htfa.datasets.fetch_development_fmri¶

htfa.datasets.fetch_development_fmri(n_subjects=None, adults_only=True, clean=True, data_dir=None, cache_dir=None)[source]¶

nilearn’s development_fmri runs, masked and cleaned for HTFA.

In this example dataset, children and adults watch Pixar’s “Partly Cloudy” during fMRI acquisition. Each run contains 168 brain volumes sampled two seconds apart. This convenience loader is specific to that study; use htfa.load or arrays for other experimental datasets.

Downloads the runs that are not yet in nilearn’s data directory (about 25 MB each), masks each one (load_development_fmri_subject, cached) and, with clean, removes a linear trend and the confounds (regress_confounds).

Parameters:

n_subjectsint, optional: Number of subjects; all of the age group by default.
adults_onlybool: Only the 33 adults (default); False adds children.
cleanbool: Regress out a linear trend and the confounds (default). False returns the masked BOLD intensities.
data_dirpath, optional: nilearn’s data directory.
cache_dirpath, optional: Cache of the masked runs (default: development_fmri/htfa in the data directory).

Returns:

DevelopmentFMRI: subject_ids, data, coords, confounds, phenotypic and tr; data and coords go straight to htfa.fit().

Examples

>>> import htfa
>>> ds = htfa.datasets.fetch_development_fmri(n_subjects=3)
>>> res = htfa.fit(ds.data, coords=ds.coords, K=20)