last review as the reference standard.

Results

The sensitivity of the MDS MSA criteria (93.2%, 95% CI = 90.5-95.2%) was significantly higher than that of the 2008 MSA criteria (83.5%, 95% CI = 79.8-86.6%) (P < 0.001). Additionally, the sensitivity of the MDS MSA criteria was maintained robustly across different subgroups, defined by diagnostic subtype, disease duration, and the type of symptom[s] at onset. Importantly, the specificities were not significantly different between the MDS MSA criteria and the 2008 MSA criteria (P > 0.05).

Conclusion

The present study demonstrated that the MDS MSA criteria exhibited good diagnostic utility for MSA. The new MDS MSA criteria should be considered as a useful diagnostic tool for clinical practice and future therapeutic trials.

Ultra-strong diffusion-weighted MRI reveals cerebellar grey matter abnormalities in movement disorders

Structural brain MRI has proven invaluable in understanding movement disorder pathophysiology. However, most work has focused on grey/white matter volumetric (macrostructural) and white matter microstructural effects, limiting understanding of frequently implicated grey matter microstructural differences. Using ultra-strong spherical tensor encoding diffusion-weighted MRI, a persistent MRI signal was seen in healthy cerebellar grey matter even at high diffusion-weightings (b ​≥ 10,000 s/mm2). Quantifying the proportion of this signal (denoted fs), previously ascertained to originate from inside small spherical spaces, provides a potential proxy for cell body density. In this work, this approach was applied for the first time to a clinical cohort, including patients with diagnosed movement disorders in which the cerebellum has been implicated in symptom pathophysiology. Five control participants (control group 1, median age 24.5 years (20-39 years), imaged at two timepoints, demonstrated consistency in measurement of all three measures - MD (Mean Diffusivity) fs, and Ds (dot diffusivity)- with intraclass correlation coefficients (ICC) of 0.98, 0.86 and 0.76, respectively. Comparison with an older control group (control group 2 (n = 5), median age 51 years (43-58 years)) found no significant differences, neither with morphometric nor microstructural (MD (p = 0.36), fs (p = 0.17) and Ds (p = 0.22)) measures. The movement disorder cohort (Parkinson's Disease, n = 5, dystonia, n = 5. Spinocerebellar Ataxia 6, n = 5) when compared to the age-matched control cohort (Control Group 2) identified significantly lower MD (p < 0.0001 and p < 0.0001) and higher fs values (p < 0.0001 and p < 0.0001) in SCA6 and dystonia cohorts respectively. Lobar division of the cerebellum found these same differences in the superior and inferior posterior lobes, while no differences were seen in either the anterior lobes or with Ds measurements. In contrast to more conventional measures from diffusion tensor imaging, this framework provides enhanced specificity to differences in restricted spherical spaces in grey matter (including small cells) by eliminating signals from cerebrospinal fluid and axons. In the context of human and animal histopathology studies, these findings potentially implicate the cerebellar Purkinje and granule cells as contributors to the observed signal differences, with both cell types having been implicated in several neurological disorders through both postmortem and animal model studies. This novel microstructural imaging approach shows promise for improving movement disorder diagnosis, prognosis, and treatment.

Imaging biomarkers for early multiple system atrophy

OBJECTIVE

To systematically evaluate structural MRI and diffusion MRI features for cross-sectional discrimination and tracking of longitudinal disease progression in early multiple system atrophy (MSA).

METHODS

In a prospective, longitudinal study of synucleinopathies with imaging on 14 controls and 29 MSA patients recruited at an early disease stage (15 predominant cerebellar ataxia subtype or MSA-C and 14 predominant parkinsonism subtype or MSA-P), we computed regional morphometric and diffusion MRI features. We identified morphometric features by ranking them based on their ability to distinguish MSA-C from controls and MSA-P from controls and evaluated diffusion changes in these regions. For the top performing regions, we evaluated their utility for tracking longitudinal disease progression using imaging from 12-month follow-up and computed sample size estimates for a hypothetical clinical trial in MSA. We also computed these selected morphometric features in an independent validation dataset.

