uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning
Linkoping Univ, Div Psychol, Dept Behav Sci & Learning, SE-58183 Linkoping, Sweden..
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
Umea Univ, Ctr Populat Studies Ageing & Living Condit, Umea, Sweden.;Umea Univ, Umea Ctr Funct Brain Imaging UFBI, Umea, Sweden..
Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6525 ED Nijmegen, Netherlands.;Kings Coll London, Inst Psychiat, Ctr Neuroimaging Sci, Dept Neuroimaging, London WC2R 2LS, England..
Show others and affiliations
2015 (English)In: Translational Psychiatry, ISSN 2158-3188, E-ISSN 2158-3188, Vol. 5, e530Article in journal (Refereed) PublishedText
Abstract [en]

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a substantial proportion relapse after treatment has ended. Predicting an individual's long-term clinical response therefore remains an important challenge. This study aimed at assessing neural predictors of long-term treatment outcome in participants with SAD 1 year after completion of Internet-delivered CBT (iCBT). Twenty-six participants diagnosed with SAD underwent iCBT including attention bias modification for a total of 13 weeks. Support vector machines (SVMs), a supervised pattern recognition method allowing predictions at the individual level, were trained to separate long-term treatment responders from nonresponders based on blood oxygen level-dependent (BOLD) responses to self-referential criticism. The Clinical Global Impression-Improvement scale was the main instrument to determine treatment response at the 1-year follow-up. Results showed that the proportion of long-term responders was 52% (12/23). From multivariate BOLD responses in the dorsal anterior cingulate cortex (dACC) together with the amygdala, we were able to predict long-term response rate of iCBT with an accuracy of 92% (confidence interval 95% 73.2-97.6). This activation pattern was, however, not predictive of improvement in the continuous Liebowitz Social Anxiety Scale-Self-report version. Follow-up psychophysiological interaction analyses revealed that lower dACC-amygdala coupling was associated with better long-term treatment response. Thus, BOLD response patterns in the fear-expressing dACC-amygdala regions were highly predictive of long-term treatment outcome of iCBT, and the initial coupling between these regions differentiated long-term responders from nonresponders. The SVM-neuroimaging approach could be of particular clinical value as it allows for accurate prediction of treatment outcome at the level of the individual.

Place, publisher, year, edition, pages
2015. Vol. 5, e530
National Category
URN: urn:nbn:se:uu:diva-275888DOI: 10.1038/tp.2015.22ISI: 000367654700004PubMedID: 25781229OAI: oai:DiVA.org:uu-275888DiVA: diva2:901619
Available from: 2016-02-08 Created: 2016-02-08 Last updated: 2016-02-08Bibliographically approved

Open Access in DiVA

fulltext(1097 kB)30 downloads
File information
File name FULLTEXT01.pdfFile size 1097 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Frick, AndreasFurmark, Tomas
By organisation
Department of Psychology
In the same journal
Translational Psychiatry

Search outside of DiVA

GoogleGoogle Scholar
Total: 30 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 45 hits
ReferencesLink to record
Permanent link

Direct link