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.

7.2.27. nilearn.datasets.fetch_cobre

nilearn.datasets.fetch_cobre(n_subjects=10, data_dir=None, url=None, verbose=1)

Fetch COBRE datasets preprocessed using NIAK 0.17 under CentOS version 6.3 with Octave version 4.0.2 and the Minc toolkit version 0.3.18.

Downloads and returns COBRE preprocessed resting state fMRI datasets, covariates and phenotypic information such as demographic, clinical variables, measure of frame displacement FD (an average FD for all the time frames left after censoring).

Each subject fmri_XXXXXXX.nii.gz is a 3D+t nifti volume (150 volumes). WARNING: no confounds were actually regressed from the data, so it can be done interactively by the user who will be able to explore different analytical paths easily.

For each subject, there is fmri_XXXXXXX.tsv files which contains the covariates such as motion parameters, mean CSF signal that should to be regressed out of the functional data.

keys_confounds.json: a json file, that describes each variable mentioned in the files fmri_XXXXXXX.tsv.gz. It also contains a list of time frames that have been removed from the time series by censoring for high motion.

phenotypic_data.tsv contains the data of clinical variables that explained in keys_phenotypic_data.json

New in version 0.3.

Parameters:

n_subjects: int, optional

The number of subjects to load from maximum of 146 subjects. By default, 10 subjects will be loaded. If n_subjects=None, all subjects will be loaded.

data_dir: str, optional

Path to the data directory. Used to force data storage in a specified location. Default: None

url: str, optional

Override download url. Used for test only (or if you setup a mirror of the data). Default: None

verbose: int, optional

Verbosity level (0 means no message).

Returns:

data: Bunch

Dictionary-like object, the attributes are:

  • ‘func’: string list
    Paths to Nifti images.
  • ‘confounds’: string list
    Paths to .tsv files of each subject, confounds.
  • ‘phenotypic’: numpy.recarray
    Contains data of clinical variables, sex, age, FD.
  • ‘description’: data description of the release and references.
  • ‘desc_con’: str
    description of the confounds variables
  • ‘desc_phenotypic’: str
    description of the phenotypic variables.

Notes

See more information about datasets structure