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.

7.2.26. nilearn.datasets.fetch_megatrawls_netmats

nilearn.datasets.fetch_megatrawls_netmats(dimensionality=100, timeseries=’eigen_regression’, matrices=’partial_correlation’, data_dir=None, resume=True, verbose=1)

Downloads and returns Network Matrices data from MegaTrawls release in HCP.

This data can be used to predict relationships between imaging data and non-imaging behavioural measures such as age, sex, education, etc. The network matrices are estimated from functional connectivity datasets of 461 subjects. Full technical details in [1] [2].

New in version 0.2.2.


dimensionality: int, optional

Valid inputs are 25, 50, 100, 200, 300. By default, network matrices estimated using Group ICA brain parcellations of 100 components/dimensions will be returned.

timeseries: str, optional

Valid inputs are ‘multiple_spatial_regression’ or ‘eigen_regression’. By default ‘eigen_regression’, matrices estimated using first principal eigen component timeseries signals extracted from each subject data parcellations will be returned. Otherwise, ‘multiple_spatial_regression’ matrices estimated using spatial regressor based timeseries signals extracted from each subject data parcellations will be returned.

matrices: str, optional

Valid inputs are ‘full_correlation’ or ‘partial_correlation’. By default, partial correlation matrices will be returned otherwise if selected full correlation matrices will be returned.

data_dir: str, default is None, optional

Path of the data directory. Used to force data storage in a specified location.

resume: bool, default is True

This parameter is required if a partially downloaded file is needed to be resumed to download again.

verbose: int, default is 1

This parameter is used to set the verbosity level to print the message to give information about the processing. 0 indicates no information will be given.


data: Bunch

dictionary-like object, the attributes are :

  • ‘dimensions’: int, consists of given input in dimensions.
  • ‘timeseries’: str, consists of given input in timeseries method.
  • ‘matrices’: str, consists of given type of specific matrices.
  • ‘correlation_matrices’: ndarray, consists of correlation matrices based on given type of matrices. Array size will depend on given dimensions (n, n).
  • ‘description’: data description


[1] Stephen Smith et al, HCP beta-release of the Functional Connectivity
MegaTrawl. April 2015 “HCP500-MegaTrawl” release.

[2] Smith, S.M. et al. Nat. Neurosci. 18, 1565-1567 (2015).

[3] N.Filippini, et al. Distinct patterns of brain activity in young
carriers of the APOE-e4 allele. Proc Natl Acad Sci USA (PNAS), 106::7209-7214, 2009.
[4] S.Smith, et al. Methods for network modelling from high quality rfMRI data.
Meeting of the Organization for Human Brain Mapping. 2014
[5] J.X. O’Reilly et al. Distinct and overlapping functional zones in the
cerebellum defined by resting state functional connectivity. Cerebral Cortex, 2009.

Note: See description for terms & conditions on data usage. Examples using nilearn.datasets.fetch_megatrawls_netmats