TY - JOUR
T1 - Effective connectivity between resting-state networks in depression
AU - DeMaster, Dana
AU - Godlewska, Beata
AU - Liang, Mingrui
AU - Vannucci, Marina
AU - Bockmann, Taya
AU - Bo, Cao
AU - Selvaraj, Sudhakar
N1 - Funding Information:
This work was supported by an MRC program grant (MR/K022202/1) and the Oxford Health NIHR Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR or the Department of Health. SS has received grants/research support from NIMH R21 (1R21MH119441 ? 01A1) and SAMHSA (FG000470-01). Research supplement funds from The University of Texas Health Science Center at Houston to SS were utilized for this study. The University of Texas Health Science Center at Houston had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or SAMHSA. We thank all the participants for their help with the study. In the last three years, SS has received speaking honoraria from Global Medical Education, British Medical Journal Publishing Group; own shares at Flow Med Tech. SS received research support from Compass pathways, Janssen and LivaNova. Dana DeMaster and Beata Godlewska, along with all other authors had no conflicting interests during the last three years.
Funding Information:
This work was supported by an MRC program grant ( MR/K022202/1 ) and the Oxford Health NIHR Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR or the Department of Health. SS has received grants/research support from NIMH R21 ( 1R21MH119441 – 01A1 ) and SAMHSA ( FG000470-01 ). Research supplement funds from The University of Texas Health Science Center at Houston to SS were utilized for this study. The University of Texas Health Science Center at Houston had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The content of this study is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or SAMHSA.
Publisher Copyright:
© 2022 The Authors
PY - 2022/6/15
Y1 - 2022/6/15
N2 - 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.
AB - 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.
KW - Depression
KW - Effective connectivity
KW - Escitalopram
KW - Resting state fMRI
KW - Treatment response
KW - Brain
KW - Magnetic Resonance Imaging
KW - Humans
KW - Brain Mapping
KW - Antidepressive Agents/pharmacology
KW - Depressive Disorder, Major/diagnostic imaging
UR - http://www.scopus.com/inward/record.url?scp=85128856254&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128856254&partnerID=8YFLogxK
U2 - 10.1016/j.jad.2022.03.041
DO - 10.1016/j.jad.2022.03.041
M3 - Article
C2 - 35331822
AN - SCOPUS:85128856254
SN - 0165-0327
VL - 307
SP - 79
EP - 86
JO - Journal of Affective Disorders
JF - Journal of Affective Disorders
ER -