netneurotools.datasets.fetch_conte69

netneurotools.datasets.fetch_conte69(force=False, data_dir=None, verbose=1)[source]

Download files for Van Essen et al., 2012 Conte69 template.

This dataset contains midthickness, inflated, and very inflated surface files in GIFTI format for the Conte69 atlas, a population-average surface template in fsLR32k space registered to MNI305 volumetric space.

If you used this data, please cite 1, 2.

Returns:

filenames – Dictionary-like object with keys [‘midthickness’, ‘inflated’, ‘vinflated’, ‘info’], where ‘midthickness’, ‘inflated’, and ‘vinflated’ are Surface namedtuples containing filepaths for the left (L) and right (R) hemisphere GIFTI files, and ‘info’ is a dictionary containing template metadata from template_description.json.

Return type:

sklearn.utils.Bunch

Other Parameters:

  • force (bool, optional) – If True, will overwrite existing dataset. Default: False

  • data_dir (str, optional) – Path to use as data directory. If not specified, will check for environmental variable ‘NNT_DATA’; if that is not set, will use ~/nnt-data instead. Default: None

  • verbose (int, optional) – Modifies verbosity of download, where higher numbers mean more updates. Default: 1

Notes

The Conte69 template is a population-average surface atlas registered to MNI305 volumetric space using the fsLR32k mesh (approximately 32k vertices per hemisphere).

The returned files include:

  • midthickness: Midthickness surface geometry (.surf.gii), halfway between white and pial surfaces.

  • inflated: Inflated surface geometry (.surf.gii) for improved visualization of sulci and gyri.

  • vinflated: Very inflated surface geometry (.surf.gii) providing additional smoothing for visualization.

  • info: Metadata dictionary containing template name, BIDS version, and references.

Example directory tree:

~/nnt-data/tpl-conte69
├── CHANGES
├── template_description.json
├── tpl-conte69_space-MNI305_variant-fsLR32k_inflated.L.surf.gii
├── tpl-conte69_space-MNI305_variant-fsLR32k_inflated.R.surf.gii
├── tpl-conte69_space-MNI305_variant-fsLR32k_midthickness.L.surf.gii
├── tpl-conte69_space-MNI305_variant-fsLR32k_midthickness.R.surf.gii
├── tpl-conte69_space-MNI305_variant-fsLR32k_vinflated.L.surf.gii
└── tpl-conte69_space-MNI305_variant-fsLR32k_vinflated.R.surf.gii

0 directories, 8 files

Examples

Load the Conte69 template surfaces:

>>> surfaces = fetch_conte69()
>>> surfaces.keys()
dict_keys(['midthickness', 'inflated', 'vinflated', 'info'])

Access the midthickness surface GIFTI files:

>>> surfaces.midthickness
Surface(L=PosixPath('~/nnt-data/tpl-conte69/tpl-conte69_space-MNI305_variant-fsLR32k_midthickness.L.surf.gii'),
        R=PosixPath('~/nnt-data/tpl-conte69/tpl-conte69_space-MNI305_variant-fsLR32k_midthickness.R.surf.gii'))

Load the left midthickness surface with nibabel:

>>> import nibabel as nib
>>> gii = nib.load(surfaces.midthickness.L)
>>> vertices = gii.agg_data('pointset')
>>> faces = gii.agg_data('triangle')
>>> print(f"Vertices: {vertices.shape}, Faces: {faces.shape}")
Vertices: (32492, 3), Faces: (64980, 3)

Examine template metadata:

>>> surfaces.info['Name']
"The 'Conte-69' template"

References