fMRI may predict treatment response in social anxiety disorder

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anxious woman - 273.92 Kb

Brain activation measured by neuroimaging provided a biomarker which predicted response to behavioral therapy in patients diagnosed with social anxiety disorder, according to a study published online Sept. 3 in Archives of General Psychiatry.

Although social anxiety disorder is a common psychiatric condition in the U.S., both primary treatment options—cognitive behavioral therapy (CBT) and pharmacotherapy—only are moderately effective. No reliable predictor of treatment response has been identified.

Neuroimaging might indicate patient variation and better predict treatment outcome, according to Oliver Doehrmann, PhD, of the department of brain and cognitive sciences at Massachusetts General Hospital in Boston, and colleagues.

To test this hypothesis, Doehrmann and colleagues enrolled 39 patients with social anxiety disorder in a study. Patients underwent a pretreatment functional MRI (fMRI) scan, during which they viewed faces or scenes with neutral or negative emotional valence.

They participated in 12 weekly CBT sessions, and measures of social anxiety prior to and after therapy were correlated with the pretreatment brain activation data.

“Pretreatment brain responses for angry vs. neutral faces in two occipitotemporal brain regions were significantly and positively associated with CBT outcome. The neuroimaging measures in combination with pretreatment severity scores predicted CBT outcome significantly better, accounting for about 40 percent of the variance in treatment response, than predictions based in LSAS [Liebowitz Social Anxiety Scale score]-pre alone, which accounted for about 12 percent of the variance in treatment response,” wrote Doehrmann and colleagues.  

The researchers found patients’ functional brain responses to faces, rather than scenes, predicted treatment response, which is consistent with the social nature of social anxiety disorder.

“Neuroimaging may offer an evidence-based path toward selection of optimal treatments, such that neuromarkers could selectively identify which patients are most likely to benefit from which treatment option,” concluded Doerhmann et al.