Functional connectomics in depression: insights into therapies

Depressed patients often have cognitive and behavioral deficits, characterized by heterogeneous symptoms, the neurobiological mechanism of which remains unknown.There are different treatment types for depression. However, the response and remission rates remain low and the neurobiological basis of treatment effects is unclear.Functional connectomics has provided useful tools to characterize depression as a brain network disorder and guide treatment decision-making.Recent work suggests that multidimensional depression symptoms are linked with abnormalities of functional connectivity and network organization in different brain networks. Each treatment type tends to improve specific symptoms by modulating specific networks, suggesting a need for combined treatments.Identifying network-guided, symptom-specific personalized treatment targets is the key for addressing heterogeneity in depression and treatment response.We propose a hypothetical model offering a theoretical framework for hypothesis testing that relates functional connectome changes with specific symptoms and therapies in major depression. Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.