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.

7.2.9. nilearn.datasets.fetch_atlas_basc_multiscale_2015

nilearn.datasets.fetch_atlas_basc_multiscale_2015(version=’sym’, data_dir=None, resume=True, verbose=1)

Downloads and loads multiscale functional brain parcellations

This atlas includes group brain parcellations generated from resting-state functional magnetic resonance images from about 200 young healthy subjects.

Multiple scales (number of networks) are available, among 7, 12, 20, 36, 64, 122, 197, 325, 444. The brain parcellations have been generated using a method called bootstrap analysis of stable clusters called as BASC, (Bellec et al., 2010) and the scales have been selected using a data-driven method called MSTEPS (Bellec, 2013).

Note that two versions of the template are available, ‘sym’ or ‘asym’. The ‘asym’ type contains brain images that have been registered in the asymmetric version of the MNI brain template (reflecting that the brain is asymmetric), while the ‘sym’ type contains images registered in the symmetric version of the MNI template. The symmetric template has been forced to be symmetric anatomically, and is therefore ideally suited to study homotopic functional connections in fMRI: finding homotopic regions simply consists of flipping the x-axis of the template.

New in version 0.2.3.


version: str, optional

Available versions are ‘sym’ or ‘asym’. By default all scales of brain parcellations of version ‘sym’ will be returned.

data_dir: str, optional

directory where data should be downloaded and unpacked.

url: str, optional

url of file to download.

resume: bool

whether to resumed download of a partly-downloaded file.

verbose: int

verbosity level (0 means no message).


data: sklearn.datasets.base.Bunch

dictionary-like object, Keys are:

  • “scale007”, “scale012”, “scale020”, “scale036”, “scale064”, “scale122”, “scale197”, “scale325”, “scale444”: str, path to Nifti file of various scales of brain parcellations.
  • “description”: details about the data release.


For more information on this dataset’s structure, see


Bellec P, Rosa-Neto P, Lyttelton OC, Benali H, Evans AC, Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51 (3), 1126-1139. URL

Bellec P, Jun. 2013. Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure. Pattern Recognition in Neuroimaging (PRNI), 2013 pp. 54-57. Examples using nilearn.datasets.fetch_atlas_basc_multiscale_2015