afni_proc.py with fMRIPrep outputs (questions on masking and clustering)

meggonsalves · February 25, 2025, 3:19pm

AFNI version info (afni -ver): 24.2.01

Hi there,

I am using afni_proc.py to analyze my block design data. The data has already been preprocessed using fMRIPrep. I have two questions:

Question #1: Masks
I decided to let afni create the mask using the epi data rather than load in the fMRIPrep mask. When I go to look at my subject-level data in the output, the mask looks wonky. There seems to be parts that extend beyond the cortex and don't fully match up with the anatomy. In some areas it almost seems like the mask has ears. Here are some images

Do you have any advice for this? Or what mask I should be using during the second level analysis? I don't want any results outside of the MNI template brain.

My second question pertains to the -regress_run_clustsim flag. I watched the AFNI bootcamp videos and it said that this flag applies cluster level thresholding at the individual level. I was wondering if this is necessary if I want to do cluster-level thresholding at the group level (p-FWE<.05) in addition to voxel thresholding (p<.001)? I don't want to overcorrect my data.

My script is pasted below.

Thank you as always for your help! This message board is fantastic
Meg

#!/bin/bash

# File containing the list of subjects
subject_file="/oscar/data/jbarredo/nstb/code/card/subjects_54.txt"

# Loop through each subject in the list
while read subID; do

    # Set the session and task
    sess='ses-01'
    task='card'

    afni_proc.py                                                         \
        -subj_id                  $subID                                 \
        -out_dir                  /oscar/data/jbarredo/nstb/derivatives/afni/$subID/$sess/$subID.$task.results  \
        -copy_anat                /oscar/data/jbarredo/nstb/derivatives/fmriprep/$subID/$sess/anat/${subID}_${sess}_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz \
        -anat_has_skull           no                                     \
        -dsets                    /oscar/data/jbarredo/nstb/derivatives/fmriprep/$subID/$sess/func/${subID}_${sess}_task-card_dir-AP_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz \
                                   /oscar/data/jbarredo/nstb/derivatives/fmriprep/$subID/$sess/func/${subID}_${sess}_task-card_dir-PA_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz \
        -blocks                   blur mask scale regress                          \
        -tcat_remove_first_trs    0                                      \
        -mask_apply               epi                              \
        -mask_epi_anat            yes                               \
        -blur_size                6.0                                    \
        -regress_stim_times       /oscar/data/jbarredo/nstb/derivatives/card/AFNI/${subID}_reward.1D \
                                   /oscar/data/jbarredo/nstb/derivatives/card/AFNI/${subID}_punish.1D \
                                   /oscar/data/jbarredo/nstb/derivatives/card/AFNI/${subID}_baseline.1D \
        -regress_stim_labels      reward punish baseline                               \
        -regress_stim_types       AM1                                   \
        -regress_basis            'dmUBLOCK'                          \
        -regress_polort           3                                     \
        -regress_opts_3dD         -jobs 4                                \
                                    -gltsym 'SYM: reward -baseline'                \
                                    -glt_label 1 reward_vs_baseline                       \
                                    -gltsym 'SYM: punish -baseline'                \
                                    -glt_label 2 punish_vs_baseline \
                                    -gltsym 'SYM: punish -reward' \
                                    -glt_label 3 punish_vs_reward                     \
                                    -gltsym 'SYM: reward -punish' \
                                    -glt_label 4 reward_vs_punish                    \
        -regress_motion_file      /oscar/data/jbarredo/nstb/derivatives/card/motion/${subID}_motion_combined.txt \
        -regress_est_blur_errts                                             \
        -regress_run_clustsim     yes                                     \
        -html_review_style        pythonic                               \
        -execute

done < $subject_file

rickr · February 26, 2025, 3:00pm

Hi Meg,

That very basic EPI mask is merely from an iterated process computing a higher intensity cluster of voxels. So "-mask_apply epi" is something we would not usually do.

But for QC purposes, which are worth strong consideration, we generally do not use '-mask_apply' in the first place. Is it important to see what results are indeed outside of where you consider the brain to be. If there is some odd artifact there, it would be better to understand it and any potential impact on the analysis, rather than not knowing about it. Masking is generally better to apply at the group level, where you can supply the MNI template mask directly.

The -regress_run_clustsim operation is usually unimportant. Note that it does not actually apply the cluster and threshold limits, it merely computes their relationship, so that if you cluster the single subject stats in the afni GUI, it will include a guide for that correction in the cluster report. It is also simply an example of what one might consider doing at the group level.

This operation at the single subject level will not alter the actual data or affect any group level cluster correction.

-rick