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_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 references.
- dimensionalityint, 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. Default=100.
- timeseriesstr, 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. Default=’eigen_regression’.
- matricesstr, 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. Default=’partial_correlation’.
Path where data should be downloaded. By default, files are downloaded in home directory.
Whether to resume download of a partly-downloaded file. Default=True.
Verbosity level (0 means no message). Default=1.
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
See description for terms & conditions on data usage.