Development and evaluation of a multimodal marker of major depressive disorder

Abstract

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

“…Imaging data and neurobiological data were included in the data fusion. In both studies, the results did not show strong support for the hypothesis and did not provide sufficient evidence for the sought biomarkers [160, 161] .…”

“…Among the methods of the multimodal MRI image fusion in depressive disorders, support vector machine (SVM) [160] and linked independent component analysis [161] was recently used, respectively, to identify biomarkers for the classification and prediction of symptom loads of heterogeneous MDD cohorts. Imaging data and neurobiological data were included in the data fusion.…”

Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

“…This all suggests a more complex relationship between SSRIs, sleep, and treatment response that demands further investigation. Furthermore, it seems that the predictability of changes in insomnia related to depression is either specific to FC or the influence of citalopram, since this factor could not be predicted in a well-powered study using structural and diffusion MRI (Yang et al, 2018 ).…”

Functional connectivity predictors of acute depression treatment outcome

“…The left IFG-tri has been associated with language processing and emotional processing. Studies of depression and non-depressed control subjects found that IFG-tri was one of four areas that contributed significantly to discriminating between the two groups (Yang et al, 2018), with greater cortical thinning in the left IFG-tri in the depressed group (Na et al, 2016), and volume reductions of the left IFG-tri in the depressed group were associated with a particular genetic polymorphism (Han et al, 2017). As left IFG-tri comprises the Broca's area, our results also raise the possibility that inner speech or self-referential dialogue may play an important role in depression.…”