Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response
Roselinde H Kaiser1, Henry W Chase2, Mary L Phillips2
1Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado; Institute of Cognitive Science, University of Colorado Boulder, Boulder, Colorado; Renée Crown Wellness Institute, University of Colorado Boulder, Boulder, Colorado.
Biological Psychiatry
|June 9, 2022
Summary
New dynamic brain network markers show promise for predicting antidepressant response. These objective measures, identified through resting-state neuroimaging, could help personalize depression treatment and monitor recovery effectively.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Effective antidepressant treatment lacks objective predictors or monitors.
- This study addresses the need for objective tools in depression treatment.
Purpose of the Study:
- To test novel dynamic resting-state functional network markers for predicting antidepressant response.
- To identify objective biomarkers for monitoring treatment efficacy in major depressive disorder.
Main Methods:
- Utilized data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.
- Employed coactivation pattern analyses on resting-state neuroimaging data from 259 participants.
- Applied multilevel modeling to assess associations between network dynamics and sertraline response.
Main Results:
- Dynamic network markers, including altered time in specific network states, predicted early sertraline response.
- Changes in these dynamic network markers mediated the effect of sertraline on depression recovery.
- Distinct network dynamics related to general symptom recovery across treatment groups.
Conclusions:
- Dynamic resting-state markers show potential for early prediction of antidepressant response.
- These findings may aid in developing clinical tools for monitoring and predicting effective interventions for depression.
