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Name


phenotype


session


datatype


guts[location]_ses-[session]_task-[task].tsv


guts[location]_ses-[session]_task-[task].json


subject


session


datatype


sub-[subjectid]_ses-[session]_task-[task]_[extra-information].[extension]


sub-[subjectid]_ses-[session]_task-[task]_[extra-information].json


The BIDS structure consists of subject folders and a phenotype folder.

Phenotype: Group-level data files that are not specific to any particular neuroimaging modality, such as questionnaires or hormone level output. A single phenotype data file typically contains information about all participants’ performance or characteristics related to a specific task within the study.

    - beh (group-level behavioral data from tasks)
    - quest (questionnaire data)
    - physio (physiological data, e.g., hormone level output)
Subject folders: Data files that are specific to a particular (neuro)imaging/recording modality. BIDS includes the following modalities:
    - func (task-based and resting-state functional MRI)
    - fmap (field inhomogeneity mapping data such as field maps)
    - anat (structural imaging such as T1, T2, PD, etc.)
    - eeg (electroencephalography)
    - beh (individual task data related to non-neuro recording files, such as ECG, skin conductance, dynamometer)

Name


derivatives


phenotype


sub-gutseur0001


sub-gutseur0002


gutseur_demographics.tsv


gutseur_demographics.json


gutseur_participation.tsv


gutseur_participation.json


This is what the 'first-level' of a data structure could look like. A phenotype folder and individual subject folders. Additionally, there is room for files with participant information.

Generally speaking, information about participants themselves (demographics) is placed in the demographics file, whereas information about participation (e.g., dates of sessions, sessions completed, etc.) is placed in a participation file.

Additionally, there is room for a derivatives folder, in which outputs from processing pipelines can be saved.

Name


phenotype


ses-01


quest


gutseur_ses-01_task-iri.tsv


gutseur_ses-01_task-iri.json


gutseur_ses-01_task-sdq.tsv


gutseur_ses-01_task-sdq.json


sub-gutseur0001


Questionnaire data consists of a single file for each questionnaire with responses from all participants (group-level data) and is therefore placed in the phenotype folder. All questionnaires get a separate TSV file per session and an accompanying JSON file.

Name


phenotype


ses-01


beh


gutseur_ses-01_task-behsddt.tsv


gutseur_ses-01_task-behsddt.json


sub-gutseur0001


ses-01


func


sub-gutseur0001_ses-01_task-fmrirest_bold.json


sub-gutseur0001_ses-01_task-fmrirest_bold.nii.gz


sub-gutseur0001_ses-01_task-fmrisddt_run-01_bold.json


sub-gutseur0001_ses-01_task-fmrisddt_run-01_bold.nii.gz


sub-gutseur0001_ses-01_task-fmrisddt_run-01_events.tsv


sub-gutseur0001_ses-01_task-fmrisddt_run-02_bold.json


sub-gutseur0001_ses-01_task-fmrisddt_run-02_bold.nii.gz


sub-gutseur0001_ses-01_task-fmrisddt_run-02_events.tsv


In (f)MRI, we have an 'fmri' and 'beh' prefix for the short name of a certain task conducted during fMRI.

For example, for the task SSDT, we have a behsddt short name that represents group-level data (in the phenotype folder). This file contains data from all individual event files containing responses/scores from participants during the specific SDDT task.

The fmrisddt data is stored per individual in the 'func' folder. This allows researchers to conduct both individual fMRI analyses and group-level behavioral analyses.

Name


phenotype


ses-01


beh


gutseur_ses-01_task-behgonogo.tsv


gutseur_ses-01_task-behgonogo.json


sub-gutseur0001


ses-01


eeg


sub-gutseur0001_ses-01_task-eeggonogo_channels.tsv


sub-gutseur0001_ses-01_task-eeggonogo_events.tsv


sub-gutseur0001_ses-01_task-eeggonogo_eeg.edf


sub-gutseur0001_ses-01_task-eeggonogo_eeg.json


In EEG, we have an 'eeg' and 'beh' prefix for the short name of a certain task conducted during EEG.

For example, for the task Go No Go, we have a behgonogo short name that represents group-level data (in the phenotype folder). This file contains data from all individual event files containing responses/scores from participants during the specific Go No Go task.

