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Differentiating multiple-system atrophy from Parkinson's disease

Ramli N, Nair SR, Ramli NM, Lim SY

Clin Radiol, 2015 May;70(5):555-64.
PMID: 25752581 DOI: 10.1016/j.crad.2015.01.005

The purpose of this review is to illustrate the differentiating features of multiple-system atrophy from Parkinson's disease at MRI. The various MRI sequences helpful in the differentiation will be discussed, including newer methods, such as diffusion tensor imaging, MR spectroscopy, and nuclear imaging.

Matched MeSH terms: Multiple System Atrophy/diagnosis*
The degree of astrocyte activation in multiple system atrophy is inversely proportional to the distance to α-synuclein inclusions

Radford R, Rcom-H'cheo-Gauthier A, Wong MB, Eaton ED, Quilty M, Blizzard C, et al.

Mol. Cell. Neurosci., 2015 Mar;65:68-81.
PMID: 25731829 DOI: 10.1016/j.mcn.2015.02.015

Multiple system atrophy (MSA) exhibits widespread astrogliosis together with α-synuclein (α-syn) glial cytoplasmic inclusions (GCIs) in mature oligodendrocytes. We quantified astrocyte activation by morphometric analysis of MSA cases, and investigated the correlation to GCI proximity. Using Imaris software, we obtained "skinned" three-dimensional models of GFAP-positive astrocytes in MSA and control tissue (n=75) from confocal z-stacks and measured the astrocyte process length and thickness and radial distance to the GCI. Astrocytes proximal to GCI-containing oligodendrocytes (r<25μm) had significantly (p, 0.05) longer and thicker processes characteristic of activation than distal astrocytes (r>25μm), with a reciprocal linear correlation (m, 90μm(2)) between mean process length and radial distance to the nearest GCI (R(2), 0.7). In primary cell culture studies, α-syn addition caused ERK-dependent activation of rat astrocytes and perinuclear α-syn inclusions in mature (MOSP-positive) rat oligodendrocytes. Activated astrocytes were also observed in close proximity to α-syn deposits in a unilateral rotenone-lesion mouse model. Moreover, unilateral injection of MSA tissue-derived α-syn into the mouse medial forebrain bundle resulted in widespread neuroinflammation in the α-syn-injected, but not sham-injected hemisphere. Taken together, our data suggests that the action of localized concentrations of α-syn may underlie both astrocyte and oligodendrocyte MSA pathological features.

Matched MeSH terms: Multiple System Atrophy/metabolism*
Fulltext A Hot Cross Bun sign from diffusion tensor imaging and tractography perspective

Loh KB, Rahmat K, Lim SY, Ramli N

Neurol India, 2011 Mar-Apr;59(2):266-9.
PMID: 21483130 DOI: 10.4103/0028-3886.79143

A "Hot Cross Bun" sign on T2-weighted MRI was described as a result of selective loss of myelinated transverse pontocerebellar fibers and neurons in the pontine raphe with preservation of the pontine tegmentum and corticospinal tracts (CST). However, neuropathologic studies showed contradicting results with no sparing of the CST. This is a pictorial and quantitative demonstration of the sign on diffusion tensor imaging and tractography, which provides the imaging evidence that is consistent with neuropathologic findings.

Matched MeSH terms: Multiple System Atrophy/pathology*
Exploring bedside clinical features of parkinsonism: A focus on differential diagnosis

Bhidayasiri R, Rattanachaisit W, Phokaewvarangkul O, Lim TT, Fernandez HH

Parkinsonism Relat Disord, 2019 Feb;59:74-81.
PMID: 30502095 DOI: 10.1016/j.parkreldis.2018.11.005

The proper diagnosis of parkinsonian disorders usually involves three steps: identifying core features of parkinsonism; excluding other causes; and collating supportive evidence based on clinical signs or investigations. While the recognition of cardinal parkinsonian features is usually straightforward, the appreciation of clinical features suggestive of specific parkinsonian disorders can be challenging, and often requires greater experience and skills. In this review, we outline the clinical features that are relevant to the differential diagnosis of common neurodegenerative parkinsonian disorders, including Parkinson's disease, multiple system atrophy, progressive supranuclear palsy, and corticobasal degeneration. We aim to make this process relatable to clinicians-in-practice, therefore, have categorised the list of clinical features into groups according to the typical sequence on how clinicians would elicit them during the examination, starting with observation of facial expression and clinical signs of the face, spotting eye movement abnormalities, examination of tremors and jerky limb movements, and finally, examination of posture and gait dysfunction. This review is not intended to be comprehensive. Rather, we have focused on the most common clinical signs that are potentially key to making the correct diagnosis and those that do not require special skills or training for interpretation. Evidence is also provided, where available, such as diagnostic criteria, consensus statements, clinicopathological studies or large multi-centre registries. Pitfalls are also discussed when relevant to the diagnosis. While no clinical signs are pathognomonic for certain parkinsonian disorders, certain clinical clues may assist in narrowing a differential diagnosis and tailoring focused investigations for the individual patient.

Matched MeSH terms: Multiple System Atrophy
A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging

Nair SR, Tan LK, Mohd Ramli N, Lim SY, Rahmat K, Mohd Nor H

Eur Radiol, 2013 Jun;23(6):1459-66.
PMID: 23300042 DOI: 10.1007/s00330-012-2759-9

OBJECTIVE: To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD).
METHODS: 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3.
RESULTS: Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified.
CONCLUSION: Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD.
KEY POINTS: • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

Matched MeSH terms: Multiple System Atrophy/diagnosis*