RESULTS

We found that morphometric changes in the cerebellar white matter, brainstem, and pons can separate early MSA-C patients from controls both cross-sectionally and longitudinally (p < 0.01). The putamen and striatum, though useful for separating early MSA-P patients from control subjects at baseline, were not useful for tracking MSA disease progression. Cerebellum white matter diffusion changes aided in capturing early disease related degeneration in MSA.

INTERPRETATION

Regardless of clinically predominant features at the time of MSA assessment, brainstem and cerebellar pathways progressively deteriorate with disease progression. Quantitative measurements of these regions are promising biomarkers for MSA diagnosis in early disease stage and potential surrogate markers for future MSA clinical trials.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease

AIMS

To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction.

METHODS

77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen.

CONCLUSION

The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Advanced brain aging in multiple system atrophy compared to Parkinson's disease

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MSA, but not PD, exhibits advanced brain aging in gray matter and white matter.

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Brain age of gray matter is correlated with that of white matter in PD.

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Brain age measures can partly reveal associations with symptom severity.

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Brain features underlying brain age difference between MSA and PD are identified.

Multiple system atrophy (MSA) and Parkinson’s disease (PD) belong to alpha-synucleinopathy, but they have very different clinical courses and prognoses. An imaging biomarker that can differentiate between the two diseases early in the disease course is desirable for appropriate treatment. Neuroimaging-based brain age paradigm provides an individualized marker to differentiate aberrant brain aging patterns in neurodegenerative diseases. In this study, patients with MSA (N = 23), PD (N = 33), and healthy controls (N = 34; HC) were recruited. A deep learning approach was used to estimate brain-predicted age difference (PAD) of gray matter (GM) and white matter (WM) based on image features extracted from T1-weighted and diffusion-weighted magnetic resonance images, respectively. Spatial normative models of image features were utilized to quantify neuroanatomical impairments in patients, which were then used to estimate the contributions of image features to brain age measures. For PAD of GM (GM-PAD), patients with MSA had significantly older brain age (9.33 years) than those with PD (0.75 years; P = 0.002) and HC (-1.47 years; P < 0.001), and no significant difference was found between PD and HC (P = 1.000). For PAD of WM (WM-PAD), it was significantly greater in MSA (9.27 years) than that in PD (1.90 years; P = 0.037) and HC (-0.74 years; P < 0.001); there was no significant difference between PD and HC (P = 0.087). The most salient image features that contributed to PAD in MSA and PD were different. For GM, they were the orbitofrontal regions and the cuneus in MSA and PD, respectively, and for WM, they were the central corpus callosum and the uncinate fasciculus in MSA and PD, respectively. Our results demonstrated that MSA revealed significantly greater PAD than PD, which might be related to markedly different neuroanatomical contributions to brain aging. The image features with distinct contributions to brain aging might be of value in the differential diagnosis of MSA and PD.

Differentiation of multiple system atrophy subtypes by gray matter atrophy

BACKGROUND AND PURPOSE

Multiple system atrophy(MSA) is a rare adult-onset synucleinopathy that can be divided in two subtypes depending on whether the prevalence of its symptoms is more parkinsonian or cerebellar (MSA-P and MSA-C, respectively). The aim of this work is to investigate the structural MRI changes able to discriminate MSA phenotypes.

METHODS

The sample includes 31 MSA patients (15 MSA-C and 16 MSA-P) and 39 healthy controls. Participants underwent a comprehensive motor and neuropsychological battery. MRI data were acquired with a 3T scanner (MAGNETOM Trio, Siemens, Germany). FreeSurfer was used to obtain volumetric and cortical thickness measures. A Support Vector Machine (SVM) algorithm was used to assess the classification between patients' group using cortical and subcortical structural data.

RESULTS

After correction for multiple comparisons, MSA-C patients had greater atrophy than MSA-P in the left cerebellum, whereas MSA-P showed reduced volume bilaterally in the pallidum and putamen. Using deep gray matter volume ratios and mean cortical thickness as features, the SVM algorithm provided a consistent classification between MSA-C and MSA-P patients (balanced accuracy 74.2%, specificity 75.0%, and sensitivity 73.3%). The cerebellum, putamen, thalamus, ventral diencephalon, pallidum, and caudate were the most contributing features to the classification decision (z > 3.28; p < .05 [false discovery rate]).

