Multi-Session fmriprep breaking summary _func.html outputs

Software Support
1

Summary of what happened:

Hi all,
We’re trying to put some intensively sampled data through fmriprep, 4 subjects ~ 10 sessions each on our HPC. The data are multi-echo with 4 or 5 runs of functional data, depending on the session. Like in some other posts I’ve read, we’re trying to avoid using the longitudinal flag and instead process each session separately. To do that, I’ve created a script that generates a subject-session-specific bids config file and fmriprep command that I can submit one-by-one. I’ve also created session-specific derivative and scratch directories to avoid issues there.

It looks like things mostly worked, but something weird is going on with the summary .html outputs that is breaking ses-0x into one _ses-0_func.html file and another ses-x_func.html file (see screenshot below). Both of these files are 2KB and have nothing when I open them (presumably because no outputs match ses-0 or ses-x?).

As far as we can tell so far, the rest of the outputs look correct. fmriprep indeed said it ran succesfully. If this is just a syntax error for the output summary, I suppose we can move forward, but I would like to confirm nothing deeper is going on here.

I’ve now replicated this with ses-01 twice as well as ses-02 for our first subject.

Command used (and if a helper script was used, a link to the helper script or the command generated):

This script generates our bids config files and fmriprep commands:

#!/bin/bash

# ensure paths are correct
projectname=night-owls 
maindir=/gpfs/scratch/tug87422/smithlab-shared/$projectname
scriptdir=$maindir/code
bidsdir=$maindir/bids
logdir=$maindir/logs
mkdir -p $logdir

#Keep scratch and derivatives dirs separate by sessions
for ses in {01..12}; do
  if [ ! -d "$maindir/scratch/ses-$ses" ]; then
    mkdir -p "$maindir/scratch/ses-$ses"
  fi
   if [ ! -d "$maindir/derivatives/ses-$ses" ]; then
    mkdir -p "$maindir/derivatives/ses-$ses"
  fi
done

# Generate configs and PBS scripts

subjects=(sub-101) 

for sub in ${subjects[@]}; do
	
    ## for sessions that have a functional image..
    sessions=( $(
        find "$bidsdir/$sub" \
            -type f \
            -path "*/ses-[0-9][0-9]/func/*.nii.gz" \
            2>/dev/null \
        | sed -n 's|.*/\(ses-[0-9][0-9]\)/.*|\1|p' \
        | sort -u
    ) )


  for ses in "${sessions[@]}"; do
    sesid=${ses#ses-}
		
    ## Create subject/session-specific BIDS filter file
    configfile=$maindir/code/fmriprep/fmriprep_config_${sub}_${ses}.json
    cat <<EOF > "$configfile"
{
  "t1w": {"datatype": "anat", "suffix": "T1w", "session": "$sesid"},
  "sbref": {"datatype": "func", "suffix": "sbref", "part": [null, "mag"], "session": "$sesid"},
  "bold": {"datatype": "func", "suffix": "bold", "part": [null, "mag"], "session": "$sesid"}
}
EOF

    # Generate subject/session-specific PBS script
    scriptfile=$maindir/code/fmriprep/fmriprep_${sub}_${ses}.sh

    cat <<EOF > "$scriptfile"
#!/bin/bash
#PBS -l walltime=8:00:00
#PBS -N fmriprep-${sub}-${ses}
#PBS -q normal
#PBS -l nodes=1:ppn=14

# load modules and go to workdir
module load fsl/6.0.2
source \$FSLDIR/etc/fslconf/fsl.sh
module load singularity
cd \$PBS_O_WORKDIR

# ensure paths are correct
projectname=night-owls 
maindir=/gpfs/scratch/tug87422/smithlab-shared/\$projectname
scriptdir=\$maindir/code
bidsdir=\$maindir/bids
logdir=\$maindir/logs
mkdir -p \$logdir

