AFNI version info (`afni -ver`

): Version ANFI_24.0.19

Dear AFNI experts and users,

I recently read a paper about using seed-based resting-state functional connectivity (RSFC) strength to predict task-related regression coefficients. The description in the paper is as follows:

To predict each voxel’s BOLD response during the Flanker task based on that voxel’s RSFC strength, we conducted a voxel-matched linear regression analysis for each voxel in the brain.Specifically, we entered each voxel’s RSFC strength as a predictor in the regression model. Subsequently, the parameter estimates for the Flanker task of that same voxel were entered as dependent variables. This resulted in a unique linear regression model for each voxel in which RSFC predicted BOLD activity induced by the Flanker task.

In practice, we first concatenated the participant-level RSFC maps for all 26 participants into a 26-volume 4-D image (1 volume/ participant). Separately we concatenated the participant-level Flanker task parameter estimates of all participants into a similar 26-volume 4-D image. Next we assessed for each voxel in the brain whether there was a significant linear relationship between RSFC and task-induced activity across participants. This analysis produced, at each voxel, a regression coefficient corresponding to the correlation across the 26 participants between RSFC and task-induced activity at that voxel.

These values were converted to Z-statistics and the resultant whole-brain Z-statistic map were corrected for multiple comparisons at the cluster level using Gaussian random field theory (Z > 2.3; cluster significance: p < 0.05, corrected).

I have some question about this analysis:

- How can I do this using AFNI, specifically using a 3D+time dataset of RSFC as the regressor and a 3D+time dataset of task-related coefficients as the dependent variable passed to a linear regression model? Or is there an alternative way to calculate this?
- When performing population-level analysis in AFNI (e.g., with
`3dttest++`

), we need at least one group of datasets (e.g., for a one-sample t-test) and use the`-Clustsim`

option to control the cluster false-positive rate (FPR) at 0.05. In this paper, the final output seems to be a Z-statistics map. Can only one Z-statistics map be used for multiple comparison correction?

Thanks,

Yang