Ulrike Lueken1, Benjamin Straube2, Yunbo Yang2, Tim Hahn3, Katja Beesdo-Baum4, Hans-Ulrich Wittchen4, Carsten Konrad2, Andreas Ströhle5, André Wittmann5, Alexander L Gerlach6, Bettina Pfleiderer7, Volker Arolt8, Tilo Kircher2. 1. Department of Psychiatry, Psychosomatics, and Psychotherapy, University Hospital Würzburg, Füchsleinstr. 15, D-97080 Würzburg, Germany; Institute of Clinical Psychology and Psychotherapy, Department of Psychology, Technische Universität Dresden, Dresden, Germany. Electronic address: Lueken_U@ukw.de. 2. Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany. 3. Department of Cognitive Psychology II, Goethe-Universität Frankfurt, Frankfurt, Germany. 4. Institute of Clinical Psychology and Psychotherapy, Department of Psychology, Technische Universität Dresden, Dresden, Germany. 5. Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-University Medicine Berlin, Berlin, Germany. 6. Department of Psychology, University of Cologne, Cologne, Germany. 7. Department of Clinical Radiology, University Hospital Münster, Münster, Germany. 8. Department of Psychiatry and Psychotherapy, University Hospital Münster, Münster, Germany.
Abstract
BACKGROUND: Depression is frequent in panic disorder (PD); yet, little is known about its influence on the neural substrates of PD. Difficulties in fear inhibition during safety signal processing have been reported as a pathophysiological feature of PD that is attenuated by depression. We investigated the impact of comorbid depression in PD with agoraphobia (AG) on the neural correlates of fear conditioning and the potential of machine learning to predict comorbidity status on the individual patient level based on neural characteristics. METHODS: Fifty-nine PD/AG patients including 26 (44%) with a comorbid depressive disorder (PD/AG+DEP) underwent functional magnetic resonance imaging (fMRI). Comorbidity status was predicted using a random undersampling tree ensemble in a leave-one-out cross-validation framework. RESULTS: PD/AG-DEP patients showed altered neural activation during safety signal processing, while +DEP patients exhibited generally decreased dorsolateral prefrontal and insular activation. Comorbidity status was correctly predicted in 79% of patients (sensitivity: 73%; specificity: 85%) based on brain activation during fear conditioning (corrected for potential confounders: accuracy: 73%; sensitivity: 77%; specificity: 70%). LIMITATIONS: No primary depressed patients were available; only medication-free patients were included. Major depression and dysthymia were collapsed (power considerations). CONCLUSIONS: Neurofunctional activation during safety signal processing differed between patients with or without comorbid depression, a finding which may explain heterogeneous results across previous studies. These findings demonstrate the relevance of comorbidity when investigating neurofunctional substrates of anxiety disorders. Predicting individual comorbidity status may translate neurofunctional data into clinically relevant information which might aid in planning individualized treatment. The study was registered with the ISRCTN: ISRCTN80046034.
BACKGROUND:Depression is frequent in panic disorder (PD); yet, little is known about its influence on the neural substrates of PD. Difficulties in fear inhibition during safety signal processing have been reported as a pathophysiological feature of PD that is attenuated by depression. We investigated the impact of comorbid depression in PD with agoraphobia (AG) on the neural correlates of fear conditioning and the potential of machine learning to predict comorbidity status on the individual patient level based on neural characteristics. METHODS: Fifty-nine PD/AG patients including 26 (44%) with a comorbid depressive disorder (PD/AG+DEP) underwent functional magnetic resonance imaging (fMRI). Comorbidity status was predicted using a random undersampling tree ensemble in a leave-one-out cross-validation framework. RESULTS:PD/AG-DEP patients showed altered neural activation during safety signal processing, while +DEP patients exhibited generally decreased dorsolateral prefrontal and insular activation. Comorbidity status was correctly predicted in 79% of patients (sensitivity: 73%; specificity: 85%) based on brain activation during fear conditioning (corrected for potential confounders: accuracy: 73%; sensitivity: 77%; specificity: 70%). LIMITATIONS: No primary depressedpatients were available; only medication-free patients were included. Major depression and dysthymia were collapsed (power considerations). CONCLUSIONS: Neurofunctional activation during safety signal processing differed between patients with or without comorbid depression, a finding which may explain heterogeneous results across previous studies. These findings demonstrate the relevance of comorbidity when investigating neurofunctional substrates of anxiety disorders. Predicting individual comorbidity status may translate neurofunctional data into clinically relevant information which might aid in planning individualized treatment. The study was registered with the ISRCTN: ISRCTN80046034.
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
Authors: Gonzalo Salazar de Pablo; Erich Studerus; Julio Vaquerizo-Serrano; Jessica Irving; Ana Catalan; Dominic Oliver; Helen Baldwin; Andrea Danese; Seena Fazel; Ewout W Steyerberg; Daniel Stahl; Paolo Fusar-Poli Journal: Schizophr Bull Date: 2021-03-16 Impact factor: 9.306