Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response

Roselinde H. Kaiser, Henry W. Chase, Mary L. Phillips, Thilo Deckersbach, Ramin V. Parsey, Maurizio Fava, Patrick J. McGrath, Myrna Weissman, Maria A. Oquendo, Melvin G. McInnis, Thomas Carmody, Crystal Cooper Cortes, Madhukar H Trivedi, Diego A. Pizzagalli

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Delivery of effective antidepressant treatment has been hampered by a lack of objective tools for predicting or monitoring treatment response. This study aimed to address this gap by testing novel dynamic resting-state functional network markers of antidepressant response. Methods: The Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study randomized adults with major depressive disorder to 8 weeks of either sertraline or placebo, and depression severity was evaluated longitudinally. Participants completed resting-state neuroimaging pretreatment and again after 1 week of treatment (n = 259 eligible for analyses). Coactivation pattern analyses identified recurrent whole-brain states of spatial coactivation, and computed time spent in each state for each participant was the main dynamic measure. Multilevel modeling estimated the associations between pretreatment network dynamics and sertraline response and between early (pretreatment to 1 week) changes in network dynamics and sertraline response. Results: Dynamic network markers of early sertraline response included increased time in network states consistent with canonical default and salience networks, together with decreased time in network states characterized by coactivation of cingulate and ventral limbic or temporal regions. The effect of sertraline on depression recovery was mediated by these dynamic network changes. In contrast, early changes in dynamic functioning of corticolimbic and frontoinsular-default networks were related to patterns of symptom recovery common across treatment groups. Conclusions: Dynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.

Original languageEnglish (US)
JournalBiological Psychiatry
DOIs
StateAccepted/In press - 2022

Keywords

  • Antidepressant
  • Biomarker
  • Depression
  • Dynamic
  • Network
  • Resting-state

ASJC Scopus subject areas

  • Biological Psychiatry

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