Outputs of pySPFM

Outputs with a selected regularization parameter lambda

For single-echo data:

Filename	Content
basename_pySPFM_activityInducing.nii.gz	Estimated activity-inducing signal
basename_pySPFM_denoised_bold.nii.gz	Denoised BOLD signal (estimated activity-inducing signal convolved with the HRF)
basename_pySPFM_innovation.nii.gz	Estimated innovation signal (if using the block model)
basename_pySPFM_lambda.nii.gz	Map of selected lambda values
basename_pySPFM_MAD.nii.gz	Map of the mean absolute deviation of the residuals (estimated level of noise in original data)
_references.txt	References to the methods used in the analysis
call.sh	Command used to run the analysis

For multi-echo data, the outputs are the same as for single-echo data, but with a file for each echo in the case of the denoised BOLD signal and the MAD.

Outputs with stability selection

The outputs of the stability selection analysis are the same for single-echo and multi-echo data:

Filename	Content
basename_pySPFM_AUC.nii.gz	Estimated area under the curve (probability of containing a neuronal-related event)
_references.txt	References to the methods used in the analysis
call.sh	Command used to run the analysis

Outputs of the auc_to_estimates function

The outputs of the auc_to_estimates function are the same as for the single \(\lambda\) analysis, with the addition of the following files:

Filename	Content
basename_pySPFM_aucThresholded.nii.gz	Map of the thresholded AUC values