This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
- nilearn.datasets.fetch_development_fmri(n_subjects=None, reduce_confounds=True, data_dir=None, resume=True, verbose=1, age_group='both')[source]#
Fetch movie watching based brain development dataset (fMRI).
The data is downsampled to 4mm resolution for convenience with a repetition time (TR) of 2 secs. The origin of the data is coming from OpenNeuro. See Notes below.
Please cite  if you are using this dataset.
New in version 0.5.2.
- n_subjectsint, optional
The number of subjects to load. If None, all the subjects are loaded. Total 155 subjects.
- reduce_confoundsbool, optional
If True, the returned confounds only include 6 motion parameters, mean framewise displacement, signal from white matter, csf, and 6 anatomical compcor parameters. This selection only serves the purpose of having realistic examples. Depending on your research question, other confounds might be more appropriate. If False, returns all fMRIPrep confounds. Default=True.
Path where data should be downloaded. By default, files are downloaded in home directory.
Whether to resume download of a partly-downloaded file. Default=True.
Verbosity level (0 means no message). Default=1.
- age_groupstr, optional
Default=’both’. Which age group to fetch
‘adults’ = fetch adults only (n=33, ages 18-39)
‘child’ = fetch children only (n=122, ages 3-12)
‘both’ = fetch full sample (n=155)
Dictionary-like object, the interest attributes are :
- ‘func’: list of str (Nifti files)
Paths to downsampled functional MRI data (4D) for each subject.
- ‘confounds’: list of str (tsv files)
Paths to confounds related to each subject.
- ‘phenotypic’: numpy.ndarray
Contains each subject age, age group, child or adult, gender, handedness.
The original data is downloaded from OpenNeuro https://openneuro.org/datasets/ds000228/versions/1.0.0
This fetcher downloads downsampled data that are available on Open Science Framework (OSF). Located here: https://osf.io/5hju4/files/
Preprocessing details: https://osf.io/wjtyq/
Note that if n_subjects > 2, and age_group is ‘both’, fetcher will return a ratio of children and adults representative of the total sample.
Extracting signals of a probabilistic atlas of functional regions
Computing a connectome with sparse inverse covariance
Producing single subject maps of seed-to-voxel correlation
Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
Group Sparse inverse covariance for multi-subject connectome
Regions extraction using dictionary learning and functional connectomes
Comparing connectomes on different reference atlases
Classification of age groups using functional connectivity
Extracting signals from a brain parcellation
Extract signals on spheres and plot a connectome
Clustering methods to learn a brain parcellation from fMRI
Comparing the means of 2 images
Simple example of NiftiMasker use
Extracting signals from brain regions using the NiftiLabelsMasker
Understanding NiftiMasker and mask computation
Multivariate decompositions: Independent component analysis of fMRI
Functional connectivity predicts age group