TEMPLATEFLOW_DIR=/gpfs/scratch/tug87422/smithlab-shared/tools/templateflow
MPLCONFIGDIR_DIR=/gpfs/scratch/tug87422/smithlab-shared/tools/mplconfigdir
export SINGULARITYENV_TEMPLATEFLOW_HOME=/opt/templateflow
export SINGULARITYENV_MPLCONFIGDIR=/opt/mplconfigdir

singularity run --cleanenv \\
	-B \${TEMPLATEFLOW_DIR}:/opt/templateflow \\
	-B \${MPLCONFIGDIR_DIR}:/opt/mplconfigdir \\
	-B \$maindir:/base \\
	-B /gpfs/scratch/tug87422/smithlab-shared/tools/licenses:/opts \\
	-B \$maindir/scratch/$ses:/scratch \\
	/gpfs/scratch/tug87422/smithlab-shared/tools/fmriprep-24.1.1.simg \\
	/base/bids /base/derivatives/$ses \\
	participant --participant_label $sub \\
	--stop-on-first-crash \\
	--skip-bids-validation \\
	--nthreads 14 \\
	--me-output-echos \\
	--output-spaces MNI152NLin6Asym \\
	--bids-filter-file /base/code/fmriprep/fmriprep_config_${sub}_${ses}.json \\
	--fs-no-reconall --fs-license-file /opts/fs_license.txt \\
	-w /scratch
EOF

    chmod +x "$scriptfile"

  done
done

Here is an example of what the config file and fmriprep script look like for sub-101, which was submitted as qsub fmriprep_sub-101_ses-01.sh

Config:

{
  "t1w": {"datatype": "anat", "suffix": "T1w", "session": "01"},
  "sbref": {"datatype": "func", "suffix": "sbref", "part": [null, "mag"], "session": "01"},
  "bold": {"datatype": "func", "suffix": "bold", "part": [null, "mag"], "session": "01"}
}

Call:

#PBS -l walltime=8:00:00
#PBS -N fmriprep-sub-101-ses-01
#PBS -q normal
#PBS -l nodes=1:ppn=14

# load modules and go to workdir
module load fsl/6.0.2
source $FSLDIR/etc/fslconf/fsl.sh
module load singularity
cd $PBS_O_WORKDIR

# ensure paths are correct
projectname=night-owls
maindir=/gpfs/scratch/tug87422/smithlab-shared/$projectname
scriptdir=$maindir/code
bidsdir=$maindir/bids
logdir=$maindir/logs
mkdir -p $logdir

TEMPLATEFLOW_DIR=/gpfs/scratch/tug87422/smithlab-shared/tools/templateflow
MPLCONFIGDIR_DIR=/gpfs/scratch/tug87422/smithlab-shared/tools/mplconfigdir
export SINGULARITYENV_TEMPLATEFLOW_HOME=/opt/templateflow
export SINGULARITYENV_MPLCONFIGDIR=/opt/mplconfigdir

singularity run --cleanenv \
        -B ${TEMPLATEFLOW_DIR}:/opt/templateflow \
        -B ${MPLCONFIGDIR_DIR}:/opt/mplconfigdir \
        -B $maindir:/base \
        -B /gpfs/scratch/tug87422/smithlab-shared/tools/licenses:/opts \
        -B $maindir/scratch/ses-01:/scratch \
        /gpfs/scratch/tug87422/smithlab-shared/tools/fmriprep-24.1.1.simg \
        /base/bids /base/derivatives/ses-01 \
        participant --participant_label sub-101 \
        --stop-on-first-crash \
        --skip-bids-validation \
        --nthreads 14 \
        --me-output-echos \
        --output-spaces MNI152NLin6Asym \
        --bids-filter-file /base/code/fmriprep/fmriprep_config_sub-101_ses-01.json \
        --fs-no-reconall --fs-license-file /opts/fs_license.txt \
        -w /scratch

Version:

24.1.1

Environment (Docker, Singularity / Apptainer, custom installation):

Singularity

Data formatted according to a validatable standard? Please provide the output of the validator:

PASTE VALIDATOR OUTPUT HERE

Relevant log outputs (up to 20 lines):

Here is the /func output, which looks normal as far as we can tell.

