rsDCM: Robust and Sparse Dynamic Causal Modelling for Functional MRI

rsDCM provides a robust and sparse method for group-level Dynamic Causal Modelling (DCM) of fMRI data, together with an R implementation of single-subject DCM ported from the MATLAB SPM25 toolbox.

Details

The package's main method is rsdcm (with the convenience wrapper rsdcm_fit): a group-level model that weights subjects by a Student-t likelihood (robustness to outliers) and selects group-level effects with a nonlocal product-moment (pMOM) spike-and-slab prior (sparsity), with ReML-estimated between-subject variance components. The “rs” is robust and sparse; it does not denote resting-state fMRI.

For single-subject inversion, the main entry point is dcm_estimate.

Group-level modelling (robust and sparse)

rsdcm: Robust, sparse group DCM (Student-t + pMOM).
rsdcm_fit: Assemble rsdcm inputs from a list of fitted DCMs.
rsdcm_options: Get or set package numerical options.

Single-subject DCM estimation (SPM25 port)

dcm_estimate: Full DCM inversion (main entry point).
dcm_nlsi_GN: Variational Laplace / Gauss-Newton inversion.
dcm_int: Bilinear-system integrator (forward model).
dcm_fmri_priors: Construct fMRI DCM priors.
dcm_fx_fmri, dcm_gx_fmri: Neural-state and BOLD observation equations.
dcm_bireduce, dcm_kernels: Bilinear reduction and Volterra kernels.
dcm_evidence, dcm_log_evidence, dcm_log_evidence_reduce: Model evidence and Bayesian model reduction.

Parametric Empirical Bayes (Gaussian group modelling)

dcm_peb_prepare, dcm_peb_run: Prepare and fit a second- or third-level PEB.
dcm_peb_of_pebs: Third-level PEB-of-PEBs over a directory of subject PEBs.
dcm_peb_design, dcm_peb_files, dcm_peb_load: Design-matrix and subject-PEB loading helpers.

Datasets

toy_dcm: Three-region single-subject DCM specification.
narps_dcm: 48-subject NARPS DCM summaries used by rsdcm.

Lower-level numerical helpers (matrix and vector utilities, numerical differentiation, matrix exponentials, and error-covariance bases) are also exported and documented; see, for example, dcm_vec, dcm_inv, dcm_diff, and dcm_Ce.

Acknowledgment

The single-subject DCM routines are a derivative work of the MATLAB SPM25 (version 25.01.02) toolbox, distributed under GPL-2 by the Wellcome Centre for Human Neuroimaging. See the LICENSE.note file in the package source for the list of ported routines.

References

Arhin, G., Sanyal, N. (2026). Robust and Sparse Group Dynamic Causal Modeling via Student-t Parametric Empirical Bayes and Nonlocal Priors. arXiv:2609.06379. doi:10.48550/arXiv.2609.06379

Botvinik-Nezer, R., Holzmeister, F., Camerer, C.F., et al. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582(7810), 84-88. doi:10.1038/s41586-020-2314-9

Friston, K.J., Harrison, L., Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273-1302. doi:10.1016/S1053-8119(03)00202-7

Friston, K.J., Mattout, J., Trujillo-Barreto, N., Ashburner, J., Penny, W. (2007). Variational free energy and the Laplace approximation. NeuroImage, 34(1), 220-234. doi:10.1016/j.neuroimage.2006.08.035

Valerio, D., Peres, A., Bergstrom, F., Seidel, P., Almeida, J. (2025). Neural and behavioral similarity-driven tuning curves for manipulable objects. Imaging Neuroscience, 3. doi:10.1162/imag_a_00482

Author

Maintainer: Godfred Arhin arhin0122@gmail.com [translator]

Authors:

Godfred Arhin arhin0122@gmail.com [translator]
Nilotpal Sanyal nsanyal@utep.edu

Other contributors:

SPM25 Authors (Original MATLAB SPM25 (v25.01.02) implementation; see LICENSE.note) [contributor, copyright holder]