Discriminating bipolar disorder from major depression based on kernel SVM using functional independent components

Shuang Gao, Elizabeth A. Osuch, Michael Wammes, Jean Theberge, Tian Zi Jiang, Vince D. Calhoun, Jing Sui

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

Abstract

Bipolar disorder (BD) and major depressive disorder (MDD) both share depressive symptoms, so how to discriminate them in early depressive episodes is a major clinical challenge. Independent components (ICs) extracted from fMRI data have been proved to carry distinguishing information and can be used for classification. Here we extend a previous method that makes use of multiple fMRI ICs to build linear subspaces for each individual, which is further used as input for classifiers. The similarity matrix between different subjects is first calculated using distance metric of principal angle, which is then projected into kernel space for support vector machine (SVM) classification among 37 BDs and 36 MDDs. In practice, we adopt forward selection technique on 20 ICs and nested 10-fold cross validation to select the most discriminative IC combinations of fMRI and determine the final diagnosis by majority voting mechanism. The results on human data demonstrate that the proposed method achieves much better performance than its initial version [8] (93% vs. 75%), and identifies 5 discriminative fMRI components for distinguishing BD and MDD patients, which are mainly located in prefrontal cortex, default mode network and thalamus etc. This work provides a new framework for helping diagnose the new patients with overlapped symptoms between BD and MDD, which not only adds to our understanding of functional deficits in mood disorders, but also may serve as potential biomarkers for their differential diagnosis.

Original languageEnglish (US)
Title of host publication2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
EditorsNaonori Ueda, Jen-Tzung Chien, Tomoko Matsui, Jan Larsen, Shinji Watanabe
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Electronic)9781509063413
DOIs
StatePublished - Dec 5 2017
Event2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Tokyo, Japan
Duration: Sep 25 2017Sep 28 2017

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2017-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
CountryJapan
CityTokyo
Period9/25/179/28/17

Keywords

  • Bipolar disorder
  • Independent component analysis
  • Kernel SVM
  • Linear subspace
  • Major depression disorder

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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