Preprocessing Pipeline

Create a BIDSDataGrabber Node to read data files
Create a IdentityInterface Node to iterate over multiple Subjects
Create following Nodes for preprocessing: (Based on Nan-kuei Chen's resting state analysis pipeline:
- [-] convert data to nii in LAS orientation (Skip if NYU is already in LAS Orientation)
- [x] Exclude 4 volumes from the functional scan
- [x] slice time correction
- [x] motion correction, {[Save motion parameter]}
- [x] Skull stripping and mask generation using mean of functional scan got using mcflirt
- [x] Apply mask to Functional image
- [x] Co-Registration with Anatomical Image
- [x] normalize functional data
- [-] regress out WM/CSF - Not doing coz of the debate that WM also has some activations
- [x] bandpass filter
Embed them into a workflow
Do the Preprocessing of 4 subjects



In [ ]:



In [1]:

    
from bids.grabbids import BIDSLayout
from nipype.interfaces.fsl import (BET, ExtractROI, FAST, FLIRT, ImageMaths,
                                   MCFLIRT, SliceTimer, Threshold,Info, ConvertXFM,MotionOutliers)
from nipype.interfaces.afni import Resample
from nipype.interfaces.io import DataSink
from nipype.pipeline import Node, MapNode, Workflow, JoinNode
from nipype.interfaces.utility import IdentityInterface, Function
import os
from os.path import join as opj
from nipype.interfaces import afni
import nibabel as nib









    



Failed to import duecredit due to No module named 'duecredit.version'

Define Paths

Let's set the directory names:

base_directory : The directory where all the output of my program will be saved
I have created 2 workflows, one onside another:
1. parent_wf_directory: The name of the folder where the top level workflow's output is saved
2. child_wf_directory: The name of the folder where the Second level workfolw's output is saved
data_directory: Directory where the BIDS data is stored.



In [176]:

    
os.chdir('/home1/varunk/Autism-Connectome-Analysis-brain_connectivity/notebooks/')
!pwd









    



/home1/varunk/Autism-Connectome-Analysis-brain_connectivity/notebooks

Adding module to read the parameters and paths from json file



In [2]:

    
import json



In [3]:

    
# Paths

path_cwd = os.getcwd()
path_split_list = path_cwd.split('/')
s = path_split_list[0:-2] # for getting to the parent dir of pwd
s = opj('/',*s) # *s converts list to path, # very important to add '/' in the begining so it is read as directory later



In [4]:

    
# json_path = opj(data_directory,'task-rest_bold.json')

json_path = '../scripts/json/paths.json'
with open(json_path, 'rt') as fp:
    task_info = json.load(fp)



In [6]:

    
# base_directory = opj(s,'result') 
# parent_wf_directory = 'preprocessPipeline_ABIDE2_GU1_withfloat'
# child_wf_directory = 'coregistrationPipeline'

# data_directory = opj(s,"data/ABIDE2-BIDS/GU1")

# datasink_name = 'datasink_preprocessed_ABIDE2_GU1_withfloat'

base_directory = opj(s,task_info["base_directory_for_results"]) 
motion_correction_bet_directory = task_info["motion_correction_bet_directory"]
parent_wf_directory = task_info["parent_wf_directory"]
coreg_reg_directory = task_info["coreg_reg_directory"]
atlas_resize_reg_directory = task_info["atlas_resize_reg_directory"]

# data_directory = opj(s,task_info["data_directory"])
data_directory = opj(s,'data/NYU_Cocaine-BIDS')
datasink_name = task_info["datasink_name"]

atlasPath = opj(s,task_info["atlas_path"])


# mask_file = '/media/varun/LENOVO4/Projects/result/preprocessPipeline/coregistrationPipeline/_subject_id_0050952/skullStrip/sub-0050952_T1w_resample_brain_mask.nii.gz'
# os.chdir(path)



In [7]:

    
s# data_directory# path_cwd









    Out[7]:





'/home1/varunk'



In [8]:

    
layout = BIDSLayout(data_directory)

# number_of_subjects = 4 # Number of subjects you wish to preprocess
number_of_subjects = len(layout.get_subjects())

Checking the Data directory Structure



In [9]:

    
# !tree /home/jovyan/work/preprocess/data/ABIDE-BIDS/NYU/



In [10]:

    
len(layout.get_subjects()) # working!Gives us list of all the subjects









    Out[10]:





50



In [11]:

    
layout.get_subjects()









    Out[11]:





['1010169',
 '1045947',
 '1065809',
 '1108539',
 '1113148',
 '1139036',
 '1166151',
 '1226307',
 '1348206',
 '1365850',
 '1388232',
 '1413797',
 '1546134',
 '1655010',
 '1731343',
 '1840159',
 '1915879',
 '1961399',
 '2071764',
 '2154093',
 '2181167',
 '2219645',
 '2248885',
 '2259903',
 '2322234',
 '2498887',
 '2565282',
 '2616448',
 '2869665',
 '2988001',
 '3003775',
 '3091757',
 '3283638',
 '3334426',
 '3529000',
 '3577037',
 '3588280',
 '3999447',
 '4084755',
 '4169147',
 '4191037',
 '4193824',
 '4210045',
 '4222184',
 '4257613',
 '6144510',
 '7749110',
 '7946505',
 '9603424',
 '9858555']

To get the metadata associated with a subject. [Takes as argumment the filename of subject ]

Create a list of subjects



In [186]:

    
subject_list = (layout.get_subjects())[0:number_of_subjects]



In [187]:

    
layout.get()









    Out[187]:





[File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/.DS_Store', type=''),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/.~lock.ABIDE_composite_phenotypic_file.csv#', type=''),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/ABIDE_composite_phenotypic_file.csv', type='file'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/.DS_Store', type=''),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051456/.DS_Store', subject='0051456', type=''),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051456/anat/sub-0051456_T1w.nii.gz', subject='0051456', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051456/func/sub-0051456_task-rest_run-1_bold.nii.gz', subject='0051456', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051457/anat/sub-0051457_T1w.nii.gz', subject='0051457', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051457/func/sub-0051457_task-rest_run-1_bold.nii.gz', subject='0051457', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051458/anat/sub-0051458_T1w.nii.gz', subject='0051458', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051458/func/sub-0051458_task-rest_run-1_bold.nii.gz', subject='0051458', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051459/anat/sub-0051459_T1w.nii.gz', subject='0051459', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051459/func/sub-0051459_task-rest_run-1_bold.nii.gz', subject='0051459', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051460/anat/sub-0051460_T1w.nii.gz', subject='0051460', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051460/func/sub-0051460_task-rest_run-1_bold.nii.gz', subject='0051460', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051461/anat/sub-0051461_T1w.nii.gz', subject='0051461', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051461/func/sub-0051461_task-rest_run-1_bold.nii.gz', subject='0051461', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051462/anat/sub-0051462_T1w.nii.gz', subject='0051462', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051462/func/sub-0051462_task-rest_run-1_bold.nii.gz', subject='0051462', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051463/anat/sub-0051463_T1w.nii.gz', subject='0051463', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051463/func/sub-0051463_task-rest_run-1_bold.nii.gz', subject='0051463', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051464/anat/sub-0051464_T1w.nii.gz', subject='0051464', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051464/func/sub-0051464_task-rest_run-1_bold.nii.gz', subject='0051464', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051465/anat/sub-0051465_T1w.nii.gz', subject='0051465', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051465/func/sub-0051465_task-rest_run-1_bold.nii.gz', subject='0051465', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051466/anat/sub-0051466_T1w.nii.gz', subject='0051466', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051466/func/sub-0051466_task-rest_run-1_bold.nii.gz', subject='0051466', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051467/anat/sub-0051467_T1w.nii.gz', subject='0051467', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051467/func/sub-0051467_task-rest_run-1_bold.nii.gz', subject='0051467', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051468/anat/sub-0051468_T1w.nii.gz', subject='0051468', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051468/func/sub-0051468_task-rest_run-1_bold.nii.gz', subject='0051468', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051469/anat/sub-0051469_T1w.nii.gz', subject='0051469', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051469/func/sub-0051469_task-rest_run-1_bold.nii.gz', subject='0051469', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051470/anat/sub-0051470_T1w.nii.gz', subject='0051470', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051470/func/sub-0051470_task-rest_run-1_bold.nii.gz', subject='0051470', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051471/anat/sub-0051471_T1w.nii.gz', subject='0051471', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051471/func/sub-0051471_task-rest_run-1_bold.nii.gz', subject='0051471', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051472/anat/sub-0051472_T1w.nii.gz', subject='0051472', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051472/func/sub-0051472_task-rest_run-1_bold.nii.gz', subject='0051472', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051473/anat/sub-0051473_T1w.nii.gz', subject='0051473', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051473/func/sub-0051473_task-rest_run-1_bold.nii.gz', subject='0051473', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051474/anat/sub-0051474_T1w.nii.gz', subject='0051474', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051474/func/sub-0051474_task-rest_run-1_bold.nii.gz', subject='0051474', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051475/anat/sub-0051475_T1w.nii.gz', subject='0051475', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051475/func/sub-0051475_task-rest_run-1_bold.nii.gz', subject='0051475', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051476/anat/sub-0051476_T1w.nii.gz', subject='0051476', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051476/func/sub-0051476_task-rest_run-1_bold.nii.gz', subject='0051476', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051477/anat/sub-0051477_T1w.nii.gz', subject='0051477', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051477/func/sub-0051477_task-rest_run-1_bold.nii.gz', subject='0051477', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051478/anat/sub-0051478_T1w.nii.gz', subject='0051478', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051478/func/sub-0051478_task-rest_run-1_bold.nii.gz', subject='0051478', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051479/anat/sub-0051479_T1w.nii.gz', subject='0051479', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051479/func/sub-0051479_task-rest_run-1_bold.nii.gz', subject='0051479', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051480/anat/sub-0051480_T1w.nii.gz', subject='0051480', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051480/func/sub-0051480_task-rest_run-1_bold.nii.gz', subject='0051480', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051481/anat/sub-0051481_T1w.nii.gz', subject='0051481', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051481/func/sub-0051481_task-rest_run-1_bold.nii.gz', subject='0051481', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051482/anat/sub-0051482_T1w.nii.gz', subject='0051482', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051482/func/sub-0051482_task-rest_run-1_bold.nii.gz', subject='0051482', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051483/anat/sub-0051483_T1w.nii.gz', subject='0051483', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051483/func/sub-0051483_task-rest_run-1_bold.nii.gz', subject='0051483', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051484/anat/sub-0051484_T1w.nii.gz', subject='0051484', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051484/func/sub-0051484_task-rest_run-1_bold.nii.gz', subject='0051484', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051485/anat/sub-0051485_T1w.nii.gz', subject='0051485', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051485/func/sub-0051485_task-rest_run-1_bold.nii.gz', subject='0051485', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051486/anat/sub-0051486_T1w.nii.gz', subject='0051486', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051486/func/sub-0051486_task-rest_run-1_bold.nii.gz', subject='0051486', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051487/anat/sub-0051487_T1w.nii.gz', subject='0051487', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051487/func/sub-0051487_task-rest_run-1_bold.nii.gz', subject='0051487', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051488/anat/sub-0051488_T1w.nii.gz', subject='0051488', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051488/func/sub-0051488_task-rest_run-1_bold.nii.gz', subject='0051488', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051489/anat/sub-0051489_T1w.nii.gz', subject='0051489', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051489/func/sub-0051489_task-rest_run-1_bold.nii.gz', subject='0051489', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051490/anat/sub-0051490_T1w.nii.gz', subject='0051490', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051490/func/sub-0051490_task-rest_run-1_bold.nii.gz', subject='0051490', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051491/anat/sub-0051491_T1w.nii.gz', subject='0051491', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051491/func/sub-0051491_task-rest_run-1_bold.nii.gz', subject='0051491', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051492/anat/sub-0051492_T1w.nii.gz', subject='0051492', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051492/func/sub-0051492_task-rest_run-1_bold.nii.gz', subject='0051492', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051493/anat/sub-0051493_T1w.nii.gz', subject='0051493', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/sub-0051493/func/sub-0051493_task-rest_run-1_bold.nii.gz', subject='0051493', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/T1w.json', type='T1w'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/Caltech/task-rest_bold.json', type='bold', task='rest'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/CMU_a/.DS_Store', type=''),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/CMU_a/sub-0050642/anat/sub-0050642_T1w.nii.gz', subject='0050642', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/CMU_a/sub-0050642/func/sub-0050642_task-rest_run-1_bold.nii.gz', subject='0050642', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/CMU_a/sub-0050646/.DS_Store', subject='0050646', type=''),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/CMU_a/sub-0050646/anat/sub-0050646_T1w.nii.gz', subject='0050646', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/CMU_a/sub-0050646/func/sub-0050646_task-rest_run-1_bold.nii.gz', subject='0050646', run='1', type='bold', task='rest', modality='func'),
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 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050166/func/sub-0050166_task-rest_run-1_bold.nii.gz', subject='0050166', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050166/func/sub-0050166_task-rest_run-2_bold.nii.gz', subject='0050166', run='2', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050166/func/sub-0050166_task-rest_run-3_bold.nii.gz', subject='0050166', run='3', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050167/anat/sub-0050167_T1w.nii.gz', subject='0050167', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050167/func/sub-0050167_task-rest_run-1_bold.nii.gz', subject='0050167', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050167/func/sub-0050167_task-rest_run-2_bold.nii.gz', subject='0050167', run='2', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050167/func/sub-0050167_task-rest_run-3_bold.nii.gz', subject='0050167', run='3', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050168/anat/sub-0050168_T1w.nii.gz', subject='0050168', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050168/func/sub-0050168_task-rest_run-1_bold.nii.gz', subject='0050168', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050168/func/sub-0050168_task-rest_run-2_bold.nii.gz', subject='0050168', run='2', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050168/func/sub-0050168_task-rest_run-3_bold.nii.gz', subject='0050168', run='3', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050169/anat/sub-0050169_T1w.nii.gz', subject='0050169', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050169/func/sub-0050169_task-rest_run-1_bold.nii.gz', subject='0050169', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050169/func/sub-0050169_task-rest_run-2_bold.nii.gz', subject='0050169', run='2', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050169/func/sub-0050169_task-rest_run-3_bold.nii.gz', subject='0050169', run='3', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050170/anat/sub-0050170_T1w.nii.gz', subject='0050170', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050170/func/sub-0050170_task-rest_run-1_bold.nii.gz', subject='0050170', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050170/func/sub-0050170_task-rest_run-2_bold.nii.gz', subject='0050170', run='2', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050170/func/sub-0050170_task-rest_run-3_bold.nii.gz', subject='0050170', run='3', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050171/anat/sub-0050171_T1w.nii.gz', subject='0050171', type='T1w', modality='anat'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050171/func/sub-0050171_task-rest_run-1_bold.nii.gz', subject='0050171', run='1', type='bold', task='rest', modality='func'),
 File(filename='/home1/varunk/data/ABIDE1/RawDataBIDs/OHSU/sub-0050171/func/sub-0050171_task-rest_run-2_bold.nii.gz', subject='0050171', run='2', type='bold', task='rest', modality='func'),
 ...]



