Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging

Oliver Doehrmann, Satrajit S Ghosh, Frida E Polli, Gretchen O Reynolds, Franziska Horn, Anisha Keshavan, Christina Triantafyllou, Zeynep M Saygin, Susan Whitfield-Gabrieli, Stefan G Hofmann, Mark Pollack, John D Gabrieli, Oliver Doehrmann, Satrajit S Ghosh, Frida E Polli, Gretchen O Reynolds, Franziska Horn, Anisha Keshavan, Christina Triantafyllou, Zeynep M Saygin, Susan Whitfield-Gabrieli, Stefan G Hofmann, Mark Pollack, John D Gabrieli

Abstract

Context: Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD.

Objective: To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT).

Design: Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli.

Setting: Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology.

Patients: Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD.

Interventions: Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT.

Main outcome measures: Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure.

Results: Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline.

Conclusions: The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient.

Figures

Figure 1
Figure 1
The functional magnetic resonance imaging task. A, Examples of stimuli for each category and color code. B and C, Visualization of stimulation blocks that cycled through all 5 experimental conditions, block timing within a block, and visualization of stimulus (S) timing within a block.
Figure 2
Figure 2
Relation and prediction using Liebowitz Social Anxiety Scale (LSAS) scores. A, Relation of initial social anxiety disorder severity score (LSAS-pre) to treatment effectiveness (change in LSAS score [LSAS-change]). D indicates D-cycloserine; and P, placebo. Left and bottom panels of part A: box plots of LSAS-change and LSAS-pre for each group. B, Relation between predicted LSAS-change from cross-validated model and actual LSAS-change using LSAS-pre and group information only.
Figure 3
Figure 3
Two right-hemisphere occipitotemporal regions in which initial activation for angry vs neutral faces significantly predicted treatment effectiveness. A and B, t Values and locations of clusters showing positive relations with change in Liebowitz Social Anxiety Scale scores (LSAS-change). C, Cluster activation means of each participant vs LSAS-change. Right panel of parts C and D: box plots of cluster means grouped by treatment group (D indicates D-cycloserine; P, placebo) showing similar results in both groups. D, Cluster activation means vs initial LSAS scores (LSAS-pre) showing no significant relation. Color bar represents t values.
Figure 4
Figure 4
Results from the prediction model created via nested cross-validation using Liebowitz Social Anxiety Scale (LSAS) scores, group information, and brain imaging data. A, Relation between predicted change in LSAS score (LSAS-change) using this model and actual LSAS-change. B, Approximate permutation test results: null distribution (gray), actual value (red). C, Voxels selected in at least 1 fold of the cross-validation. Color bar indicates the number of folds in which a particular voxel was selected.

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