sub-101_ses-01_task-mid_run-1_echo-1_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-1_echo-1_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-1_echo-2_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-1_echo-2_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-1_echo-3_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-1_echo-3_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-1_echo-4_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-1_echo-4_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-1_from-boldref_to-T1w_mode-image_desc-coreg_xfm.json
sub-101_ses-01_task-mid_run-1_from-boldref_to-T1w_mode-image_desc-coreg_xfm.txt
sub-101_ses-01_task-mid_run-1_from-orig_to-boldref_mode-image_desc-hmc_xfm.json
sub-101_ses-01_task-mid_run-1_from-orig_to-boldref_mode-image_desc-hmc_xfm.txt
sub-101_ses-01_task-mid_run-1_part-mag_desc-brain_mask.json
sub-101_ses-01_task-mid_run-1_part-mag_desc-brain_mask.nii.gz
sub-101_ses-01_task-mid_run-1_part-mag_desc-confounds_timeseries.json
sub-101_ses-01_task-mid_run-1_part-mag_desc-confounds_timeseries.tsv
sub-101_ses-01_task-mid_run-1_part-mag_desc-coreg_boldref.json
sub-101_ses-01_task-mid_run-1_part-mag_desc-coreg_boldref.nii.gz
sub-101_ses-01_task-mid_run-1_part-mag_desc-hmc_boldref.json
sub-101_ses-01_task-mid_run-1_part-mag_desc-hmc_boldref.nii.gz
sub-101_ses-01_task-mid_run-1_part-mag_space-MNI152NLin6Asym_boldref.json
sub-101_ses-01_task-mid_run-1_part-mag_space-MNI152NLin6Asym_boldref.nii.gz
sub-101_ses-01_task-mid_run-1_part-mag_space-MNI152NLin6Asym_desc-brain_mask.json
sub-101_ses-01_task-mid_run-1_part-mag_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
sub-101_ses-01_task-mid_run-1_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-1_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-1_space-MNI152NLin6Asym_T2starmap.json
sub-101_ses-01_task-mid_run-1_space-MNI152NLin6Asym_T2starmap.nii.gz
sub-101_ses-01_task-mid_run-2_echo-1_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-2_echo-1_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-2_echo-2_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-2_echo-2_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-2_echo-3_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-2_echo-3_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-2_echo-4_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-2_echo-4_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-2_from-boldref_to-T1w_mode-image_desc-coreg_xfm.json
sub-101_ses-01_task-mid_run-2_from-boldref_to-T1w_mode-image_desc-coreg_xfm.txt
sub-101_ses-01_task-mid_run-2_from-orig_to-boldref_mode-image_desc-hmc_xfm.json
sub-101_ses-01_task-mid_run-2_from-orig_to-boldref_mode-image_desc-hmc_xfm.txt
sub-101_ses-01_task-mid_run-2_part-mag_desc-brain_mask.json
sub-101_ses-01_task-mid_run-2_part-mag_desc-brain_mask.nii.gz
sub-101_ses-01_task-mid_run-2_part-mag_desc-confounds_timeseries.json
sub-101_ses-01_task-mid_run-2_part-mag_desc-confounds_timeseries.tsv
sub-101_ses-01_task-mid_run-2_part-mag_desc-coreg_boldref.json
sub-101_ses-01_task-mid_run-2_part-mag_desc-coreg_boldref.nii.gz
sub-101_ses-01_task-mid_run-2_part-mag_desc-hmc_boldref.json
sub-101_ses-01_task-mid_run-2_part-mag_desc-hmc_boldref.nii.gz
sub-101_ses-01_task-mid_run-2_part-mag_space-MNI152NLin6Asym_boldref.json
sub-101_ses-01_task-mid_run-2_part-mag_space-MNI152NLin6Asym_boldref.nii.gz
sub-101_ses-01_task-mid_run-2_part-mag_space-MNI152NLin6Asym_desc-brain_mask.json
sub-101_ses-01_task-mid_run-2_part-mag_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
sub-101_ses-01_task-mid_run-2_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.json
sub-101_ses-01_task-mid_run-2_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz
sub-101_ses-01_task-mid_run-2_space-MNI152NLin6Asym_T2starmap.json
sub-101_ses-01_task-mid_run-2_space-MNI152NLin6Asym_T2starmap.nii.gz
sub-101_ses-01_task-rest_run-1_echo-1_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-rest_run-1_echo-1_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-rest_run-1_echo-2_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-rest_run-1_echo-2_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-rest_run-1_echo-3_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-rest_run-1_echo-3_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-rest_run-1_echo-4_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-rest_run-1_echo-4_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-rest_run-1_from-boldref_to-T1w_mode-image_desc-coreg_xfm.json
sub-101_ses-01_task-rest_run-1_from-boldref_to-T1w_mode-image_desc-coreg_xfm.txt
sub-101_ses-01_task-rest_run-1_from-orig_to-boldref_mode-image_desc-hmc_xfm.json
sub-101_ses-01_task-rest_run-1_from-orig_to-boldref_mode-image_desc-hmc_xfm.txt
sub-101_ses-01_task-rest_run-1_part-mag_desc-brain_mask.json
sub-101_ses-01_task-rest_run-1_part-mag_desc-brain_mask.nii.gz
sub-101_ses-01_task-rest_run-1_part-mag_desc-confounds_timeseries.json
sub-101_ses-01_task-rest_run-1_part-mag_desc-confounds_timeseries.tsv
sub-101_ses-01_task-rest_run-1_part-mag_desc-coreg_boldref.json
