Investigating Functional Connectivity in Adolescent Depression and Suicide Attempt during Neurofeedback Sessions: A Multivariate Random Covariance Model Approach

Abstract

Background A recent neurofeedback functional magnetic resonance imaging (NF, fMRI) study on depressed vs. healthy adolescents elicited differential functional connectivity (FC) amongst brain regions of interest (ROIs). Previous results employed univariate methods and included only two seed areas of FC (amygdala and hippocampus). In this study, we propose a new multivariate analysis for whole-network FC estimation. Methods Primary analyses concerned a pre-identified network of 17 salient ROIs reflecting key regions in self-processing and emotion regulation. A random covariance model (RCM) was applied to jointly estimate participant- and group-specific connectivity, where FC was measured by partial correlation conditioned on or adjusted for rest-of-network connectivity patterns. Secondary analyses concerned participant-specific network association with mental functioning changes and the AAL3 whole-brain atlas. Results New findings suggested that depressed adolescents with a suicide attempt expressed significantly higher positive FC between the left temporal gyrus and the left amygdala during NF, compared to negative FC in non-attempting depressed youth, while healthy controls displayed negative FC between the insula, inferior frontal gyrus to inferior parietal lobe connection, compared to mild negative connectivity in depressed adolescents. Previous cross-hemispheric findings in depressed vs. healthy adolescents were corroborated. Conclusion A multivariate RCM uncovered key ROI-pairwise connections differentiating FC patterns between depressed youth vs. healthy controls and among depressed youth, with and without a suicide attempt. Findings were strengthened by enhanced inference vs. univariate methods, and corroboration of previous NF secondary analyses demonstrated future utility for participant-specific study in association with clinical outcomes and/or whole-brain analyses with larger sample sizes.