Source code for CPAC.cwas.pipeline

import os
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.utility as util
from nipype import config

from CPAC.utils.interfaces.function import Function

from .cwas import (

[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 outputspec.z_map : string (nifti file) Significance p values converted to z-scores 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/' ) 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, Cameron Craddock R, Emerson JW, McMahon K, Copland DA, Castellanos FX, Milham MP. A multivariate distance-based analytic framework for connectome-wide association studies. Neuroimage. 2014 Jun;93 Pt 1(0 1):74-94. doi: 10.1016/j.neuroimage.2014.02.024. Epub 2014 Feb 28. PMID: 24583255; PMCID: PMC4138049. """ 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), 'crashfile_format': 'txt'} inputspec = pe.Node(util.IdentityInterface(fields=['roi', 'subjects', 'regressor', 'participant_column', 'columns', 'permutations', 'parallel_nodes', 'z_score']), name='inputspec') outputspec = pe.Node(util.IdentityInterface(fields=['F_map', 'p_map', 'neglog_p_map', 'one_p_map', 'z_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', 'z_score', 'permutations'], output_names=['F_file', 'p_file', 'neglog_p_file', 'one_p_file', 'z_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(inputspec, 'z_score', mcwasb, 'z_score') workflow.connect(inputspec, 'permutations', mcwasb, 'permutations') 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') workflow.connect(mcwasb, 'one_p_file', outputspec, 'one_p_map') workflow.connect(mcwasb, 'z_file', outputspec, 'z_map') return workflow