liu.seSearch for publications in DiVA
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.
Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences. (Internet, health and clinical psychology research group)
Uppsala Univ, Dept Psychol, Uppsala, Sweden.
Umea Univ, Umea, Sweden.
Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands, Kings Coll London, Inst Psychiat, Ctr Neuroimaging Sci, Dept Neuroimaging, London, England.
Show others and affiliations
2015 (English)In: Translational psychiatry, ISSN 2158-3188, Vol. 5, e530- p.Article in journal (Refereed) Published
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- p.
National Category
Applied Psychology
URN: urn:nbn:se:liu:diva-117119DOI: 10.1038/tp.2015.22ISI: 000367654700004PubMedID: 25781229OAI: diva2:805831

Funding agencies: Swedish Research Council; Linkoping University; Swedish Research Council for Health, Working Life and Welfare; PRIMA Psychiatry Research Foundation; Kings College London Centre of Excellence in Medical Engineering - Wellcome Trust; Engineering and Physica

Available from: 2015-04-16 Created: 2015-04-16 Last updated: 2016-02-01

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Månsson, Kristoffer N TAndersson, Gerhard
By organisation
PsychologyFaculty of Arts and Sciences
Applied Psychology

Search outside of DiVA

GoogleGoogle Scholar
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: 196 hits
ReferencesLink to record
Permanent link

Direct link