This repository contains the code used to analyze differences in effective brain connectivity associated with neurofeedback training assessed with DCM fMRI.
Please contact at this e-mail address if you have any question: [email protected]. Gabriela Vargas.
- DCM Matfiles:
- 1st family-inference : model_space.mat and BMS.mat.
- 2nd family-inferernce: model_space.mat and BMS.mat
- Bayesian model average: BMA.m
- Plots:
- Data: fMRI with contrasts
- Generate masks: Here we use the following functions: generaunMask.m
- Generate VOI's: .nii files. Here we used the following functions generaunROI.m
- Build and Generate 1st set of DCM models specifying A and C parameters. Here we use the following functions generaunDCM.m
- Bayesian model comparison: RFX group and Family comparison (both use the batch and the function 'spm_run_dcm_bms(job)' )
- Selection of winning model parameter configuration by model evidence
- Build and Generate 2nd set of DCM specifying B parameter. Here we use the same function generaunDCM.m
- Bayesian model comparison: RFX group and Family comparison (both use the batch and the function 'spm_run_dcm_bms(job)' )
- Bayesian model averaging (BMA). Here we use the function BMA.m by the batch.