title: Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis.
abstract: OBJECTIVE
  Aberrant brain functional connectivity patterns have been reported in major depressive disorder (MDD). It is unknown whether they can be used in discriminant analysis for diagnosis of MDD. In the present study we examined the efficiency of discriminant analysis of MDD by individualized computer-assisted diagnosis.
  
  
  METHODS
  Based on resting-state functional magnetic resonance imaging data, a new approach was adopted to investigate functional connectivity changes in 39 MDD patients and 37 well-matched healthy controls. By using the proposed feature selection method, we identified significant altered functional connections in patients. They were subsequently applied to our analysis as discriminant features using a support vector machine classification method. Furthermore, the relative contribution of functional connectivity was estimated.
  
  
  RESULTS
  After subset selection of high-dimension features, the support vector machine classifier reached up to approximately 84% with leave-one-out training during the discrimination process. Through summarizing the classification contribution of functional connectivities, we obtained four obvious contribution modules: inferior orbitofrontal module, supramarginal gyrus module, inferior parietal lobule-posterior cingulated gyrus module and middle temporal gyrus-inferior temporal gyrus module.
  
  
  CONCLUSIONS
  The experimental results demonstrated that the proposed method is effective in discriminating MDD patients from healthy controls. Functional connectivities might be useful as new biomarkers to assist clinicians in computer auxiliary diagnosis of MDD.
authors.affiliation: Mental Health Institute of The Second Xiangya Hospital, Hunan Province Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Hunan, China.
authors.forename: Longlong
authors.initials: L
authors.lastname: Cao
authors.forename: Shuixia
authors.initials: S
authors.lastname: Guo
authors.forename: Zhimin
authors.initials: Z
authors.lastname: Xue
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
authors.forename: Haihong
authors.initials: H
authors.lastname: Liu
authors.forename: Tumbwene E
authors.initials: TE
authors.lastname: Mwansisya
authors.forename: Weidan
authors.initials: W
authors.lastname: Pu
authors.forename: Bo
authors.initials: B
authors.lastname: Yang
authors.forename: Chang
authors.initials: C
authors.lastname: Liu
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
authors.forename: Eric Y H
authors.initials: EY
authors.lastname: Chen
authors.forename: Zhening
authors.initials: Z
authors.lastname: Liu
mesh_terms: D000293:Adolescent; D000328:Adult; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000503:physiopathology; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000503:physiopathology*; D009434:Neural Pathways / Q000503:physiopathology; D060388:Support Vector Machine; D055815:Young Adult
keywords: discriminant analysis; functional magnetic resonance imaging; machine learning; major depressive disorder; support vector machine