CONCLUSIONS

MSA-C and MSA-P with similar disease severity and duration have a differential distribution of gray matter atrophy. Although cerebellar atrophy is a clear differentiator between groups, thalamic and basal ganglia structures are also relevant contributors to distinguishing MSA subtypes.

Individual voxel-based morphometry adjusting covariates in multiple system atrophy

INTRODUCTION

This study aimed to evaluate whether novel individual voxel-based morphometry adjusting covariates (iVAC), such as age, sex, and total intracranial volume, could increase the accuracy of a diagnosis of multiple system atrophy (MSA) and enable the differentiation of MSA from Parkinson's disease (PD).

METHODS

We included 53 MSA patients (MSA-C: 33, MSA-P: 20), 53 PD patients, and 189 healthy controls in this study. All participants underwent high-resolution T1-weighted imaging (WI) and T2-WI with a 3.0-T MRI scanner. We evaluated the occurrence of significant atrophic findings in the pons/middle cerebellar peduncle (MCP) and putamen on iVAC and compared these findings with characteristic changes on T2-WI.

RESULTS

On iVAC, abnormal findings were observed in the pons/MCP of 96.2% of MSA patients and in the putamen of 80% of MSA patients; however, on T2-WI, they were both observed at a frequency of 60.4% in MSA patients. On iVAC, all but one MSA-P patient (98.1%) showed significant atrophic changes in the pons/MCP or putamen. By contrast, 69.8% of patients with MSA showed abnormal signal changes in the pons/MCP or putamen on T2-WI. iVAC yielded 95.0% sensitivity and 96.2% specificity for differentiating MSA-P from PD.

CONCLUSION

iVAC enabled us to recognize the morphological characteristics of MSA visually and with high accuracy compared to T2-WI, indicating that iVAC is a potential diagnostic screening tool for MSA.

Update on neuroimaging for categorization of Parkinson's disease and atypical parkinsonism

PURPOSE OF REVIEW

Differential diagnosis of Parkinsonism may be difficult. The objective of this review is to present the work of the last three years in the field of imaging for diagnostic categorization of parkinsonian syndromes focusing on progressive supranuclear palsy (PSP) and multiple system atrophy (MSA).

RECENT FINDINGS

Two main complementary approaches are being pursued. The first seeks to develop and validate manual qualitative or semi-quantitative imaging markers that can be easily used in clinical practice. The second is based on quantitative measurements of magnetic resonance imaging abnormalities integrated in a multimodal approach and in automatic categorization machine learning tools.

SUMMARY

These two complementary approaches obtained high diagnostic around 90% and above in the classical Richardson form of PSP and probable MSA. Future work will determine if these techniques can improve diagnosis in other PSP variants and early forms of the diseases when all clinical criteria are not fully met.

Dermal and cardiac autonomic fiber involvement in Parkinson's disease and multiple system atrophy

Pathological aggregates of alpha-synuclein in peripheral dermal nerve fibers can be detected in patients with idiopathic Parkinson's disease and multiple system atrophy. This study combines skin biopsy staining for p-alpha-synuclein depositions and radionuclide imaging of the heart with [¹²³I]-metaiodobenzylguanidine to explore peripheral denervation in both diseases. To this purpose, 42 patients with a clinical diagnosis of Parkinson's disease or multiple system atrophy were enrolled. All patients underwent a standardized clinical work-up including neurological evaluation, neurography, and blood samples. Skin biopsies were obtained from the distal and proximal leg, back, and neck for immunofluorescence double labeling with anti-p-alpha-synuclein and anti-PGP9.5. All patients underwent myocardial [¹²³I]-metaiodobenzylguanidine scintigraphy. Dermal p-alpha-synuclein was observed in 47.6% of Parkinson's disease patients and was mainly found in autonomic structures. 81.0% of multiple system atrophy patients had deposits with most of cases in somatosensory fibers. The [¹²³I]-metaiodobenzylguanidine heart-to-mediastinum ratio was lower in Parkinson's disease than in multiple system atrophy patients (1.94 ± 0.63 vs. 2.91 ± 0.96; p < 0.0001). Irrespective of the diagnosis, uptake was lower in patients with than without p-alpha-synuclein in autonomic structures (1.42 ± 0.51 vs. 2.74 ± 0.83; p < 0.0001). Rare cases of Parkinson's disease with p-alpha-synuclein in somatosensory fibers and multiple system atrophy patients with deposits in autonomic structures or both fiber types presented with clinically overlapping features. In conclusion, this study suggests that alpha-synuclein contributes to peripheral neurodegeneration and mediates the impairment of cardiac sympathetic neurons in patients with synucleinopathies. Furthermore, it indicates that Parkinson's disease and multiple system atrophy share pathophysiologic mechanisms of peripheral nervous system dysfunction with a clinical overlap.

