USCBrain Atlas Description

Description of USCBrain atlas

    The USCBrain atlas is a new high-resolution single-subject atlas, constructed using both anatomical and functional information to guide the parcellation of the cortex. Individual structural scans can be registered to this atlas using BrainSuite. We also provide uncertainty maps that give probabilistic parcellations.

    Anatomical parcellation

BCI-USCBrainA high-resolution 3D MPRAGE scan (TE=4.33 ms; TR=2070 ms; TI=1100ms; flip angle=12 degrees; resolution=0.547×0.547×0.802 mm) was acquired on a 3T Siemens MAGNETOM Tim Trio using a 32-channel head coil. Data were acquired 5 times and averaged. The subject is a typical right-handed woman in her mid-thirties with a brachiocephalic brain and no rare anomalies.
Preprocessing and labeling of the MPRAGE image was performed using BrainSuite [1,2]. This MRI volume was used to generate the BCI-DNI atlas as described here.

    Functional subparcellation

Functional subparcellations of the gyral ROIs were then generated from 40 minimally preprocessed rfMRI datasets from the Human connectome database [3]. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects using the HCP gray-ordinate space as a reference. For each subject, each gyral ROI was subdivided using the rfMRI data by applying the normalized cuts spectral clustering algorithm to a similarity matrix computed from the correlation between voxels [4]. The number of subdivisions was chosen based on a Silhouette analysis [5]. The subparcellations were then transferred back to the original cortical mesh to create the USCBrain atlas with a total of 65 cortical regions per hemisphere. Probabilistic map of the subdivisions was generated by measuring agreement of the subdivisions across the 40 subjects [6].

The USCBrain atlas can be used with BrainSuite, FreeSurfer and FSL softwares.


Using the atlas with BrainSuite, FreeSurfer and FSL


1. Damasio, H., (1995), ‘Human brain anatomy in computerized images’, Oxford university press.
2. Joshi, A., (2012), ‘A method for automated cortical surface registration and labeling’, International Workshop on Biomedical Image Registration, pp 180-189.
3. Glasser, M. (2013), ‘The minimal preprocessing pipelines for the human connectome project’, NeuroImage, vol. 80, pp. 105–124
4. Shi, J. (2000), ‘Normalized cuts and image segmentation’, IEEE Trans on PAMI, 22(8), 888-905.
5. Rousseeuw P. (1987), ‘Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis’, Computational and Applied Mathematics 20: 53-65.
6. Hubert, L., (1985), ‘Comparing partitions’, Journal of Classification, Vol 2, pp 193.
7. Joshi, A et al. (2017), ‘A Whole Brain Atlas with Sub-parcellation of Cortical Gyri using Resting fMRI’, SPIE Medical Imaging, 101330O-101330O-9.
8. Joshi, A. ‘USCBrain Atlas: A Volumetric and Surface Atlas Delineated by Anatomical and Functional MRI’, OHBM 2017. e-poster