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 Richardson et al.[1] if you are using this dataset.
New in version 0.5.2.
- Parameters:
- n_subjects
int
, optional The number of subjects to load. If None, all the subjects are loaded. Total 155 subjects.
- reduce_confounds
bool
, 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_dir
pathlib.Path
orstr
, 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 alsonilearn.datasets.utils.get_data_dirs
.- resume
bool
, default=True Whether to resume download of a partly-downloaded file.
- verbose
int
, default=1 Verbosity level (0 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)
- n_subjects
- Returns:
- dataBunch
Dictionary-like object, the interest attributes are :
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
#
![](../../_images/sphx_glr_plot_inverse_covariance_connectome_thumb.png)
Computing a connectome with sparse inverse covariance
![](../../_images/sphx_glr_plot_probabilistic_atlas_extraction_thumb.png)
Extracting signals of a probabilistic atlas of functional regions
![](../../_images/sphx_glr_plot_multi_subject_connectome_thumb.png)
Group Sparse inverse covariance for multi-subject connectome
![](../../_images/sphx_glr_plot_seed_to_voxel_correlation_thumb.png)
Producing single subject maps of seed-to-voxel correlation
![](../../_images/sphx_glr_plot_compare_decomposition_thumb.png)
Deriving spatial maps from group fMRI data using ICA and Dictionary Learning
![](../../_images/sphx_glr_plot_extract_regions_dictlearning_maps_thumb.png)
Regions extraction using dictionary learning and functional connectomes
![](../../_images/sphx_glr_plot_atlas_comparison_thumb.png)
Comparing connectomes on different reference atlases
![](../../_images/sphx_glr_plot_group_level_connectivity_thumb.png)
Classification of age groups using functional connectivity
![](../../_images/sphx_glr_plot_data_driven_parcellations_thumb.png)
Clustering methods to learn a brain parcellation from fMRI
![](../../_images/sphx_glr_plot_nifti_labels_simple_thumb.png)
Extracting signals from brain regions using the NiftiLabelsMasker
![](../../_images/sphx_glr_plot_ica_resting_state_thumb.png)
Multivariate decompositions: Independent component analysis of fMRI