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Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder.

Journal article
Authors Andreas Frick
Jonas Engman
Iman Alaie
Johannes Björkstrand
Malin Gingnell
Elna-Marie Larsson
Elias Eriksson
Kurt Wahlstedt
Mats Fredrikson
Tomas Furmark
Published in Journal of affective disorders
Volume 261
Pages 230-237
ISSN 1573-2517
Publication year 2020
Published at Institute of Neuroscience and Physiology, Department of Pharmacology
Pages 230-237
Language en
Keywords Social anxiety disorder
Subject categories Pharmacology and Toxicology


Correct prediction of treatment response is a central goal of precision psychiatry. Here, we tested the predictive accuracy of a variety of pre-treatment patient characteristics, including clinical, demographic, molecular genetic, and neuroimaging markers, for treatment response in patients with social anxiety disorder (SAD).Forty-seven SAD patients (mean±SD age 33.9 ± 9.4 years, 24 women) were randomized and commenced 9 weeks' Internet-delivered cognitive behavior therapy (CBT) combined either with the selective serotonin reuptake inhibitor (SSRI) escitalopram (20 mg daily [10 mg first week], SSRI+CBT, n = 24) or placebo (placebo+CBT, n = 23). Treatment responders were defined from the Clinical Global Impression-Improvement scale (CGI-I ≤ 2). Before treatment, patients underwent functional magnetic resonance imaging and the Multi-Source Interference Task taxing cognitive interference. Support vector machines (SVMs) were trained to separate responders from nonresponders based on pre-treatment neural reactivity in the dorsal anterior cingulate cortex (dACC), amygdala, and occipital cortex, as well as molecular genetic, demographic, and clinical data. SVM models were tested using leave-one-subject-out cross-validation.The best model separated treatment responders (n = 24) from nonresponders based on pre-treatment dACC reactivity (83% accuracy, P = 0.001). Responders had greater pre-treatment dACC reactivity than nonresponders especially in the SSRI+CBT group. No other variable was associated with clinical response or added predictive accuracy to the dACC SVM model.Small sample size, especially for genetic analyses. No replication or validation samples were available.The findings demonstrate that treatment outcome predictions based on neural cingulate activity, at the individual level, outperform genetic, demographic, and clinical variables for medication-assisted Internet-delivered CBT, supporting the use of neuroimaging in precision psychiatry.

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