import os
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.utility as util
from CPAC.utils.interfaces.function import Function
from .cwas import (
joint_mask,
create_cwas_batches,
merge_cwas_batches,
nifti_cwas,
)
[docs]def create_cwas(name='cwas', working_dir=None, crash_dir=None):
"""
Connectome Wide Association Studies
This workflow performs CWAS on a group of subjects.
Parameters
----------
name : string, optional
Name of the workflow.
Returns
-------
cwas : nipype.pipeline.engine.Workflow
CWAS workflow.
Notes
-----
Workflow Inputs::
inputspec.subjects : dict (subject id: nifti files)
4-D timeseries of a group of subjects normalized to MNI space
inputspec.roi : string (nifti file)
Mask of region(s) of interest
inputspec.regressor : list (float)
Corresponding list of the regressor variable of shape (`N`) or (`N`,`1`), `N` subjects
inputspec.cols : list (int)
todo
inputspec.f_samples : int
Number of permutation samples to draw from the pseudo F distribution
inputspec.parallel_nodes : integer
Number of nodes to create and potentially parallelize over
Workflow Outputs::
outputspec.F_map : string (nifti file)
Pseudo F values of CWAS
outputspec.p_map : string (nifti file)
Significance p values calculated from permutation tests
CWAS Procedure:
1. Calculate spatial correlation of a voxel
2. Correlate spatial z-score maps for every subject pair
3. Convert matrix to distance matrix, `1-r`
4. Calculate MDMR statistics for the voxel
5. Determine significance of MDMR statistics with permutation tests
.. exec::
from CPAC.cwas import create_cwas
wf = create_cwas()
wf.write_graph(
graph2use='orig',
dotfilename='./images/generated/create_cwas.dot'
)
Workflow Graph:
.. image:: ../../images/generated/cwas.png
:width: 500
Detailed Workflow Graph:
.. image:: ../../images/generated/cwas_detailed.png
:width: 500
References
----------
.. [1] Shehzad Z, Kelly C, Reiss PT, Emerson JW, McMahon K, Copland DA, Castellanos FX, Milham MP. An Analytic Framework for Connectome-Wide Association Studies. Under Review.
"""
if not working_dir:
working_dir = os.path.join(os.getcwd(), 'MDMR_work_dir')
if not crash_dir:
crash_dir = os.path.join(os.getcwd(), 'MDMR_crash_dir')
workflow = pe.Workflow(name=name)
workflow.base_dir = working_dir
workflow.config['execution'] = {'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(crash_dir)}
inputspec = pe.Node(util.IdentityInterface(fields=['roi',
'subjects',
'regressor',
'participant_column',
'columns',
'permutations',
'parallel_nodes']),
name='inputspec')
outputspec = pe.Node(util.IdentityInterface(fields=['F_map',
'p_map',
'neglog_p_map']),
name='outputspec')
ccb = pe.Node(Function(input_names=['mask_file',
'batches'],
output_names='batch_list',
function=create_cwas_batches,
as_module=True),
name='cwas_batches')
ncwas = pe.MapNode(Function(input_names=['subjects',
'mask_file',
'regressor_file',
'participant_column',
'columns_string',
'permutations',
'voxel_range'],
output_names=['result_batch'],
function=nifti_cwas,
as_module=True),
name='cwas_batch',
iterfield='voxel_range')
jmask = pe.Node(Function(input_names=['subjects',
'mask_file'],
output_names=['joint_mask'],
function=joint_mask,
as_module=True),
name='joint_mask')
mcwasb = pe.Node(Function(input_names=['cwas_batches',
'mask_file'],
output_names=['F_file',
'p_file',
'neglog_p_file'],
function=merge_cwas_batches,
as_module=True),
name='cwas_volumes')
#Compute the joint mask
workflow.connect(inputspec, 'subjects',
jmask, 'subjects')
workflow.connect(inputspec, 'roi',
jmask, 'mask_file')
#Create batches based on the joint mask
workflow.connect(jmask, 'joint_mask',
ccb, 'mask_file')
workflow.connect(inputspec, 'parallel_nodes',
ccb, 'batches')
#Compute CWAS over batches of voxels
workflow.connect(jmask, 'joint_mask',
ncwas, 'mask_file')
workflow.connect(inputspec, 'subjects',
ncwas, 'subjects')
workflow.connect(inputspec, 'regressor',
ncwas, 'regressor_file')
workflow.connect(inputspec, 'permutations',
ncwas, 'permutations')
workflow.connect(inputspec, 'participant_column',
ncwas, 'participant_column')
workflow.connect(inputspec, 'columns',
ncwas, 'columns_string')
workflow.connect(ccb, 'batch_list',
ncwas, 'voxel_range')
#Merge the computed CWAS data
workflow.connect(ncwas, 'result_batch',
mcwasb, 'cwas_batches')
workflow.connect(jmask, 'joint_mask',
mcwasb, 'mask_file')
workflow.connect(mcwasb, 'F_file', outputspec, 'F_map')
workflow.connect(mcwasb, 'p_file', outputspec, 'p_map')
workflow.connect(mcwasb, 'neglog_p_file', outputspec, 'neglog_p_map')
return workflow
