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.
Stephen Smith1 Diego Vidaurre2 Matthew Glasser, Anderson Winkler1 Paul McCarthy1 Emma Robinson, Xu Chen4 William Horton3 Mark Jenkinson, and Eugene Duff1 Christian Beckmann. Hcp beta-release of the functional connectivity megatrawl. In humanconnectome. 2015. URL: https://www.humanconnectome.org/storage/app/media/documentation/s500/HCP500_MegaTrawl_April2015.pdf.
Stephen M Smith, Thomas E Nichols, Diego Vidaurre, Anderson M Winkler, Timothy EJ Behrens, Matthew F Glasser, Kamil Ugurbil, Deanna M Barch, David C Van Essen, and Karla L Miller. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature neuroscience, 18(11):1565–1567, 2015.
Nicola Filippini, Bradley J. MacIntosh, Morgan G. Hough, Guy M. Goodwin, Giovanni B. Frisoni, Stephen M. Smith, Paul M. Matthews, Christian F. Beckmann, and Clare E. Mackay. Distinct patterns of brain activity in young carriers of the apoe-ε4 allele. Proceedings of the National Academy of Sciences, 106(17):7209–7214, 2009. URL: https://www.pnas.org/content/106/17/7209, arXiv:https://www.pnas.org/content/106/17/7209.full.pdf, doi:10.1073/pnas.0811879106.
SM Smith, MF Glasser, E Robinson, G Salimi-Khorshidi, E Duff, DC Van Essen, MW Woolrich, M Jenkinson, and CF Beckmann. Methods for network modelling from high quality rfmri data. In OHBM 2014 Annual Meeting. 2014.
Jill X. O’Reilly, Christian F. Beckmann, Valentina Tomassini, Narender Ramnani, and Heidi Johansen-Berg. Distinct and Overlapping Functional Zones in the Cerebellum Defined by Resting State Functional Connectivity. Cerebral Cortex, 20(4):953–965, 08 2009. URL: https://doi.org/10.1093/cercor/bhp157, arXiv:https://academic.oup.com/cercor/article-pdf/20/4/953/17303287/bhp157.pdf, doi:10.1093/cercor/bhp157.