Abstract

Geriatric depression and anxiety disorders often manifest as neuropsychiatric symptoms among those with mild cognitive impairment. Both tend to co-occur, and overlap in symptomology and etiology. Such commonalities are likely to be reflected in the brain as common neural correlates. Using connectome-based predictive modeling (CPM), we examined the functional and structural connectomes predicting depression and anxiety symptoms, and subsequently the overlap and cross-syndrome generalization of the connectomes associated with either disorder. Ninety-one older adults completed self-reported measures of depression and anxiety, and underwent diffusion tensor imaging and resting-state functional magnetic resonance imaging. Functional connectivity (FC) and structural connectivity (SC) matrices were derived from these scans and, in various combinations, entered into CPM models to predict either type of symptoms. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (ρpredicted-observed). While FC or SC features alone significantly predicted either type of symptoms, these symptoms were best predicted by models that consisted of both FC and SC features (depression: ρpredicted-observed = 0.497; anxiety: ρpredicted-observed = 0.455). The features common to depression and anxiety were identified and entered into another model which was similarly accurate in predicting either type of symptoms. Moreover, cross-syndrome generalization was observed— the depression-associated features significantly predicted anxiety symptoms (ρpredicted-observed = 0.403) and vice-versa (ρpredicted-observed = 0.378). These FC and SC features are complementary biomarkers of geriatric depression and anxiety symptoms. Both types of symptoms are largely underpinned by common patterns of altered FC and SC, alluding to the transdiagnostic neurobiological susceptibility in both disorders.

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