Multimodal phenotypic classification of generalized anxiety and panic using structural MRI data and psychosocial factors: machine learning results from the German National Cohort (NAKO) study.
Authors
Affiliations (41)
Affiliations (41)
- Department of Psychiatry, Psychosomatic and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany. [email protected].
- Department of Psychology III, University of Würzburg, Würzburg, Germany. [email protected].
- Department of Psychiatry, Psychosomatic and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany.
- Department of Psychology I, University of Würzburg, Würzburg, Germany.
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany.
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- German Center for Mental Health (DZPG), Partner Site Mannheim, Heidelberg - Ulm, Germany.
- Institute for Anatomy I, Medical Faculty & Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany.
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
- Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
- University Hospital Essen, Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), Essen, Germany.
- University of Applied Sciences and Arts Dortmund (FH Dortmund), Department of Computer Science, Dortmund, Germany.
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany.
- Institute of Medical Epidemiology, Biometrics and Informatics, Medical Faculty of the Martin-Luther University Halle-Wittenberg, Halle, Wittenberg, Germany.
- German Center for Mental Health (DZPG), Site Halle-Jena-Magdeburg, Halle (Saale), Germany.
- Center for Intervention and Research on adaptive and maladaptive brain - Circuits underlying mental health (C-I-R-C), Halle-Jena-Magdeburg, Halle (Saale), Germany.
- Institute of Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
- State Institute of Health I, Bavarian Health and Food Safety Authority, Erlangen, Germany.
- Department for Epidemiology, Helmholtz Centre for Infection Research (HZI), Brunswick, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Heidelberg Institute of Global Health (HIGH), Medical Faculty and University Hospital, Heidelberg University, Heidelberg, Germany.
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA.
- Africa Health Research Institute (AHRI), Somkhele and Durban, Durban, South Africa.
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany.
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC) Biobank Technology Platform, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Institute for Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany.
- Department of Neurology, medbo District Hospital and University Hospital of Regensburg, Regensburg, Germany.
- Leipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany.
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany.
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
- German Centre for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.
- Max Planck Institute of Psychiatry, Munich, Germany.
Abstract
Anxiety disorders are common and impairing mental health conditions. Using data from 26,378 adults in the German National Cohort Study (NAKO), we investigated psychosocial and neuroimaging predictors of generalized anxiety disorder (GAD) symptoms and panic attacks. We conducted machine-learning analyses of 246 regions of interest from whole-brain imaging data in combination with psychosocial variables. Neuroimaging data alone showed suboptimal classification performance, whereas psychosocial variables alone - particularly depressive symptoms, stress, and childhood trauma - achieved the strongest discrimination for GAD symptoms and panic attacks. Adding neuroimaging features to psychosocial models modestly improved unbalanced accuracy and specificity by reducing false-positive classifications, indicating a conditional and complementary contribution of neuroanatomical information. Within the multivariate models, features from anxiety-related circuits, including the amygdala and superior parietal lobule, were consistently selected. Overall, these findings suggest that psychosocial factors dominate classification of anxiety outcomes, while structural MRI measures may provide complementary information within multimodal frameworks aimed at refining classification and supporting the development of individualized risk profiles to guide tailored therapeutic and preventive strategies.