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_haxby#

nilearn.datasets.fetch_haxby(data_dir=None, subjects=(2,), fetch_stimuli=False, url=None, resume=True, verbose=1)[source]#

Download and loads complete haxby dataset.

See Haxby et al.[1].

Parameters:
data_dirpathlib.Path or str, 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.

subjectslist or int, default=(2,)

Either a list of subjects or the number of subjects to load, from 1 to 6. By default, 2nd subject will be loaded. Empty list returns no subject data.

fetch_stimuliboolean, default=False

Indicate if stimuli images must be downloaded. They will be presented as a dictionary of categories.

urlstr, default=None

URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data).

resumebool, default=True

Whether to resume download of a partly-downloaded file.

verboseint, default=1

Verbosity level (0 means no message).

Returns:
datasklearn.datasets.base.Bunch

Dictionary-like object, the interest attributes are :

  • ‘anat’: list of str. Paths to anatomic images.

  • ‘func’: list of str. Paths to nifti file with BOLD data.

  • ‘session_target’: list of str. Paths to text file containing run and target data.

  • ‘mask’: str. Path to fullbrain mask file.

  • ‘mask_vt’: list of str. Paths to nifti ventral temporal mask file.

  • ‘mask_face’: list of str. Paths to nifti with face-reponsive brain regions.

  • ‘mask_face_little’: list of str. Spatially more constrained version of the above.

  • ‘mask_house’: list of str. Paths to nifti with house-reponsive brain regions.

  • ‘mask_house_little’: list of str. Spatially more constrained version of the above.

Notes

PyMVPA provides a tutorial making use of this dataset: http://www.pymvpa.org/tutorial.html

More information about its structure: http://dev.pymvpa.org/datadb/haxby2001.html

See additional information <https://www.science.org/doi/10.1126/science.1063736>

Run 8 in subject 5 does not contain any task labels. The anatomical image for subject 6 is unavailable.

References

Examples using nilearn.datasets.fetch_haxby#

A introduction tutorial to fMRI decoding

A introduction tutorial to fMRI decoding

NeuroImaging volumes visualization

NeuroImaging volumes visualization

Plot Haxby masks

Plot Haxby masks

Plotting tools in nilearn

Plotting tools in nilearn

More plotting tools from nilearn

More plotting tools from nilearn

Show stimuli of Haxby et al. dataset

Show stimuli of Haxby et al. dataset

Cortical surface-based searchlight decoding

Cortical surface-based searchlight decoding

Decoding with FREM: face vs house vs chair object recognition

Decoding with FREM: face vs house vs chair object recognition

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

The haxby dataset: different multi-class strategies

The haxby dataset: different multi-class strategies

Searchlight analysis of face vs house recognition

Searchlight analysis of face vs house recognition

Decoding of a dataset after GLM fit for signal extraction

Decoding of a dataset after GLM fit for signal extraction

ROI-based decoding analysis in Haxby et al. dataset

ROI-based decoding analysis in Haxby et al. dataset

Setting a parameter by cross-validation

Setting a parameter by cross-validation

Different classifiers in decoding the Haxby dataset

Different classifiers in decoding the Haxby dataset

Computing a Region of Interest (ROI) mask manually

Computing a Region of Interest (ROI) mask manually

Massively univariate analysis of face vs house recognition

Massively univariate analysis of face vs house recognition

Advanced decoding using scikit learn

Advanced decoding using scikit learn