sub-101_ses-01_task-rest_run-1_part-mag_desc-coreg_boldref.nii.gz
sub-101_ses-01_task-rest_run-1_part-mag_desc-hmc_boldref.json
sub-101_ses-01_task-rest_run-1_part-mag_desc-hmc_boldref.nii.gz
sub-101_ses-01_task-rest_run-1_part-mag_space-MNI152NLin6Asym_boldref.json
sub-101_ses-01_task-rest_run-1_part-mag_space-MNI152NLin6Asym_boldref.nii.gz
sub-101_ses-01_task-rest_run-1_part-mag_space-MNI152NLin6Asym_desc-brain_mask.json
sub-101_ses-01_task-rest_run-1_part-mag_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
sub-101_ses-01_task-rest_run-1_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.json
sub-101_ses-01_task-rest_run-1_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz
sub-101_ses-01_task-rest_run-1_space-MNI152NLin6Asym_T2starmap.json
sub-101_ses-01_task-rest_run-1_space-MNI152NLin6Asym_T2starmap.nii.gz
sub-101_ses-01_task-sharedreward_run-1_echo-1_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-1_echo-1_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-1_echo-2_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-1_echo-2_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-1_echo-3_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-1_echo-3_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-1_echo-4_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-1_echo-4_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-1_from-boldref_to-T1w_mode-image_desc-coreg_xfm.json
sub-101_ses-01_task-sharedreward_run-1_from-boldref_to-T1w_mode-image_desc-coreg_xfm.txt
sub-101_ses-01_task-sharedreward_run-1_from-orig_to-boldref_mode-image_desc-hmc_xfm.json
sub-101_ses-01_task-sharedreward_run-1_from-orig_to-boldref_mode-image_desc-hmc_xfm.txt
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-brain_mask.json
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-brain_mask.nii.gz
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-confounds_timeseries.json
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-confounds_timeseries.tsv
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-coreg_boldref.json
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-coreg_boldref.nii.gz
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-hmc_boldref.json
sub-101_ses-01_task-sharedreward_run-1_part-mag_desc-hmc_boldref.nii.gz
sub-101_ses-01_task-sharedreward_run-1_part-mag_space-MNI152NLin6Asym_boldref.json
sub-101_ses-01_task-sharedreward_run-1_part-mag_space-MNI152NLin6Asym_boldref.nii.gz
sub-101_ses-01_task-sharedreward_run-1_part-mag_space-MNI152NLin6Asym_desc-brain_mask.json
sub-101_ses-01_task-sharedreward_run-1_part-mag_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
sub-101_ses-01_task-sharedreward_run-1_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-1_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-1_space-MNI152NLin6Asym_T2starmap.json
sub-101_ses-01_task-sharedreward_run-1_space-MNI152NLin6Asym_T2starmap.nii.gz
sub-101_ses-01_task-sharedreward_run-2_echo-1_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-2_echo-1_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-2_echo-2_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-2_echo-2_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-2_echo-3_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-2_echo-3_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-2_echo-4_part-mag_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-2_echo-4_part-mag_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-2_from-boldref_to-T1w_mode-image_desc-coreg_xfm.json
sub-101_ses-01_task-sharedreward_run-2_from-boldref_to-T1w_mode-image_desc-coreg_xfm.txt
sub-101_ses-01_task-sharedreward_run-2_from-orig_to-boldref_mode-image_desc-hmc_xfm.json
sub-101_ses-01_task-sharedreward_run-2_from-orig_to-boldref_mode-image_desc-hmc_xfm.txt
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-brain_mask.json
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-brain_mask.nii.gz
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-confounds_timeseries.json
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-confounds_timeseries.tsv
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-coreg_boldref.json
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-coreg_boldref.nii.gz
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-hmc_boldref.json
sub-101_ses-01_task-sharedreward_run-2_part-mag_desc-hmc_boldref.nii.gz
sub-101_ses-01_task-sharedreward_run-2_part-mag_space-MNI152NLin6Asym_boldref.json
sub-101_ses-01_task-sharedreward_run-2_part-mag_space-MNI152NLin6Asym_boldref.nii.gz
sub-101_ses-01_task-sharedreward_run-2_part-mag_space-MNI152NLin6Asym_desc-brain_mask.json
sub-101_ses-01_task-sharedreward_run-2_part-mag_space-MNI152NLin6Asym_desc-brain_mask.nii.gz
sub-101_ses-01_task-sharedreward_run-2_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.json
sub-101_ses-01_task-sharedreward_run-2_part-mag_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz
sub-101_ses-01_task-sharedreward_run-2_space-MNI152NLin6Asym_T2starmap.json
sub-101_ses-01_task-sharedreward_run-2_space-MNI152NLin6Asym_T2starmap.nii.gz

