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_dir
pathlib.Path
orstr
, 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 alsonilearn.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.
- url
str
, default=None URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data).
- resume
bool
, default=True Whether to resume download of a partly-downloaded file.
- verbose
int
, default=1 Verbosity level (0 means no message).
- data_dir
- Returns:
- data
sklearn.utils.Bunch
Dictionary-like object, the interest attributes are :
‘session_target’:
list
ofstr
. Paths to text file containing run and target data.‘mask’:
str
. Path to fullbrain mask file.‘mask_vt’:
list
ofstr
. Paths to nifti ventral temporal mask file.‘mask_face’:
list
ofstr
. Paths to nifti with face-reponsive brain regions.‘mask_face_little’:
list
ofstr
. Spatially more constrained version of the above.‘mask_house’:
list
ofstr
. Paths to nifti with house-reponsive brain regions.‘mask_house_little’:
list
ofstr
. Spatially more constrained version of the above.
- data
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
¶

Basic nilearn example: manipulating and looking at data

Decoding with FREM: face vs house vs chair object recognition

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

The haxby dataset: different multi-class strategies

Decoding of a dataset after GLM fit for signal extraction

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