Characterization and diagnostic potential of diffusion tractography in multiple system atrophy

INTRODUCTION

Microstructural integrity of the middle cerebellar peduncle (MCP) and the putamen captured by diffusion-tensor imaging (DTI) is differentially affected in the parkinsonian and cerebellar variants of multiple system atrophy (MSA-P, MSA-C) compared to Parkinson's disease (PD). The current study applied DTI and tractography in order to 1) characterize the distribution of DTI metrics along the tracts of the MCP and from the putamen in MSA variants, and 2) evaluate the usefulness of combining these measures for the differential diagnosis of MSA-P against PD in the clinical setting.

METHODS

Twenty-nine MSA patients (MSA-C, n = 10; MSA-P, n = 19), with a mean disease duration of 2.8 ± 1.7 years, 19 PD patients, and 27 healthy controls (HC) were included in the study. Automatized tractography with a masking procedure was employed to isolate the MCP tracts. DTI measures along the tracts of the MCP and within the putamen were acquired and jointly used to classify MSA vs. PD, and MSA-P vs. PD. Putamen volume was additionally tested as classification feature in post hoc analyses.

RESULTS

DTI measures within the MCP and putamen showed significant alterations in MSA variants compared to HC and PD. Classification accuracy for MSA vs. PD and MSA-P vs PD using diffusion measures was 91.7% and 89.5%, respectively. When replacing the putaminal DTI measure by a normalized measure of putamen volume classification accuracy improved to 95.8% and 94.7%, respectively.

CONCLUSION

Multimodal information from MCP tractography and putamen volume yields excellent diagnostic accuracy to discriminate between early-to-moderately advanced patients with MSA and PD.

MRI-Based Radiomics of Basal Nuclei in Differentiating Idiopathic Parkinson's Disease From Parkinsonian Variants of Multiple System Atrophy: A Susceptibility-Weighted Imaging Study

Objectives

To investigate the value of MRI-based radiomic model based on the radiomic features of different basal nuclei in differentiating idiopathic Parkinson's disease (IPD) from Parkinsonian variants of multiple system atrophy (MSA-P).

Methods

Radiomics was applied to the 3T susceptibility- weighted imaging (SWI) from 102 MSA-P patients and 83 IPD patients (allocated to a training and a testing cohort, 7:3 ratio). The substantia nigra (SN), caudate nucleus (CN), putamen (PUT), globus pallidus (GP), red nucleus (RN), and subthalamic nucleus (STN) were manually segmented, and 396 features were extracted. After feature selection, support vector machine (SVM) was generated, and its predictive performance was calculated in both the training and testing cohorts using the area under receiver operating characteristic curve (AUC).

Results

Seven radiomic features were selected from the PUT, by which the SVM classifier achieved the best diagnostic performance with an AUC of 0.867 in the training cohort and an AUC of 0.862 in the testing cohort. Furthermore, the combined model, which incorporating part III of the Parkinson's Disease Rating Scale (UPDRSIII) scores into radiomic features of the PUT, further improved the diagnostic performance. However, radiomic features extracted from RN, SN, GP, CN, and STN had moderate to poor diagnostic performance, with AUC values that ranged from 0.610 to 0.788 in the training cohort and 0.583 to 0.766 in the testing cohort.

