Discriminating bipolar disorder from major depression using whole-brain functional connectivity: A feature selection analysis with SVM-FoBA algorithm

Nan Feng Jie, Elizabeth A. Osuch, Mao Hu Zhu, Xiao Ying Ma, Michael Wammes, Tian Zi Jiang, Jing Sui, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is known that both bipolar disorder (BD) and major depressive disorder (MDD) indicate depressive symptoms, especially in the early phase of illness. Therefore, discriminating BD from MDD is a major clinical challenge due to the absence of biomarkers. Feature selection is especially important in neuroimaging applications, yet high feature dimensions, low sample size and model understanding present huge challenges. Here we propose an advanced feature selection algorithm, 'SVM-FoBa', which enables adaptive selection of informative feature subsets from high dimensional brain functional connectives (FC) resulted from fMRI. With 38 significant FCs chosen from 6,670 ones, classification accuracy between BD and MDD was achieved up to 88% with leave-one-out cross validation. Further, by conducting weight analysis, the most discriminative FCs were revealed, which adds our understanding on functional deficits and may serve as potential biomarkers for mood disorders.

Original languageEnglish (US)
Title of host publication2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015
EditorsDeniz Erdogmus, Serdar Kozat, Jan Larsen, Murat Akcakaya
PublisherIEEE Computer Society
ISBN (Electronic)9781467374545
DOIs
StatePublished - Nov 10 2015
Externally publishedYes
Event25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 - Boston, United States
Duration: Sep 17 2015Sep 20 2015

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2015-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015
CountryUnited States
CityBoston
Period9/17/159/20/15

Keywords

  • Functional connectivity
  • SVM-FoBa
  • bipolar disorder
  • feature selection
  • major depression disorder

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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