In [188]:

    
# subject_list[960:980]



In [189]:

    
# To debug some error
for subject_id in subject_list:
#     subject_id = '50273'
    anat_file_path = [f.filename for f in layout.get(subject=subject_id, type='T1w', extensions=['nii', 'nii.gz'])]
    func_file_path = [f.filename for f in layout.get(subject=subject_id, type='bold', extensions=['nii', 'nii.gz'])]
    print('In Subject: ',subject_id)
    x = anat_file_path[0]
    y = func_file_path[0]









    



In Subject:  0050002
In Subject:  0050003
In Subject:  0050004
In Subject:  0050005
In Subject:  0050006
In Subject:  0050007
In Subject:  0050008
In Subject:  0050009
In Subject:  0050010
In Subject:  0050011
In Subject:  0050012
In Subject:  0050013
In Subject:  0050014
In Subject:  0050015
In Subject:  0050016
In Subject:  0050017
In Subject:  0050019
In Subject:  0050020
In Subject:  0050022
In Subject:  0050023
In Subject:  0050024
In Subject:  0050025
In Subject:  0050026
In Subject:  0050027
In Subject:  0050028
In Subject:  0050029
In Subject:  0050030
In Subject:  0050031
In Subject:  0050032
In Subject:  0050033
In Subject:  0050034
In Subject:  0050035
In Subject:  0050036
In Subject:  0050037
In Subject:  0050038
In Subject:  0050039
In Subject:  0050040
In Subject:  0050041
In Subject:  0050042
In Subject:  0050043
In Subject:  0050044
In Subject:  0050045
In Subject:  0050046
In Subject:  0050047
In Subject:  0050048
In Subject:  0050049
In Subject:  0050050
In Subject:  0050051
In Subject:  0050052
In Subject:  0050053
In Subject:  0050054
In Subject:  0050055
In Subject:  0050056
In Subject:  0050057
In Subject:  0050058
In Subject:  0050059
In Subject:  0050060
In Subject:  0050102
In Subject:  0050103
In Subject:  0050104
In Subject:  0050105
In Subject:  0050106
In Subject:  0050107
In Subject:  0050108
In Subject:  0050109
In Subject:  0050110
In Subject:  0050111
In Subject:  0050112
In Subject:  0050113
In Subject:  0050114
In Subject:  0050115
In Subject:  0050116
In Subject:  0050117
In Subject:  0050118
In Subject:  0050119
In Subject:  0050120
In Subject:  0050121
In Subject:  0050122
In Subject:  0050123
In Subject:  0050124
In Subject:  0050125
In Subject:  0050126
In Subject:  0050127
In Subject:  0050128
In Subject:  0050129
In Subject:  0050130
In Subject:  0050131
In Subject:  0050132
In Subject:  0050133
In Subject:  0050134
In Subject:  0050135
In Subject:  0050136
In Subject:  0050137
In Subject:  0050142
In Subject:  0050143
In Subject:  0050144
In Subject:  0050145
In Subject:  0050146
In Subject:  0050147
In Subject:  0050148
In Subject:  0050149
In Subject:  0050150
In Subject:  0050152
In Subject:  0050153
In Subject:  0050155
In Subject:  0050156
In Subject:  0050157
In Subject:  0050158
In Subject:  0050159
In Subject:  0050160
In Subject:  0050161
In Subject:  0050162
In Subject:  0050163
In Subject:  0050164
In Subject:  0050165
In Subject:  0050166
In Subject:  0050167
In Subject:  0050168
In Subject:  0050169
In Subject:  0050170
In Subject:  0050171
In Subject:  0050182
In Subject:  0050183
In Subject:  0050184
In Subject:  0050185
In Subject:  0050186
In Subject:  0050187
In Subject:  0050188
In Subject:  0050189
In Subject:  0050190
In Subject:  0050191
In Subject:  0050192
In Subject:  0050193
In Subject:  0050194
In Subject:  0050195
In Subject:  0050196
In Subject:  0050197
In Subject:  0050198
In Subject:  0050199
In Subject:  0050200
In Subject:  0050201
In Subject:  0050202
In Subject:  0050203
In Subject:  0050204
In Subject:  0050205
In Subject:  0050206
In Subject:  0050207
In Subject:  0050208
In Subject:  0050209
In Subject:  0050210
In Subject:  0050211
In Subject:  0050212
In Subject:  0050213
In Subject:  0050214
In Subject:  0050215
In Subject:  0050216
In Subject:  0050217
In Subject:  0050232
In Subject:  0050233
In Subject:  0050234
In Subject:  0050235
In Subject:  0050236
In Subject:  0050237
In Subject:  0050238
In Subject:  0050239
In Subject:  0050240
In Subject:  0050241
In Subject:  0050242
In Subject:  0050243
In Subject:  0050244
In Subject:  0050245
In Subject:  0050246
In Subject:  0050247
In Subject:  0050248
In Subject:  0050249
In Subject:  0050250
In Subject:  0050251
In Subject:  0050252
In Subject:  0050253
In Subject:  0050254
In Subject:  0050255
In Subject:  0050257
In Subject:  0050259
In Subject:  0050260
In Subject:  0050261
In Subject:  0050262
In Subject:  0050263
In Subject:  0050264
In Subject:  0050265
In Subject:  0050266
In Subject:  0050267
In Subject:  0050268
In Subject:  0050269
In Subject:  0050270
In Subject:  0050271
In Subject:  0050272
In Subject:  0050273
In Subject:  0050274
In Subject:  0050275
In Subject:  0050276
In Subject:  0050277
In Subject:  0050278
In Subject:  0050279
In Subject:  0050280
In Subject:  0050281
In Subject:  0050282
In Subject:  0050283
In Subject:  0050284
In Subject:  0050285
In Subject:  0050286
In Subject:  0050287
In Subject:  0050288
In Subject:  0050289
In Subject:  0050290
In Subject:  0050291
In Subject:  0050292
In Subject:  0050293
In Subject:  0050294
In Subject:  0050295
In Subject:  0050296
In Subject:  0050297
In Subject:  0050298
In Subject:  0050299
In Subject:  0050300
In Subject:  0050301
In Subject:  0050302
In Subject:  0050303
In Subject:  0050304
In Subject:  0050305
In Subject:  0050306
In Subject:  0050307
In Subject:  0050308
In Subject:  0050309
In Subject:  0050310
In Subject:  0050311
In Subject:  0050312
In Subject:  0050313
In Subject:  0050314
In Subject:  0050315
In Subject:  0050316
In Subject:  0050317
In Subject:  0050318
In Subject:  0050319
In Subject:  0050320
In Subject:  0050321
In Subject:  0050322
In Subject:  0050323
In Subject:  0050324
In Subject:  0050325
In Subject:  0050326
In Subject:  0050327
In Subject:  0050328
In Subject:  0050329
In Subject:  0050330
In Subject:  0050331
In Subject:  0050332
In Subject:  0050333
In Subject:  0050334
In Subject:  0050335
In Subject:  0050336
In Subject:  0050337
In Subject:  0050338
In Subject:  0050339
In Subject:  0050340
In Subject:  0050341
In Subject:  0050342
In Subject:  0050343
In Subject:  0050344
In Subject:  0050345
In Subject:  0050346
In Subject:  0050347
In Subject:  0050348
In Subject:  0050349
In Subject:  0050350
In Subject:  0050351
In Subject:  0050352
In Subject:  0050353
In Subject:  0050354
In Subject:  0050355
In Subject:  0050356
In Subject:  0050357
In Subject:  0050358
In Subject:  0050359
In Subject:  0050360
In Subject:  0050361
In Subject:  0050362
In Subject:  0050363
In Subject:  0050364
In Subject:  0050365
In Subject:  0050366
In Subject:  0050367
In Subject:  0050368
In Subject:  0050369
In Subject:  0050370
In Subject:  0050371
In Subject:  0050372
In Subject:  0050373
In Subject:  0050374
In Subject:  0050375
In Subject:  0050376
In Subject:  0050377
In Subject:  0050378
In Subject:  0050379
In Subject:  0050380
In Subject:  0050381
In Subject:  0050382
In Subject:  0050383
In Subject:  0050385
In Subject:  0050386
In Subject:  0050387
In Subject:  0050388
In Subject:  0050390
In Subject:  0050391
In Subject:  0050397
In Subject:  0050399
In Subject:  0050402
In Subject:  0050403
In Subject:  0050404
In Subject:  0050405
In Subject:  0050406
In Subject:  0050407
In Subject:  0050408
In Subject:  0050410
In Subject:  0050411
In Subject:  0050412
In Subject:  0050413
In Subject:  0050414
In Subject:  0050415
In Subject:  0050416
In Subject:  0050417
In Subject:  0050418
In Subject:  0050419
In Subject:  0050421
In Subject:  0050422
In Subject:  0050423
In Subject:  0050424
In Subject:  0050425
In Subject:  0050426
In Subject:  0050427
In Subject:  0050428
In Subject:  0050432
In Subject:  0050433
In Subject:  0050434
In Subject:  0050435
In Subject:  0050436
In Subject:  0050437
In Subject:  0050438
In Subject:  0050439
In Subject:  0050440
In Subject:  0050441
In Subject:  0050442
In Subject:  0050443
In Subject:  0050444
In Subject:  0050445
In Subject:  0050446
In Subject:  0050447
In Subject:  0050448
In Subject:  0050449
In Subject:  0050450
In Subject:  0050451
In Subject:  0050452
In Subject:  0050453
In Subject:  0050454
In Subject:  0050455
In Subject:  0050456
In Subject:  0050457
In Subject:  0050458
In Subject:  0050459
In Subject:  0050460
In Subject:  0050461
In Subject:  0050462
In Subject:  0050463
In Subject:  0050464
In Subject:  0050465
In Subject:  0050466
In Subject:  0050467
In Subject:  0050468
In Subject:  0050469
In Subject:  0050470
In Subject:  0050471
In Subject:  0050472
In Subject:  0050473
In Subject:  0050474
In Subject:  0050475
In Subject:  0050476
In Subject:  0050477
In Subject:  0050478
In Subject:  0050479
In Subject:  0050480
In Subject:  0050481
In Subject:  0050482
In Subject:  0050483
In Subject:  0050484
In Subject:  0050485
In Subject:  0050486
In Subject:  0050487
In Subject:  0050488
In Subject:  0050489
In Subject:  0050490
In Subject:  0050491
In Subject:  0050492
In Subject:  0050493
In Subject:  0050494
In Subject:  0050495
In Subject:  0050496
In Subject:  0050497
In Subject:  0050498
In Subject:  0050499
In Subject:  0050500
In Subject:  0050501
In Subject:  0050502
In Subject:  0050503
In Subject:  0050504
In Subject:  0050505
In Subject:  0050506
In Subject:  0050507
In Subject:  0050508
In Subject:  0050509
In Subject:  0050510
In Subject:  0050511
In Subject:  0050512
In Subject:  0050513
In Subject:  0050514
In Subject:  0050515
In Subject:  0050516
In Subject:  0050517
In Subject:  0050518
In Subject:  0050519
In Subject:  0050520
In Subject:  0050521
In Subject:  0050522
In Subject:  0050523
In Subject:  0050524
In Subject:  0050525
In Subject:  0050526
In Subject:  0050527
In Subject:  0050528
In Subject:  0050529
In Subject:  0050530
In Subject:  0050531
In Subject:  0050532
In Subject:  0050551
In Subject:  0050552
In Subject:  0050553
