Skip to content

Latest commit

 

History

History
executable file
·
137 lines (91 loc) · 5.82 KB

README.md

File metadata and controls

executable file
·
137 lines (91 loc) · 5.82 KB
Raw

rDCM Logo

rDCM - regression Dynamic Causal Modeling.

This ReadMe file provides the relevant information on the regression dynamic causal modeling (rDCM) toolbox.

General information

  • Authors: Stefan Frässle ([email protected]), Ekaterina I. Lomakina
  • Copyright (C) 2016-2021
  • Translational Neuromodeling Unit (TNU)
  • Institute for Biomedical Engineering
  • University of Zurich & ETH Zurich

Download

Purpose

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

For the accompanying technical papers, detailing the methodology presented in this toolbox, please see 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 an early stage of development 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 Manual and the tutorial script tapas_rdcm_tutorial() as starting points 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.

Cite Me

Please cite the following paper (main TAPAS reference):

  1. 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.I., 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. Frontiers in Psychiatry 12, 857. https://doi.org/10.3389/fpsyt.2021.680811

In addition, please cite the following key references for the rDCM Toolbox: 2. 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 3. 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

Finally, when using rDCM for resting-state fMRI data, feel free to also cite: 4. Frässle, S., Harrison, S.J., Heinzle, J., Clementz, B.A., Tamminga, C.A., Sweeney, J.A., Gershon, E.S., Keshavan, M.S., Pearlson, G.D., Powers, A., Stephan, K.E., 2021. Regression dynamic causal modeling for resting-state fMRI. Human Brain Mapping 42, 2159-2180. https://doi.org/10.1002/hbm.25357

You can include for example the following snippet in your Methods section:

The analysis was performed using the regression dynamic causal modeling (rDCM) toolbox (Frässle et al., 2017; 2018), open-source code available as part of the TAPAS software collection (Frässle et al., 2021).

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_.