舍曲林
共激活
抗抑郁药
默认模式网络
静息状态功能磁共振成像
重性抑郁障碍
心理学
动态网络分析
医学
神经科学
功能磁共振成像
认知
数学
组合数学
肌电图
海马体
作者
Roselinde H. Kaiser,Henry W. Chase,Mary L. Phillips,Thilo Deckersbach,Ramin V. Parsey,Maurizio Fava,Patrick J. McGrath,Myrna M. Weissman,Maria A. Oquendo,Melvin G. McInnis,Thomas Carmody,Crystal Cooper,Madhukar H. Trivedi,Diego A. Pizzagalli
标识
DOI:10.1016/j.biopsych.2022.03.020
摘要
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.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.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.Dynamic resting-state markers of early antidepressant response or general recovery may assist development of clinical tools for monitoring and predicting effective intervention.
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