Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

• Published in: Frontiers in computational neuroscience Vol. 9; p. 66
• Main Authors: Zhang, Yudong, Dong, Zhengchao, Phillips, Preetha, Wang, Shuihua, Ji, Genlin, Yang, Jiquan, Yuan, Ti-Fei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 02.06.2015; Frontiers Media S.A
• Subjects: Accuracy; Aging; Alzheimer's disease; Artificial intelligence; Automation; Brain research; Classification; Dementia; Diagnosis; Entropy; Learning algorithms; Machine learning; Machine Vision; Magnetic Resonance Imaging; Methods; Neurodegenerative diseases; Neuroscience; NMR; Nuclear magnetic resonance; prediction; Principal components analysis; Student's t-test; New York; United States > US; China; eigenbrain; magnetic resonance imaging; support vector machine; Welch's t-test; particle swarm optimization; machine learning; Alzheimer's disease; machine vision
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2015.00066

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. 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. 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. The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.