Kevin Hilbert, Joscha Böhnlein, Charlotte Meinke, Alice Chavanne, Till Langhammer, Lara Stumpe, Nils Winter, Ramona Leenings, Dirk Adolph, Volker Arolt, Sophie Bischoff, Jan Cwik, Jürgen Deckert, Katharina Domschke, Thomas Fydrich, Bettina Gathmann, Alfons Hamm, Ingmar Heinig, Martin Herrmann, Maike Hollandt, Jürgen Hoyer, Markus Junghöfer, Tilo Kircher, Katja Koelkebeck, Martin Lotze, Jürgen Margraf, Jennifer Mumm, Peter Neudeck, Paul Pauli, Andre Pittig, Jens Plag, Jan Richter, Isabelle Ridderbusch, Winfried Rief, Silvia Schneider, Hanna Schwarzmeier, Fabian Seeger, Niklas Siminski, Benjamin Benjamin, Thomas Straube, Andreas Ströhle, Hans-Ulrich Wittchen, Adrian Wroblewski, Yunbo Yang, Kati Roesmann, Elisabeth Leehr, Udo Dannlowski and Ulrike Lueken

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: a machine learning study in two large multi-site samples in anxiety disorders


Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.