rDCM - regression Dynamic Causal Modeling.

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

NOTE: There is a new implementation of rDCM in the Julia language which can be found here.

---

General information

• Authors: Stefan Frässle, Ekaterina I. Lomakina
• Copyright (C) 2016-2024

Translational Neuromodeling Unit (TNU)
Institute for Biomedical Engineering
University of Zurich & ETH Zurich

---

Download

• Please download the latest stable versions of the rDCM Toolbox on GitHub.
• The latest bugfixes can be found in the GitHub Issue Forum or by request to the authors.
• Changes between all versions will be documented in the CHANGELOG.

---

Purpose

The regression dynamic causal modeling (rDCM) toolbox 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 rDCM 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

---

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 of the rDCM toolbox.

---

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 published as part of the Statistical Parametric Mapping (SPM) toolbox. The respective functions are marked with the prefix tapas_rdcm_spm_.