title: Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa: Efficient Feature Selection With Multimodal Brain Imaging Data.
abstract: Discriminating between bipolar disorder (BD) and major depressive disorder (MDD) is a major clinical challenge due to the absence of known biomarkers; hence a better understanding of their pathophysiology and brain alterations is urgently needed. Given the complexity, feature selection is especially important in neuroimaging applications, however, feature dimension and model understanding present serious challenges. In this study, a novel feature selection approach based on linear support vector machine with a forward-backward search strategy (SVM-FoBa) was developed and applied to structural and resting-state functional magnetic resonance imaging data collected from 21 BD, 25 MDD and 23 healthy controls. Discriminative features were drawn from both data modalities, with which the classification of BD and MDD achieved an accuracy of 92.1% (1,000 bootstrap resamples). Weight analysis of the selected features further revealed that the inferior frontal gyrus may characterize a central role in BD-MDD differentiation, in addition to the default mode network and the cerebellum. A modality-wise comparison also suggested that functional information outweighs anatomical by a large margin when classifying the two clinical disorders. This work validated the advantages of multimodal joint analysis and the effectiveness of SVM-FoBa, which has potential for use in identifying possible biomarkers for several mental disorders.
authors: Brainnetome center and the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Nan-Feng
authors: NF
authors: Jie
authors: Elementary Educational College, Jiangxi Normal University, Nanchang, China.
authors: Mao-Hu
authors: MH
authors: Zhu
authors: School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
authors: Xiao-Ying
authors: XY
authors: Ma
authors: Dept. of Psychiatry, University of Western Ontario and the Lawson Health Research Institute Imaging Division, London, Ontario, Canada.
authors: Elizabeth A
authors: EA
authors: Osuch
authors: Dept. of Psychiatry, University of Western Ontario and the Lawson Health Research Institute Imaging Division, London, Ontario, Canada.
authors: Michael
authors: M
authors: Wammes
authors: Dept. of Medical Biophysics, University of Western Ontario and the Lawson Health Research Institute Imaging Division, London, Ontario, Canada.
authors: Jean
authors: J
authors: Théberge
authors: Brainnetome center and the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Huan-Dong
authors: HD
authors: Li
authors: Brainnetome center and the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Yu
authors: Y
authors: Zhang
authors: Brainnetome center and the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Tian-Zi
authors: TZ
authors: Jiang
authors: Brainnetome center and the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors: Jing
authors: J
authors: Sui
authors: Mind Research Network/LBERI.
authors: Vince D
authors: VD
authors: Calhoun
keywords: bipolar disorder; classification; feature selection; major depression; multimodal fusion