In Subject:  0050554
In Subject:  0050555
In Subject:  0050556
In Subject:  0050557
In Subject:  0050558
In Subject:  0050559
In Subject:  0050560
In Subject:  0050561
In Subject:  0050562
In Subject:  0050563
In Subject:  0050564
In Subject:  0050565
In Subject:  0050566
In Subject:  0050567
In Subject:  0050568
In Subject:  0050569
In Subject:  0050570
In Subject:  0050571
In Subject:  0050572
In Subject:  0050573
In Subject:  0050574
In Subject:  0050575
In Subject:  0050576
In Subject:  0050577
In Subject:  0050578
In Subject:  0050601
In Subject:  0050602
In Subject:  0050603
In Subject:  0050604
In Subject:  0050605
In Subject:  0050606
In Subject:  0050607
In Subject:  0050608
In Subject:  0050609
In Subject:  0050610
In Subject:  0050611
In Subject:  0050612
In Subject:  0050613
In Subject:  0050614
In Subject:  0050615
In Subject:  0050616
In Subject:  0050617
In Subject:  0050618
In Subject:  0050619
In Subject:  0050620
In Subject:  0050621
In Subject:  0050622
In Subject:  0050623
In Subject:  0050624
In Subject:  0050625
In Subject:  0050626
In Subject:  0050627
In Subject:  0050628
In Subject:  0050642
In Subject:  0050643
In Subject:  0050644
In Subject:  0050645
In Subject:  0050646
In Subject:  0050647
In Subject:  0050648
In Subject:  0050649
In Subject:  0050650
In Subject:  0050651
In Subject:  0050652
In Subject:  0050653
In Subject:  0050654
In Subject:  0050655
In Subject:  0050656
In Subject:  0050657
In Subject:  0050658
In Subject:  0050659
In Subject:  0050660
In Subject:  0050661
In Subject:  0050663
In Subject:  0050664
In Subject:  0050665
In Subject:  0050666
In Subject:  0050667
In Subject:  0050668
In Subject:  0050669
In Subject:  0050682
In Subject:  0050683
In Subject:  0050685
In Subject:  0050686
In Subject:  0050687
In Subject:  0050688
In Subject:  0050689
In Subject:  0050690
In Subject:  0050691
In Subject:  0050692
In Subject:  0050693
In Subject:  0050694
In Subject:  0050695
In Subject:  0050696
In Subject:  0050697
In Subject:  0050698
In Subject:  0050699
In Subject:  0050700
In Subject:  0050701
In Subject:  0050702
In Subject:  0050703
In Subject:  0050704
In Subject:  0050705
In Subject:  0050706
In Subject:  0050707
In Subject:  0050708
In Subject:  0050709
In Subject:  0050710
In Subject:  0050711
In Subject:  0050722
In Subject:  0050723
In Subject:  0050724
In Subject:  0050725
In Subject:  0050726
In Subject:  0050727
In Subject:  0050728
In Subject:  0050730
In Subject:  0050731
In Subject:  0050732
In Subject:  0050733
In Subject:  0050734
In Subject:  0050735
In Subject:  0050736
In Subject:  0050737
In Subject:  0050738
In Subject:  0050739
In Subject:  0050740
In Subject:  0050741
In Subject:  0050742
In Subject:  0050743
In Subject:  0050744
In Subject:  0050745
In Subject:  0050746
In Subject:  0050747
In Subject:  0050748
In Subject:  0050749
In Subject:  0050750
In Subject:  0050751
In Subject:  0050752
In Subject:  0050753
In Subject:  0050754
In Subject:  0050755
In Subject:  0050756
In Subject:  0050757
In Subject:  0050772
In Subject:  0050773
In Subject:  0050774
In Subject:  0050775
In Subject:  0050776
In Subject:  0050777
In Subject:  0050778
In Subject:  0050779
In Subject:  0050780
In Subject:  0050781
In Subject:  0050782
In Subject:  0050783
In Subject:  0050784
In Subject:  0050785
In Subject:  0050786
In Subject:  0050787
In Subject:  0050788
In Subject:  0050789
In Subject:  0050790
In Subject:  0050791
In Subject:  0050792
In Subject:  0050793
In Subject:  0050794
In Subject:  0050795
In Subject:  0050796
In Subject:  0050797
In Subject:  0050798
In Subject:  0050799
In Subject:  0050800
In Subject:  0050801
In Subject:  0050802
In Subject:  0050803
In Subject:  0050804
In Subject:  0050805
In Subject:  0050806
In Subject:  0050807
In Subject:  0050808
In Subject:  0050809
In Subject:  0050810
In Subject:  0050811
In Subject:  0050812
In Subject:  0050813
In Subject:  0050814
In Subject:  0050815
In Subject:  0050816
In Subject:  0050817
In Subject:  0050818
In Subject:  0050819
In Subject:  0050820
In Subject:  0050821
In Subject:  0050822
In Subject:  0050823
In Subject:  0050824
In Subject:  0050825
In Subject:  0050826
In Subject:  0050952
In Subject:  0050953
In Subject:  0050954
In Subject:  0050955
In Subject:  0050956
In Subject:  0050957
In Subject:  0050958
In Subject:  0050959
In Subject:  0050960
In Subject:  0050961
In Subject:  0050962
In Subject:  0050964
In Subject:  0050965
In Subject:  0050966
In Subject:  0050967
In Subject:  0050968
In Subject:  0050969
In Subject:  0050970
In Subject:  0050971
In Subject:  0050972
In Subject:  0050973
In Subject:  0050974
In Subject:  0050975
In Subject:  0050976
In Subject:  0050977
In Subject:  0050978
In Subject:  0050979
In Subject:  0050980
In Subject:  0050981
In Subject:  0050982
In Subject:  0050983
In Subject:  0050984
In Subject:  0050985
In Subject:  0050986
In Subject:  0050987
In Subject:  0050988
In Subject:  0050989
In Subject:  0050990
In Subject:  0050991
In Subject:  0050992
In Subject:  0050993
In Subject:  0050994
In Subject:  0050995
In Subject:  0050996
In Subject:  0050997
In Subject:  0050998
In Subject:  0050999
In Subject:  0051000
In Subject:  0051001
In Subject:  0051002
In Subject:  0051003
In Subject:  0051006
In Subject:  0051007
In Subject:  0051008
In Subject:  0051009
In Subject:  0051010
In Subject:  0051011
In Subject:  0051012
In Subject:  0051013
In Subject:  0051014
In Subject:  0051015
In Subject:  0051016
In Subject:  0051017
In Subject:  0051018
In Subject:  0051019
In Subject:  0051020
In Subject:  0051021
In Subject:  0051023
In Subject:  0051024
In Subject:  0051025
In Subject:  0051026
In Subject:  0051027
In Subject:  0051028
In Subject:  0051029
In Subject:  0051030
In Subject:  0051032
In Subject:  0051033
In Subject:  0051034
In Subject:  0051035
In Subject:  0051036
In Subject:  0051038
In Subject:  0051039
In Subject:  0051040
In Subject:  0051041
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In [190]:

    
# anat_file_path,func_file_path

Create our own custom function - BIDSDataGrabber using a Function Interface.



In [191]:

    
def get_nifti_filenames(subject_id,data_dir):
#     Remember that all the necesary imports need to be INSIDE the function for the Function Interface to work!
    from bids.grabbids import BIDSLayout
    
    layout = BIDSLayout(data_dir)
    
    anat_file_path = [f.filename for f in layout.get(subject=subject_id, type='T1w', extensions=['nii', 'nii.gz'])]
    func_file_path = [f.filename for f in layout.get(subject=subject_id, type='bold', run='1', extensions=['nii', 'nii.gz'])]
    
    return anat_file_path[0],func_file_path[0]

# Refer to Supplementary material section One for info on arguments for layout.get()

Wrap it inside a Node



In [192]:

    
BIDSDataGrabber = Node(Function(function=get_nifti_filenames, input_names=['subject_id','data_dir'],
                                output_names=['anat_file_path','func_file_path']), name='BIDSDataGrabber')
# BIDSDataGrabber.iterables = [('subject_id',subject_list)]
BIDSDataGrabber.inputs.data_dir = data_directory

%%bash cd /home1/varunk/Autism-Connectome-Analysis-bids-related



In [ ]:



In [193]:

    
# To test the function wrapped in the node
# os.chdir('/home1/varunk/Autism-Connectome-Analysis-bids-related')
# BIDSDataGrabber.inputs.data_dir = data_directory
# BIDSDataGrabber.inputs.subject_id = layout.get_subjects()[0] # gives the first subject's ID
# res = BIDSDataGrabber.run()

# res.outputs

Return TR



In [194]:

    
def get_TR(in_file):
    from bids.grabbids import BIDSLayout

    data_directory = '/home1/varunk/data/ABIDE1/RawDataBIDs'
    layout = BIDSLayout(data_directory)
    metadata = layout.get_metadata(path=in_file)
    TR  = metadata['RepetitionTime']
    return TR



In [195]:

    
# type(get_TR('/home1/varunk/data/ABIDE1/RawDataBIDs/Pitt/sub-0050002/func/sub-0050002_task-rest_run-1_bold.nii.gz'))



In [196]:

    
# in_file = '/home1/varunk/data/ABIDE1/RawDataBIDs/Pitt/sub-0050002/func/sub-0050002_task-rest_run-1_bold.nii.gz'
in_file = '/home1/varunk/data/ABIDE1/RawDataBIDs/SBL/sub-0051556/func/sub-0051556_task-rest_run-1_bold.nii.gz'

metadata = layout.get_metadata(path=in_file)

metadata['RepetitionTime'], metadata['SliceAcquisitionOrder']









    Out[196]:





(2.2, 'Sequential Descending')



In [197]:

    
def _getMetadata(in_file):
    from bids.grabbids import BIDSLayout

    interleaved = True
    index_dir = False
    data_directory = '/home1/varunk/data/ABIDE1/RawDataBIDs'
    layout = BIDSLayout(data_directory)
    metadata = layout.get_metadata(path=in_file)
    tr  = metadata['RepetitionTime']
    slice_order = metadata['SliceAcquisitionOrder']
    
    if slice_order.split(' ')[0] == 'Sequential':
        interleaved =  False
    if slice_order.split(' ')[1] == 'Descending': 
        index_dir = True

    return tr, index_dir, interleaved



In [198]:

    
getMetadata = Node(Function(function=_getMetadata, input_names=['in_file'],
                                output_names=['tr','index_dir','interleaved']), name='getMetadata')



In [199]:

    
# Test run
# getMetadata.inputs.in_file = in_file
# res = getMetadata.run()









    



171213-02:06:51,6 workflow INFO:
	 Executing node getMetadata in dir: /tmp/tmprajuzlw_/getMetadata



In [200]:

    
# res.outputs









    Out[200]:





index_dir = True
interleaved = False
tr = 2.2

Skipping 4 starting scans

Extract ROI for skipping first 4 scans of the functional data

Arguments:
t_min: (corresponds to time dimension) Denotes the starting time of the inclusion
t_size: Denotes the number of scans to include

The logic behind skipping 4 initial scans is to take scans after the subject has stabalized in the scanner.



