Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity

Runa Bhaumik, Lisanne M. Jenkins, Jennifer R. Gowins, Rachel H. Jacobs, Alyssa Barba, Dulal K. Bhaumik, Scott A. Langenecker*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD). To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI) to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r) population. In this study, we examined the efficiency of support vector machine (SVM) classifier to successfully discriminate rMDD individuals from healthy controls (HCs) in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9%) by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention.

Original languageEnglish (US)
Pages (from-to)390-398
Number of pages9
JournalNeuroImage: Clinical
Volume16
DOIs
StatePublished - 2017
Externally publishedYes

Keywords

  • MVPA
  • Machine learning
  • Major depressive disorder
  • Resting state fMRI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience

Fingerprint

Dive into the research topics of 'Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity'. Together they form a unique fingerprint.

Cite this