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.

8.2.15. nilearn.datasets.fetch_abide_pcp

nilearn.datasets.fetch_abide_pcp(data_dir=None, n_subjects=None, pipeline='cpac', band_pass_filtering=False, global_signal_regression=False, derivatives=['func_preproc'], quality_checked=True, url=None, verbose=1, **kwargs)

Fetch ABIDE dataset.

Fetch the Autism Brain Imaging Data Exchange (ABIDE) dataset wrt criteria that can be passed as parameter. Note that this is the preprocessed version of ABIDE provided by the preprocess connectome projects (PCP). See [1].

Parameters

data_dirstring, optional

Path of the data directory. Used to force data storage in a specified location. Default: None

n_subjectsint, optional

The number of subjects to load. If None is given, all available subjects are used (this number depends on the preprocessing pipeline used).

pipelinestring {‘cpac’, ‘css’, ‘dparsf’, ‘niak’}, optional

Possible pipelines are “ccs”, “cpac”, “dparsf” and “niak”. Default=’cpac’.

band_pass_filteringboolean, optional

Due to controversies in the literature, band pass filtering is optional. If true, signal is band filtered between 0.01Hz and 0.1Hz. Default=False.

global_signal_regressionboolean optional

Indicates if global signal regression should be applied on the signals. Default=False.

derivativesstring list, optional

Types of downloaded files. Possible values are: alff, degree_binarize, degree_weighted, dual_regression, eigenvector_binarize, eigenvector_weighted, falff, func_mask, func_mean, func_preproc, lfcd, reho, rois_aal, rois_cc200, rois_cc400, rois_dosenbach160, rois_ez, rois_ho, rois_tt, and vmhc. Please refer to the PCP site for more details. Default=[‘func_preproc’].

quality_checkedboolean, optional

If true (default), restrict the list of the subjects to the one that passed quality assessment for all raters. Default=True.

kwargsparameter list, optional

Any extra keyword argument will be used to filter downloaded subjects according to the CSV phenotypic file. Some examples of filters are indicated below.

SUB_IDlist of integers in [50001, 50607], optional

Ids of the subjects to be loaded.

DX_GROUPinteger in {1, 2}, optional

1 is autism, 2 is control.

DSM_IV_TRinteger in [0, 4], optional

O is control, 1 is autism, 2 is Asperger, 3 is PPD-NOS, 4 is Asperger or PPD-NOS.

AGE_AT_SCANfloat in [6.47, 64], optional

Age of the subject.

SEXinteger in {1, 2}, optional

1 is male, 2 is female.

HANDEDNESS_CATEGORYstring in {‘R’, ‘L’, ‘Mixed’, ‘Ambi’}, optional

R = Right, L = Left, Ambi = Ambidextrous.

HANDEDNESS_SCOREinteger in [-100, 100], optional

Positive = Right, Negative = Left, 0 = Ambidextrous.

Notes

Code and description of preprocessing pipelines are provided on the PCP website <http://preprocessed-connectomes-project.github.io/>.

References

1

Nielsen, Jared A., et al. “Multisite functional connectivity MRI classification of autism: ABIDE results.” Frontiers in human neuroscience 7 (2013).