In [201]:

    
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=4, t_size=-1),
               output_type='NIFTI',
               name="extract")

Slice time correction

Created a Node that does slice time correction

Arguments:
index_dir=False -> Slices were taken bottom to top i.e. in ascending order
interleaved=True means odd slices were acquired first and then even slices [or vice versa(Not sure)]



In [202]:

    
slicetimer = Node(SliceTimer(
                             output_type='NIFTI'
                             ),
                  name="slicetimer")

# index_dir=False, interleaved=True,



In [203]:

    
# To test Slicetimer

# subject_id = layout.get_subjects()[0] # gives the first subject's ID
# func_file_path = [f.filename for f in layout.get(subject=subject_id, type='bold', extensions=['nii', 'nii.gz'])] 
# slicetimer.inputs.in_file = func_file_path[0]
# res = slicetimer.run()
# res.outputs

Motion Correction

Motion correction is done using fsl's mcflirt. It alligns all the volumes of a functional scan to each other



In [204]:

    
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT( mean_vol=True,
                       save_plots=True,
                       output_type='NIFTI'),
               name="mcflirt")

#  ref_vol = 1,



In [205]:

    
# To test mcflirt

# subject_id = layout.get_subjects()[0] # gives the first subject's ID
# func_file_path = [f.filename for f in layout.get(subject=subject_id, type='bold', extensions=['nii', 'nii.gz'])] 
# mcflirt.inputs.in_file = func_file_path[0]
# res_mcflirt = mcflirt.run()
# res_mcflirt.outputs

%%bash cat /tmp/tmpc2wmdeci/mcflirt/sub-28741_task-rest_run-1_bold_mcf.nii.par

Skull striping

I used fsl's BET



In [206]:

    
skullStrip = Node(BET(mask=False, frac=0.3, robust=True ),name='skullStrip')

Note: Do not include special characters in name field above coz then wf.writegraph will cause issues



In [207]:

    
# BET.help(); # Useful to see what are the parameters taken by BET



In [ ]:

Atlas



In [208]:

    
# Put in the path of atlas you wish to use
# atlasPath = opj(s,'atlas/Full_brain_atlas_thr0-2mm/fullbrain_atlas_thr0-2mm.nii.gz')



In [209]:

    
# # Read the atlas
# atlasObject = nib.load(atlasPath)
# atlas = atlasObject.get_data()



In [ ]:

Resample

I needed to resample the anatomical file from 1mm to 2mm. Because registering a 1mm file was taking a huge amount of time.



In [ ]:



In [210]:

    
# Resample - resample anatomy to 3x3x3 voxel resolution
resample_mni = Node(Resample(voxel_size=(3, 3, 3), resample_mode='Cu', # cubic interpolation
                         outputtype='NIFTI'),
                name="resample_mni")

resample_anat = Node(Resample(voxel_size=(3, 3, 3), resample_mode='Cu', # cubic interpolation
                         outputtype='NIFTI'),
                name="resample_anat")



In [211]:

    
resample_atlas = Node(Resample(voxel_size=(3, 3, 3), resample_mode='NN', # cubic interpolation
                         outputtype='NIFTI'),
                name="resample_atlas")

resample_atlas.inputs.in_file = atlasPath



In [212]:

    
# Resample.help() # To understand what all parameters Resample supports



In [213]:

    
# resample.outputs

Matrix operations

For concatenating the transformation matrices



In [214]:

    
concat_xform = Node(ConvertXFM(concat_xfm=True),name='concat_xform')
# .cmdline

For finding the inverse of a transformation matrix



In [215]:

    
# Node to calculate the inverse of func2std matrix
inv_mat = Node(ConvertXFM(invert_xfm=True), name='inv_mat')



In [216]:

    
# inv_mat.inputs

Extracting the mean brain



In [217]:

    
meanfunc = Node(interface=ImageMaths(op_string='-Tmean',
                                            suffix='_mean'),
                   name='meanfunc')

# preproc.connect(motion_correct, ('out_file', pickfirst), meanfunc, 'in_file')



In [218]:

    
# in_file = '/home1/varunk/data/ABIDE1/RawDataBIDs/Pitt/sub-0050002/func/sub-0050002_task-rest_run-1_bold.nii.gz'
# meanfunc.inputs.in_file = in_file
# res = meanfunc.run()



In [ ]:



In [ ]:

Creating mask using the mean brain



In [219]:

    
meanfuncmask = Node(interface=BET(mask=True,
                                         no_output=True,
                                         frac=0.3),
                       name='meanfuncmask')



In [220]:

    
# in_file = '/home1/varunk/data/ABIDE1/RawDataBIDs/Pitt/sub-0050002/func/sub-0050002_task-rest_run-1_bold.nii.gz'
# meanfuncmask.inputs.in_file = in_file
# res = meanfuncmask.run()



In [ ]:

Apply Mask



In [221]:

    
# Does BET (masking) on the whole func scan [Not using this, creates bug for join node]
maskfunc = Node(interface=ImageMaths(suffix='_bet',
                                               op_string='-mas'),
                      name='maskfunc')

# Does BET (masking) on the mean func scan 
maskfunc4mean = Node(interface=ImageMaths(suffix='_bet',
                                               op_string='-mas'),
                      name='maskfunc4mean')



In [ ]:



In [222]:

    
# in_file = '/home1/varunk/data/ABIDE1/RawDataBIDs/Pitt/sub-0050002/func/sub-0050002_task-rest_run-1_bold.nii.gz'

# in_file = '/usr/local/fsl/data/standard/MNI152_T1_2mm.nii.gz'
# in_file2 = '/usr/local/fsl/data/standard/MNI152_T1_2mm_brain_mask.nii.gz'
# maskfunc.inputs.in_file = in_file
# maskfunc.inputs.in_file2 = in_file2
# res = maskfunc.run()



In [223]:

    
# res.outputs.out_file



In [ ]:



In [ ]:

Datasink

I needed to define the structure of what files are saved and where.



In [224]:

    
# Create DataSink object
dataSink = Node(DataSink(), name='datasink')

# Name of the output folder
dataSink.inputs.base_directory = opj(base_directory,datasink_name)



In [225]:

    
base_directory









    Out[225]:





'/home1/varunk/results_sliceTime'

To create the substitutions I looked the datasink folder where I was redirecting the output. I manually selected the part of file/folder name that I wanted to change and copied below to be substituted.

TODO: Using datasink create a hierarchical directory structure i.e. folder in folder - to exactly match BIDS.



In [226]:

    
# Define substitution strings so that the data is similar to BIDS
substitutions = [('_subject_id_', 'sub-'),
                 ('_resample_brain_flirt.nii_brain', ''),
                 ('_roi_st_mcf_flirt.nii_brain_flirt', ''),
                 ('task-rest_run-1_bold_roi_st_mcf.nii','motion_params'),
                 ('T1w_resample_brain_flirt_sub-0050002_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt','fun2std')
                ]

# Feed the substitution strings to the DataSink node
dataSink.inputs.substitutions = substitutions

Apply Mask to functional data

Mean file of the motion corrected functional scan is sent to skullStrip to get just the brain and the mask_image. Mask_image is just a binary file (containing 1 where brain is present and 0 where it isn't).
After getting the mask_image form skullStrip, apply that mask to aligned functional image to extract its brain and remove the skull



In [227]:

    
# Function
# in_file: The file on which you want to apply mask
# in_file2 = mask_file:  The mask you want to use. Make sure that mask_file has same size as in_file
# out_file : Result of applying mask in in_file -> Gives the path of the output file

def applyMask_func(in_file, in_file2):
    import numpy as np
    import nibabel as nib
    import os
    from os.path import join as opj

    # convert from unicode to string : u'/tmp/tmp8daO2Q/..' -> '/tmp/tmp8daO2Q/..' i.e. removes the prefix 'u'
    mask_file = in_file2

    brain_data = nib.load(in_file)
    mask_data = nib.load(mask_file)

    brain = brain_data.get_data().astype('float32')
    mask = mask_data.get_data()
    
    # applying mask by multiplying elementwise to the binary mask

    if len(brain.shape) == 3: # Anat file
        brain = np.multiply(brain,mask)
    elif len(brain.shape) > 3: # Functional File
        for t in range(brain.shape[-1]):
            brain[:,:,:,t] = np.multiply(brain[:,:,:,t],mask)
    else:
        pass

    # Saving the brain file
    
    path = os.getcwd()
    

    in_file_split_list = in_file.split('/')
    in_file_name = in_file_split_list[-1]

    out_file = in_file_name + '_brain.nii.gz' # changing name
    brain_with_header = nib.Nifti1Image(brain, affine=brain_data.affine,header = brain_data.header)
    nib.save(brain_with_header,out_file)
    
    out_file = opj(path,out_file)
    out_file2 = in_file2
    
    return out_file, out_file2

Things learnt:

I found out that whenever a node is being executed, it becomes the current directory and whatever file you create now, will be stored here.
#from IPython.core.debugger import Tracer; Tracer()() # Debugger doesnt work in nipype

Wrap the above function inside a Node



In [228]:

    
applyMask = Node(Function(function=applyMask_func, input_names=['in_file','in_file2'],
                                output_names=['out_file','out_file2']), name='applyMask')



In [ ]:



In [ ]:



In [ ]:



In [ ]:

Some nodes needed for Co-registration and Normalization

I observed using fslsyes that the brain is enlarged if you Normalize a brain resampled to 2mm brain. This in turn causes the functional data to enlarge as well after normalization. So, I will apply MNI152_2mm brain mask to the resample brain after it has been normalized.

For that let's first create a Node - anat2std_reg_masking that applies the MNI152_2mm brain mask to the Output of anat2std_reg.



In [229]:

    
# FLIRT.help()



In [230]:

    
# Node for getting the xformation matrix 
func2anat_reg = Node(FLIRT(output_type='NIFTI'), name="func2anat_reg")

# Node for applying xformation matrix to functional data
func2std_xform = Node(FLIRT(output_type='NIFTI',
                         apply_xfm=True), name="func2std_xform")

# Node for applying xformation matrix to standard space brain data
std2func_xform = Node(FLIRT(output_type='NIFTI',
                         apply_xfm=True, interp='nearestneighbour'), name="std2func_xform")


# Node for Normalizing/Standardizing the anatomical and getting the xformation matrix
anat2std_reg = Node(FLIRT(output_type='NIFTI'), name="anat2std_reg")

I wanted to use the MNI file as input to the workflow so I created an Identity Node that reads the MNI file path and outputs the same MNI file path. Then I connected this node to whereever it was needed.



