Seitzman 2018 atlas¶

Access¶

See nilearn.datasets.fetch_coords_seitzman_2018.

Notes¶

300 regions coordinates in cortical, subcortical and cerebellar regions.

These regions cover cortical, subcortical and cerebellar regions and are assigned to one of 13 networks: (Auditory, CinguloOpercular, DefaultMode, DorsalAttention, FrontoParietal, MedialTemporalLobe, ParietoMedial, Reward, Salience, SomatomotorDorsal, SomatomotorLateral, VentralAttention, Visual) and have a regional label (cortexL, cortexR, cerebellum, thalamus, hippocampus, basalGanglia, amygdala, cortexMid).

Here, we apply a winner-take-all partitioning method to resting-state fMRI data and careful consideration of anatomy to generate novel functionally-constrained regions in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these regions in three datasets via several anatomical and functional criteria, including known anatomical divisions and functions, as well as agreement with existing literature. Further, we demonstrate that combining these regions with established cortical regions recapitulates and extends previously described functional network organization.

See Seitzman et al.[1].

For more information see: https://greenelab.ucsd.edu/data_software

ROI coordinates downloaded from: https://wustl.app.box.com/s/twpyb1pflj6vrlxgh3rohyqanxbdpelw

Content¶

atlas_type: str. Type of atlas. See Probabilistic atlas and Deterministic atlas.
networks: numpy.ndarray. Names of the corresponding network for each region.
radius: numpy.ndarray. Radius of each region in mm.
regions: numpy.ndarray. Names of the regions.
rois: pandas.DataFrame. Coordinates of the 300 regions in MNI space with x, y, z coordinates.
template: str. The standardized space of analysis in which the data is provided.

References¶

License¶

unknown