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- Månsson, Kristoffer, et al.
(författare)
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Predicting Long-Term Outcome of Cognitive Behaviour Therapy for Social Anxiety Disorder Using fMRI and Support Vector Machiness
- 2014
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Ingår i: Abstracts from the 44th Congress of the European Association for Behavioural & Cognitive Therapies.
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Konferensbidrag (refereegranskat)abstract
- Background: Cognitive behavioural therapy (CBT) has yielded robust treatment effects for social anxiety disorder (SAD) but still many patients do not respond fully to treatment, and a substantial proportion relapse after treatment has ended. Identification of robust predictors of sustained treatment responses could be of high clinical importance.Methods: We used functional magnetic resonance imaging (fMRI; 3T General Electric) to assess 26 patients (85% women, mean age 32.3 years) with SAD. Blood-oxygen-level dependent (BOLD) responses to self-referential criticism, i.e. reading sentences such as "Nobody likes you" were compared to criticism referring to other individuals. Responses in the fear network, i.e. the amygdala, hippocampus, anterior cingulate cortex (ACC), and insula, were evaluated in a Support Vector Machine (SVM) approach to predict treatment outcome one-year after Internet-delivered CBT. We applied leave-one-out cross-validation to increase the generalizability of the data.Results: At one-year follow-up, three patients had dropped out. Twelve (52%) of the assessed patients met the response criteria, i.e. very much or much improved according to the Clinical Global Impression-Improvement scale (CGI-I). SVM on initial BOLD response, accurately classified patients according to responder status, based on multi-voxel patterns in the ACC (balanced accuracy of 91.7%, p=.001), and the ACC together with the amygdala (83.0%, p=.004) as well as the hippocampus (73.9%,p=.032).Conclusions: We demonstrate that initial multi-voxel BOLD response patterns to self-referential criticism in the ACC, amygdala, and hippocampus are highly predictive of long-term improvement of CBT in patients with SAD.
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| 3. |
- Bas-Hoogendam, Janna Marie, et al.
(författare)
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Voxel-based morphometry multi-center mega-analysis of brain structure in social anxiety disorder
- 2017
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Ingår i: NeuroImage. - : Elsevier BV. - 2213-1582. ; 16, s. 678-688
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Tidskriftsartikel (refereegranskat)abstract
- Social anxiety disorder (SAD) is a prevalent and disabling mental disorder, associated with significant psychiatric co-morbidity. Previous research on structural brain alterations associated with SAD has yielded inconsistent results concerning the direction of the changes in gray matter (GM) in various brain regions, as well as on the relationship between brain structure and SAD-symptomatology. These heterogeneous findings are possibly due to limited sample sizes. Multi-site imaging offers new opportunities to investigate SAD-related alterations in brain structure in larger samples.An international multi-center mega-analysis on the largest database of SAD structural T1-weighted 3T MRI scans to date was performed to compare GM volume of SAD-patients (n = 174) and healthy control (HC)-participants (n = 213) using voxel-based morphometry. A hypothesis-driven region of interest (ROI) approach was used, focusing on the basal ganglia, the amygdala-hippocampal complex, the prefrontal cortex, and the parietal cortex. SAD-patients had larger GM volume in the dorsal striatum when compared to HC-participants. This increase correlated positively with the severity of self-reported social anxiety symptoms. No SAD-related differences in GM volume were present in the other ROIs. Thereby, the results of this mega-analysis suggest a role for the dorsal striatum in SAD, but previously reported SAD-related changes in GM in the amygdala, hippocampus, precuneus, prefrontal cortex and parietal regions were not replicated. Our findings emphasize the importance of large sample imaging studies and the need for meta-analyses like those performed by the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium.
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| 4. |
- Månsson, Kristoffer N T, et al.
(författare)
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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
- 2015
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Ingår i: Translational Psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- 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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