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_localizer_first_level

nilearn.datasets.fetch_localizer_first_level(data_dir=None, verbose=1)[source]

Download a first-level localizer fMRI dataset.

For more information see the dataset description.

Parameters:
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.

verbosebool or int, default=1

Verbosity level (0 or False means no message).

Returns:
datasklearn.utils.Bunch

Dictionary-like object, with the keys:

  • epi_img: the input 4D image

  • events: a csv file describing the paradigm

  • description: data description

  • t_r: repetition time of the function data in seconds

  • slice_time_ref:

    slice timing reference used during slice timing correction

  • ‘template’str

    The standardized space of analysis in which the atlas results are provided. When known it should be a valid template name taken from the spaces described in the BIDS specification.

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.

Examples using nilearn.datasets.fetch_localizer_first_level

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Example of surface-based first-level analysis

Example of surface-based first-level analysis

Understanding parameters of the first-level model

Understanding parameters of the first-level model