Screenshots / relevant information:

image
image2783×1543 209 KB

2

Apologies, tagging @effigies and @oesteban to see if there’s any ideas. We (@dvsmith ) are mainly trying to determine if anything functionally went wrong during fmriprep, or if this is just a naming error for the html summary. We haven’t noticed anything yet besides the issue summary html naming (which we imaging leads to the summary file being empty).

3

@mattonim this looks like a bug in the session-wise report. Can you please file an issue on the github repo? Sign in to GitHub · GitHub

That said, I think your approach of breaking up sessions would be way more consistent if you first run your subjects with --anat-only, which, unless you also play with the longitudinal option (you shouldn’t), will generate preprocessed anatomical data for each subject across all sessions. Once you have that, then you do something like what you are doing, ensuring the anatomical results are reused in each session.

4

Hi @oesteban thanks for the help! I will make a bug report shortly.

Following up on your 2nd point. Is the ‘more consistent’ approach you’re referencing just for the goal of having the same anatomical across all sessions for registration purposes?
And the workflow you suggest with --anat-only first, am I misunderstanding that this would essentially be replicating the --longitudinal workflow?

5

Yes, running first --anat-only will put together into a single reference all the T1w images across sessions for a given subject. So unless you expect substantial anatomical differences (e.g., surgery), I think this is most likely the most reliable option.

By splitting by session you are manually implementing --longitudinal. What I’m suggesting is to run the equivalent of a massive fMRIPrep run with all sessions together, but decoupling the anatomical from the functional processing so it is broken up into smaller chunks.

6

I’m a bit confused. From what I understand of the docs, the --longitudinal flag creates an average T1 image from all sessions (" For consistency, in the case of multiple images, fMRIPrep constructs templates aligned to the first image, unless passed the --longitudinal flag, which forces the estimation of an unbiased template."). In my understanding, this seems consistent with what the --anat-only approach you’re suggesting would be. I thought my approach was completely separating all sessions so I don’t understand how this could be replicating the longitudinal flag. I’m also seeing your comments in a previous thread that are consistent with what you’re saying now (and another user seemingly shared my confusion), but things aren’t clicking for me how this connects with what the documentation is saying.

I’m also under the general impression folks are trying to steer away from the longitudinal flag from prior threads and apparant plans to depracate it..

Apologies for stringing this along - but either I’m grossly misunderstanding something or the documentation appears inconsistent with my read of your responses here

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You are right, the difference between having --longitudinal or not is whether the template is created in reference to the first T1w image or an unbiased “middle point” across all available T1w images. We are discussing the --longitudinal flag because it is misleading (I even fell for it in the first message!): a better name would have been --unbiased-anat or something like that.

By splitting sessions, you are taking a truly longitudinal approach: each session has its own anatomical reference. As I mentioned above, unless you expect substantial anatomical changes over time (e.g., pre-/post-surgery, or sessions separated many years apart, etc), I don’t think you would want to split sessions. If you are not to split sessions then the next question is whether you want to use --longitudinal or not. In this case, a dataset involving sessions at 5, 7, 10, 15, 20, 25 years old will have very different T1w images over time. Without --longitudinal, everything will be aligned into the 5 yo. T1w image, which is something probably undesirable in this context: in this case, you probably want an “unbiased” reference that is representative of all sessions.

TL;DR besides my confusion about --longitudinal the recommendation remains the same. In your case, it looks like generating a single anatomical reference without the extra computation of --longitudinal first (--anat-only) and then processing sessions in a second pass (either separating sessions or giving a try to all sessions in one run) is the better approach.