Brain markers predicting response to cognitive-behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015

被引:11
作者
Ashar, Yoni K. [1 ]
Clark, Joseph [2 ]
Gunning, Faith M. [1 ]
Goldin, Philippe [3 ]
Gross, James J. [4 ]
Wager, Tor D. [5 ]
机构
[1] Weill Cornell Med, Dept Psychiat, New York, NY 10011 USA
[2] Univ Colorado, Dept Psychol & Neurosci, Boulder, CO 80309 USA
[3] Univ Calif Davis, Betty Irene Moore Sch Nursing, Davis, CA 95616 USA
[4] Stanford Univ, Dept Psychol, Palo Alto, CA 94304 USA
[5] Dartmouth Coll, Psychol & Brain Sci Dept, Hanover, NH 03755 USA
关键词
FUNCTIONAL CONNECTIVITY; SAMPLE-SIZE; SELF-REPORT; REAPPRAISAL; RELIABILITY; PLACEBO; SCALE; MOOD;
D O I
10.1038/s41398-021-01366-y
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
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(1). 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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页数:9
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