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.

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, contains:

  • epi_img: str. Full path the 4D BOLD nifti image.

  • events: str. Full path to TSV file describing the paradigm.

  • slice_time_ref: float. Slice timing reference used during slice timing correction.

  • t_r: float. Repetition time in seconds.

  • template: str. The standardized space of analysis in which the data is provided.

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 Impulse Response (FIR) model

Analysis of an fMRI dataset with a Finite Impulse 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