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Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Ekselius: Psychiatry.ORCID iD: 0000-0003-2516-9075
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Child and Adolescent Psychiatry.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology. Univ Southern Denmark, Dept Psychol, Odense, Denmark;Lund Univ, Dept Psychol, Lund, Sweden.
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2020 (English)In: Journal of Affective Disorders, ISSN 0165-0327, E-ISSN 1573-2517, Vol. 261, p. 230-237Article 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
Elsevier BV , 2020. Vol. 261, p. 230-237
Keywords [en]
Social phobia, SSRI, CBT, Personalized medicine, SVM, Pattern recognition
National Category
Psychiatry
Identifiers
URN: urn:nbn:se:uu:diva-402003DOI: 10.1016/j.jad.2019.10.027ISI: 000499616400031PubMedID: 31655378OAI: oai:DiVA.org:uu-402003DiVA, id: diva2:1386862
Funder
Swedish Research CouncilRiksbankens JubileumsfondForte, Swedish Research Council for Health, Working Life and WelfareAvailable from: 2020-01-20 Created: 2020-01-20 Last updated: 2021-09-01Bibliographically approved

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Frick, AndreasEngman, JonasAlaie, ImanBjörkstrand, JohannesGingnell, MalinLarsson, Elna-MarieFredrikson, MatsFurmark, Tomas

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