Theranostic markers for personalized therapy of spider phobia: Methods of a bicentric external cross-validation machine learning approach

Hanna Schwarzmeier, Elisabeth Johanna Leehr, Joscha Böhnlein, Fabian Reinhard Seeger, Kati Roesmann, Bettina Gathmann, Martin J Herrmann, Niklas Siminski, Markus Junghöfer, Thomas Straube, Dominik Grotegerd, Udo Dannlowski, Hanna Schwarzmeier, Elisabeth Johanna Leehr, Joscha Böhnlein, Fabian Reinhard Seeger, Kati Roesmann, Bettina Gathmann, Martin J Herrmann, Niklas Siminski, Markus Junghöfer, Thomas Straube, Dominik Grotegerd, Udo Dannlowski

Abstract

Objectives: Embedded in the Collaborative Research Center "Fear, Anxiety, Anxiety Disorders" (CRC-TRR58), this bicentric clinical study aims at identifying biobehavioral markers of treatment (non-)response by applying machine learning methodology with an external cross-validation protocol. We hypothesize that a priori prediction of treatment (non-)response is possible in a second, independent sample based on multimodal markers.

Methods: One-session virtual reality exposure treatment (VRET) with patients with spider phobia was conducted on two sites. Clinical, neuroimaging, and genetic data were assessed at baseline, post-treatment and after 6 months. The primary and secondary outcomes defining treatment response are as follows: 30% reduction regarding the individual score in the Spider Phobia Questionnaire and 50% reduction regarding the individual distance in the behavioral avoidance test.

Results: N = 204 patients have been included (n = 100 in Würzburg, n = 104 in Münster). Sample characteristics for both sites are comparable.

Discussion: This study will offer cross-validated theranostic markers for predicting the individual success of exposure-based therapy. Findings will support clinical decision-making on personalized therapy, bridge the gap between basic and clinical research, and bring stratified therapy into reach. The study is registered at ClinicalTrials.gov (ID: NCT03208400).

Keywords: machine learning; spider phobia; theranostic markers.

Conflict of interest statement

The following authors report no conflicts of interest concerning the content of this paper: H. Schwarzmeier, E. J. Leehr, J. Böhnlein, F. R. Seeger, K. Roesmann, B. Gathmann, M. J. Herrmann, N. Siminski, M. Junghöfer, T. Straube, D. Grotegerd, and U. Dannlowski.

© 2019 The Authors. International Journal of Methods in Psychiatric Research Published by John Wiley & Sons Ltd.

Figures

Figure 1
Figure 1
Schematic representation of the study protocol. Pre‐treatment assessment encompasses a baseline assessment to gather clinical and psychometric data, and a blood sample for genetic and epigenetic analyses is drawn. The behavioral avoidance test (BAT) serves as a quantification of avoidance behavior. A separate MRI session (structural and functional) completes the pre‐treatment assessments, which will be used for prediction of treatment outcome. At the Münster site, baseline measurements are accompanied by an additional MEG measurement (prior to fMRI measurement); at both sites, there takes an additional fMRI measurement place, which is not part of the prediction track. Treatment itself consists of a one‐session massed exposure therapy in virtual reality. Approx. 1 week after treatment, clinical, psychometric, behavioral, and epigenetic data are collected again, followed by an MEG‐ and fMRI measurement at the Münster site and optional fMRI measurement at Würzburg. Baseline measurements will be repeated at the follow‐up assessment 6 months after the post‐treatment assessment
Figure 2
Figure 2
In vivo behavioral avoidance test (BAT). A bird spider placed in a plastic box with a closed lid is used to assess generalization of treatment effects to a real spider. The box is placed on a slide 3 m away from the patient who then slowly drags the box with the spider toward himself as close as possible using a crank. The final distance between patient and spider (i.e., quantification of avoidance behavior) serves as the dependent variable. Patients are asked to rate their fear, observation of behavior is noted using a standardized scheme, and electrodermal activity (EDA) is recorded alongside. All outcomes are assessed pre‐treatment, post‐treatment, and after 6‐month follow‐up
Figure 3
Figure 3
Schematic representation of the machine learning procedure. The pattern classifier is trained at site A to generate a discriminating pattern maximally distinguishing responders from nonresponders based on data from the two groups. The classifier is then tested at site B by classifying a new subject as responder or nonresponder

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