netneurotools.datasets.fetch_fslr_curated

netneurotools.datasets.fetch_fslr_curated(version='fslr32k', force=False, data_dir=None, verbose=1)

Download files for HCP fsLR template.

This dataset contains surface geometry files (midthickness, inflated, veryinflated [where available], sphere), medial wall labels, and surface shape files (sulcal depth and vertex area) in GIFTI format for the HCP fsLR template at various densities.

If you used this data, please cite 1, 2, 3.

Parameters:

version (str, optional) – One of {“fslr4k”, “fslr8k”, “fslr32k”, “fslr164k”}. Default: ‘fslr32k’

Returns:

filenames – Dictionary-like object with keys [‘midthickness’, ‘inflated’, ‘veryinflated’ (except for ‘fslr4k’/’fslr8k’), ‘sphere’, ‘medial’, ‘sulc’, ‘vaavg’], where corresponding values are Surface namedtuples containing filepaths for the left (L) and right (R) hemisphere files in GIFTI format.

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

This function fetches curated fsLR surfaces from the neuromaps package (see neuromaps.datasets.fetch_fslr). All files are provided in GIFTI format (.gii). The fsLR template is the HCP standard mesh used for group analyses and cross-subject alignment.

The returned files include:

• midthickness: Midthickness surface geometry (.surf.gii), halfway

  between white and pial surfaces; often preferred for data mapping.

• inflated: Inflated surface geometry (.surf.gii) for improved

  visualization of sulci and gyri.

• veryinflated: Very inflated surface geometry (.surf.gii) providing

  additional smoothing; not available for ‘fslr4k’/’fslr8k’.

• sphere: Spherical surface geometry (.surf.gii) used for surface-based

  registration and applying parcellations.

• medial: Medial wall mask (.label.gii) indicating vertices to exclude

  from analyses (vertices with no cortex).

• sulc: Sulcal depth map (.shape.gii) providing sulcal/gyral patterns

  on the midthickness surface.

• vaavg: Vertex area map (.shape.gii) representing the average vertex

  area on the midthickness surface.

The vertex density varies by version: fslr4k (≈4k vertices), fslr8k (≈8k), fslr32k (≈32k), and fslr164k (≈164k) per hemisphere.

Example directory tree:

