Longitudinal studies with multiple sessions (visits)

Multiple sessions (visits) are encoded by adding an extra layer of directories and file names in the form of ses-<label>. Session label can consist only of alphanumeric characters [a-zA-Z0-9] and should be consistent across subjects. If numbers are used in session labels we recommend using zero padding (for example ses-01, ses-11 instead of ses-1, ses-11). This makes results of alphabetical sorting more intuitive. Acquisition time of session can be defined in the sessions file (see below for details).

The extra session layer (at least one /ses-<label> subfolder) should be added for all subjects if at least one subject in the dataset has more than one session. Skipping the session layer for only some subjects in the dataset is not allowed. If a /ses-<label> subfolder is included as part of the directory hierarchy, then the same ses-<label> tag must also be included as part of the file names themselves.

sub-control01/
    ses-predrug/
        anat/
            sub-control01_ses-predrug_T1w.nii.gz
            sub-control01_ses-predrug_T1w.json
            sub-control01_ses-predrug_T2w.nii.gz
            sub-control01_ses-predrug_T2w.json
        func/
            sub-control01_ses-predrug_task-nback_bold.nii.gz
            sub-control01_ses-predrug_task-nback_bold.json
            sub-control01_ses-predrug_task-nback_events.tsv
            sub-control01_ses-predrug_task-nback_cont-physio.tsv.gz
            sub-control01_ses-predrug_task-nback_cont-physio.json
            sub-control01_ses-predrug_task-nback_sbref.nii.gz
        dwi/
            sub-control01_ses-predrug_dwi.nii.gz
            sub-control01_ses-predrug_dwi.bval
            sub-control01_ses-predrug_dwi.bvec
        fmap/
            sub-control01_ses-predrug_phasediff.nii.gz
            sub-control01_ses-predrug_phasediff.json
            sub-control01_ses-predrug_magnitude1.nii.gz
        sub-control01_ses-predrug_scans.tsv
    ses-postdrug/
        func/
            sub-control01_ses-postdrug_task-nback_bold.nii.gz
            sub-control01_ses-postdrug_task-nback_bold.json
            sub-control01_ses-postdrug_task-nback_events.tsv
            sub-control01_ses-postdrug_task-nback_cont-physio.tsv.gz
            sub-control01_ses-postdrug_task-nback_cont-physio.json
            sub-control01_ses-postdrug_task-nback_sbref.nii.gz
        fmap/
            sub-control01_ses-postdrug_phasediff.nii.gz
            sub-control01_ses-postdrug_phasediff.json
            sub-control01_ses-postdrug_magnitude1.nii.gz
        sub-control01_ses-postdrug_scans.tsv
    sub-control01_sessions.tsv
participants.tsv
dataset_description.json
README
CHANGES

Sessions file

Template:

sub-<label>/
    sub-<label>_sessions.tsv

Optional: Yes

In case of multiple sessions there is an option of adding an additional participant key files describing variables changing between sessions. In such case one file per participant should be added. These files need to include compulsory session_id column and describe each session by one and only one row. Column names in per participant key files have to be different from group level participant key column names.

_sessions.tsv example:

session_id  acq_time  systolic_blood_pressure
ses-predrug 2009-06-15T13:45:30 120
ses-postdrug  2009-06-16T13:45:30 100
ses-followup  2009-06-17T13:45:30 110

Multi-site or multi-center studies

This version of the BIDS specification does not explicitly cover studies with data coming from multiple sites or multiple centers (such extension is planned in BIDS 2.0.0). There are however ways to model your data without any loss in terms of metadata.

Treat each site/center as a separate dataset

The simplest way of dealing with multiple sites is to treat data from each site as a separate and independent BIDS dataset with a separate participants.tsv and other metadata files. This way you can feed each dataset individually to BIDS Apps and everything should just work.

Option 2: Combining sites/centers into one dataset

Alternatively you can combine data from all sites into one dataset. To identify which site each subjects comes from you can add a site column in the participants.tsv file indicating the source site. This solution allows you to analyze all of the subjects together in one dataset. One caveat is that subjects from all sites will have to have unique labels. To enforce that and improve readability you can use a subject label prefix identifying the site. For example sub-NUY001, sub-MIT002, sub-MPG002 etc. Remember that hyphens and underscores are not allowed in subject labels.