PREACT

This subproject has a service and a science component, the first will provide an infrastructural basis regarding hard- and software for data and model exchange as well as high-performance computing, and the other will drive genuine scientific model development for prediction of treatment response in internalizing mental disorders. Within the service component, we will 1) securely store and organize retrospective and prospective data in a way that all participating Principal Investigators have access to and 2) provide hardware for processing complex biomedical data with state-of-the-art machine learning algorithms including deep learning. Within the science component, we will develop a methods toolbox relying on predictive analytics and machine learning algorithms for identifying (non-) responders on a single-subject level. This will be done separately for the different disorders as well as across disorders to investigate if treatment response operates on an individual or cross-diagnostic scale. We will systematically compare the predictive value of different data domains, such as clinical data, ambulatory assessment, electrophysiological data, and neuroimaging data, and will evaluate different methods for data fusion. Specifically, we will experiment with sophisticated machine learning techniques such as convolutional neural networks for analyzing neuroimaging data and will employ transfer learning for dealing with comparatively low sample sizes.