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_coords_seitzman_2018

nilearn.datasets.fetch_coords_seitzman_2018(ordered_regions=True, legacy_format=False)

Load the Seitzman et al. 300 ROIs.

These ROIs cover cortical, subcortical and cerebellar regions and are assigned to one of 13 networks (Auditory, CinguloOpercular, DefaultMode, DorsalAttention, FrontoParietal, MedialTemporalLobe, ParietoMedial, Reward, Salience, SomatomotorDorsal, SomatomotorLateral, VentralAttention, Visual) and have a regional label (cortexL, cortexR, cerebellum, thalamus, hippocampus, basalGanglia, amygdala, cortexMid).

See Seitzman et al.[1].

Added in version 0.5.1.

Parameters:

ordered_regionsbool, default=True

ROIs from same networks are grouped together and ordered with respect to their locations (anterior to posterior).

legacy_formatbool, default=True

If set to True, the fetcher will return recarrays. Otherwise, it will return pandas dataframes.

Returns:

datasklearn.utils.Bunch

Dictionary-like object, contains:

• ‘rois’: numpy.recarray, rec array with the coordinates of the 300 ROIs in MNI space. If legacy_format is set to False, this is a pandas.DataFrame.

• ‘radius’: numpy.ndarray of int, radius of each ROI in mm.

• ‘networks’: numpy.ndarray of str, names of the corresponding network for each ROI.

• ‘regions’: numpy.ndarray of str, names of the regions.

• ‘description’: str, description of the dataset.

References

[1]

Benjamin A. Seitzman, Caterina Gratton, Scott Marek, Ryan V. Raut, Nico U.F. Dosenbach, Bradley L. Schlaggar, Steven E. Petersen, and Deanna J. Greene. A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum. NeuroImage, 206:116290, 2020. doi:10.1016/j.neuroimage.2019.116290.