Brain markers predicting response to cognitive-behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015.
Brain markers predicting response to cognitive-behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015.
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DOI：
10.1038/s41398-021-01366-y
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发表时间：
2021-05-01
影响因子：
6.8
通讯作者：
Wager TD
中科院分区：
文献类型：
--
作者：
Ashar YK;Clark J;Gunning FM;Goldin P;Gross JJ;Wager TD
预测性脑标志物有望在科学、临床和社会等诸多方面得到重要应用。已发表的报告中描述了600多种预测性脑标志物，但很少有在独立重复验证中得到检验。在此，我们对先前发表的一种标志物进行了独立重复验证，该标志物通过静息态功能磁共振成像杏仁核连接模式来预测社交焦虑障碍的认知行为疗法的治疗反应。重复验证是在一个与原始报告所用数据集相似的现有数据集中进行的，由一组独立研究人员在与原始作者协商后开展。原始报告中所描述的精确模型在重复验证数据集中对治疗结果有正向预测作用，但统计显著性较弱，置换检验p = 0.1。在重复验证数据集中效应量大幅减小，该模型对治疗结果方差的解释率为2%，而原始报告中为21%。包括当前重复验证在内的多条证据表明，杏仁核功能或结构的特征或许能够预测焦虑障碍的治疗反应。然而，对于科学和临床应用而言，需要能够解释独立数据集中大量方差的预测模型。
Predictive brain markers promise a number of important scientific, clinical, and societal applications. Over 600 predictive brain markers have been described in published reports, but very few have been tested in independent replication attempts. Here, we conducted an independent replication of a previously published marker predicting treatment response to cognitive-behavioral therapy for social anxiety disorder from patterns of resting-state fMRI amygdala connectivity. The replication attempt was conducted in an existing dataset similar to the dataset used in the original report, by a team of independent investigators in consultation with the original authors. The precise model described in the original report positively predicted treatment outcomes in the replication dataset, but with marginal statistical significance, permutation test p = 0.1. The effect size was substantially smaller in the replication dataset, with the model explaining 2% of the variance in treatment outcomes, as compared to 21% in the original report. Several lines of evidence, including the current replication attempt, suggest that features of amygdala function or structure may be able to predict treatment response in anxiety disorders. However, predictive models that explain a substantial amount of variance in independent datasets will be needed for scientific and clinical applications.
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影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
DOI：
10.1176/appi.ajp.2015.14091200
发表时间：
2016-04-01
期刊：
The American journal of psychiatry
影响因子：
--
作者：
Clementz BA;Sweeney JA;Hamm JP;Ivleva EI;Ethridge LE;Pearlson GD;Keshavan MS;Tamminga CA
通讯作者：
Tamminga CA
影响因子：
12.6
作者：
Conway, Christopher C.;Forbes, Miriam K.;Eaton, Nicholas R.
通讯作者：
Eaton, Nicholas R.
影响因子：
6.9
作者：
Fresco, DM;Coles, ME;Goetz, D
通讯作者：
Goetz, D
DOI：
10.1016/j.pscychresns.2016.12.012
发表时间：
2017-03-30
期刊：
Psychiatry research. Neuroimaging
影响因子：
--
作者：
Burklund LJ;Torre JB;Lieberman MD;Taylor SE;Craske MG
通讯作者：
Craske MG