In [231]:

    
MNI152_2mm = Node(IdentityInterface(fields=['standard_file','mask_file']),
                  name="MNI152_2mm")
# Set the mask_file and standard_file input in the Node. This setting sets the input mask_file permanently.
MNI152_2mm.inputs.mask_file = os.path.expandvars('$FSLDIR/data/standard/MNI152_T1_2mm_brain_mask.nii.gz')

MNI152_2mm.inputs.standard_file = os.path.expandvars('$FSLDIR/data/standard/MNI152_T1_2mm_brain.nii.gz')
# MNI152_2mm.inputs.mask_file = '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain_mask.nii.gz'
# MNI152_2mm.inputs.standard_file = '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain.nii.gz'



In [232]:

    
# /usr/local/fsl/data/standard/



In [233]:

    
# Testing

# res = MNI152_2mm_mask.run()
# res.outputs



In [234]:

    
# afni.Bandpass.help()

Band Pass Filtering

Let's do a band pass filtering on the data using the code from https://neurostars.org/t/bandpass-filtering-different-outputs-from-fsl-and-nipype-custom-function/824/2



In [235]:

    
### AFNI

# bandpass = Node(afni.Bandpass(highpass=0.008, lowpass=0.08, 
#                          despike=False, no_detrend=True, notrans=True, 
#                          outputtype='NIFTI_GZ'),name='bandpass')

bandpass = Node(afni.Bandpass(highpass=0.01, lowpass=0.1, 
                         despike=False, no_detrend=True, notrans=True, 
                         tr=2.0,outputtype='NIFTI_GZ'),name='bandpass')


# bandpass.inputs.mask = MNI152_2mm.outputs.mask_file



In [236]:

    
# Testing bandpass on the func data in subject's space

# First comment out the bandpass.inputs.mask as it is in standard space.

# subject_id = layout.get_subjects()[0] # gives the first subject's ID
# func_file_path = [f.filename for f in layout.get(subject=subject_id, type='bold', extensions=['nii', 'nii.gz'])] 
# bandpass.inputs.in_file = func_file_path[0]
# res = bandpass.run();



In [237]:

    
# res.outputs.out_file



In [238]:

    
# To view in fsl I need to save this file. You can change the the location as per your need.
# First run utility functions section. It contains the load_and_save function
# load_and_save(res.outputs.out_file,'/home/jovyan/work/preprocess/result/filtered_func.nii')



In [239]:

    
# afni.Bandpass.help() # to see what all parameters are supported by Bandpass filter of afni

Following is a Join Node that collects the preprocessed file paths and saves them in a file



In [240]:

    
def save_file_list_function(in_brain, in_mask, in_motion_params, in_motion_outliers, in_joint_xformation_matrix, in_tr, in_atlas):
    # Imports
    import numpy as np
    import os
    from os.path import join as opj
    
    
    file_list = np.asarray(in_brain)
    print('######################## File List ######################: \n',file_list)

    np.save('brain_file_list',file_list)
    file_name = 'brain_file_list.npy'
    out_brain = opj(os.getcwd(),file_name) # path

    
    file_list2 = np.asarray(in_mask)
    print('######################## File List ######################: \n',file_list2)
        
    np.save('mask_file_list',file_list2)
    file_name2 = 'mask_file_list.npy'
    out_mask = opj(os.getcwd(),file_name2) # path


    file_list3 = np.asarray(in_motion_params)
    print('######################## File List ######################: \n',file_list3)
        
    np.save('motion_params_file_list',file_list3)
    file_name3 = 'motion_params_file_list.npy'
    out_motion_params = opj(os.getcwd(),file_name3) # path

    
    file_list4 = np.asarray(in_motion_outliers)
    print('######################## File List ######################: \n',file_list4)
        
    np.save('motion_outliers_file_list',file_list4)
    file_name4 = 'motion_outliers_file_list.npy'
    out_motion_outliers = opj(os.getcwd(),file_name4) # path
    
    
    file_list5 = np.asarray(in_joint_xformation_matrix)
    print('######################## File List ######################: \n',file_list5)
        
    np.save('joint_xformation_matrix_file_list',file_list5)
    file_name5 = 'joint_xformation_matrix_file_list.npy'
    out_joint_xformation_matrix = opj(os.getcwd(),file_name5) # path
    
    tr_list = np.asarray(in_tr)
    print('######################## TR List ######################: \n',tr_list)
        
    np.save('tr_list',tr_list)
    file_name6 = 'tr_list.npy'
    out_tr = opj(os.getcwd(),file_name6) # path
    
    
    file_list7 = np.asarray(in_atlas)
    print('######################## File List ######################: \n',file_list7)
        
    np.save('atlas_file_list',file_list7)
    file_name7 = 'atlas_file_list.npy'
    out_atlas = opj(os.getcwd(),file_name7) # path
    
    
    
    
    return out_brain, out_mask, out_motion_params, out_motion_outliers, out_joint_xformation_matrix, out_tr , out_atlas



In [241]:

    
save_file_list = JoinNode(Function(function=save_file_list_function, input_names=['in_brain', 'in_mask', 'in_motion_params','in_motion_outliers','in_joint_xformation_matrix', 'in_tr', 'in_atlas'],
                 output_names=['out_brain','out_mask','out_motion_params','out_motion_outliers','out_joint_xformation_matrix','out_tr', 'out_atlas']),
                 joinsource="infosource",
                 joinfield=['in_brain', 'in_mask', 'in_motion_params','in_motion_outliers','in_joint_xformation_matrix','in_tr', 'in_atlas'],
                 name="save_file_list")



In [242]:

    
# ------------------Change it in the program below -- all the names of parameters iin the workflow..

AFNI's filter is working good:

[x] Add the mask as parameter to the afni Node
[] Add the Node to the workflow
[x] Improve the data sink
[] Create Voxel pair FC map

Motion outliers



In [243]:

    
motionOutliers = Node(MotionOutliers(no_motion_correction=True, out_metric_plot = 'refrms_plot.png',
                                     out_metric_values='refrms_raw.txt'),name='motionOutliers')



In [244]:

    
# (MotionOutliers(in_file = 'var.nii',no_motion_correction=False, out_metric_plot = 'refrms_plot',
#                                      out_metric_values='refrms_raw')).cmdline

Lets see the number of regions in the atlas and display the atlas.



In [245]:

    
# num_ROIs = int((np.max(atlas) - np.min(atlas) ))

# print('Min Index:', np.min(atlas),'Max Index', np.max(atlas))
# print('Total Number of Parcellations = ',num_ROIs)



In [ ]:



In [ ]:

Workflow for atlas registration from std to functional



In [246]:

    
wf_atlas_resize_reg = Workflow(name=atlas_resize_reg_directory)

wf_atlas_resize_reg.connect([
    
            # Apply the inverse matrix to the 3mm Atlas to transform it to func space  
    
            (maskfunc4mean, std2func_xform, [(('out_file','reference'))]),           
                                
            (resample_atlas, std2func_xform, [('out_file','in_file')] ), 
                  
            # Now, applying the inverse matrix
                  
            (inv_mat, std2func_xform, [('out_file','in_matrix_file')]), # output: Atlas in func space
    
            (std2func_xform, save_file_list, [('out_file','in_atlas')]),
       
            # ---------------------------Save the required files --------------------------------------------
    
            (save_file_list, dataSink, [('out_motion_params','motion_params_paths.@out_motion_params')]),
            (save_file_list, dataSink, [('out_motion_outliers','motion_outliers_paths.@out_motion_outliers')]),
            (save_file_list, dataSink, [('out_brain','preprocessed_brain_paths.@out_brain')]),
            (save_file_list, dataSink, [('out_mask','preprocessed_mask_paths.@out_mask')]),
    
            (save_file_list, dataSink, [('out_joint_xformation_matrix',
                                         'joint_xformation_matrix_paths.@out_joint_xformation_matrix')]),
    
            (save_file_list, dataSink, [('out_tr','tr_paths.@out_tr')]),
    
            (save_file_list, dataSink, [('out_atlas','atlas_paths.@out_atlas')])
    
    
    
])



In [247]:

    
wf_coreg_reg = Workflow(name=coreg_reg_directory)
# wf_coreg_reg.base_dir = base_directory 
# Dir where all the outputs will be stored(inside coregistrationPipeline folder).


wf_coreg_reg.connect([
            
             
            (BIDSDataGrabber,skullStrip,[('anat_file_path','in_file')]), # Resampled the anat file to 3mm
            
            (skullStrip,resample_anat,[('out_file','in_file')]),
            
            (resample_anat,func2anat_reg,[('out_file','reference')]), # Make the resampled file as reference in func2anat_reg

            # Sec 1. The above 3 steps registers the mean image to resampled anat image and 
            # calculates the xformation matrix .. I hope the xformation matrix will be saved 
    
            (MNI152_2mm, resample_mni, [('standard_file','in_file')]),
    
            (resample_mni, anat2std_reg, [('out_file','reference')]),
    
            (resample_anat, anat2std_reg,  [('out_file','in_file')]),
    
    
            # Calculates the Xformationmatrix from anat3mm to MNI 3mm
    
            # We can get those matrices by refering to func2anat_reg.outputs.out_matrix_file and similarly for anat2std_reg
    
            
            
            (func2anat_reg, concat_xform, [('out_matrix_file','in_file')]),
    
            (anat2std_reg, concat_xform, [('out_matrix_file','in_file2')]),
    
            (concat_xform, dataSink, [('out_file', 'tranformation_matrix_fun2std.@out_file')]),
    
            (concat_xform, save_file_list, [('out_file', 'in_joint_xformation_matrix')]), #func2std xformation mat files
    
            # Now inverse the func2std MAT to std2func 
            (concat_xform, wf_atlas_resize_reg, [('out_file','inv_mat.in_file')])
])



In [248]:

    
# wf_coreg_reg = Workflow(name=coreg_reg_directory)
# # wf_coreg_reg.base_dir = base_directory 
# # Dir where all the outputs will be stored(inside coregistrationPipeline folder).


