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Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience.ORCID iD: 0000-0003-2516-9075
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience.
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2019 (English)In: Journal of Affective Disorders, ISSN 0165-0327, E-ISSN 1573-2517, Vol. 261, p. 230-237, article id S0165-0327(19)30886-9Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: 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).

METHODS: 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.

RESULTS: 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.

LIMITATIONS: Small sample size, especially for genetic analyses. No replication or validation samples were available.

CONCLUSIONS: 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.

Place, publisher, year, edition, pages
2019. Vol. 261, p. 230-237, article id S0165-0327(19)30886-9
Keywords [en]
CBT, Pattern recognition, Personalized medicine, SSRI, SVM, Social phobia
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-397548DOI: 10.1016/j.jad.2019.10.027PubMedID: 31655378OAI: oai:DiVA.org:uu-397548DiVA, id: diva2:1372003
Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-12-19

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