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) at Zurich. These tools have been developed to support translational neuroscience, particularly concerning the application of neuroimaging and computational modeling to research questions in psychiatry and neurology. Problems that can be addressed by tools in TAPAS presently include:

  • PhysIO: Physiological noise correction of fMRI data.
  • HGF: Hierarchical Gaussian Filtering (Bayesian inference on computational processes from observed behaviour).
  • MICP: Mixed-effects inference on classification performance.
  • VBLM: Variational Bayes for linear regression models.
  • mpdcm: Massively parallel DCM (efficient integration of dynamical systems in DCM).

TAPAS is written in MATLAB and distributed as open source code under the GNU General Public License (GPL, Version 3).

Download

The software can be downloaded after completing a  registration form. The information will be used for  generating statistics regarding the usage of this software.  Submitting the form will take you to the page where the latest version can be downloaded. Older versions of TAPAS can be downloaded here.

Mailing List

For discussion/queries/suggestions, please subscribe to the mailing list at TAPAS@sympa.ethz.ch.  Users without an ETH ID/password can register with their email-ID by clicking the “Subscribe” link on the left side of the mailing list page.

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. The same mailing list can be used for reporting any bugs in the software.  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.

Documentation

Detailed description of the software can be found in the Documentation section.

Data

Sample code and datasets can be found in the Data section.

Publications

The publications associated with the software are listed in the Publications section.