Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 571289, 10 pages
http://dx.doi.org/10.1155/2013/571289
Research Article

Differential Diagnosis Tool for Parkinsonian Syndrome Using Multiple Structural Brain Measures

Miho Ota,1 Yasuhiro Nakata,2 Kimiteru Ito,2 Kouhei Kamiya,2 Masafumi Ogawa,3 Miho Murata,3 Satoko Obu,1 Hiroshi Kunugi,1 and Noriko Sato2

1Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8502, Japan
2Department of Radiology, National Center of Neurology and Psychiatry Hospital, 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan
3Department of Neurology, National Center of Neurology and Psychiatry Hospital, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan

Received 28 November 2012; Revised 18 February 2013; Accepted 18 February 2013

Academic Editor: Wenxiang Cong

Copyright © 2013 Miho Ota et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Clinical differentiation of parkinsonian syndromes such as the Parkinson variant of multiple system atrophy (MSA-P) and cerebellar subtype (MSA-C) from Parkinson's disease is difficult in the early stage of the disease. To identify the correlative pattern of brain changes for differentiating parkinsonian syndromes, we applied discriminant analysis techniques by magnetic resonance imaging (MRI). T1-weighted volume data and diffusion tensor images were obtained by MRI in eighteen patients with MSA-C, 12 patients with MSA-P, 21 patients with Parkinson’s disease, and 21 healthy controls. They were evaluated using voxel-based morphometry and tract-based spatial statistics, respectively. Discriminant functions derived by step wise methods resulted in correct classification rates of 0.89. When differentiating these diseases with the use of three independent variables together, the correct classification rate was the same as that obtained with step wise methods. These findings support the view that each parkinsonian syndrome has structural deviations in multiple brain areas and that a combination of structural brain measures can help to distinguish parkinsonian syndromes.