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Regression dynamic causal modeling (rDCM)

Authors: Stefan Frässle ([email protected]), Ekaterina I. Lomakina

Copyright (C) 2016-2018

Translational Neuromodeling Unit (TNU)

Institute for Biomedical Engineering

University of Zurich & ETH Zurich

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Purpose

The regression dynamic causal modeling (rDCM) toobox implements a novel variant of DCM for fMRI that enables extremely efficient inference on effective (i.e., directed) connectivity among brain regions. Due to its computational efficiency, inversion of large network models becomes feasible.

The accompanying technical papers about the toolbox concept and methodology can be found in Frässle et al., 2017 and Frässle et al., 2018.

Installation

Matlab

  1. Unzip the TAPAS archive in your folder of choice
  2. Open Matlab
  3. Add the rDCM Toolbox to your Matlab path
  4. Use the Manual and the tutorial script tapas_rdcm_tutorial() as starting points

Important Notes

Please note that rDCM is a method that is still in it's infancy and thus subject to various limiations. Due to these limitations, requirements of rDCM in terms of fMRI data quality (i.e., fast TR, high SNR) are high. For data that does not meet these conditions, the method might not give reliable results. It remains the responsibility of each user to ensure that his/her dataset fulfills these requirements. Please refer to the main toolbox references (see below) for more detailed explanations.

Contact/Support

We are very happy to provide support on how to use the rDCM Toolbox. However, due to time constraints, we might not provide a detailed answer to your request, but just some general pointers and templates. Before you contact us, please try the following:

  1. First, look at the FAQ (which is frequently extended) for answers to your questions.
  2. For new requests, we would like to ask you to submit them as issues on our github release page for TAPAS.

References

Main Toolbox References

  1. 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. doi:10.1016/j.neuroimage.2017.02.090
  2. 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. doi:10.1016/j.neuroimage.2018.05.058

Copying/License

The rDCM Toolbox is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program (see the file LICENSE). If not, see http://www.gnu.org/licenses/.

Acknowledgment

We would like to highlight and acknowledge that the rDCM toolbox uses some functions that were publised as part of the Statistical Parameteric Mapping (SPM) toolbox. The respective functions are marked with the prefix tapas_rdcm_spm_.