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  1. Roles/

Service project

The Q projects integrate a recruitment platform, a data management platform, an integrated research training group as well as the coordination service project. Following are the key issues of the central projects:

  1. Coordinated data assessments across different patient groups (transdiagnostic approach) in all centers.

  2. A stringent longitudinal design with repeated measurements of aggressive behaviors and biomarkers across different age groups (developmental approach) starting in child/adolescent age extending into adult psychiatric patients.

By facilitating the understanding of transdiagnostic multimodal and developmental aspects (see objectives), the longitudinal focus help to identify aggression biosignatures and predict their stability in AMD.

Projects

Q01: Recruitment and biotyping transdiagnostic risk mechanisms for aggressive behaviors in mental disorders across the life span

Andreas Reif Annika Maas Antonia Fritsch Catherine Barnes-Scheufler Celina Müller Christiane Licht Christina Neczewicz Christine Margarete Freitag Dario Müller Henry Schirok Ina Kuschel Jaqueline Scharf Kerstin Konrad Philippa Hüpen Robert Krämer Sabine Herpertz Thilo Kellermann Thomas Frodl Tobias Banaschewski Ute Habel Service project RWTH Aachen Goethe University Frankfurt Heidelberg University Central Institute of Mental Health Biological Psychiatry Clinical Psychiatry, Psychotherapy, Child and Adolescent Psychiatry

The central recruitment platform for collecting and curating a longitudinal dataset for studying individual aggression dynamics related to the neural, cognitive-emotional, neurobiological, psychopathological and environmental factors in patient groups.

Q02: Data management for computational modelling

Alessio Giacomel Arezoo Taebi Christine Ecker Gabriele Ende Klaus Mathiak Michael Hanke Richard Nkrumah Stephan Heunis Traute Demirakca Wiebke Hennig Service project RWTH Aachen Goethe University Frankfurt Research Center Jülich (FZJ) Central Institute of Mental Health

Data management and training platform. A decentralized data management infrastructure will help focus on developmental and therapeutic longitudinal data, training all participating researchers in the necessary skills for future use. This strategy will lay the foundations for further data-driven computational modelling projects in the next funding period. This is a distributed project, with representatives at all main TRR379 sites. As a key software solution, this project employs DataLad. DataLad is a data management software designed to facilitate the organization, sharing, and reproducibility of scientific datasets. It integrates version control with data handling, allowing researchers to track changes, collaborate efficiently, and ensure the accessibility and integrity of their data. By leveraging tools like Git and Git- annex, DataLad provides a streamlined way to manage large datasets, making it particularly valuable in fields like neuroimaging and bioinformatics.