Regional Homogeneity Approach to fMRI data analysis

CPAC.reho.create_reho()[source]

Regional Homogeneity(ReHo) approach to fMRI data analysis

This workflow computes the ReHo map, z-score on map

None

reHo : workflow
Regional Homogeneity Workflow

Source

Workflow Inputs:

inputspec.rest_res_filt : string (existing nifti file)
    Input EPI 4D Volume

inputspec.rest_mask : string (existing nifti file)
    Input Whole Brain Mask of EPI 4D Volume

inputspec.cluster_size : integer
    For a brain voxel the number of neighbouring brain voxels to use for KCC.
    Possible values are 27, 19, 7. Recommended value 27

Workflow Outputs:

outputspec.raw_reho_map : string (nifti file)

outputspec.z_score : string (nifti file)

ReHo Workflow Procedure:

  1. Generate ReHo map from the input EPI 4D volume, EPI mask and cluster_size
  2. Compute Z score of the ReHo map by subtracting mean and dividing by standard deviation

Workflow Graph:

workflows/../images/reho.dot.png

Detailed Workflow Graph:

workflows/../images/reho_detailed.dot.png
[1]Zang, Y., Jiang, T., Lu, Y., He, Y., Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage, 22(1), 394, 400. doi:10.1016/j.neuroimage.2003.12.030
>>> from CPAC import reho
>>> wf = reho.create_reho()
>>> wf.inputs.inputspec.rest_res_filt = '/home/data/Project/subject/func/rest_res_filt.nii.gz'
>>> wf.inputs.inputspec.rest_mask = '/home/data/Project/subject/func/rest_mask.nii.gz'
>>> wf.inputs.inputspec.cluster_size = 27
>>> wf.run()
CPAC.reho.f_kendall(timeseries_matrix)[source]

Calculates the Kendall’s coefficient of concordance for a number of time-series in the input matrix

timeseries_matrix : ndarray
A matrix of ranks of a subset subject’s brain voxels
kcc : float
Kendall’s coefficient of concordance on the given input matrix
CPAC.reho.getOpString(mean, std_dev)[source]

Generate the Operand String to be used in workflow nodes to supply mean and std deviation to alff workflow nodes

mean: string
mean value in string format
std_dev : string
std deviation value in string format

op_string : string

CPAC.reho.compute_reho(in_file, mask_file, cluster_size)[source]

Computes the ReHo Map, by computing tied ranks of the timepoints, followed by computing Kendall’s coefficient concordance(KCC) of a timeseries with its neighbours

in_file : nifti file
4D EPI File
mask_file : nifti file
Mask of the EPI File(Only Compute ReHo of voxels in the mask)
cluster_size : integer
for a brain voxel the number of neighbouring brain voxels to use for KCC.
out_file : nifti file
ReHo map of the input EPI image