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_dosenbach_2010(ordered_regions=True, legacy_format=True)[source]#

Load the Dosenbach et al. 160 ROIs. These ROIs cover much of the cerebral cortex and cerebellum and are assigned to 6 networks.

See 1.

ordered_regionsbool, optional

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

legacy_formatbool, optional

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


Dictionary-like object, contains:

  • ‘rois’: numpy.recarray, rec array with the coordinates of the 160 ROIs in MNI space. If legacy_format is set to False, this is a pandas.DataFrame.

  • ‘labels’: numpy.ndarray of str, list of label names for the 160 ROIs.

  • ‘networks’: numpy.ndarray of str, list of network names for the 160 ROI.

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



Nico U. F. Dosenbach, Binyam Nardos, Alexander L. Cohen, Damien A. Fair, Jonathan D. Power, Jessica A. Church, Steven M. Nelson, Gagan S. Wig, Alecia C. Vogel, Christina N. Lessov-Schlaggar, Kelly Anne Barnes, Joseph W. Dubis, Eric Feczko, Rebecca S. Coalson, John R. Pruett, Deanna M. Barch, Steven E. Petersen, and Bradley L. Schlaggar. Prediction of individual brain maturity using fmri. Science, 329(5997):1358–1361, 2010. URL:, arXiv:, doi:10.1126/science.1194144.

Examples using nilearn.datasets.fetch_coords_dosenbach_2010#

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Extract signals on spheres and plot a connectome

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