Timeseries Analysis

CPAC.timeseries.gen_roi_timeseries(data_file, template, output_type)

Extract mean of voxel across all timepoints for each node in roi mask.

Parameters:

• data_file (string) – path to input functional data

• template (string) – path to input roi mask in functional native space

• output_type (list) – list of two boolean values suggesting the output types - numpy npz file and csv format

Returns:

out_list – list of 1D file, txt file, csv file and/or npz file containing mean timeseries for each scan corresponding to each node in roi mask

Return type:

list

Raises:

Exception –

CPAC.timeseries.gen_vertices_timeseries(rh_surface_file, lh_surface_file)

Extract timeseries from vertices of a freesurfer surface file.

Parameters:

• rh_surface_file (string (mgz/mgh file)) – left hemisphere FreeSurfer surface file

• lh_surface_file (string (mgz/mgh file)) – right hemisphere FreeSurfer surface file

Returns:

out_list – list of vertices timeseries csv files

Return type:

string (list of file)

CPAC.timeseries.gen_voxel_timeseries(data_file: str, template: str) → str

Extract timeseries for each voxel in the data that is present in the input mask.

Parameters:

• datafile (string (nifti file)) – path to input functional data

• template (string (nifti file)) – path to input mask in functional native space

Returns:

oneD_file – Path to the created .1D file containing the mean timeseries vector.

Return type:

str

Raises:

Exception –

CPAC.timeseries.get_roi_timeseries(wf_name: str = 'roi_timeseries') → Workflow

Extract timeseries for each node in the ROI mask.

For each node, mean across all the timepoint is calculated and stored in csv and npz format.

Parameters:

wf_name (string) – name of the workflow

Notes

Source

Workflow Inputs:

inputspec.rest : string  (nifti file)
    path to input functional data
inputspec.output_type : string (list of boolean)
    list of boolean for csv and npz file formats
input_roi.roi : string (nifti file)
    path to ROI mask

Workflow Outputs:

outputspec.roi_ts : numpy array
    Voxel time series stored in numpy array, which is used to create ndmg graphs.

outputspec.roi_outputs : string (list of files)
    Voxel time series stored in 1D (column wise timeseries for each node),
    csv and/or npz files. By default it outputs timeseries in a 1D file.
    The 1D file is compatible with afni interfaces.

Example

>>> import CPAC.timeseries.timeseries_analysis as t
>>> wf = t.get_roi_timeseries()
>>> wf.inputs.inputspec.rest = '/home/data/rest.nii.gz'  
>>> wf.inputs.input_roi.roi = '/usr/local/fsl/data/atlases/HarvardOxford/HarvardOxford-cort-maxprob-thr0-2mm.nii.gz'  
>>> wf.inputs.inputspec.output_type = [True,True]  
>>> wf.base_dir = './'
>>> wf.run()  

CPAC.timeseries.get_spatial_map_timeseries(wf_name: str = 'spatial_map_timeseries') → Workflow

Regress each provided spatial map to the subjects functional 4D file…

…in order to return a timeseries for each of the maps.

Parameters:

wf_name (string) – name of the workflow

Notes

Source

Workflow Inputs:

inputspec.subject_rest : string  (nifti file)
    path to input functional data
inputspec.subject_mask : string (nifti file)
    path to subject functional mask
inputspec.spatial_map : string (nifti file)
    path to Spatial Maps
inputspec.demean : Boolean
    control whether to demean model and data

Workflow Outputs:

outputspec.subject_timeseries: string (txt file)
    list of time series stored in a space separated
    txt file
    the columns are spatial maps, rows are timepoints

Example

>>> import CPAC.timeseries.timeseries_analysis as t
>>> wf = t.get_spatial_map_timeseries()
>>> wf.inputs.inputspec.subject_rest = '/home/data/rest.nii.gz'  
>>> wf.inputs.inputspec.subject_mask = '/home/data/rest_mask.nii.gz'  
>>> wf.inputs.inputspec.ICA_map = '/home/data/spatialmaps/spatial_map.nii.gz'  
>>> wf.inputs.inputspec.demean = True
>>> wf.base_dir = './'
>>> wf.run()  

CPAC.timeseries.get_vertices_timeseries(wf_name='vertices_timeseries')

Workflow to get vertices time series from a FreeSurfer surface file.

Parameters:

wf_name (string) – name of the workflow

Returns:

wflow – workflow object

Return type:

workflow object

Notes

Source

Workflow Inputs:

inputspec.lh_surface_file : string (nifti file)
    left hemishpere surface file
inputspec.rh_surface_file : string (nifti file)
    right hemisphere surface file

Workflow Outputs:

outputspec.surface_outputs: string (csv and/or npz files)
    list of timeseries matrices stored in csv and/or
    npz files

Example

>>> import CPAC.timeseries.timeseries_analysis as t
>>> wf = t.get_vertices_timeseries()
>>> wf.inputs.inputspec.lh_surface_file = '/home/data/outputs/SurfaceRegistration/lh_surface_file.nii.gz'  
>>> wf.inputs.inputspec.rh_surface_file = '/home/data/outputs/SurfaceRegistration/rh_surface_file.nii.gz'  
>>> wf.base_dir = './'
>>> wf.run()  

CPAC.timeseries.get_voxel_timeseries(wf_name: str = 'voxel_timeseries') → Workflow

Extract time series for each voxel in data that are present in the input mask.

Parameters:

wf_name (string) – name of the workflow

Notes

Source

Workflow Inputs:

inputspec.rest : string  (nifti file)
    path to input functional data
inputspec.output_type : string (list of boolean)
    list of boolean for csv and npz file formats
input_mask.masks : string (nifti file)
    path to ROI mask

Workflow Outputs:

outputspec.mask_outputs: string (1D, csv and/or npz files)
    list of time series matrices stored in csv and/or
    npz files.By default it outputs mean of voxels
    across each time point in a afni compatible 1D file.

High Level Workflow Graph:

Example

>>> import CPAC.timeseries.timeseries_analysis as t
>>> wf = t.get_voxel_timeseries()
>>> wf.inputs.inputspec.rest = '/home/data/rest.nii.gz'  
>>> wf.inputs.input_mask.mask = '/usr/local/fsl/data/standard/MNI152_T1_2mm_brain.nii.gz'  
>>> wf.inputs.inputspec.output_type = [True,True]
>>> wf.base_dir = './'
>>> wf.run()