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

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.

Keywords: MVPA; Machine learning; Major depressive disorder; Resting state fMRI.

Fig. 1

The Classification Framework.

Fig. 2

Significant functional connections based on two sample unadjusted t-test (p < /05). Note: * represents the FCs based on t-test with FDR.

Fig. 3

Classification accuracy for rMDD across top connections for two filter methods.

Fig. 4

Classification results for rMDD varying by alpha parameter (α) in Elastic Net method. Note. At α = 1, Elastic Net becomes LASSO.

Fig. 5

The contributing functional connections detection of rMDD selected by different feature selection algorithms. Note. The cell values represent the percentages of training folds in which a given connection was selected during the classification (more than 50% were selected as consensus functional connectivities).