Note

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

nilearn.datasets.fetch_development_fmri(n_subjects=None, reduce_confounds=True, data_dir=None, resume=True, verbose=1, age_group='both')

Fetch movie watching based brain development dataset (fMRI).

The data is downsampled to 4mm resolution for convenience with a repetition time (t_r) of 2 secs. The origin of the data is coming from OpenNeuro. See Notes below.

Please cite Richardson et al.[1] if you are using this dataset.

Added in Nilearn 0.5.2.

Parameters:

n_subjectsint, default=None

The number of subjects to load. If None, all the subjects are loaded. Total 155 subjects.

reduce_confoundsbool, default=True

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.

data_dirpathlib.Path or str or None, optional

Path where data should be downloaded. By default, files are downloaded in a nilearn_data folder in the home directory of the user. See also nilearn.datasets.utils.get_data_dirs.

resumebool, default=True

Whether to resume download of a partly-downloaded file.

verbosebool or int, default=1

Verbosity level (0 or False means no message).

age_groupstr, 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)

Returns:

dataBunch

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’: pandas.DataFame

  Contains each subject age, age group, child or adult, gender, handedness.

• ‘t_r’: float

  Repetition time of the functional data.

Notes

If the dataset files are already present in the user’s Nilearn data directory, this fetcher will not re-download them. To force a fresh download, you can remove the existing dataset folder from your local Nilearn data directory.

For more details on how Nilearn stores datasets.

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.

References

[1]

Hilary Richardson, Grace Lisandrelli, Alexa Riobueno-Naylor, and Rebecca Saxe. Development of the social brain from age three to twelve years. Nature communications, 9(1):1–12, 2018.

Examples using nilearn.datasets.fetch_development_fmri

Classification of age groups using functional connectivity

Classification of age groups using functional connectivity

Clustering methods to learn a brain parcellation from fMRI

Clustering methods to learn a brain parcellation from fMRI

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Computing a connectome with sparse inverse covariance

Computing a connectome with sparse inverse covariance

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome

Extracting signals from a brain parcellation

Extracting signals from a brain parcellation

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions

Group Sparse inverse covariance for multi-subject connectome

Group Sparse inverse covariance for multi-subject connectome

Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation

Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes

Comparing the means of 2 images

Comparing the means of 2 images

Extracting signals from brain regions using the NiftiLabelsMasker

Extracting signals from brain regions using the NiftiLabelsMasker

Simple example of NiftiMasker use

Simple example of NiftiMasker use

Smoothing an image

Smoothing an image

Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation

Functional connectivity predicts age group

Functional connectivity predicts age group

Multivariate decompositions: Independent component analysis of fMRI

Multivariate decompositions: Independent component analysis of fMRI