# wf_coreg_reg.connect([
            
             
#             (BIDSDataGrabber,resample_anat,[('anat_file_path','in_file')]), # Resampled the anat file to 3mm
            
#             (resample_anat,skullStrip,[('out_file','in_file')]),
            
#             (skullStrip,func2anat_reg,[('out_file','reference')]), # Make the resampled file as reference in func2anat_reg

#             # Sec 1. The above 3 steps registers the mean image to resampled anat image and 
#             # calculates the xformation matrix .. I hope the xformation matrix will be saved 
    
#             (MNI152_2mm, resample_mni, [('standard_file','in_file')]),
    
#             (resample_mni, anat2std_reg, [('out_file','reference')]),
    
#             (skullStrip, anat2std_reg,  [('out_file','in_file')]),
    
    
#             # Calculates the Xformationmatrix from anat3mm to MNI 3mm
    
#             # We can get those matrices by refering to func2anat_reg.outputs.out_matrix_file and similarly for anat2std_reg
    
            
            
#             (func2anat_reg, concat_xform, [('out_matrix_file','in_file')]),
    
#             (anat2std_reg, concat_xform, [('out_matrix_file','in_file2')]),
    
#             (concat_xform, dataSink, [('out_file', 'tranformation_matrix_fun2std.@out_file')]),
    
#             (concat_xform, save_file_list, [('out_file', 'in_joint_xformation_matrix')]), #func2std xformation mat files
    
#             # Now inverse the func2std MAT to std2func 
#             (concat_xform, wf_atlas_resize_reg, [('out_file','inv_mat.in_file')])
    
# -------------------------------------------------

    
    
#             # Apply the transformation to 3mm Atlas to transform it to func space
    
#             (maskfunc4mean, std2func_xform, [(('standard_file','reference'))]),           
                                
#             (resample_atlas, std2func_xform, [('out_file','in_file')] ), # Applies the transform to the ...
#             # ... atlas       
                  
#             # Now, apply the inverse matrix to the atlas  
                  
#             (inv_mat, std2func_xform, [('out_file','in_matrix_file')]),
    
    
# ------------------------------------------    
    
    
    
#             (save_file_list, dataSink, [('out_motion_params','motion_params_paths.@out_motion_params')]),
#             (save_file_list, dataSink, [('out_motion_outliers','motion_outliers_paths.@out_motion_outliers')]),
#             (save_file_list, dataSink, [('out_brain','preprocessed_brain_paths.@out_brain')]),
#             (save_file_list, dataSink, [('out_mask','preprocessed_mask_paths.@out_mask')]),
    
#             (save_file_list, dataSink, [('out_joint_xformation_matrix',
#                                          'joint_xformation_matrix_paths.@out_joint_xformation_matrix')]),
    
#             (save_file_list, dataSink, [('out_tr',
#                                          'tr_paths.@out_tr')])
    
            
# ])

Co-Registration, Normalization and Bandpass Workflow

Co-registration means alligning the func to anat
Normalization means aligning func/anat to standard
Applied band pass filtering in range - highpass=0.008, lowpass=0.08



In [249]:

    
wf_motion_correction_bet = Workflow(name=motion_correction_bet_directory)
# wf_motion_correction_bet.base_dir = base_directory 

wf_motion_correction_bet.connect([
    
            (mcflirt,dataSink,[('par_file','motion_params.@par_file')]), # saves the motion parameters calculated before
            
            (mcflirt,save_file_list,[('par_file','in_motion_params')]),
    
#             (save_file_list, dataSink, [('out_motion_params','motion_params_paths.@out_motion_params')]),
                     
            (mcflirt, meanfunc, [('out_file','in_file')]),
    
            (meanfunc, meanfuncmask, [('out_file','in_file')]),
    
            (slicetimer,motionOutliers,[('slice_time_corrected_file','in_file')]),
    
#             (mcflirt, motionOutliers, [('out_file','in_file')]),
            
            (meanfuncmask, motionOutliers, [('mask_file','mask')]),
    
            (motionOutliers, dataSink, [('out_file','motionOutliers.@out_file')]),
    
            (motionOutliers, dataSink, [('out_metric_plot','motionOutliers.@out_metric_plot')]),
    
            (motionOutliers, dataSink, [('out_metric_values','motionOutliers.@out_metric_values')]),
    
            (motionOutliers, save_file_list, [('out_file','in_motion_outliers')]),
    
#             (save_file_list, dataSink, [('out_motion_outliers','motion_outliers_paths.@out_motion_outliers')]),
    
    
            (mcflirt,applyMask , [('out_file','in_file')]), # 1
            
            (meanfuncmask, applyMask, [('mask_file','in_file2')]), # 2 output: 1&2,  BET on coregistered fmri scan
    
            (meanfunc, maskfunc4mean, [('out_file', 'in_file')]), # 3
    
            (meanfuncmask, maskfunc4mean, [('mask_file','in_file2')]), # 4 output: 3&4, BET on mean func scan
     
                
            (applyMask, save_file_list, [('out_file', 'in_brain')]),
            (applyMask, save_file_list, [('out_file2', 'in_mask')]),
            
                  
#             (save_file_list, dataSink, [('out_brain','preprocessed_brain_paths.@out_brain')]),
    
#             (save_file_list, dataSink, [('out_mask','preprocessed_mask_paths.@out_mask')]),
    
            (maskfunc4mean, wf_coreg_reg, [('out_file','func2anat_reg.in_file')])


    
])

Observation:

Applying masking again on the Normalized func file greately reduced the size from ~600MB -> ~150MB. I think Normalizing might have generated some extra voxels in the region of 'no brain'. Masking again got rid of them. Hence, reduced size.

Adding a module to select only selected participants

KKI
Leuven_1
Leuven_2
SBL
Stanford (N.A)
Trinity
UM_1
UM_2



In [250]:

    
import pandas as pd
import numpy as np

df = pd.read_csv('/home1/varunk/data/ABIDE1/RawDataBIDs/composite_phenotypic_file.csv') # , index_col='SUB_ID'

df = df.sort_values(['SUB_ID'])
df
selected_participants = df.loc[(df['SITE_ID'] == 'KKI') | (df['SITE_ID'] == 'Leuven_1') | (df['SITE_ID'] == 'Leuven_2') \
                              | (df['SITE_ID'] == 'SBL') | (df['SITE_ID'] == 'Trinity') | (df['SITE_ID'] == 'UM_1') \
                              | (df['SITE_ID'] == 'UM_2')]

selected_participants = list(map(str, selected_participants.as_matrix(['SUB_ID']).squeeze()))
# selected_participants[0]

Main Workflow



In [251]:

    
subject_list = selected_participants[0:2]
subject_list =  [str(item).zfill(7) for item in subject_list]
subject_list









    Out[251]:





['0050272', '0050273']



In [252]:

    
infosource = Node(IdentityInterface(fields=['subject_id']),
                  name="infosource")

infosource.iterables = [('subject_id',subject_list)]

# infosource.inputs.subject_id = subject_list[0]
# res = infosource.run()



In [253]:

    
# res.outputs



In [254]:

    
# Create the workflow
# Refer to Supplementary material's Section Two. for more on workspaces

wf = Workflow(name=parent_wf_directory)
# base_dir = opj(s,'result')
wf.base_dir = base_directory # Dir where all the outputs will be stored(inside BETFlow folder).

wf.connect([      (infosource, BIDSDataGrabber, [('subject_id','subject_id')]),
                  (BIDSDataGrabber, extract, [('func_file_path','in_file')]),
                  
                  (BIDSDataGrabber,getMetadata, [('func_file_path','in_file')]),
            
                  (getMetadata,slicetimer, [('tr','time_repetition')]),

                
                  (getMetadata,slicetimer, [('index_dir','index_dir')]),
            
                  (getMetadata,slicetimer, [('interleaved','interleaved')]),
            
                  (getMetadata,save_file_list, [('tr','in_tr')]),
                
                  (extract,slicetimer,[('roi_file','in_file')]),
                  (slicetimer,wf_motion_correction_bet,[('slice_time_corrected_file','mcflirt.in_file')])
           ])
# Run it in parallel
%time wf.run('MultiProc', plugin_args={'n_procs': 6})

# (BIDSDataGrabber,slicetimer, [(('func_file_path', get_TR ),'time_repetition')]),









    



171213-02:06:56,641 workflow INFO:
	 Workflow ABIDE1_Preprocess settings: ['check', 'execution', 'logging']
171213-02:06:56,666 workflow INFO:
	 Running in parallel.
171213-02:06:56,669 workflow INFO:
	 Executing: BIDSDataGrabber.a1 ID: 0
171213-02:06:56,671 workflow INFO:
	 Executing: BIDSDataGrabber.a0 ID: 13
171213-02:06:56,672 workflow INFO:
	 Executing: resample_atlas ID: 26
171213-02:06:56,672 workflow INFO:
	 Executing node BIDSDataGrabber.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/BIDSDataGrabber171213-02:06:56,674 workflow INFO:
	 Executing: resample_mni ID: 27
171213-02:06:56,673 workflow INFO:
	 Executing node BIDSDataGrabber.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/BIDSDataGrabber
171213-02:06:56,677 workflow INFO:
	 Executing node resample_mni in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/resample_mni171213-02:06:56,675 workflow INFO:
	 Executing node resample_atlas in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/resample_atlas


171213-02:06:56,681 workflow INFO:
	 Running: 3dresample -prefix fullbrain_atlas_thr0-2mm_resample.nii -rmode NN -dxyz 3.000000 3.000000 3.000000 -inset /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/resample_atlas/fullbrain_atlas_thr0-2mm.nii.gz171213-02:06:56,683 workflow INFO:
	 Running: 3dresample -prefix MNI152_T1_2mm_brain_resample.nii -rmode Cu -dxyz 3.000000 3.000000 3.000000 -inset /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/resample_mni/MNI152_T1_2mm_brain.nii.gz

171213-02:06:56,756 workflow INFO:
	 [Job finished] jobname: resample_atlas jobid: 26
171213-02:06:56,777 workflow INFO:
	 [Job finished] jobname: resample_mni jobid: 27
171213-02:06:56,940 workflow INFO:
	 [Job finished] jobname: BIDSDataGrabber.a1 jobid: 0
171213-02:06:56,944 workflow INFO:
	 Executing: extract.a1 ID: 1
171213-02:06:56,946 workflow INFO:
	 Executing: getMetadata.a1 ID: 2
171213-02:06:56,949 workflow INFO:
	 Executing: skullStrip.a1 ID: 10
171213-02:06:56,948 workflow INFO:
	 Executing node extract.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050273/extract
171213-02:06:56,950 workflow INFO:
	 Executing node getMetadata.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050273/getMetadata
171213-02:06:56,955 workflow INFO:
	 Running: fslroi /home1/varunk/data/ABIDE1/RawDataBIDs/UM_1/sub-0050273/func/sub-0050273_task-rest_run-1_bold.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050273/extract/sub-0050273_task-rest_run-1_bold_roi.nii.gz 4 -1
171213-02:06:56,961 workflow INFO:
	 [Job finished] jobname: BIDSDataGrabber.a0 jobid: 13
171213-02:06:56,961 workflow INFO:
	 Executing node skullStrip.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/skullStrip
171213-02:06:56,965 workflow INFO:
	 Executing: extract.a0 ID: 14
171213-02:06:56,969 workflow INFO:
	 Running: bet /home1/varunk/data/ABIDE1/RawDataBIDs/UM_1/sub-0050273/anat/sub-0050273_T1w.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/skullStrip/sub-0050273_T1w_brain.nii.gz -f 0.30 -R171213-02:06:56,969 workflow INFO:
	 Executing node extract.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050272/extract171213-02:06:56,968 workflow INFO:
	 Executing: getMetadata.a0 ID: 15


171213-02:06:56,972 workflow INFO:
	 Executing: skullStrip.a0 ID: 23
171213-02:06:56,973 workflow INFO:
	 Executing node getMetadata.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050272/getMetadata171213-02:06:56,976 workflow INFO:
	 Running: fslroi /home1/varunk/data/ABIDE1/RawDataBIDs/UM_1/sub-0050272/func/sub-0050272_task-rest_run-1_bold.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050272/extract/sub-0050272_task-rest_run-1_bold_roi.nii.gz 4 -1
171213-02:06:56,977 workflow INFO:
	 Executing node skullStrip.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/skullStrip

