TAPAS - Translational Algorithms for Psychiatry-Advancing Science
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. Problems that can be addressed by tools in TAPAS presently include:
- 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.
Please use the Issues Forum pages on GitHub to submit any bug reports, discussions, feature requests and code improvements (you will need a GitHub account). This issue forum is searchable, so please have a look if your question has been asked before.
For older versions or more general questions, please also have a look at our now deprecated E-Mail List TAPAS@sympa.ethz.ch, which has a searchable Archive.
We are in a continuous process of improving our software, hence it would be helpful for us to know about any bugs that you encounter in the current version. For ease of management, please mention the specific operation/toolbox, where the error was found, in the subject line, along with details/snapshots of the error message.
The latest documentation of TAPAS can be found in the README downloaded with the software, and on the GitHub Wiki of the TAPAS GitHub page. In general, each toolbox comes with their own documentation as Wiki, PDF, matlab tutorials etc.
Documentation for older versions (<= 18.104.22.168) is provided on the Documentation section of this page.
Sample code and datasets can be found in the Data section.
The publications associated with the software are listed in the Publications section.