title: Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information.
abstract: OBJECTIVE
  Although neuroimaging research has made substantial progress in identifying the large-scale neural substrate of anxiety disorders, its value for clinical application lags behind expectations. Machine-learning approaches have predictive potential for individual-patient prognostic purposes and might thus aid translational efforts in psychiatric research.
  
  
  OBJECTIVE
  To predict treatment response to cognitive behavioral therapy (CBT) on an individual-patient level based on functional magnetic resonance imaging data in patients with panic disorder with agoraphobia (PD/AG).
  
  
  METHODS
  We included 49 patients free of medication for at least 4 weeks and with a primary diagnosis of PD/AG in a longitudinal study performed at 8 clinical research institutes and outpatient centers across Germany. The functional magnetic resonance imaging study was conducted between July 2007 and March 2010.
  
  
  METHODS
  Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure.
  
  
  METHODS
  Treatment response was defined as exceeding a 50% reduction in Hamilton Anxiety Rating Scale scores. Blood oxygenation level-dependent signal was measured during a differential fear-conditioning task. Regional and whole-brain gaussian process classifiers using a nested leave-one-out cross-validation were used to predict the treatment response from data acquired before CBT.
  
  
  RESULTS
  Although no single brain region was predictive of treatment response, integrating regional classifiers based on data from the acquisition and the extinction phases of the fear-conditioning task for the whole brain yielded good predictive performance (accuracy, 82%; sensitivity, 92%; specificity, 72%; P < .001). Data from the acquisition phase enabled 73% correct individual-patient classifications (sensitivity, 80%; specificity, 67%; P < .001), whereas data from the extinction phase led to an accuracy of 74% (sensitivity, 64%; specificity, 83%; P < .001). Conservative reanalyses under consideration of potential confounders yielded nominally lower but comparable accuracy rates (acquisition phase, 70%; extinction phase, 71%; combined, 79%).
  
  
  CONCLUSIONS
  Predicting treatment response to CBT based on functional neuroimaging data in PD/AG is possible with high accuracy on an individual-patient level. This novel machine-learning approach brings personalized medicine within reach, directly supporting clinical decisions for the selection of treatment options, thus helping to improve response rates.
authors.affiliation: Department of Cognitive Psychology II, Goethe University Frankfurt am Main, Frankfurt am Main, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Hahn
authors.affiliation: Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany.
authors.forename: Tilo
authors.initials: T
authors.lastname: Kircher
authors.affiliation: Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Straube
authors.affiliation: Institute of Clinical Psychology and Psychotherapy, Department of Psychology, Technische Universität Dresden, Dresden, Germany4Neuroimaging Center, Department of Psychology, Technische Universität Dresden, Dresden, Germany.
authors.forename: Hans-Ulrich
authors.initials: HU
authors.lastname: Wittchen
authors.affiliation: Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany.
authors.forename: Carsten
authors.initials: C
authors.lastname: Konrad
authors.affiliation: Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-University Medicine Berlin, Berlin, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Ströhle
authors.affiliation: Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-University Medicine Berlin, Berlin, Germany.
authors.forename: André
authors.initials: A
authors.lastname: Wittmann
authors.affiliation: Department of Clinical Radiology, University Hospital Münster, Münster, Germany.
authors.forename: Bettina
authors.initials: B
authors.lastname: Pfleiderer
authors.affiliation: Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Würzburg, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Reif
authors.affiliation: Department of Psychiatry and Psychotherapy, University Hospital Münster, Münster, Germany.
authors.forename: Volker
authors.initials: V
authors.lastname: Arolt
authors.affiliation: Institute of Clinical Psychology and Psychotherapy, Department of Psychology, Technische Universität Dresden, Dresden, Germany4Neuroimaging Center, Department of Psychology, Technische Universität Dresden, Dresden, Germany.
authors.forename: Ulrike
authors.initials: U
authors.lastname: Lueken
mesh_terms: D000328:Adult; D000379:Agoraphobia / Q000175:diagnosis*; D015928:Cognitive Behavioral Therapy / Q000379:methods; D005260:Female; D059907:Functional Neuroimaging / Q000379:methods; D005858:Germany; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D016584:Panic Disorder / Q000175:diagnosis / Q000503:physiopathology / Q000523:psychology / Q000628:therapy; D018579:Patient Selection; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D012680:Sensitivity and Specificity