171213-02:06:56,992 workflow INFO:
	 Running: bet /home1/varunk/data/ABIDE1/RawDataBIDs/UM_1/sub-0050272/anat/sub-0050272_T1w.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/skullStrip/sub-0050272_T1w_brain.nii.gz -f 0.30 -R
171213-02:06:57,371 workflow INFO:
	 [Job finished] jobname: getMetadata.a1 jobid: 2
171213-02:06:57,382 workflow INFO:
	 [Job finished] jobname: getMetadata.a0 jobid: 15
171213-02:07:02,974 workflow INFO:
	 [Job finished] jobname: skullStrip.a1 jobid: 10
171213-02:07:02,976 workflow INFO:
	 Executing: resample_anat.a1 ID: 11
171213-02:07:02,978 workflow INFO:
	 Executing node resample_anat.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/resample_anat
171213-02:07:02,982 workflow INFO:
	 Running: 3dresample -prefix sub-0050273_T1w_brain_resample.nii -rmode Cu -dxyz 3.000000 3.000000 3.000000 -inset /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/resample_anat/sub-0050273_T1w_brain.nii.gz
171213-02:07:03,3 workflow INFO:
	 [Job finished] jobname: skullStrip.a0 jobid: 23
171213-02:07:03,6 workflow INFO:
	 Executing: resample_anat.a0 ID: 24
171213-02:07:03,8 workflow INFO:
	 Executing node resample_anat.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/resample_anat
171213-02:07:03,12 workflow INFO:
	 Running: 3dresample -prefix sub-0050272_T1w_brain_resample.nii -rmode Cu -dxyz 3.000000 3.000000 3.000000 -inset /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/resample_anat/sub-0050272_T1w_brain.nii.gz
171213-02:07:03,164 workflow INFO:
	 [Job finished] jobname: resample_anat.a0 jobid: 24
171213-02:07:03,167 workflow INFO:
	 Executing: anat2std_reg.a0 ID: 28
171213-02:07:03,169 workflow INFO:
	 [Job finished] jobname: resample_anat.a1 jobid: 11
171213-02:07:03,170 workflow INFO:
	 Executing node anat2std_reg.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/anat2std_reg
171213-02:07:03,173 workflow INFO:
	 Executing: anat2std_reg.a1 ID: 32
171213-02:07:03,176 workflow INFO:
	 Running: flirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/resample_anat/sub-0050272_T1w_brain_resample.nii -ref /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/resample_mni/MNI152_T1_2mm_brain_resample.nii -out sub-0050272_T1w_brain_resample_flirt.nii -omat sub-0050272_T1w_brain_resample_flirt.mat
171213-02:07:03,178 workflow INFO:
	 Executing node anat2std_reg.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/anat2std_reg
171213-02:07:03,183 workflow INFO:
	 Running: flirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/resample_anat/sub-0050273_T1w_brain_resample.nii -ref /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/resample_mni/MNI152_T1_2mm_brain_resample.nii -out sub-0050273_T1w_brain_resample_flirt.nii -omat sub-0050273_T1w_brain_resample_flirt.mat
171213-02:07:04,770 workflow INFO:
	 [Job finished] jobname: extract.a0 jobid: 14
171213-02:07:04,772 workflow INFO:
	 Executing: slicetimer.a0 ID: 16
171213-02:07:04,775 workflow INFO:
	 Executing node slicetimer.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/slicetimer
171213-02:07:04,779 workflow INFO:
	 Running: slicetimer --in=/home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050272/extract/sub-0050272_task-rest_run-1_bold_roi.nii.gz --down --out=/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/slicetimer/sub-0050272_task-rest_run-1_bold_roi_st.nii --repeat=2.200000
171213-02:07:04,820 workflow INFO:
	 [Job finished] jobname: extract.a1 jobid: 1
171213-02:07:04,824 workflow INFO:
	 Executing: slicetimer.a1 ID: 3
171213-02:07:04,828 workflow INFO:
	 Executing node slicetimer.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/slicetimer
171213-02:07:04,832 workflow INFO:
	 Running: slicetimer --in=/home1/varunk/results_sliceTime/ABIDE1_Preprocess/_subject_id_0050273/extract/sub-0050273_task-rest_run-1_bold_roi.nii.gz --down --out=/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/slicetimer/sub-0050273_task-rest_run-1_bold_roi_st.nii --repeat=2.200000
171213-02:07:06,235 workflow INFO:
	 [Job finished] jobname: anat2std_reg.a1 jobid: 32
171213-02:07:06,263 workflow INFO:
	 [Job finished] jobname: anat2std_reg.a0 jobid: 28
171213-02:07:13,733 workflow INFO:
	 [Job finished] jobname: slicetimer.a1 jobid: 3
171213-02:07:13,737 workflow INFO:
	 Executing: mcflirt.a1 ID: 4
171213-02:07:13,740 workflow INFO:
	 Executing node mcflirt.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/mcflirt
171213-02:07:13,746 workflow INFO:
	 Running: mcflirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/slicetimer/sub-0050273_task-rest_run-1_bold_roi_st.nii -meanvol -out /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/mcflirt/sub-0050273_task-rest_run-1_bold_roi_st_mcf.nii -plots
171213-02:07:13,748 workflow INFO:
	 [Job finished] jobname: slicetimer.a0 jobid: 16
171213-02:07:13,751 workflow INFO:
	 Executing: mcflirt.a0 ID: 17
171213-02:07:13,753 workflow INFO:
	 Executing node mcflirt.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/mcflirt
171213-02:07:13,757 workflow INFO:
	 Running: mcflirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/slicetimer/sub-0050272_task-rest_run-1_bold_roi_st.nii -meanvol -out /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/mcflirt/sub-0050272_task-rest_run-1_bold_roi_st_mcf.nii -plots
171213-02:08:31,519 workflow INFO:
	 [Job finished] jobname: mcflirt.a0 jobid: 17
171213-02:08:31,522 workflow INFO:
	 Executing: meanfunc.a0 ID: 18
171213-02:08:31,526 workflow INFO:
	 Executing node meanfunc.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfunc
171213-02:08:31,531 workflow INFO:
	 Running: fslmaths /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/mcflirt/sub-0050272_task-rest_run-1_bold_roi_st_mcf.nii -Tmean /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfunc/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean.nii.gz
171213-02:08:31,610 workflow INFO:
	 [Job finished] jobname: mcflirt.a1 jobid: 4
171213-02:08:31,614 workflow INFO:
	 Executing: meanfunc.a1 ID: 5
171213-02:08:31,617 workflow INFO:
	 Executing node meanfunc.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfunc
171213-02:08:31,622 workflow INFO:
	 Running: fslmaths /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/mcflirt/sub-0050273_task-rest_run-1_bold_roi_st_mcf.nii -Tmean /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfunc/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean.nii.gz
171213-02:08:32,517 workflow INFO:
	 [Job finished] jobname: meanfunc.a0 jobid: 18
171213-02:08:32,520 workflow INFO:
	 Executing: meanfuncmask.a0 ID: 19
171213-02:08:32,523 workflow INFO:
	 Executing node meanfuncmask.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfuncmask
171213-02:08:32,528 workflow INFO:
	 Running: bet /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfunc/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfuncmask/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_brain.nii.gz -f 0.30 -m -n
171213-02:08:32,600 workflow INFO:
	 [Job finished] jobname: meanfunc.a1 jobid: 5
171213-02:08:32,603 workflow INFO:
	 Executing: meanfuncmask.a1 ID: 6
171213-02:08:32,606 workflow INFO:
	 Executing node meanfuncmask.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfuncmask
171213-02:08:32,610 workflow INFO:
	 Running: bet /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfunc/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfuncmask/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_brain.nii.gz -f 0.30 -m -n
171213-02:08:33,572 workflow INFO:
	 [Job finished] jobname: meanfuncmask.a0 jobid: 19
171213-02:08:33,575 workflow INFO:
	 Executing: maskfunc4mean.a0 ID: 20
171213-02:08:33,577 workflow INFO:
	 Executing: applyMask.a0 ID: 21
171213-02:08:33,578 workflow INFO:
	 Executing node maskfunc4mean.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/maskfunc4mean
171213-02:08:33,580 workflow INFO:
	 Executing: motionOutliers.a0 ID: 22
171213-02:08:33,580 workflow INFO:
	 Executing node applyMask.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/applyMask
171213-02:08:33,583 workflow INFO:
	 Running: fslmaths /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfunc/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean.nii.gz -mas /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfuncmask/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_brain_mask.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/maskfunc4mean/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet.nii.gz
171213-02:08:33,585 workflow INFO:
	 Executing node motionOutliers.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/motionOutliers
171213-02:08:33,590 workflow INFO:
	 Running: fsl_motion_outliers -i /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/slicetimer/sub-0050272_task-rest_run-1_bold_roi_st.nii -m /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfuncmask/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_brain_mask.nii.gz --nomoco -o sub-0050272_task-rest_run-1_bold_roi_st_outliers.txt -p refrms_plot.png -s refrms_raw.txt
171213-02:08:33,621 workflow INFO:
	 [Job finished] jobname: meanfuncmask.a1 jobid: 6
171213-02:08:33,623 workflow INFO:
	 Executing: maskfunc4mean.a1 ID: 7
171213-02:08:33,626 workflow INFO:
	 Executing: applyMask.a1 ID: 8
171213-02:08:33,627 workflow INFO:
	 Executing node maskfunc4mean.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/maskfunc4mean
171213-02:08:33,628 workflow INFO:
	 Executing: motionOutliers.a1 ID: 9
171213-02:08:33,630 workflow INFO:
	 Executing node applyMask.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/applyMask171213-02:08:33,631 workflow INFO:
	 Executing node motionOutliers.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/motionOutliers
171213-02:08:33,635 workflow INFO:
	 Running: fslmaths /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfunc/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean.nii.gz -mas /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfuncmask/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_brain_mask.nii.gz /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/maskfunc4mean/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet.nii.gz171213-02:08:33,636 workflow INFO:
	 Running: fsl_motion_outliers -i /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/slicetimer/sub-0050273_task-rest_run-1_bold_roi_st.nii -m /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfuncmask/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_brain_mask.nii.gz --nomoco -o sub-0050273_task-rest_run-1_bold_roi_st_outliers.txt -p refrms_plot.png -s refrms_raw.txt


