Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study.

Journal: Depression and anxiety
PMID:

Abstract

Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with clinical routine data have only resulted in moderate prediction accuracies. Neuroimaging data showed promise to predict treatment outcome, but previous prediction attempts have been exploratory and reported small clinical sample sizes. Herein, we aimed to examine the incremental predictive value of neuroimaging data in contrast to clinical and demographic data alone (for which results were previously published), using a two-level multimodal ensemble machine-learning strategy. We used pretreatment structural and task-based fMRI data to predict virtual reality exposure therapy outcome in a bicentric sample of = 190 patients with spider phobia. First, eight 1st-level random forest classifications were conducted using separate data modalities (clinical questionnaire scores and sociodemographic data, cortical thickness and gray matter volumes, functional activation, connectivity, connectivity-derived graph metrics, and BOLD signal variance). Then, the resulting predictions were used to train a 2nd-level classifier that produced a final prediction. No 1st-level or 2nd-level classifier performed above chance level except BOLD signal variance, which showed potential as a contributor to higher-level prediction from multiple regions across the brain (1st-level balanced accuracy = 0.63). Overall, neuroimaging data did not provide any incremental accuracy for treatment outcome prediction in patients with spider phobia with respect to clinical and sociodemographic data alone. Thus, we advise caution in the interpretation of prediction performances from small-scale, single-site patient samples. Larger multimodal datasets are needed to further investigate individual-level neuroimaging predictors of therapy response in anxiety disorders.

Authors

  • Alice V Chavanne
    Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
  • Charlotte Meinke
    Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
  • Till Langhammer
    Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
  • Kati Roesmann
    Institute of Clinical Psychology and Psychotherapy, University of Siegen, Germany.
  • Joscha Boehnlein
    Institute for Translational Psychiatry, University of Münster, Germany.
  • Bettina Gathmann
    Institute of Medical Psychology and Systems Neuroscience, University of Münster, Germany.
  • Martin J Herrmann
    Department of Psychiatry, Psychosomatics, and Psychotherapy, Center for Mental Health, University Hospital of Würzburg, Germany.
  • Markus Junghoefer
    Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany.
  • Luisa Klahn
    Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
  • Hanna Schwarzmeier
    Department of Psychiatry, Psychosomatics, and Psychotherapy, Center for Mental Health, University Hospital of Würzburg, Germany.
  • Fabian R Seeger
    Department of Psychiatry, Psychosomatics, and Psychotherapy, Center for Mental Health, University Hospital of Würzburg, Germany.
  • Niklas Siminski
    Department of Psychiatry, Psychosomatics, and Psychotherapy, Center for Mental Health, University Hospital of Würzburg, Germany.
  • Thomas Straube
    Institute for Translational Psychiatry, University of Münster, Germany.
  • Udo Dannlowski
    Department of Psychiatry and Psychotherapy, University of Münster, Germany.
  • Ulrike Lueken
    Department of Psychology, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Chemnitzer Straße 46, 01187, Dresden, Germany, ulrike.lueken@tu-dresden.de.
  • Elisabeth J Leehr
    Institute for Translational Psychiatry, University of Münster, Germany.
  • Kevin Hilbert