Dear Experts,

I recently discovered the rs-HRF toolbox for estimating and modeling the resting-state HRF from resting-state fMRI data.

I have successfully installed rs-HRF on SPM in MATLAB and followed the tutorials and documentation. However, I couldn't find clear instructions on estimating the HRF from .mat files containing preprocessed time series for each ROI.

Could you please clarify which option I should select in the GUI (Voxels, ROI volume, Signals, Vertices, etc.) to estimate the HRF from my ROI time series?

Your guidance would be greatly appreciated.

Best regards,

Abir.

you should select 'Signals' for ROI time series analysis.

Dear Guorong Wu,

Thank you for your clarifications, they are greatly appreciated.

I encountered another issue after preprocessing the fMRI data using the CONN toolbox. I obtained a single rp.txt file for regressors, and for each subject, I obtained an ROI.mat file containing time series for all segmented ROIs.

To model the HRF response for the DMN, I extracted the time series corresponding to the DMN from the ROI.mat file. However, when I ran the rsHRF toolbox, I encountered an error indicating a size mismatch between rp.txt and DMN_time_series.mat.

Could you please help me resolve this issue?

Best regards,

Abir.

ensure the ROI signals matrix is structured as (time points)*(number of ROIs)

Dear Guorong Wu,

Thank you again for this information, I truly appreciate it.

After reviewing the documentation in detail, I have some ambiguity regarding the nuisance.txt file. Specifically, when working with ROI signals stored in .mat files, is it correct that there is no need to download this regression variable file?

Additionally, could you please clarify how I can define the temporal mask to exclude spurious events?

Thanks again,

Best,
Abir

Indeed it's supposed that when the data has been already organized in ROIs, the nuisance regression has been made already. The temporal mask can be defined from the framewise displacement, as explained on page 8 of the matlab manual. It indicates if there are points in time that should be excluded (akin to the "scrubbing" approach by Power et al.)