GitHub - yuzhounh/SLIC_2: Scripts for the paper: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data.

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Scripts for the paper: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data.

www.frontiersin.org/articles/10.3389/fnhum.2016.00659/full

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Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
LICENSE		LICENSE
MKSC_parc.m		MKSC_parc.m
MSC_mean_parc.m		MSC_mean_parc.m
MSC_sub_parc.m		MSC_sub_parc.m
MSC_twolevel_eigen.m		MSC_twolevel_eigen.m
MSC_twolevel_parc.m		MSC_twolevel_parc.m
MSC_twolevel_weight.m		MSC_twolevel_weight.m
Ncut_eigen.m		Ncut_eigen.m
Ncut_kway.m		Ncut_kway.m
Readme		Readme
Readme_plus		Readme_plus
SLIC.m		SLIC.m
SLIC_mean_parc.m		SLIC_mean_parc.m
SLIC_sub_parc.m		SLIC_sub_parc.m
SLIC_twolevel_eigen.m		SLIC_twolevel_eigen.m
SLIC_twolevel_parc.m		SLIC_twolevel_parc.m
SLIC_twolevel_weight.m		SLIC_twolevel_weight.m
T1_4mm.nii		T1_4mm.nii
eval_dice.m		eval_dice.m
eval_dice_sub.m		eval_dice_sub.m
eval_homogeneity.m		eval_homogeneity.m
eval_sdi.m		eval_sdi.m
graymatter.nii		graymatter.nii
main.m		main.m
main_algorithm.m		main_algorithm.m
main_evaluation.m		main_evaluation.m
main_histogram.fig		main_histogram.fig
main_histogram.m		main_histogram.m
main_illustration.fig		main_illustration.fig
main_illustration.m		main_illustration.m
main_plot.fig		main_plot.fig
main_plot.m		main_plot.m
main_preparation.m		main_preparation.m
mean_eigen.m		mean_eigen.m
mean_weight.m		mean_weight.m
parc_diag.m		parc_diag.m
parc_disp.m		parc_disp.m
parc_distinct.m		parc_distinct.m
parc_nii.m		parc_nii.m
parc_norm.m		parc_norm.m
parc_normalize.m		parc_normalize.m
parc_orthview.m		parc_orthview.m
parc_parpool.m		parc_parpool.m
parc_searchlight.m		parc_searchlight.m
parc_slice.m		parc_slice.m
perct.m		perct.m
sub_eigen.m		sub_eigen.m
sub_weight.m		sub_weight.m

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SLIC: a whole brain parcellation toolbox
Copyright (C) 2016 Jing Wang

The SLIC toolbox contains five whole brain parcellation approaches that 
operates on resting-state fMRI data. Three of them are reproduced from the 
Ncut-based approaches in (Craddock et al., 2012, HBM) and (Shen et al., 
2013, Neuroimage). The remaining two are the mean SLIC and two-level SLIC 
approaches, which combine Ncut and SLIC to perform whole brain 
parcellation. By running this demo, you could reproduce the major results 
in our paper. See the Readme_plus file for further information. This 
project is also shared on NITRC, https://www.nitrc.org/projects/slic/. 

Usage:
1. Download the preprocessed fMRI data from NITRC, and then uncompress
the data. 
   https://www.nitrc.org/frs/?group_id=1030
2. Run main.m to play this demo. It takes about 10 hours on a server with
   40 CPUs and 256 GB memory. 

Changes:
1. Don't discard the eigenvectors corresponding to the trivial eigenvalues 
   (<10^-4) anymore because this step is not necessary. 
2. Set the error tolerance to 1e-3 and set the maximum iteration number to
   100 for iterations.
3. Store usefull information in sInfo.mat.

Related Codes:
SLIC, https://github.com/yuzhounh/SLIC
SLIC_2, https://github.com/yuzhounh/SLIC_2

Reference:
Jing Wang, Haixian Wang. A supervoxel-based method for groupwise whole 
brain parcellation with resting-state fMRI data. Frontiers in Human 
Neuroscience. DOI: 10.3389/fnhum.2016.00659

Contact information:
Jing Wang
[email protected]
[email protected]

2018-6-20 15:11:46