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.
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.
Authors: Ronald C Kessler; Patricia Berglund; Olga Demler; Robert Jin; Kathleen R Merikangas; Ellen E Walters Journal: Arch Gen Psychiatry Date: 2005-06
Authors: Carlos Blanco; Richard G Heimberg; Franklin R Schneier; David M Fresco; Henian Chen; Cynthia L Turk; Donna Vermes; Brigette A Erwin; Andrew B Schmidt; Harlan R Juster; Raphael Campeas; Michael R Liebowitz Journal: Arch Gen Psychiatry Date: 2010-03
Authors: Krzysztof Gorgolewski; Christopher D Burns; Cindee Madison; Dav Clark; Yaroslav O Halchenko; Michael L Waskom; Satrajit S Ghosh Journal: Front Neuroinform Date: 2011-08-22 Impact factor: 4.081
Authors: Jonathan R T Davidson; Edna B Foa; Jonathan D Huppert; Francis J Keefe; Martin E Franklin; Jill S Compton; Ning Zhao; Kathryn M Connor; Thomas R Lynch; Kishore M Gadde Journal: Arch Gen Psychiatry Date: 2004-10
Authors: Stefan G Hofmann; Alicia E Meuret; Jasper A J Smits; Naomi M Simon; Mark H Pollack; Katherine Eisenmenger; Michael Shiekh; Michael W Otto Journal: Arch Gen Psychiatry Date: 2006-03
Authors: Cynthia H Y Fu; Steve C R Williams; Michael J Brammer; John Suckling; Jieun Kim; Anthony J Cleare; Nicholas D Walsh; Martina T Mitterschiffthaler; Chris M Andrew; Emilio Merlo Pich; Edward T Bullmore Journal: Am J Psychiatry Date: 2007-04 Impact factor: 18.112
Authors: Autumn Kujawa; Katie L Burkhouse; Shannon R Karich; Kate D Fitzgerald; Christopher S Monk; K Luan Phan Journal: J Child Adolesc Psychopharmacol Date: 2019-05-07 Impact factor: 2.576
Authors: V R Steiger; A B Brühl; S Weidt; A Delsignore; M Rufer; L Jäncke; U Herwig; J Hänggi Journal: Mol Psychiatry Date: 2016-12-06 Impact factor: 15.992
Authors: Andrea L Gold; Elizabeth R Steuber; Lauren K White; Jennifer Pacheco; Jessica F Sachs; David Pagliaccio; Erin Berman; Ellen Leibenluft; Daniel S Pine Journal: Neuropsychopharmacology Date: 2017-04-24 Impact factor: 7.853