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, optional: URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data). Default=None.
resumebool, optional: Whether to resume download of a partly-downloaded file. Default=True.
verboseint, optional: Verbosity level (0 means no message). Default=1.

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

1: James V. Haxby, M. Ida Gobbini, Maura L. Furey, Alumit Ishai, Jennifer L. Schouten, and Pietro Pietrini. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539):2425–2430, 2001. URL: https://science.sciencemag.org/content/293/5539/2425, arXiv:https://science.sciencemag.org/content/293/5539/2425.full.pdf, doi:10.1126/science.1063736.

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

Decoding with FREM: face vs house object recognition

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

Cortical surface-based searchlight decoding

The haxby dataset: different multi-class strategies

Searchlight analysis of face vs house recognition

Decoding of a dataset after GLM fit for signal extraction

Setting a parameter by cross-validation

ROI-based decoding analysis in Haxby et al. dataset

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`#