Brain connectivity in major depressive disorder: a precision component of treatment modalities?

2023 | journal article. A publication with affiliation to the University of Göttingen.

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​Brain connectivity in major depressive disorder: a precision component of treatment modalities?​
Tura, A. & Goya-Maldonado, R.​ (2023) 
Translational Psychiatry13(1).​ DOI: https://doi.org/10.1038/s41398-023-02499-y 

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Authors
Tura, Asude; Goya-Maldonado, Roberto
Abstract
Abstract Major depressive disorder (MDD) is a very prevalent mental disorder that imposes an enormous burden on individuals, society, and health care systems. Most patients benefit from commonly used treatment methods such as pharmacotherapy, psychotherapy, electroconvulsive therapy (ECT), and repetitive transcranial magnetic stimulation (rTMS). However, the clinical decision on which treatment method to use remains generally informed and the individual clinical response is difficult to predict. Most likely, a combination of neural variability and heterogeneity in MDD still impedes a full understanding of the disorder, as well as influences treatment success in many cases. With the help of neuroimaging methods like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the brain can be understood as a modular set of functional and structural networks. In recent years, many studies have investigated baseline connectivity biomarkers of treatment response and the connectivity changes after successful treatment. Here, we systematically review the literature and summarize findings from longitudinal interventional studies investigating the functional and structural connectivity in MDD. By compiling and discussing these findings, we recommend the scientific and clinical community to deepen the systematization of findings to pave the way for future systems neuroscience roadmaps that include brain connectivity parameters as a possible precision component of the clinical evaluation and therapeutic decision.
Issue Date
2023
Journal
Translational Psychiatry 
eISSN
2158-3188
Language
English

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