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.

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=None, quality_checked=True, url=None, verbose=1, legacy_format=False, **kwargs)[source]

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 Nielsen et al.[1].

Parameters:
data_dirpathlib.Path or str, optional

Path where data should be downloaded. By default, files are downloaded in a nilearn_data folder in the home directory of the user. See also nilearn.datasets.utils.get_data_dirs.

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).

pipelinestr {‘cpac’, ‘css’, ‘dparsf’, ‘niak’}, default=’cpac’

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

band_pass_filteringbool, default=False

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_regressionbool, default=False

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

derivativeslist of str, default=[‘func_preproc’]

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. Will default to ['func_preproc'] if None is passed.

quality_checkedbool, default=True

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

urlstr, default=None

URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data).

verboseint, default=1

Verbosity level (0 means no message).

legacy_formatbool, default=True

If set to True, the fetcher will return recarrays. Otherwise, it will return pandas dataframes.

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.

Returns:
datasklearn.utils.Bunch

Dictionary-like object, the keys are described below.

  • ‘description’: str, description of the dataset.
  • ‘phenotypic’: pandas.DataFrame

    phenotypic information for each subject.

  • Specific Derivative Keys:

    Additional keys,’func_preproc’ being the default, are introduced based on the provided ‘derivatives’ parameter during fetching. Any combination of the parameters below may occur.

    • ‘func_preproc’ (default): numpy.ndarray, paths to preprocessed functional MRI data in NIfTI format. This key is present by default when fetching the dataset.

    • ‘alff’: numpy.ndarray, amplitude values of low-frequency fluctuations in functional MRI data.

    • ‘degree_binarize’: numpy.ndarray, data specific to binarized node degree in brain networks.

    • ‘degree_weighted’: numpy.ndarray, data specific to weighted node degree, considering connectivity strength in brain networks.

    • ‘dual_regression’: numpy.ndarray, results from dual regression analysis, often involving the identification of resting-state networks.

    • ‘eigenvector_binarize’: numpy.ndarray, data specific to binarized eigenvector centrality, a measure of node influence in brain networks.

    • ‘eigenvector_weighted’: numpy.ndarray, data specific to weighted eigenvector centrality, reflecting node influence with consideration of connectivity strength.

    • ‘falff’: numpy.ndarray, data specific to fractional amplitude values of low-frequency fluctuations.

    • ‘func_mask’: numpy.ndarray, functional mask data, often used to define regions of interest.

    • ‘func_mean’: numpy.ndarray, mean functional MRI data, representing average activity across the brain.

    • ‘lfcd’: numpy.ndarray, data specific to local functional connectivity density in brain networks.

    • ‘reho’: numpy.ndarray, data specific to regional homogeneity in functional MRI data.

    • ‘rois_aal’: numpy.ndarray, data specific to anatomical regions defined by the Automatic Anatomical Labeling atlas.

    • ‘rois_cc200’: numpy.ndarray data specific to regions defined by the Craddock 200 atlas.

    • ‘rois_cc400’: numpy.ndarray, data specific to regions defined by the Craddock 400 atlas.

    • ‘rois_dosenbach160’: numpy.ndarray, data specific to regions defined by the Dosenbach 160 atlas.

    • ‘rois_ez’: numpy.ndarray, data specific to regions defined by the EZ atlas.

    • ‘rois_ho’: numpy.ndarray, data specific to regions defined by the Harvard-Oxford atlas.

    • ‘rois_tt’: numpy.ndarray, data specific to regions defined by the Talairach atlas.

    • ‘vmhc’: numpy.ndarray, data specific to voxel-mirrored homotopic connectivity in functional MRI data.

Notes

Code and description of preprocessing pipelines are provided on the PCP website.

References