Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering

• Published in: Neural Information Processing pp. 131 - 139
• Main Authors: Wang, Jing, Hu, Zilan, Wang, Haixian
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Functional connectivity; Individualized brain atlas; Resting-state fMRI; Supervoxel; Whole brain parcellation
• ISBN: 3319466747; 9783319466743
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-46675-0_15

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.