Regression Dynamic Causal Modeling (rDCM) toolbox
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- Adapted "Cite Me" information to include TAPAS paper and update existing rDCM references
- Corrected typo in the comments of tapas_rdcm_estimate.m
- Corrected bug in construction of the full posterior covariance matrix (from the region-wise posterior covariance matrices)
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- Facilitation of using rDCM for resting-state fMRI data. This includes changes to the MATLAB functions as well as information added to the Manual of the toolbox.
- Possibility to include multiple confounds rather than a simple constant for baseline shifts.
- Helper function for specification of whole-brain dynamic causal models (i.e., DCM structure).
- Corrected small bug in the specification of regressors
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- rDCM now stores the actual (measured) and predicted derivatives of the signal (in frequency domain). This is relevant because it represents the data feature that is actually fitted. Additionally, a routine has been added to tapas_rdcm_visualize.m to plot these fits in terms of the power spectral density in frequency domain.
- Option to inform the sparsity constraints (p0) of rDCM using external binary information (e.g., from anatatomical or functional connectivity)
- Small changes to the console output of rDCM
- rDCM stores experimental inputs and the run-time in the output structure
- rDCM checks for empty input regressors and sets them to zero (if necessary)
- Corrected small bug in the evaluation of the log-determinant
- Corrected small bug in the storage of the posterior covariance matrix
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- Original release of the regression dynamic causal modeling (rDCM) toolbox (v1.0) as part of the open-source software package TAPAS [3.0].
- MATLAB functions implementing the necessary steps of an rDCM analysis have been added
- A brief tutorial tapas_rdcm_tutorial.m that demonstrates how to implement an rDCM analysis has been added
- Documentation has been added in the Manual.pdf, ReadMe.md, and throughout the code
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