DiFuMo atlas

Access

See nilearn.datasets.fetch_atlas_difumo.

Notes

We provide Dictionaries of Functional Modes “DiFuMo” (Dadi et al.[1]) that can serve as atlases to extract functional signals, for example to serve as IDPs, with different dimensionalities (64, 128, 256, 512, and 1024). These modes are optimized to represent well raw BOLD timeseries, over a with range of experimental conditions.

• All atlases are available in .nii.gz format and sampled to MNI space

Additionally, we provide meaningful names for these modes, based on their anatomical location, to facilitate reporting of results.

• Anatomical names are available for each resolution as pandas dataframe

Direct download links from OSF:

• 64: https://osf.io/pqu9r/download

• 128: https://osf.io/wjvd5/download

• 256: https://osf.io/3vrct/download

• 512: https://osf.io/9b76y/download

• 1024: https://osf.io/34792/download

For more information about this dataset’s structure: https://inria.hal.science/hal-02904869

See also Poldrack et al.[2] and Mensch et al.[3].

Content

• atlas_type: str. Type of atlas. See Probabilistic atlas and Deterministic atlas.

• labels: pandas.DataFrame. Containing the labels of the regions. The length of the dataframe corresponds to the number of dimensions requested. data.labels[i] is the label corresponding to volume i in the ‘maps’ image.

• maps: str. Full path to 4D nifti file containing regions definition. The shape of the image is (104, 123, 104, dimension) where dimension is the requested dimension of the atlas.

• template: str. The standardized space of analysis in which the data is provided.

References

[1]

Kamalaker Dadi, Gaël Varoquaux, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann, Bertrand Thirion, and Arthur Mensch. Fine-grain atlases of functional modes for fmri analysis. NeuroImage, 221:117126, 2020. URL: https://inria.hal.science/hal-02904869, doi:10.1016/j.neuroimage.2020.117126.

[2]

Russell A. Poldrack, Deanna M. Barch, Jason Mitchell, Tor Wager, Anthony D. Wagner, Joseph T. Devlin, Chad Cumba, Oluwasanmi Koyejo, and Michael Milham. Toward open sharing of task-based fmri data: the openfmri project. Frontiers in Neuroinformatics, 2013. URL: https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2013.00012, doi:10.3389/fninf.2013.00012.

[3]

Arthur Mensch, Julien Mairal, Bertrand Thirion, and Gaël Varoquaux. Stochastic subsampling for factorizing huge matrices. IEEE Transactions on Signal Processing, 66(1):113–128, 2018. doi:10.1109/TSP.2017.2752697.

License

usage is unrestricted for non-commercial research purposes.