Model estimation

The tmfc_estimate_updated_GLMs function estimates updated GLMs with nuisance regressors. It is called automatically by the main function TMFC_denoise if the user has selected the corresponding options, or it can be run manually:

output_paths = tmfc_estimate_updated_GLMs(SPM_paths,masks,options);

The outputs are saved in the TMFC_denoise/[WM*e*]_[CSF*e*]_[GM*d*]/GLM_* subfolders, where folder names encode the selected denoising and masking parameters (see Mask Generation).

  • WM*e* — probability threshold and number of erosion cycles.

  • CSF*e* — threshold and number of erosion cycles.

  • GM*d* — threshold and number of dilation cycles.

Each selected denoising option appends a corresponding suffix to the updated GLM subfolder (see Denoising Options). For example, GLM_[24HMP]_[aCompCor50]_[rWLS] indicates that the updated GLM includes 24 head-motion regressors, a variable number of aCompCor regressors explaining 50% of WM and CSF variance, and was estimated using rWLS.

The updated GLM subfolders contain the standard outputs from SPM model estimation, as well as GLM_batch.m files, which store matlabbatch structures that can be reopened in the SPM batch system.

The SPM.mat files in these subfolders can be used as input to the TMFC toolbox, which implements gPPI and BSC-LSS methods with or without FIR task regression (Masharipov et al., 2024). gPPI and LSS models automatically include nuisance regressors and, optionally, FIR regressors, along with high-pass filter regressors. Therefore, noise regression, FIR task co-activation regression (optional), and high-pass filtering are performed in a single step, which avoids reintroducing signal related to nuisance covariates (Lindquist et al., 2019).