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.
5.2.1. sammba.data_fetchers.fetch_zurich_test_retest¶
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sammba.data_fetchers.fetch_zurich_test_retest(subjects=range(0, 15), sessions=[1], data_dir=None, url=None, resume=True, verbose=1, correct_headers=False)¶ Download and loads the ETH-Zurich test-retest dataset.
- Parameters
subjects : sequence of int or None, optional
ids of subjects to load, default to loading all subjects.
sessions : iterable of int, optional
The sessions to load. Load only the first session by default.
data_dir : string, optional
Path of the data directory. Used to force data storage in a specified location. Default: None
resume : bool, optional (default True)
If true, try resuming download if possible.
verbose : int, optional (default 0)
Defines the level of verbosity of the output.
- Returns
data : sklearn.datasets.base.Bunch
Dictionary-like object, the interest attributes are :
‘func’: string list. Paths to functional images.
‘anat’: string list. Paths to anatomic images.
‘session’: numpy array. List of ids corresponding to images sessions.
Notes
This dataset is composed of 2 sessions of 15 male mice. For each mice, 2 resting-state scans of continuous EPI functional volumes were collected, both with their anatomical scan. Session 2 was collected 15-20 days after Session 1.
References
- Download
- Reference
Mapping the Mouse Brain with Rs-fMRI: An Optimized Pipeline for Functional Network Identification NeuroImage 123 (2015): 11-21. V. Zerbi, J. Grandjean, M. Rudin, and N. Wenderoth.