Tim Hahn1, Tilo Kircher2, Benjamin Straube2, Hans-Ulrich Wittchen3, Carsten Konrad2, Andreas Ströhle4, André Wittmann4, Bettina Pfleiderer5, Andreas Reif6, Volker Arolt7, Ulrike Lueken3. 1. Department of Cognitive Psychology II, Goethe University Frankfurt am Main, Frankfurt am Main, Germany. 2. Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany. 3. Institute of Clinical Psychology and Psychotherapy, Department of Psychology, Technische Universität Dresden, Dresden, Germany4Neuroimaging Center, Department of Psychology, Technische Universität Dresden, Dresden, Germany. 4. Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-University Medicine Berlin, Berlin, Germany. 5. Department of Clinical Radiology, University Hospital Münster, Münster, Germany. 6. Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of Würzburg, Würzburg, Germany. 7. Department of Psychiatry and Psychotherapy, University Hospital Münster, Münster, Germany.
Abstract
IMPORTANCE: 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). DESIGN, SETTING, AND PARTICIPANTS: 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. INTERVENTIONS: Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure. MAIN OUTCOMES AND MEASURES: 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 AND RELEVANCE: 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.
IMPORTANCE: 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). DESIGN, SETTING, AND PARTICIPANTS: 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. INTERVENTIONS: Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure. MAIN OUTCOMES AND MEASURES: 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 AND RELEVANCE: 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: Ingmar Heinig; Andre Pittig; Jan Richter; Katrin Hummel; Isabel Alt; Kristina Dickhöver; Jennifer Gamer; Maike Hollandt; Katja Koelkebeck; Anne Maenz; Sophia Tennie; Christina Totzeck; Yunbo Yang; Volker Arolt; Jürgen Deckert; Katharina Domschke; Thomas Fydrich; Alfons Hamm; Jürgen Hoyer; Tilo Kircher; Ulrike Lueken; Jürgen Margraf; Peter Neudeck; Paul Pauli; Winfried Rief; Silvia Schneider; Benjamin Straube; Andreas Ströhle; Hans-Ulrich Wittchen Journal: Int J Methods Psychiatr Res Date: 2017-03-21 Impact factor: 4.035
Authors: Tilo Kircher; Markus Wöhr; Igor Nenadic; Rainer Schwarting; Gerhard Schratt; Judith Alferink; Carsten Culmsee; Holger Garn; Tim Hahn; Bertram Müller-Myhsok; Astrid Dempfle; Maik Hahmann; Andreas Jansen; Petra Pfefferle; Harald Renz; Marcella Rietschel; Stephanie H Witt; Markus Nöthen; Axel Krug; Udo Dannlowski Journal: Eur Arch Psychiatry Clin Neurosci Date: 2018-09-28 Impact factor: 5.270