Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review.

ABSTRACT

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Individuals with major depressive disorder (MDD) vary in their response to available treatments, rendering treatment selection a challenging task. In this paper, we review studies applying computational models for predicting treatment response in MDD based on measures of brain activity. We discuss methodological differences across studies, focusing on how they incorporate existing knowledge about MDD and how that affects interpretability of model predictions. In this context, we argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach for treatment response prediction. Finally, we identify several other important limitations that are holding back the translation of these tools into clinical practice.