Cited by 163Functional Brain Connectivity StudiesTreatment of Major DepressionMental Health Research Topics

Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder

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Korgaonkar, Mayuresh S., Goldstein-Piekarski, Andrea N., Fornito, Alexander, Williams, Leanne M.·2020·Molecular Psychiatry

Abstract

Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.

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Authors
Korgaonkar, M. S., Goldstein-Piekarski, A. N., Fornito, A., & Williams, L. M.
Affiliations
  • The University of Sydney (AU)
  • Monash University (AU)
Corresponding author
  • Korgaonkar, Mayuresh S.
  • Williams, Leanne M.
Venue
Molecular Psychiatry
Published
2019-11-06
Volume
25
Issue
7
Pages
1537-1549
ISSN
  • 1359-4184 (ISSN-L)
  • 1476-5578
OpenAlex
W2986842001
Cited by
163
References
55
Citations by year
2026: 15 · 2025: 23 · 2024: 32 · 2023: 26 · 2022: 32 · 2021: 18 · 2020: 16 · 2019: 1
FWCI
9.74
Type
journal article
Language
en
Open access
Locations
  • Molecular Psychiatry (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Brain Resource Ltd, San Francisco & Sydney
  • Brain Resource Ltd
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
major depressive disorder, remission, antidepressant response, functional connectivity, predictive biomarkers, default mode network, large-scale brain networks, escitalopram, sertraline, venlafaxine
MeSH terms
Venlafaxine Hydrochloride, Adult, Antidepressive Agents, Brain, Major Depressive Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Neural Pathways, Remission Induction, Citalopram, Biomarkers, Sertraline, Young Adult, Connectome

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