Diffusion tensor imaging in the characterization of multiple system atrophy
Authors Rulseh A, Keller J, Rusz J, Syka M, Brozova H, Rusina R, Havrankova P, Zarubova K, Malikova H, Jech R, Vymazal J
Received 24 March 2016
Accepted for publication 26 May 2016
Published 26 August 2016 Volume 2016:12 Pages 2181—2187
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Roger Pinder
Aaron Michael Rulseh,1–3 Jiri Keller,1,4 Jan Rusz,5,6 Michael Syka,1 Hana Brozova,6 Robert Rusina,6,7 Petra Havrankova,6 Katerina Zarubova,8 Hana Malikova,1 Robert Jech,6 Josef Vymazal1
1Department of Radiology, Na Homolce Hospital, Prague, Czech Republic; 2Department of Radiology, 1st Faculty of Medicine, General University Hospital, Charles University in Prague, Prague, Czech Republic; 3National Institute of Mental Health, Klecany, Czech Republic; 43rd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic; 5Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic; 6Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic; 7Thomayer Hospital, Prague, Czech Republic; 8Department of Neurology, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
Purpose: Multiple system atrophy (MSA) is a rare neurodegenerative disease that remains poorly understood, and the diagnosis of MSA continues to be challenging. We endeavored to improve the diagnostic process and understanding of in vivo characteristics of MSA by diffusion tensor imaging (DTI).
Materials and methods: Twenty MSA subjects, ten parkinsonian dominant (MSA-P), ten cerebellar dominant (MSA-C), and 20 healthy volunteer subjects were recruited. Fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity maps were processed using tract-based spatial statistics. Diffusion data were additionally evaluated in the basal ganglia. A support vector machine was used to assess diagnostic utility, leave-one-out cross-validation in the evaluation of classification schemes, and receiver operating characteristic analyses to determine cutoff values.
Results: We detected widespread changes in the brain white matter of MSA subjects; however, no group-wise differences were found between MSA-C and MSA-P subgroups. Altered DTI metrics in the putamen and middle cerebellar peduncles were associated with a positive parkinsonian and cerebellar phenotype, respectively. Concerning clinical applicability, we achieved high classification performance on mean diffusivity data in the combined bilateral putamen and middle cerebellar peduncle (accuracy 90.3%±9%, sensitivity 86.5%±11%, and specificity 99.3%±4%).
Conclusion: DTI in the middle cerebellar peduncle and putamen may be used in the diagnosis of MSA with a high degree of accuracy.
Keywords: multiple system atrophy, diffusion tensor imaging, magnetic resonance imaging, neuroimaging, diagnostic imaging
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