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')[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 [1] if you are using this dataset.

New in version 0.5.2.

Parameters:
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.

data_dirpathlib.Path or str, optional

Path where data should be downloaded. By default, files are downloaded in home directory.

resumebool, optional

Whether to resume download of a partly-downloaded file. Default=True.

verboseint, optional

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)

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’: numpy.ndarray

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

Notes

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

Examples using nilearn.datasets.fetch_development_fmri#

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions
Computing a connectome with sparse inverse covariance

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

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Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation
Group Sparse inverse covariance for multi-subject connectome

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Regions extraction using dictionary learning and functional connectomes

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Regions extraction using dictionary learning and functional connectomes
Comparing connectomes on different reference atlases

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Extracting signals from a brain parcellation

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Comparing the means of 2 images

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Smoothing an image

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Simple example of NiftiMasker use

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Extracting signals from brain regions using the NiftiLabelsMasker

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Understanding NiftiMasker and mask computation

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Multivariate decompositions: Independent component analysis of fMRI

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Functional connectivity predicts age group

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