Autor:innen:
Julia-Katharina Pfarr, Marburg (Germany)
Katharina Brosch, Marburg (Germany)
Tina Meller, Marburg (Germany)
Kai Gustav Ringwald, Marburg (Germany)
Simon Schmitt, Marburg (Germany)
Frederike Stein, Marburg (Germany)
Andreas Jansen, Marburg (Germany)
Udo Dannlowski, Münster (Germany)
Axel Krug, Bonn (Germany)
Tilo T. J. Kircher, Marburg (Germany)
Igor Nenadic, Marburg (Germany)
Aberrant brain structural connectivity in major depressive disorder (MDD) has been repeatedly reported, yet intercorrelations of multiple phenotypes on a biomarker level are poorly understood. Respecting the dimensionality of those markers rather than their categories, contributes to disentangle the full range of psychiatric disorders. Multivariate models including single features of MDD and brain structural variation can help to unravel those relationships and can further be used for treatment improvement.
In n=595 MDD patients, we used structural equation modelling (SEM) to test the intercorrelations between anhedonia, state anxiety, and neuroticism, as well as cognition (cognitive control / executive function) in one comprehensive model. We then separately analyzed diffusion tensor imaging (DTI) connectivity measures and their association with those clinical variables, and finally integrated brain connectivity, clinical, and cognitive variables into a multivariate SEM.
We first confirmed our clinical/cognitive SEM, replicating and extending earlier studies. DTI analyses (FWE-corr.) showed a positive correlation of anhedonia with fractional anisotropy (FA) in the right anterior thalamic radiation (ATR) and forceps minor/corpus callosum, while neuroticism was negatively correlated with axial diffusivity (AD) in the left uncinate fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF). An extended SEM integrating the aforementioned brain structural associations showed an impact of both, the anhedonia-ATR as well as the neuroticism-UF association on cognitive control.
Our findings provide evidence for a differential impact of state and trait variables of MDD on brain connectivity and cognition. This multivariate approach shows feasibility of explaining heterogeneity within MDD and track this to specific brain circuits, thus adding to better understanding of heterogeneity on the biological level.