Resting-state functional connectivity graph-properties correlate with bipolar disorder-risk in young medication-free depressed subjects: Bipolar-risk Resting State Functional Connectivity in Major Depression

Abstract

BackgroundMajor Depressive Disorder (MDD) is frequently associated with risk factors for the development of Bipolar Disorder (BD). Using graph theory, we investigated brain network properties associated with BD risk factors in young MDD subjects. MethodsResting-state fMRI was acquired from a large cohort (N= 104) of medication-free currently depressed participants (25 BD depression (BDD), 79 MDD). Lifetime mania symptom count (LMSC), current Young Mania Rating Scale (YMRS) score, and family history of mood disorders (FHMD) were examined as BD risk factors. Functional connectivity matrices from 280 regions of interests (ROIs) were first entered into the Network Based Statistic (NBS) toolbox to identify connections that varied with each risk factor. Next, within the correlated network for each risk factor, global and nodal graph properties for the top five linked nodes were calculated. Last, using identified graph properties, machine learning classification (MLC) between BDD, MDD with BD risk factors (MDD+), and without BD risk factors (MDD-) was conducted. ResultsLMSC positively correlated with left lateral orbitofrontal cortex (LOFC) Communication Efficiency and with left middle temporal Eigenvector Centrality. Current YMRS score positively correlated with right amygdala Communication Efficiency and Closeness Centrality. FHMD positively correlated with right insula Eigenvector Centrality. Acceptable MLC accuracy was seen between BDD and MDD- using middle temporal Eigenvector Centrality, whereas moderate accuracy was seen between MDD+ and MDD- using OFC Communication Efficiency. LimitationAlthough participants were medication-free, they were not medication-naïve. ConclusionFunctional connectome graph properties may serve as BD vulnerability biomarkers in young individuals with MDD.