Conclusion

Radiomic features derived from the PUT had optimal value in differentiating IPD from MSA-P. A combined radiomic model, which contained radiomic features of the PUT and UPDRSIII scores, further improved performance and may represent a promising tool for distinguishing between IPD and MSA-P.

The diagnostic potential of multimodal neuroimaging measures in Parkinson's disease and atypical parkinsonism

INTRODUCTION

For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have been known as well, though less clearly seen, and thus, the addition of functional measures to structural measures is expected to be more informative for the diagnosis. Here, we aimed to assess whether multimodal neuroimaging measures of structural and functional alterations could have potential for enhancing performance in diverse diagnostic classification problems.

METHODS

For 77 patients with PD, 86 patients with AP comprising multiple system atrophy and progressive supranuclear palsy, and 53 healthy controls (HC), structural and functional MRI data were collected. Gray matter (GM) volume was acquired as a structural measure, and GM regional homogeneity and degree centrality were acquired as functional measures. The measures were used as predictors individually or in combination in support vector machine classifiers for different problems of distinguishing between HC and each diagnostic type and between different diagnostic types.

RESULTS

In statistical comparisons of the measures, structural alterations were extensively seen in all diagnostic types, whereas functional alterations were limited to specific diagnostic types. The addition of functional measures to the structural measure generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone.

CONCLUSION

We suggest the fusion of multimodal neuroimaging measures as an effective strategy that could generally cope with diverse prediction problems of clinical concerns.

Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.

Changes of Amide Proton Transfer Imaging in Multiple System Atrophy Parkinsonism Type

Multiple system atrophy (MSA), an atypical parkinsonism of alpha-synucleinopathies, has no specific biomarker of diagnosis. According to different combinations of symptoms, MSA can be classified as parkinsonism-type MSA (MSA-P) and cerebellar-type MSA (MSA-C; Watanabe et al., 2018). Amide proton transfer (APT) imaging is by far the most studied chemical exchange saturation transfer imaging for its sensitivity to mobile protons and peptides in tissues. We hypothesize that APT imaging may be a feasible biomarker of MSA-P. Twenty MSA-P patients and 20 age-matched normal controls were enrolled in this study and underwent MR exams on a 3.0-T MR scanner. Magnetization transfer spectra at 3.5 ppm were acquired at two transverse slices of the head, including the midbrain and the basal ganglia. Mann-Whitney U test was used to compare the asymmetrical magnetization transfer ratio (MTR_asym) difference between MSA-P patients and normal controls. The APT MTR_asym values of MSA patients in the red nucleus (RN) (SN; P = 0.000), substantia nigra (P = 0.000), thalamus (P = 0.000), and putamen (P = 0.013) were significantly higher than those in normal controls. There was a negative correlation between APT MTR_asym and the score of part III of the Unified Parkinson Disease Rating Scale (R = -0.338, P = 0.044) in the putamen, while there was a positive correlation between the APT MTR_asym and the rate of motor symptom progression (R = 0.406, P = 0.017) in the RN. These findings suggest that APT MTR_asym changes are found and may be of value in the diagnosis of MSA-P.

Automated Analysis of Diffusion-Weighted Magnetic Resonance Imaging for the Differential Diagnosis of Multiple System Atrophy from Parkinson's Disease

Background

Manual region‐of‐interest analysis of putaminal and middle cerebellar peduncle diffusivity distinguishes patients with multiple system atrophy (MSA) and Parkinson's disease (PD) with high diagnostic accuracy. However, a recent meta‐analysis found substantial between‐study heterogeneity of diagnostic accuracy due to the lack of harmonized imaging protocols and standardized analyses pipelines.

Objective

Evaluation of diagnostic accuracy of observer‐independent analysis of microstructural integrity as measured by diffusion‐tensor imaging in patients with MSA and PD.

Methods

A total of 29 patients with MSA and 19 patients with PD (matched for age, gender, and disease duration) with 3 years of follow‐up were investigated with diffusion‐tensor imaging and T1‐weighted magnetic resonance imaging. Automated localization of relevant brain regions was obtained, and mean diffusivity and fractional anisotropy values were averaged within the regions of interest. The classification was performed using a C5.0 hierachical decision tree algorithm.