The eeggonogo data is stored per individual in the 'eeg' folder. This allows researchers to conduct both individual EEG analyses and group-level behavioral analyses.

Name


phenotype


ses-01


beh


gutseur_ses-01_task-behsocialeffort.tsv


gutseur_ses-01_task-behsocialeffort.json


sub-gutseur0001


ses-01


beh


sub-gutseur0001_ses-01_task-dynosocialeffort_events.tsv


sub-gutseur0001_ses-01_task-dynosocialeffort_physio.acq


sub-gutseur0001_ses-01_task-dynosocialeffort_markers.tsv


sub-gutseur0001_ses-01_task-dynosocialeffort_events.json


sub-gutseur0001_ses-01_task-dynosocialeffort_markers.json


sub-gutseur0001_ses-01_task-dynosocialeffort_physio.json


We treat non-neuro recordings (ECG, EDA, Dynamometer) in a similar manner as neuro-recordings, as there is something (strength, heart rate, skin conductance) being measured while performing a task. For (f)MRI data, we have fmrisddt and behsddt. For the dynamometer data, we do the same:

For example, for the task with the short name 'prosocial-effort':

dynosocialeffort and behsocialeffort, where dynosocialeffort is in a subject folder with events, markers, and logs. The strength acquisition file (with _physio here) is then the main file (like a nii.gz file in fMRI).

Instead of the 'func' or 'eeg' folder, the data is stored in the 'beh' folder as this is the location for data from experiments performed with no neural recordings, according to BIDS.

Name


phenotype


ses-01


beh


gutseur_ses-01_task-behself.tsv


gutseur_ses-01_task-behself.json


sub-gutseur0001


ses-01


beh


sub-gutseur0001_ses-01_task-ecgself_channels.tsv


sub-gutseur0001_ses-01_task-ecgself_ecg.set


sub-gutseur0001_ses-01_task-ecgself_ecg.json


sub-gutseur0001_ses-01_task-ecgself_events.tsv


sub-gutseur0001_ses-01_task-ecgself_events.json


For ECG data, the same holds as for Dynamometer data. For example, if you measure heart rate while participants perform a task with the short name 'self,' there will be behself files in the phenotype folder, and there will be ecgself files in the individual beh folder.

Name


phenotype


ses-01


physio


gutseur_ses-01_task-salivacort.tsv


gutseur_ses-01_task-salivacort.json


gutseur_ses-01_task-salivatesto.tsv


gutseur_ses-01_task-salivatesto.json


sub-gutseur0001


Hormone analysis output will consist of one file per measure that includes data of all participants and will therefore be placed in the ‘physio’ folder in phenotype as it's at the group level. Additionally, equal to all other TSV files, an accompanying JSON file will be placed in the same folder for each file.

Name


derivatives


phenotype


ses-01


quest


gutseur_ses-01_task-iri_desc-totalscores.tsv


neurobio


gutseur_ses-01_task-smri_desc-freesurfer-corticalvol.tsv


gutseur_ses-01_task-smri_desc-freesurfer-corticalthickness.tsv


fmriprep


freesurfer


sub-gutseur0001


ses-01


surf


stats


mri


group


ses-01


gutseur_ses-01_task-smri_desc-freesurfer-corticalvol.tsv


gutseur_ses-01_task-smri_desc-freesurfer-corticalthickness.tsv


phenotype


sub-gutseur0001


In the derivative folder, there is place for files that are the result of data going through some sort of processing pipeline. The derivative folder contains:

1. Pipeline folders
2. A phenotype folder

For example, calculated total/subscale scores of a questionnaire or freesurfer output or neurobiological group-level files manually created from freesurfer output can be placed in the derivative phenotype folder.

You can also opt to place the latter in a 'group' folder within the pipeline folder, so that all pipeline data output stays together.

"Desc"stands for description and describes what kind of data is derived from the primary file. In this case, it describes what kind of data was derived from the smri data (through freesurfer) and the questionnaire iri. Note that information about the original file should be placed in the accompanying json file.

Make sure task information is included in the derivative data structure. Preferably in the file name:

'sub-gutseur0001_ses-01_task-fmrisddt_desc-brain_mask.nii.gz'.

In case, that is not possible, it should be in the pipeline name:
'fmriprep_task-fmrisddt''