Stefan Frässle studied Physics at the University of Konstanz and the Philipps-University Marburg. He obtained his PhD at the Laboratory for Multimodal Neuroimaging (Philipps-University Marburg) under the supervision of Andreas Jansen, including guest visits in the Vision Science Laboratory at the Harvard University and the Translational Neuromodeling Unit (TNU) at the University of Zurich and ETH Zurich. During his graduate training, Stefan developed a dynamic causal modeling (DCM) framework for studying hemispheric lateralization of the human brain. He also investigated the stability of DCM in terms of its test-retest reliability, which denotes a necessary prerequisite for the use of such models in a clinical setting. In March 2016, Stefan has joined the TNU as an ETH Zurich Postdoctoral Fellow under the supervision of Klaas Enno Stephan and is now a Senior Research Fellow. At the TNU, Stefan develops new variants of DCM which advance the utility of computational models for clinical diagnosis. Specifically, Stefan develops a large-scale DCM approach (regression DCM) that enables studying effective connectivity at the whole-brain level. To demonstrate the validity and utility of the approach for clinical applications, regression DCM will be challenged with real-world data from pharmacological and patient studies.
ExInit PhD Scholarship (Philipps-University Marburg): 2012-2015
ETH Zurich Postdoctoral Fellowship: 2016-2018
UZH Forschungskredit Postdoc: 2018-2019
CRPP Pain (University of Zurich): Since 2021
Frässle, S., Aponte, E.A., Bollmann, S., Brodersen, K.H., Do, C.T., Harrison, O.K., Harrison, S.J., Heinzle, J, Iglesias, S., Kasper, L., Lomakina, E., Mathys, C., Müller-Schrader, M., Pereira, I., Petzschner, F.H., Raman, S., Schöbi, D., Toussaint, B., Weber, L.A., Yao, Y., Stephan, K.E. (2021) TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry. Front Psychiatry. in press.
Frässle, S., Marquand, A.F., Schmaal, L., Dinga, R., Veltman, D.J., van der Wee, N.J.A., van Tol, M.-J., Schöbi, D., Penninx, B.W.J.H., Stephan, K.E. (2020). Predicting individual clinical trajectories of depression with generative embedding. NeuroImage:Clinical 26:102213.
Frässle, S.*, Lomakina E.I.*, Kasper L., Manjaly Z.M., Leff A., Pruessmann K.P., Buhmann J.M., Stephan K.E. (2018). A generative model of whole-brain effective connectivity. Neuroimage 179:505-529.
Frässle, S., Yao, Y., Schöbi, D., Aponte, E.A., Heinzle, J. & Stephan, K.E., 2018. Generative models for clinical applications in computational psychiatry. WIREs Cognitive Science 9(3):e1460. doi: 10.1002/wcs.1460.
Frässle, S.*, Lomakina E.I.*, Razi, A., Friston, K.J., Buhmann, J.M. & Stephan, K.E., 2017. Regression DCM for fMRI. Neuroimage 155:406-421.
Frässle, S., Stephan, K.E., Friston, K.J., Steup, M., Krach, S., Paulus, F.M. & Jansen, A., 2015. Test-retest reliability of dynamic causal modeling for fMRI. Neuroimage 117:56-66.
Regression dynamic causal modeling (rDCM) toolbox as part of the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software suite.
- User-friendly and well-curated MATLAB toolbox implementing the rDCM approach
- Active user base with >3000 downloads of TAPAS in the last two years alone
- TAPAS forum where users can ask questions and annual tutorials on rDCM