Altered effective connectivity in sensorimotor cortices: a novel signature of severity and clinical course in depression

Abstract

Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and Dynamic Causal Modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex: an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (non-pharmacological) treatment, depression associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.Significance StatementResearch into neurobiology of depression primarily focuses on its complex psychological aspects. Here, we propose an alternative approach and target sensorimotor alterations - a prominent but often neglected feature of depression. We demonstrated using resting-state fMRI data and computational modelling that top-down and bottom-up information flow in sensory and motor cortices is altered with increasing depression severity in a way that is consistent with depression symptoms. Depression associated changes were found to be consistent across sessions, amenable to treatment and of effect size sufficiently large to predict whether somebody has mild or severe depression. These results pave the way for a new avenue of research into the neural underpinnings of mental health conditions.