Classification of mood disorders from functional brain network topology: a latent profile analysis
Date
2020
Authors
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Publisher
University of Delaware
Abstract
Differentiation of Bipolar Disorder I (BPI), Bipolar Disorder II (BPII), and Major Depressive Disorder (MDD) is difficult due to the episodic nature of these disorders and their phenotypic and genetic overlap. This results in frequent misdiagnosis of MDD in BP patients which can have devastating real-world consequences as MDD treatments (e.g., selective serotonin reuptake inhibitors) can precipitate the onset of mania in some depressed individuals. Additionally, the current diagnostic system relies solely upon either self-reported or observable symptoms, which can be problematic as more than one pathway may lead to the same presentation. Thus, inclusion of additional data sources (e.g., neural metrics) may help refine diagnostic categories. However, extant neuroscience research has almost uniformly focused on differentiating between disorders using the current nosology. In a sample of patients (BPI, BPII, and MDD) and healthy controls, we addressed this gap in the literature by deriving latent classes from emergent brain network attributes in a manner that was blind to diagnostic group. In particular, we entered three global metrics of functional brain network organization (assortativity, algebraic connectivity, and total clustering coefficient) as indicators into a latent profile analysis. We examined models with 2 to 4 classes and found a 3-class model to be superior. Next, we examined the extent to which these 3 classes mapped onto existing DSM diagnostic groupings. Findings demonstrated that these classes were related to, but did not match, diagnostic groupings. In particular, the functional brain network organization of individuals with BPII appeared to be particularly distinct from that of the other patient groups (i.e., BPI, MDD) and HC. This also appeared to be the case for MDD, although the evidence was less definitive.
Description
Keywords
Bipolar disorder, Brain network, Depression, Graph theory