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
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:
Generate ReHo map from the input EPI 4D volume, EPI mask and cluster_size
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)[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