• The European Union has awarded PRONIA with 6 Million Euro within the 7th Framework Programme.

    The European Union has awarded PRONIA with 6 Million Euro within the 7th Framework Programme.

  • 1700 study participants are being recruited for PRONIA in six European centres and one in Australia.

    1700 study participants are being recruited for PRONIA in six European centres and one in Australia.

  • Psychoses typically commence in the most productive and critical period of life – late adolescence and early adulthood.

    Psychoses typically commence in the most productive and critical period of life – late adolescence and early adulthood.

PRONIA

Workpackage 4: Neurocognitive Predictors

Workpackage leader

Paolo Brambilla

Paolo Brambilla

University of Udine

Objectives of the Workpackage

Workpackage 4 (WP 4) has two main objectives; the first objective is to examine study participants with a cross-domain neuropsychological test battery at baseline and after 9 months of observation. The second objective is to develop, optimise and cross-validate neurocognitive predictors of clinical endpoints by analysing neurocognitive data with undefinedWP 2 machine learning methods.

Description of the tasks

WP 4 is the responsible for the development of the neuropsychological test battery. We will standardise measurements of cross-domain neurocognitive performance by means of a computerized neuropsychological assessment tool which will be distributed to the other partners of PRONIA at the Universities of undefinedMunich, undefinedBirmingham, undefinedBasel, undefinedCologne, and undefinedTurku. Specifically, a fully computerised neuropsychological assessment tool will be implemented for collecting and analysing neurocognitive data, available in English, Finnish, German and Italian. Neurocognitive data acquisition will be collected in all centres at baseline and after 9 months. Beside the standardisation of neuropsychological testing, which will be facilitated by our computerised assessment battery, we will implement procedures to ensure a high quality and reliability of the neuropsychological evaluation throughout the funding period by supplying regular training to clinical neuropsychologists. Then, we will use the neurocognitive 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 neurocognitive information allows for a refinement of prognostic models compared to predictors that only make use of the baseline data

 

WP 3: Neuroimaging <<

>> WP 5: Genomics

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