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.

<table style="width:100%; table-layout:fixed;"> <tr> <td><img width="400px" src="m6_illustrations.png"></td> <td><img width="400px" src="m7_comparison.png"></td> </tr> <tr> <td>Illustrations</td> <td>Comparison</td> </tr> </table>

Usage:

Run main.m to play the demo.

Notes:

1. You may download the NIFTI toolbox and the demo data manually.
2. 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.

Related codes:

1. 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
2. Scripts for the paper: Parcellating whole brain for individuals by simple linear iterative clustering.
   SLIC_individual: https://github.com/yuzhounh/SLIC_individual

References:

1. 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).
2. 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.

Contact information:

Jing Wang
wangjing0@seu.edu.cn
yuzhounh@163.com
2017-12-14 17:14:19