Effective connectivity between resting-state networks in depression

Journal article

Rationale: Although depression has been widely researched, findings characterizing how brain regions influence each other remains scarce, yet this is critical for research on antidepressant treatments and individual responses to particular treatments.

Objectives: To identify pre-treatment resting state effective connectivity (rsEC) patterns in patients with major depressive disorder (MDD) and explore their relationship with treatment response.

Methods: Thirty-four drug-free MDD patients had an MRI scan and were subsequently treated for 6 weeks with an SSRI escitalopram 10 mg daily; the response was defined as ≥50% decrease in Hamilton Depression Rating Scale (HAMD) score.

Results: rsEC networks in default mode, central executive, and salience networks were identified for patients with depression. Exploratory analyses indicated higher connectivity strength related to baseline depression severity and response to treatment.

Conclusions: Preliminary analyses revealed widespread dysfunction of rsEC in depression. Functional rsEC may be useful as a predictive tool for antidepressant treatment response. A primary limitation of the current study was the small size; however, the group was carefully chosen, well-characterized, and included only medication-free patients. Further research in large samples of placebo-controlled studies would be required to confirm the results.

Authors: DeMaster, D; Godlewska, BR; Liang, M; Vannucci, M; Bockmann, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Journal of Affective Disorders
• Volume: 307
• Pages: 79-86
• Publication date: 2022-03-21
• Acceptance date: 2022-03-17
• DOI: 10.1016/j.jad.2022.03.041
• EISSN: 1573-2517
• ISSN: 0165-0327
• Pmid: 35331822
• Language: English
• Keywords: Resting state fMRI; depression; treatment response; escitalopram; effective connectivity; FFR