Workpackage 3: Neuroimaging predictors

Workpackage leader

Stefan Borgwardt

Stefan Borwardt University of Basel



Objectives of the Workpackage

Workpackage 3 (WP 3) has three main objectives. The first one is to examine study participants with a multimodal MRI protocol at baseline and after follow up. The second objective is to develop, optimise and validateunimodal and multi-modal MRI biomarkers of clinical endpoints by analysing baseline and longitudinal MRI descriptors with undefinedWP 2 machine learning methods. The third objective is perform a multi-centre calibration study to enable quality control procedures for cross-centre data acquisition based and the implementation of quality of assessment protocol.

Description of the tasks

WP 3 is the responsible for the development of the multimodal MRI protocol and the cross-centre calibration study. We will standardise imaging measurements in a cross-centre MRI calibration study which will be performed at all partners of PRONIA at the Universities of undefinedMunich, undefinedBasel, undefinedBirmingham, undefinedUdine, undefinedCologneundefinedTurku and undefinedMilan. Furthermore, an  MRI data quality assurance protocol will be implemented for collecting and pre-processing of  imaging data. Neurocognitive data acquisition will be collected in all centres at baseline and after 9 months. Then, we will use the MRI data acquired at baseline along with the machine learning systems produced by undefinedWP 2 to predict clinical and functional outcomes in our help-seeking cohorts. Finally, we will evaluate whether the machine learning-based analysis of longitudinal imaging data allow for a refinement of prognostic models compared to predictors that only make use of the baseline data.


WP 2: Surrogate Marker <<

>> WP 4: Neurocognition


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