A network-based approach reveals higher plasticity levels in bipolar than major depressive disorder

Background: Bipolar disorder (BD) and major depressive disorder (MDD) are often misdiagnosed due to overlapping symptomatology, leading to inappropriate treatments and poor outcomes. Thus, novel approaches for effective early differential diagnosis are warranted. Plasticity – the ability to modify brain functioning and mental state – is increasingly recognized as a crucial process in psychiatry, as it underlies transitions from psychopathology to wellbeing. Accordingly, enhanced plasticity is reported to increase mental state transitions. Since BD patients experience transitioning between depression and mania, we hypothesized this disorder to be characterized by higher plasticity levels than MDD. Methods: We leveraged a recent network-based approach to measure plasticity, operationalizing it as the inverse of the connectivity strength among network symptoms. We analyzed 211 BD and 136 MDD patients undergoing a major depressive episode. Symptoms were employed to generate unregularized networks via Gaussian Graphical Model, with Spearman's Rank correlation estimating the input matrix. Connectivity strength was compared between disorders using the Network Comparison Test function. Results: BD patients exhibit higher plasticity levels than MDD patients, as demonstrated by weaker symptom connectivity, despite both groups having comparable depressive symptom severity (p = 0.008). Within the BD group, type I patients showed also higher plasticity compared to type II (p = 0.04), further corroborating the association between high plasticity and mood instability. Conclusions: Our findings support the hypothesis that plasticity levels differ across psychopathological conditions based on their likelihood of mental transitions. Consequently, measuring of plasticity through symptom network dynamics holds promise for the early differential diagnosis of BD and MDD.