Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A. and Fox, P.T. (2000) Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping, 1, 120-131.
http://dx.doi.org/10.1002/1097-0193(200007)10:3<120::AID-HBM30>3.0.CO;2-8

has been cited by the following article:

• TITLE: Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

  AUTHORS: Yasuo Yamashita, Hidetaka Arimura, Takashi Yoshiura, Chiaki Tokunaga, Ohara Tomoyuki, Koji Kobayashi, Yasuhiko Nakamura, Nobuyoshi Ohya, Hiroshi Honda, Fukai Toyofuku

  KEYWORDS: Computer-aided Classification (CAD); Alzheimer’s Disease; Magnetic Resonance Imaging (MRI); Arterial Spin Labeling (ASL); Fuzzy Membership Image; Cortical Thickness; Cerebral Blood Flow (CBF)

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.11, November 25, 2013

  ABSTRACT: Alzheimer’s disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (ANN) and a support vector machine (SVM), which were trained with 4 morphological and 6 functional image features, to 15 AD patients and 15 clinically normal (CN) subjects for classification of AD. The area under the receiver operating characteristic curve (AUC) values for the two systems based on the ANN and SVM with both imagefeatures were 0.901 and 0.915, respectively. The AUC values for the ANN-and SVM-based systems with the morphological features were 0.710 and 0.660, respectively, and those with the functional features were 0.878 and 0.903, respectively. Our preliminary results suggest that the proposed method may have potential for assisting radiologists in the differential diagnosis of AD patients by using morphological and functional image features.