Regional Homogeneity Approach to fMRI data analysis#

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

Parameters:

  • 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.

Returns:

out_file – ReHo map of the input EPI image

Return type:

nifti file

CPAC.reho.create_reho(wf_name)[source]#

Regional Homogeneity(ReHo) approach to fMRI data analysis

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

Parameters:

None –

Returns:

reHo – Regional Homogeneity Workflow

Return type:

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:

images/generated/reho.png

Detailed Workflow Graph:

images/generated/reho_detailed.png

References

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)[source]#

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

Parameters:

timeseries_matrix (ndarray) – A matrix of ranks of a subset subject’s brain voxels

Returns:

kcc – Kendall’s coefficient of concordance on the given input matrix

Return type:

float

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

Parameters:

  • mean (string) – mean value in string format

  • std_dev (string) – std deviation value in string format

Returns:

op_string

Return type:

string