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 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.
subjectslist or tuple 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_stimulibool, default=False: Indicate if stimuli images must be downloaded. They will be presented as a dictionary of categories.
urlstr or None, 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.utils.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

Basic nilearn example: manipulating and looking at data

More plotting tools from nilearn

NeuroImaging volumes visualization

Plot Haxby masks

Plotting tools in nilearn

Cortical surface-based searchlight decoding

Decoding of a dataset after GLM fit for signal extraction

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

Decoding with FREM: face vs house vs chair object recognition

Different classifiers in decoding the Haxby dataset

ROI-based decoding analysis in Haxby et al. dataset

Searchlight analysis of face vs house recognition

Setting a parameter by cross-validation

Show stimuli of Haxby et al. dataset

The haxby dataset: different multi-class strategies

Understanding Decoder

Computing a Region of Interest (ROI) mask manually

Advanced decoding using scikit learn

Massively univariate analysis of face vs house recognition

nilearn.datasets.fetch_haxby¶

Examples using nilearn.datasets.fetch_haxby¶

Examples using `nilearn.datasets.fetch_haxby`¶