Results

Mean diffusivity of the middle cerebellar peduncle and cerebellar gray and white matter compartment as well as the putamen were significantly increased in patients with MSA and showed superior effect sizes compared to the volumetric analysis of these regions. A classifier model identified mean diffusivity of the middle cerebellar peduncle and putamen as the most predictive parameters. Cross‐validation of the classification model yields a Cohen's κ and overall diagnostic accuracy of 0.823 and 0.914, respectively.

Conclusion

Analysis of microstructural integrity within the middle cerebellar peduncle and putamen yielded a superior effect size compared to the volumetric measures, resulting in excellent diagnostic accuracy to discriminate patients with MSA from PD in the early to moderate disease stages. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Can Autonomic Testing and Imaging Contribute to the Early Diagnosis of Multiple System Atrophy? A Systematic Review and Recommendations by the Movement Disorder Society Multiple System Atrophy Study Group

Background

In the current consensus diagnostic criteria, the diagnosis of probable multiple system atrophy (MSA) is based solely on clinical findings, whereas neuroimaging findings are listed as aid for the diagnosis of possible MSA. There are overlapping phenotypes between MSA-parkinsonian type and Parkinson's disease, progressive supranuclear palsy, and dementia with Lewy bodies, and between MSA-cerebellar type and sporadic adult-onset ataxia resulting in a significant diagnostic delay and misdiagnosis of MSA during life.

Objectives

In light of an ongoing effort to revise the current consensus criteria for MSA, the Movement Disorders Society Multiple System Atrophy Study Group performed a systematic review of original articles published before August 2019.

Methods

We included articles that studied at least 10 patients with MSA as well as participants with another disorder or control group for comparison purposes. MSA was defined by neuropathological confirmation, or as clinically probable, or clinically probable plus possible according to consensus diagnostic criteria.

Results

We discuss the pitfalls and benefits of each diagnostic test and provide specific recommendations on how to evaluate patients in whom MSA is suspected.

Conclusions

This systematic review of relevant studies indicates that imaging and autonomic function tests significantly contribute to increasing the accuracy of a diagnosis of MSA.

Multiple system atrophy

Multiple system atrophy (MSA) is a sporadic, adult-onset, relentlessly progressive neurodegenerative disorder, clinically characterized by various combinations of autonomic failure, parkinsonism and ataxia. The neuropathological hallmark of MSA are glial cytoplasmic inclusions consisting of misfolded α-synuclein. Selective atrophy and neuronal loss in striatonigral and olivopontocerebellar systems underlie the division into two main motor phenotypes of MSA-parkinsonian type and MSA-cerebellar type. Isolated autonomic failure and REM sleep behavior disorder are common premotor features of MSA. Beyond the core clinical symptoms, MSA manifests with a number of non-motor and motor features. Red flags highly specific for MSA may provide clues for a correct diagnosis, but in general the diagnostic accuracy of the second consensus criteria is suboptimal, particularly in early disease stages. In this chapter, the authors discuss the historical milestones, etiopathogenesis, neuropathological findings, clinical features, red flags, differential diagnosis, diagnostic criteria, imaging and other biomarkers, current treatment, unmet needs and future treatments for MSA.

Multiple System Atrophy: Recent Developments and Future Perspectives

Multiple system atrophy (MSA) is a rare and fatal neurodegenerative disorder characterized by a variable combination of parkinsonism, cerebellar impairment, and autonomic dysfunction. The pathologic hallmark is the accumulation of aggregated α-synuclein in oligodendrocytes, forming glial cytoplasmic inclusions, which qualifies MSA as a synucleinopathy together with Parkinson's disease and dementia with Lewy bodies. The underlying pathogenesis is still not well understood. Some symptomatic treatments are available, whereas neuroprotection remains an urgent unmet treatment need. In this review, we critically appraise significant developments of the past decade with emphasis on pathogenesis, diagnosis, prognosis, and treatment development. We further discuss unsolved questions and highlight some perspectives. © 2019 International Parkinson and Movement Disorder Society.