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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 universitet, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Samhällsvetenskapliga fakulteten, Institutionen för psykologi.
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..
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2015 (Engelska)Ingår i: Translational Psychiatry, ISSN 2158-3188, E-ISSN 2158-3188, Vol. 5, artikel-id e530Artikel i tidskrift (Refereegranskat) Published
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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.

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2015. Vol. 5, artikel-id e530
Nationell ämneskategori
Psykologi
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URN: urn:nbn:se:uu:diva-275888DOI: 10.1038/tp.2015.22ISI: 000367654700004PubMedID: 25781229OAI: oai:DiVA.org:uu-275888DiVA, id: diva2:901619
Tillgänglig från: 2016-02-08 Skapad: 2016-02-08 Senast uppdaterad: 2017-11-30Bibliografiskt granskad

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