MRI-based neuroimaging: atypical parkinsonisms and other movement disorders

Kassubek, Jan

doi:10.1097/wco.0000000000000578

Cited by 14 publications

(15 citation statements)

References 33 publications

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“…Automated ROI analysis eliminates bias in the process of selecting images and drawing ROIs (30). Computer-based MRI analyses using machine learning techniques and support vector machine (SVM) classification have proven potential in the discrimination of parkinsonian syndromes and the tracking of disease progression (31, 32). SVM pattern recognition of SWI data can accurately discriminate PD from APS (32).…”

Section: Current Mri Methods For Detection and Quantification Of Braimentioning

confidence: 99%

“…Furthermore, the red nucleus and globus pallidus are the two most valuable nuclei in the diagnosis of PSP (34, 46). However, direct comparisons among studies are difficult because of differences in patient characteristics (e.g., clinical severity and clinical subtypes) and methodology (e.g., MRI parameters, selected brain structures, and analysis methods) (31). The role of iron-sensitive MRI in other APS, such as diffuse Lewy body disease and corticobasal degeneration, has not yet been determined.…”

Section: Clinical Implications Of Brain Iron Deposition In Apsmentioning

confidence: 99%

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

Lee

2019

Front. Neurol.

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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.

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Section: Current Mri Methods For Detection and Quantification Of Braimentioning

confidence: 99%

Section: Clinical Implications Of Brain Iron Deposition In Apsmentioning

confidence: 99%

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

Lee

2019

Front. Neurol.

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“…Structural brain imaging using conventional MRI with visual assessment of T2-and T1-weighted sequences is usually normal in patients with PD. Its main role is to detect or exclude secondary causes of Parkinsonism (such as vascular encephalopathy, demyelinating lesions, tumors or normal pressure hydrocephalus) and to look for signs of atypical Parkinsonism (10,13,19,42). Manual morphometric measurements of the brainstem, including the anteroposterior midbrain diameter (43,44), the midsagittal midbrain area, the midbrain to pons area ratio (43,45) and the magnetic resonance Parkinsonism index (MRPI), are normal in PD (45,46).…”

Section: Brainstemmentioning

confidence: 99%

“…Multimodal MRI is defined as the combination of information provided by these different sequences. Brain abnormalities can be assessed in several ways: (i) qualitatively, by visual inspection of regional brain atrophy and signal changes using conventional structural MRI, or (ii) quantitatively, by measurements of changes in volumes, diffusion metrics and iron-related signals ( 8 – 10 , 13 , 14 ). The main MRI techniques and their respective contributions are summarized in Table 1 .…”

Section: Mri Techniques and Biomarkersmentioning

confidence: 99%

“…Neuroimaging plays a major role in the diagnosis of Parkinsonian disorders and the differentiation of PD from atypical Parkinsonism. Magnetic resonance imaging (MRI) improves diagnostic accuracy, reduces the rate of misdiagnosis, facilitates early diagnosis, and may be useful for the follow-up of disease progression (8)(9)(10)(11). Usually, the diagnosis of sporadic PD does not require an MRI examination when the clinical presentation is typical.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

et al. 2020

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Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty‐two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA‐P), and 23 with MSA of the cerebellar variant (MSA‐C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner‐dependent effects, we tested two types of normalizations using patient data or healthy control data. Results In the replication cohort, high accuracies were achieved using volumetry in the classification of PD–PSP, PD–MSA‐C, PSP–MSA‐C, and PD‐atypical parkinsonism (balanced accuracies: 0.840–0.983, area under the receiver operating characteristic curves: 0.907–0.995). Performances were lower for the classification of PD–MSA‐P, MSA‐C–MSA‐P (balanced accuracies: 0.765–0.784, area under the receiver operating characteristic curve: 0.839–0.871) and PD–PSP–MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI. Conclusions A machine learning approach based on volumetry enabled accurate classification of subjects with early‐stage parkinsonism, examined on different MRI systems, as part of their clinical assessment. © 2020 International Parkinson and Movement Disorder Society

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MRI-based neuroimaging: atypical parkinsonisms and other movement disorders

Cited by 14 publications

References 33 publications

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

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

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

Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

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