171213-02:08:33,651 workflow INFO:
	 [Job finished] jobname: maskfunc4mean.a0 jobid: 20
171213-02:08:33,655 workflow INFO:
	 Executing: func2anat_reg.a0 ID: 25
171213-02:08:33,659 workflow INFO:
	 Executing node func2anat_reg.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/func2anat_reg
171213-02:08:33,667 workflow INFO:
	 Running: flirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/maskfunc4mean/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet.nii.gz -ref /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/resample_anat/sub-0050272_T1w_brain_resample.nii -out sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt.nii -omat sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt.mat
171213-02:08:33,706 workflow INFO:
	 [Job finished] jobname: maskfunc4mean.a1 jobid: 7
171213-02:08:33,711 workflow INFO:
	 Executing: func2anat_reg.a1 ID: 12
171213-02:08:33,715 workflow INFO:
	 Executing node func2anat_reg.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/func2anat_reg
171213-02:08:33,728 workflow INFO:
	 Running: flirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/maskfunc4mean/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet.nii.gz -ref /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/resample_anat/sub-0050273_T1w_brain_resample.nii -out sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt.nii -omat sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt.mat
171213-02:08:36,763 workflow INFO:
	 [Job finished] jobname: applyMask.a0 jobid: 21
171213-02:08:36,993 workflow INFO:
	 [Job finished] jobname: func2anat_reg.a1 jobid: 12
171213-02:08:36,999 workflow INFO:
	 Executing: concat_xform.a1 ID: 33
171213-02:08:37,3 workflow INFO:
	 Executing node concat_xform.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/concat_xform
171213-02:08:37,13 workflow INFO:
	 Running: convert_xfm -omat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/concat_xform/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050273_T1w_brain_resample_flirt.mat -concat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/anat2std_reg/sub-0050273_T1w_brain_resample_flirt.mat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/func2anat_reg/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt.mat
171213-02:08:37,72 workflow INFO:
	 [Job finished] jobname: concat_xform.a1 jobid: 33
171213-02:08:37,77 workflow INFO:
	 Executing: inv_mat.a1 ID: 34
171213-02:08:37,82 workflow INFO:
	 Executing node inv_mat.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/inv_mat
171213-02:08:37,88 workflow INFO:
	 Running: convert_xfm -omat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/inv_mat/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050273_T1w_brain_resample_flirt_inv.mat -inverse /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/concat_xform/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050273_T1w_brain_resample_flirt.mat
171213-02:08:37,145 workflow INFO:
	 [Job finished] jobname: inv_mat.a1 jobid: 34
171213-02:08:37,149 workflow INFO:
	 Executing: std2func_xform.a1 ID: 35
171213-02:08:37,154 workflow INFO:
	 Executing node std2func_xform.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/std2func_xform
171213-02:08:37,163 workflow INFO:
	 Running: flirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/resample_atlas/fullbrain_atlas_thr0-2mm_resample.nii -ref /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/maskfunc4mean/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet.nii.gz -out fullbrain_atlas_thr0-2mm_resample_flirt.nii -omat fullbrain_atlas_thr0-2mm_resample_flirt.mat -applyxfm -init /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/inv_mat/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050273_T1w_brain_resample_flirt_inv.mat -interp nearestneighbour
171213-02:08:37,174 workflow INFO:
	 [Job finished] jobname: applyMask.a1 jobid: 8
171213-02:08:37,212 workflow INFO:
	 [Job finished] jobname: std2func_xform.a1 jobid: 35
171213-02:08:37,459 workflow INFO:
	 [Job finished] jobname: func2anat_reg.a0 jobid: 25
171213-02:08:37,462 workflow INFO:
	 Executing: concat_xform.a0 ID: 29
171213-02:08:37,465 workflow INFO:
	 Executing node concat_xform.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/concat_xform
171213-02:08:37,469 workflow INFO:
	 Running: convert_xfm -omat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/concat_xform/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050272_T1w_brain_resample_flirt.mat -concat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/anat2std_reg/sub-0050272_T1w_brain_resample_flirt.mat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/func2anat_reg/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt.mat
171213-02:08:37,501 workflow INFO:
	 [Job finished] jobname: concat_xform.a0 jobid: 29
171213-02:08:37,504 workflow INFO:
	 Executing: inv_mat.a0 ID: 30
171213-02:08:37,508 workflow INFO:
	 Executing node inv_mat.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/inv_mat
171213-02:08:37,512 workflow INFO:
	 Running: convert_xfm -omat /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/inv_mat/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050272_T1w_brain_resample_flirt_inv.mat -inverse /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/concat_xform/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050272_T1w_brain_resample_flirt.mat
171213-02:08:37,546 workflow INFO:
	 [Job finished] jobname: inv_mat.a0 jobid: 30
171213-02:08:37,549 workflow INFO:
	 Executing: std2func_xform.a0 ID: 31
171213-02:08:37,552 workflow INFO:
	 Executing node std2func_xform.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/std2func_xform
171213-02:08:37,560 workflow INFO:
	 Running: flirt -in /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/resample_atlas/fullbrain_atlas_thr0-2mm_resample.nii -ref /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/maskfunc4mean/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet.nii.gz -out fullbrain_atlas_thr0-2mm_resample_flirt.nii -omat fullbrain_atlas_thr0-2mm_resample_flirt.mat -applyxfm -init /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/inv_mat/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050272_T1w_brain_resample_flirt_inv.mat -interp nearestneighbour
171213-02:08:37,610 workflow INFO:
	 [Job finished] jobname: std2func_xform.a0 jobid: 31
171213-02:08:41,2 workflow INFO:
	 [Job finished] jobname: motionOutliers.a0 jobid: 22
171213-02:08:41,326 workflow INFO:
	 [Job finished] jobname: motionOutliers.a1 jobid: 9
171213-02:08:41,329 workflow INFO:
	 Executing: save_file_list ID: 36
171213-02:08:41,334 workflow INFO:
	 Executing node save_file_list in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/save_file_list
######################## File List ######################: 
 [ '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/applyMask/sub-0050272_task-rest_run-1_bold_roi_st_mcf.nii_brain.nii.gz'
 '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/applyMask/sub-0050273_task-rest_run-1_bold_roi_st_mcf.nii_brain.nii.gz']
######################## File List ######################: 
 [ '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/meanfuncmask/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_brain_mask.nii.gz'
 '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/meanfuncmask/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_brain_mask.nii.gz']
######################## File List ######################: 
 [ '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/mcflirt/sub-0050272_task-rest_run-1_bold_roi_st_mcf.nii.par'
 '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/mcflirt/sub-0050273_task-rest_run-1_bold_roi_st_mcf.nii.par']
######################## File List ######################: 
 [ '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050272/motionOutliers/sub-0050272_task-rest_run-1_bold_roi_st_outliers.txt'
 '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050273/motionOutliers/sub-0050273_task-rest_run-1_bold_roi_st_outliers.txt']
######################## File List ######################: 
 [ '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050272/concat_xform/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050272_T1w_brain_resample_flirt.mat'
 '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/_subject_id_0050273/concat_xform/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050273_T1w_brain_resample_flirt.mat']
######################## TR List ######################: 
 [ 2.2  2.2]
######################## File List ######################: 
 [ '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/std2func_xform/fullbrain_atlas_thr0-2mm_resample_flirt.nii'
 '/home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/std2func_xform/fullbrain_atlas_thr0-2mm_resample_flirt.nii']
171213-02:08:41,355 workflow INFO:
	 [Job finished] jobname: save_file_list jobid: 36
171213-02:08:41,357 workflow INFO:
	 Executing: datasink.a0 ID: 37
171213-02:08:41,363 workflow INFO:
	 Executing: datasink.a1 ID: 38
171213-02:08:41,364 workflow INFO:
	 Executing node datasink.a0 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050272/datasink
171213-02:08:41,370 workflow INFO:
	 Executing node datasink.a1 in dir: /home1/varunk/results_sliceTime/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050273/datasink
171213-02:08:41,371 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motion_params/_subject_id_0050272/sub-0050272_task-rest_run-1_bold_roi_st_mcf.nii.par -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motion_params/sub-0050272/sub-0050272_motion_params.par
171213-02:08:41,373 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/_subject_id_0050272/sub-0050272_task-rest_run-1_bold_roi_st_outliers.txt -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/sub-0050272/sub-0050272_task-rest_run-1_bold_roi_st_outliers.txt
171213-02:08:41,375 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/_subject_id_0050272/refrms_plot.png -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/sub-0050272/refrms_plot.png
171213-02:08:41,376 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motion_params/_subject_id_0050273/sub-0050273_task-rest_run-1_bold_roi_st_mcf.nii.par -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motion_params/sub-0050273/sub-0050273_motion_params.par171213-02:08:41,376 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/_subject_id_0050272/refrms_raw.txt -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/sub-0050272/refrms_raw.txt

171213-02:08:41,378 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/tranformation_matrix_fun2std/_subject_id_0050272/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050272_T1w_brain_resample_flirt.mat -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/tranformation_matrix_fun2std/sub-0050272/sub-0050272_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050272_T1w_brain_resample_flirt.mat171213-02:08:41,378 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/_subject_id_0050273/sub-0050273_task-rest_run-1_bold_roi_st_outliers.txt -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/sub-0050273/sub-0050273_task-rest_run-1_bold_roi_st_outliers.txt

171213-02:08:41,379 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/_subject_id_0050273/refrms_plot.png -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/sub-0050273/refrms_plot.png
171213-02:08:41,380 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/_subject_id_0050273/refrms_raw.txt -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/motionOutliers/sub-0050273/refrms_raw.txt
171213-02:08:41,382 interface INFO:
	 sub: /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/tranformation_matrix_fun2std/_subject_id_0050273/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050273_T1w_brain_resample_flirt.mat -> /home1/varunk/results_sliceTime/ABIDE1_Preprocess_Datasink/tranformation_matrix_fun2std/sub-0050273/sub-0050273_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050273_T1w_brain_resample_flirt.mat
171213-02:08:41,387 workflow INFO:
	 [Job finished] jobname: datasink.a0 jobid: 37
171213-02:08:41,389 workflow INFO:
	 [Job finished] jobname: datasink.a1 jobid: 38
CPU times: user 556 ms, sys: 144 ms, total: 700 ms
Wall time: 1min 44s






    Out[254]:





<networkx.classes.digraph.DiGraph at 0x7fa0ee3c5390>

Summary:



In [255]:

    
error









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-255-899fb4c51d0d> in <module>()
----> 1 error

NameError: name 'error' is not defined



In [ ]:



In [ ]:

    
# Visualize the detailed graph
from IPython.display import Image
wf.write_graph(graph2use='exec', format='png', simple_form=True)
file_name = opj(base_directory,parent_wf_directory,'graph_detailed.dot.png')
Image(filename=file_name)

Summary [Incomplete]

wf.connect([(infosource, BIDSDataGrabber, [('data_dir','data_dir'), ('subject_id', 'subject_id'),]),
                  (BIDSDataGrabber, extract, [('func_file_path','in_file')]),
                  (extract,slicetimer,[('roi_file','in_file')]),
                  (slicetimer,mcflirt,[('slice_time_corrected_file','in_file')]),
                  (mcflirt, skullStrip, [('mean_img', 'in_file')]),
                  (mcflirt,applyMask,[('out_file','brain_file')]),
                  (skullStrip, applyMask, [('mask_file', 'mask_file')]), 
                  ])

In the above created workflow the infosource node iterates over the subject_id, it creates a Node and for each Subject ID it sends data_dir (path where the data resides) and the subject specific subject_id to BIDSDataGrabber Node.

BIDSDataGrabber Node accepts the above 2 parameters, calls the function get_nifti_filenames(subject_id,data_dir)which returns the path of the anatomical and BOLD files of the subject with given subject_id and hence the Node produces output that I call func_file_path and anat_file_path. I have used only func_file_pathright now.

The file path denoted by 'func_file_path' is then fed as input to extract that removes 4 initial brain volumes of the functional scan.

Its output is called - slice_time_corrected_file which is fed to mcflirt node to correct the movion between volumes of an individual subject. This is called Motion Correction.

In next step the mean_image from mcflirt is sent to skullStrip to get the mask. The role of skullStrip is just to obtain mask from the mean EPI image.

The mask got above is then applied to the functional volume to get rif of skull.

The final results are stored in the directory : /home/jovyan/work/preprocess/result/BETFlow. Every node has its own folder where its results are stored.

TODO

Make a single workflow
See how the file looks like after the transformation is applied
Change the FC code such that the FC maps is calculated instead of just matrices.
Transform these FC maps to Standard space
Do FDR correction (Check it with the MATLAB Fdr code)



In [ ]:

    
# os.chdir('../results/ABIDE1_Preprocess/motion_correction_bet/_subject_id_0050002/applyMask')

# in_file = 'sub-0050002_task-rest_run-1_bold_roi_st_mcf.nii_brain.nii.gz'

# in_matrix_file = 'sub-0050002_T1w_resample_brain_flirt_sub-0050002_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt.mat'



In [ ]:

    
# func2std_xform.inputs



In [ ]:

    
import numpy as np
X = np.load('../results_again_again/ABIDE1_Preprocess_Datasink/preprocessed_brain_paths/brain_file_list.npy')



In [ ]:

    
X



In [ ]:

    
X = np.load('../results_again_again/ABIDE1_Preprocess_Datasink/preprocessed_mask_paths/mask_file_list.npy')
X



In [ ]:

    
! cat ../results_again_again/ABIDE1_Preprocess_Datasink/motion_params/sub-0050002/sub-0050002_motion_params.par



In [ ]:

    
! cat ../results_again_again/ABIDE1_Preprocess_Datasink/tranformation_matrix_fun2std/sub-0050002/sub-0050002_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050002_T1w_resample_brain_flirt.mat



In [ ]:

    
! cat ../results_again_again/ABIDE1_Preprocess_Datasink/tranformation_matrix_fun2std/sub-0050003/sub-0050003_task-rest_run-1_bold_roi_st_mcf_mean_bet_flirt_sub-0050003_T1w_resample_brain_flirt.mat

$ \alpha_2 $ -- Just checking latex embedding



In [ ]:

    
#TR
X = np.load('../results_again_again/ABIDE1_Preprocess_Datasink/tr_paths/tr_list.npy')
X



In [ ]:

    
X = np.load('../results_again_again//ABIDE1_Preprocess_Datasink/atlas_paths/atlas_file_list.npy')
X, X.shape



In [ ]:

    
# Read the std atlas

atlasObject = nib.load(atlasPath)
atlas = atlasObject.get_data()

num_ROIs = int((np.max(atlas) - np.min(atlas) ))



In [ ]:

    
num_ROIs



In [ ]:

    
# Read the func atlas
atlaspath2 = '/home1/varunk/results_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050002/std2func_xform/fullbrain_atlas_thr0-2mm_resample_flirt.nii'
atlasObject = nib.load(atlaspath2)
atlas = atlasObject.get_data()

num_ROIs = int((np.max(atlas) - np.min(atlas) ))



In [ ]:

    
num_ROIs



In [ ]:

    
'/home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050004/std2func_xform/fullbrain_atlas_thr0-2mm_resample_flirt.nii'