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.

7.2.12. 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).

Parameters:

data_dir: string, optional

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

n_subjects: int, optional

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

pipeline: string, optional

Possible pipelines are “ccs”, “cpac”, “dparsf” and “niak”

band_pass_filtering: boolean, optional

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

global_signal_regression: boolean optional

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

derivatives: string 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.

quality_checked: boolean, optional

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

kwargs: parameter 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_ID: list of integers in [50001, 50607], optional

Ids of the subjects to be loaded.

DX_GROUP: integer in {1, 2}, optional

1 is autism, 2 is control

DSM_IV_TR: integer 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_SCAN: float in [6.47, 64], optional

Age of the subject

SEX: integer in {1, 2}, optional

1 is male, 2 is female

HANDEDNESS_CATEGORY: string in {‘R’, ‘L’, ‘Mixed’, ‘Ambi’}, optional

R = Right, L = Left, Ambi = Ambidextrous

HANDEDNESS_SCORE: integer 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

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