Intrinsic Functional Connectomes Characterize Neuroticism in Major Depressive Disorder and Predict Antidepressant Treatment Outcomes

Abstract

BACKGROUND: Antidepressant efficacy in people with major depressive disorder remains modest, yet identifying treatment-predictive neurobiological markers may improve outcomes. While disruptions in functional connectivity within and between large-scale brain networks predict poorer treatment outcome, it is unclear whether higher trait neuroticism, which has been associated with generally poorer outcomes, contributes to these disruptions and to antidepressant-specific treatment outcomes. Here, we used whole-brain functional connectivity analysis to identify a neural connectomic signature of neuroticism and tested whether this signature predicted antidepressant treatment outcome. METHODS: Participants were 226 adults with major depressive disorder and 68 healthy control subjects who underwent functional magnetic resonance imaging and were assessed on clinical features at baseline. Participants with major depressive disorder were then randomized to 1 of 3 commonly prescribed antidepressants and after 8 weeks completed a second functional magnetic resonance imaging and were reassessed for depressive symptom remission/response. Baseline intrinsic functional connectivity between each pair of 436 brain regions was analyzed using network-based statistics to identify connectomic features associated with neuroticism. Features were then assessed on their ability to predict treatment outcome and whether they changed after 8 weeks of treatment. RESULTS: Higher baseline neuroticism was associated with greater connectivity within and between the salience, executive control, and somatomotor brain networks. Greater connectivity across these networks predicted poorer treatment outcome that was not mediated by baseline neuroticism, and connectivity strength decreased after antidepressant treatment. CONCLUSIONS: Our findings demonstrate that neuroticism is associated with organization of intrinsic neural networks that predict treatment outcome, elucidating its biological underpinnings and opportunity for better treatment personalization.