Regional Homogeneity Approach to fMRI data analysis

CPAC.reho.compute_reho(in_file, mask_file, cluster_size)

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

Parameters:

in_filenifti file

4D EPI File

mask_filenifti file

Mask of the EPI File(Only Compute ReHo of voxels in the mask)

cluster_sizeinteger

for a brain voxel the number of neighbouring brain voxels to use for KCC.

Returns:

out_filenifti file

ReHo map of the input EPI image

CPAC.reho.create_reho(wf_name)

Regional Homogeneity(ReHo) approach to fMRI data analysis

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

Parameters:

None

Returns:

reHoworkflow

Regional Homogeneity Workflow

Notes

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

Error

Unable to execute python code at exec.py:31:

create_reho() missing 1 required positional argument: ‘wf_name’

Workflow Graph:

Detailed Workflow Graph:

References

[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

Examples

>>> from CPAC import reho
>>> wf = reho.create_reho('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)

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

Parameters:

timeseries_matrixndarray

A matrix of ranks of a subset subject’s brain voxels

Returns:

kccfloat

Kendall’s coefficient of concordance on the given input matrix

CPAC.reho.getOpString(mean, std_dev)

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

Parameters:

meanstring

mean value in string format

std_devstring

std deviation value in string format

Returns:

op_stringstring