Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering
This paper utilizes a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain into functional subunits using resting-state fMRI data. The parcellation algorithm is directly applied on the resting-state fMRI time series without feature extraction, and the parcellation is conducted on the individual subject level. In order to obtain parcellations with multiple granularities, we vary the cluster number in a wide range. To demonstrate the reasonability of the proposed approach, we compare it with a state-of-the-art whole brain parcellation approach, i.e., the normalized cuts (Ncut) approach. The experimental results show that the proposed approach achieves satisfying performances in terms of spatial contiguity, functional homogeneity and reproducibility. The proposed approach could be used to generate individualized brain atlases for applications such as personalized medicine.
KeywordsWhole brain parcellation Supervoxel Resting-state fMRI Functional connectivity Individualized brain atlas
This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61375118, and the Research Foundation for Young Teachers in Anhui University of Technology under Grant QZ201516.
- 2.Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002)CrossRefGoogle Scholar
- 13.Yan, C.G., Zang, Y.F.: DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 1–7 (2010)Google Scholar
- 14.Yu, S.X., Shi, J.B.: Multiclass spectral clustering. In: Proceedings of IEEE International Conference on Computer Vision, pp. 313–319 (2003)Google Scholar