Generating individual atlases with whole brain resting-state fMRI data by learning the graph and parcellation simultaneously
Copyright (C) 2017 Jing Wang
This toolbox includes three individual subject level whole-brain parcellation approaches, i.e., normalized cuts (Ncut), simple linear iterative clustering (SLIC), and graph-without-cut (GWC). A demo which applies the three approaches on the resting-state fMRI data of three subjects from the Beijing_Zang dataset (of the fcon_1000 project) is provided in this toolbox.
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Run main.m to play the demo.
- You may download the NIFTI toolbox and the demo data manually.
- For parallel computing, carefully choose the number of parallel workers to make the most of the hardware resources and to avoid problems such as the out of memory problem.
- Scripts for the paper: A supervoxel-based method for groupwise whole
brain parcellation with resting-state fMRI data.
SLIC: http://www.nitrc.org/projects/slic
SLIC: https://github.com/yuzhounh/SLIC
SLIC_atlas: https://github.com/yuzhounh/SLIC_atlas - Scripts for the paper: Parcellating whole brain for individuals by
simple linear iterative clustering.
SLIC_individual: https://github.com/yuzhounh/SLIC_individual
- Jing Wang, and Haixian Wang. "A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data." Frontiers in human neuroscience 10 (2016).
- Jing Wang, Zilan Hu, and Haixian Wang. "Parcellating whole brain for individuals by simple linear iterative clustering." International Conference on Neural Information Processing. Springer International Publishing, 2016.
Jing Wang
wangjing0@seu.edu.cn
yuzhounh@163.com
2017-12-14 17:14:19