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 [1].

Parameters:

data_dirpathlib.Path or str, optional: Path where data should be downloaded. By default, files are downloaded in home directory.
subjectslist or int, optional: 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. Default=(2,).
fetch_stimuliboolean, optional: Indicate if stimuli images must be downloaded. They will be presented as a dictionary of categories. Default=False.
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’: string list. Paths to anatomic images.
‘func’: string list. Paths to nifti file with bold data.
‘session_target’: string list. Paths to text file containing session and target data.
‘mask’: string. Path to fullbrain mask file.
‘mask_vt’: string list. Paths to nifti ventral temporal mask file.
‘mask_face’: string list. Paths to nifti ventral temporal mask file.
‘mask_house’: string list. Paths to nifti ventral temporal mask file.
‘mask_face_little’: string list. Paths to nifti ventral temporal mask file.
‘mask_house_little’: string list. Paths to nifti ventral temporal mask file.

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 <http://www.sciencemag.org/content/293/5539/2425>

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

NeuroImaging volumes visualization

Plot Haxby masks

Plotting tools in nilearn

More plotting tools from nilearn

Show stimuli of Haxby et al. dataset

Cortical surface-based searchlight decoding

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

Decoding with FREM: face vs house vs chair object recognition

The haxby dataset: different multi-class strategies

Searchlight analysis of face vs house recognition

Decoding of a dataset after GLM fit for signal extraction

ROI-based decoding analysis in Haxby et al. dataset

Setting a parameter by cross-validation

Different classifiers in decoding the Haxby dataset

Computing a Region of Interest (ROI) mask manually

Massively univariate analysis of face vs house recognition

Advanced decoding using scikit learn

nilearn.datasets.fetch_haxby#

Examples using nilearn.datasets.fetch_haxby#

Examples using `nilearn.datasets.fetch_haxby`#