title: Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning.
abstract: OBJECTIVE
  Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.
  
  
  METHODS
  First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.
  
  
  RESULTS
  The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.
  
  
  CONCLUSIONS
  The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.
authors.affiliation: School of Computer Science and Technology, Nanjing Normal University Nanjing, China.
authors.forename: Yudong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Division of Translational Imaging and MRI Unit, New York State Psychiatric Institute, Columbia University New York, NY, USA.
authors.forename: Zhengchao
authors.initials: Z
authors.lastname: Dong
authors.affiliation: School of Natural Sciences and Mathematics, Shepherd University Shepherdstown, WV, USA.
authors.forename: Preetha
authors.initials: P
authors.lastname: Phillips
authors.affiliation: School of Computer Science and Technology, Nanjing Normal University Nanjing, China ; School of Electronic Science and Engineering, Nanjing University Nanjing, China.
authors.forename: Shuihua
authors.initials: S
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology, Nanjing Normal University Nanjing, China ; Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing, China.
authors.forename: Genlin
authors.initials: G
authors.lastname: Ji
authors.affiliation: Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing, China.
authors.forename: Jiquan
authors.initials: J
authors.lastname: Yang
authors.affiliation: School of Psychology, Nanjing Normal University Nanjing, China.
authors.forename: Ti-Fei
authors.initials: TF
authors.lastname: Yuan
keywords: Alzheimer's disease; Welch's t-test; eigenbrain; machine learning; machine vision; magnetic resonance imaging; particle swarm optimization; support vector machine