~/nnt-data/tpl-fslr_curated
├── fslr164k
│   ├── README.md
│   ├── tpl-fsLR_den-164k_hemi-L_desc-nomedialwall_dparc.label.gii
│   ├── tpl-fsLR_den-164k_hemi-L_desc-sulc_midthickness.shape.gii
│   ├── tpl-fsLR_den-164k_hemi-L_desc-vaavg_midthickness.shape.gii
│   ├── tpl-fsLR_den-164k_hemi-L_inflated.surf.gii
│   ├── tpl-fsLR_den-164k_hemi-L_midthickness.surf.gii
│   ├── tpl-fsLR_den-164k_hemi-L_sphere.surf.gii
│   ├── tpl-fsLR_den-164k_hemi-L_veryinflated.surf.gii
│   ├── tpl-fsLR_den-164k_hemi-R_desc-nomedialwall_dparc.label.gii
│   ├── tpl-fsLR_den-164k_hemi-R_desc-sulc_midthickness.shape.gii
│   ├── tpl-fsLR_den-164k_hemi-R_desc-vaavg_midthickness.shape.gii
│   ├── tpl-fsLR_den-164k_hemi-R_inflated.surf.gii
│   ├── tpl-fsLR_den-164k_hemi-R_midthickness.surf.gii
│   ├── tpl-fsLR_den-164k_hemi-R_sphere.surf.gii
│   ├── tpl-fsLR_den-164k_hemi-R_veryinflated.surf.gii
│   ├── tpl-fsLR_space-fsaverage_den-164k_hemi-L_sphere.surf.gii
│   └── tpl-fsLR_space-fsaverage_den-164k_hemi-R_sphere.surf.gii
├── fslr32k
│   ├── README.md
│   ├── tpl-fsLR_den-32k_hemi-L_desc-nomedialwall_dparc.label.gii
│   ├── tpl-fsLR_den-32k_hemi-L_desc-sulc_midthickness.shape.gii
│   ├── tpl-fsLR_den-32k_hemi-L_desc-vaavg_midthickness.shape.gii
│   ├── tpl-fsLR_den-32k_hemi-L_inflated.surf.gii
│   ├── tpl-fsLR_den-32k_hemi-L_midthickness.surf.gii
│   ├── tpl-fsLR_den-32k_hemi-L_sphere.surf.gii
│   ├── tpl-fsLR_den-32k_hemi-L_veryinflated.surf.gii
│   ├── tpl-fsLR_den-32k_hemi-R_desc-nomedialwall_dparc.label.gii
│   ├── tpl-fsLR_den-32k_hemi-R_desc-sulc_midthickness.shape.gii
│   ├── tpl-fsLR_den-32k_hemi-R_desc-vaavg_midthickness.shape.gii
│   ├── tpl-fsLR_den-32k_hemi-R_inflated.surf.gii
│   ├── tpl-fsLR_den-32k_hemi-R_midthickness.surf.gii
│   ├── tpl-fsLR_den-32k_hemi-R_sphere.surf.gii
│   ├── tpl-fsLR_den-32k_hemi-R_veryinflated.surf.gii
│   ├── tpl-fsLR_space-fsaverage_den-32k_hemi-L_sphere.surf.gii
│   └── tpl-fsLR_space-fsaverage_den-32k_hemi-R_sphere.surf.gii
├── fslr4k
│   ├── tpl-fsLR_den-4k_hemi-L_desc-nomedialwall_dparc.label.gii
│   ├── tpl-fsLR_den-4k_hemi-L_desc-sulc_midthickness.shape.gii
│   ├── tpl-fsLR_den-4k_hemi-L_desc-vaavg_midthickness.shape.gii
│   ├── tpl-fsLR_den-4k_hemi-L_inflated.surf.gii
│   ├── tpl-fsLR_den-4k_hemi-L_midthickness.surf.gii
│   ├── tpl-fsLR_den-4k_hemi-L_sphere.surf.gii
│   ├── tpl-fsLR_den-4k_hemi-R_desc-nomedialwall_dparc.label.gii
│   ├── tpl-fsLR_den-4k_hemi-R_desc-sulc_midthickness.shape.gii
│   ├── tpl-fsLR_den-4k_hemi-R_desc-vaavg_midthickness.shape.gii
│   ├── tpl-fsLR_den-4k_hemi-R_inflated.surf.gii
│   ├── tpl-fsLR_den-4k_hemi-R_midthickness.surf.gii
│   ├── tpl-fsLR_den-4k_hemi-R_sphere.surf.gii
│   ├── tpl-fsLR_space-fsaverage_den-4k_hemi-L_sphere.surf.gii
│   └── tpl-fsLR_space-fsaverage_den-4k_hemi-R_sphere.surf.gii
└── fslr8k
    ├── tpl-fsLR_den-8k_hemi-L_desc-nomedialwall_dparc.label.gii
    ├── tpl-fsLR_den-8k_hemi-L_desc-sulc_midthickness.shape.gii
    ├── tpl-fsLR_den-8k_hemi-L_desc-vaavg_midthickness.shape.gii
    ├── tpl-fsLR_den-8k_hemi-L_inflated.surf.gii
    ├── tpl-fsLR_den-8k_hemi-L_midthickness.surf.gii
    ├── tpl-fsLR_den-8k_hemi-L_sphere.surf.gii
    ├── tpl-fsLR_den-8k_hemi-R_desc-nomedialwall_dparc.label.gii
    ├── tpl-fsLR_den-8k_hemi-R_desc-sulc_midthickness.shape.gii
    ├── tpl-fsLR_den-8k_hemi-R_desc-vaavg_midthickness.shape.gii
    ├── tpl-fsLR_den-8k_hemi-R_inflated.surf.gii
    ├── tpl-fsLR_den-8k_hemi-R_midthickness.surf.gii
    ├── tpl-fsLR_den-8k_hemi-R_sphere.surf.gii
    ├── tpl-fsLR_space-fsaverage_den-8k_hemi-L_sphere.surf.gii
    └── tpl-fsLR_space-fsaverage_den-8k_hemi-R_sphere.surf.gii

4 directories, 62 files

Examples

Load the fsLR curated template surfaces:

>>> surfaces = fetch_fslr_curated(version='fslr32k')
>>> surfaces.keys()
dict_keys(['midthickness', 'inflated', 'veryinflated', 'sphere', 'medial',
           'sulc', 'vaavg'])

Access the midthickness surface GIFTI files:

>>> surfaces.midthickness
Surface(L=PosixPath('~/nnt-data/tpl-fslr_curated/fslr32k/tpl-fsLR_den-32k_hemi-L_midthickness.surf.gii'),
        R=PosixPath('~/nnt-data/tpl-fslr_curated/fslr32k/tpl-fsLR_den-32k_hemi-R_midthickness.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(vertices.shape, faces.shape)
(32492, 3) (64980, 3)

Load and examine the sulcal depth data:

>>> sulc_left = nib.load(surfaces.sulc.L)
>>> sulc_data = sulc_left.agg_data()
>>> float(sulc_data.min()), float(sulc_data.max())
(-1.6234848499298096, 1.1611071825027466)

References

[1]

David C Van Essen, Kamil Ugurbil, Edward Auerbach, Deanna Barch,Timothy EJ Behrens, Richard Bucholz, Acer Chang, Liyong Chen, Maurizio Corbetta, Sandra W Curtiss, and others. The human connectome project: a data acquisition perspective. Neuroimage, 62(4):2222–2231, 2012.

[2]

Matthew F Glasser, Stamatios N Sotiropoulos, J Anthony Wilson, Timothy S Coalson, Bruce Fischl, Jesper L Andersson, Junqian Xu, Saad Jbabdi, Matthew Webster, Jonathan R Polimeni, and others. The minimal preprocessing pipelines for the human connectome project. Neuroimage, 80:105–124, 2013.

[3]

Ross D Markello, Justine Y Hansen, Zhen-Qi Liu, Vincent Bazinet, Golia Shafiei, Laura E Suárez, Nadia Blostein, Jakob Seidlitz, Sylvain Baillet, Theodore D Satterthwaite, and others. Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11):1472–1479, 2022.