Fitlins slice time correction to event onsets

ColleenHughes · September 5, 2025, 7:48pm

Summary of what happened:

I am using fitlins via apptainer (singularity) on event-related task fMRI data in cifti fsLR format preprocessed through fMRIPrep. My fMRIPrep call enabled slice time correction. The notes here indicate I should shift my onsets in *events.tsv so the model matches the preprocessed data. Makes sense! But, pybids-transforms-v1 currently does not have the same formula transformation as in bidspm/bids-matlab. Hardcoding the ‘onset’ value in events.tsv to be the ‘shifted_onset’ value works but isn’t ideal for reproducibility. I tried a few variations of existing transforms but haven’t found the right combination to adjust the onsets within the BIDS stats model file. For example, I tried to ‘assign’ the ‘shifted_onsets’ to ‘onset’ but got an error about the Target column not being sparse. I might try adding a sparse column to events.tsv then re-trying ‘assign’. Then, I tried to delete ‘onset’ and rename ‘shifted_onsets’ but ‘onset’ was not listed as editable/available for that transform.

My question is: is there a way with the existing transforms?

Alternately, I could try to edit the manually prepared package to add the transform I want or use nilearn directly but I’m not super python savvy so I thought I’d ask here first. Thanks!

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

# run fitlins
apptainer run \
  -B ${BIDS_root}:/data/:ro \
  -B ${fp_dir}:/fp/:ro \
  -B ${out_dir}:/out/ \
  -B ${work_dir}:/work/ \
  -B ${mod_file}:/mod/ \
  --cleanenv ${cont_img} \
  --verbose \
  --database-path /data/sourcedata/workdir/dbcache \
  --space fsLR \
  --desc-label '' \
  --model /data/models/model-falsebelief_smdl.json \
  --derivatives /fp/ \
  --smoothing 6:run:iso \
  --estimator nilearn \
  --drift-model cosine \
  --work-dir /work/ \
  /data/ /out/ run

My model file

{
  "Name": "FalseBeliefTask",
  "BIDSModelVersion": "1.0.0",
  "Description": "created by Colleen Hughes Sept 2025",
  "Input": {"subject": ["10"], "task": ["falsebelief"]},
  "Nodes": [
    {
      "Level": "Run",
      "Name": "run_level",
      "GroupBy": ["run", "subject"],
      "Transformations":{
        "Transformer":"pybids-transforms-v1",
        "Instructions":[
	       {
          "Name":"Factor",
          "Input":["trial_type"]
          },
        {
          "Name":"Convolve",
          "Input":["trial_type.FalseBelief_story",
                  "trial_type.FalseBelief_statement",
                  "trial_type.FalsePhoto_story",
                  "trial_type.FalsePhoto_statement"
                  ],
          "Model": "spm"
        }
             ]
      },
      "Model": {"X": [1,
                      "trial_type.FalseBelief_story",
                      "trial_type.FalseBelief_statement",
                      "trial_type.FalsePhoto_story",
                      "trial_type.FalsePhoto_statement",
                     "trans_x",
                     "trans_y",
     	               "trans_z",
                      "rot_x",
                      "rot_y",
                      "rot_z",
                      "non_steady_state*"
                    ],
                "Type": "glm"},
      "Contrasts": [
        {
          "Name": "allbelief_v_allphoto",
          "ConditionList": ["trial_type.FalseBelief_story",
                            "trial_type.FalseBelief_statement",
                            "trial_type.FalsePhoto_story",
                            "trial_type.FalsePhoto_statement"],
          "Weights": [0.5,0.5,-0.5,-0.5],
          "Test": "t"
        },
        { 
          "Name": "statement_belief_v_photo",
          "ConditionList": ["trial_type.FalseBelief_statement",
                            "trial_type.FalsePhoto_statement"],
          "Weights": [1,-1],
          "Test": "t"
        }
      ]
    },
    {
      "Level": "Subject",
      "Name": "subject_level",
      "GroupBy": ["subject", "contrast"],
      "Model": {"X": [1], "Type": "meta"},
      "DummyContrasts": {"Test": "t"}
    },
    {
      "Level": "Dataset",
      "Name": "one-sample_dataset",
      "GroupBy": ["contrast"],
      "Model": {"X": [1], "Type": "glm"},
      "DummyContrasts": {"Test": "t"}
    }
  ]
}

Version: 0.11.0

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

Apptainer

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

Validated model file here: https://bids-standard.github.io/stats-models/validator.html