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.
7.2.28. 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 nonimaging 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.
Parameters: 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.
Returns: data: Bunch
dictionarylike 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
References
 [1] Stephen Smith et al, HCP betarelease of the Functional Connectivity
 MegaTrawl. April 2015 “HCP500MegaTrawl” release. https://db.humanconnectome.org/megatrawl/
[2] Smith, S.M. et al. Nat. Neurosci. 18, 15651567 (2015).
 [3] N.Filippini, et al. Distinct patterns of brain activity in young
 carriers of the APOEe4 allele. Proc Natl Acad Sci USA (PNAS), 106::72097214, 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.