A collection of software tools developed to support clinical neuromodeling, particularly computational psychiatry, computational neurology, and computational psychosomatics.
Lars Kasper, Steffen Bollmann, Andreea O. Diaconescu, Chloe Hutton, Jakob Heinzle, Sandra Iglesias, Tobias U. Hauser, Miriam Sebold, Zina-Mary Manjaly, Klaas P. Pruessmann, Klaas E. Stephan, Kay H.Brodersen9, Christoph Mathys10
Stable release v188.8.131.52, GNU General Public License (GPL, Version 3)
TAPAS is a collection of algorithms and software tools that are developed by the Translational Neuromodeling Unit (TNU, Zurich) and collaborators. These tools have been developed to support translational neuroscience, particularly concerning the application of neuroimaging and computational modeling to research questions in psychiatry, neurology and psychosomatics. TAPAS is written in MATLAB and presently is comprises the next tools:
- HGF: Hierarchical Gaussian Filtering (Bayesian inference on computational processes from observed behaviour).
- MICP: Mixed-effects inference on classification performance.
- MPDCM: Massively parallel DCM (efficient integration of dynamical systems, i.e., DCMs, using massive parallelization).
- PhysIO: Physiological noise correction of fMRI data.
- SEM: SERIA Model for Eye Movements (saccades and anti-saccades) and Reaction Times
- VBLM: Variational Bayes for linear regression models.
Kasper, L., Bollmann, S., Diaconescu, A.O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T.U., Sebold, M., Manjaly, Z.-M., Pruessmann, K.P., Stephan, K.E., 2017. The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data. Journal of Neuroscience Methods 276, 56–72 [PhysIO]
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in Human Neuroscience, 8:825. [HGF]
Brodersen KH, Mathys C, Chumbley JR, Daunizeau J, Ong CS, Buhmann JM,Stephan KE (2012) Bayesian mixed-effects inference on classification performance in hierarchical datasets. Journal of Machine Learning Research 13: 3133-3176. [MICP]
1Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland
2Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland
3Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, 4072, Australia
4Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK
5Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1 B 5EH, UK
6Department of Psychiatry and Psychotherapy, Campus Mitte, Charité Universitätsmedizin, 10117 Berlin, Germany
7Department of Neurology, Schulthess Clinic, 8008 Zurich, Switzerland
8Max Planck Institute for Metabolism Research, 50931 Cologne, Germany
10Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom