In this example we will take the preprocessed output from the first example and run for each subject a 1st-level analysis. For this we need to do the following steps:
In the previous example, we used two different smoothing kernels of fwhm=4 and fwhm=8. Therefore, let us also run the 1st-level analysis for those two versions.
So, let's begin!
In [ ]:
%pylab inline
from os.path import join as opj
from nipype.interfaces.spm import Level1Design, EstimateModel, EstimateContrast
from nipype.algorithms.modelgen import SpecifySPMModel
from nipype.interfaces.utility import Function, IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.pipeline.engine import Workflow, Node
In [ ]:
experiment_dir = '/output'
output_dir = 'datasink'
working_dir = 'workingdir'
# list of subject identifiers
subject_list = ['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05']
# list of session identifiers
session_list = ['run-1', 'run-2']
# TR of functional images
TR = 2
# Smoothing withds used during preprocessing
fwhm = [4, 8]
In [ ]:
# SpecifyModel - Generates SPM-specific Model
modelspec = Node(SpecifySPMModel(concatenate_runs=False,
input_units='secs',
output_units='secs',
time_repetition=TR,
high_pass_filter_cutoff=128),
name="modelspec")
# Level1Design - Generates an SPM design matrix
level1design = Node(Level1Design(bases={'hrf': {'derivs': [0, 0]}},
timing_units='secs',
interscan_interval=TR,
model_serial_correlations='AR(1)'),
name="level1design")
# EstimateModel - estimate the parameters of the model
level1estimate = Node(EstimateModel(estimation_method={'Classical': 1}),
name="level1estimate")
# EstimateContrast - estimates contrasts
level1conest = Node(EstimateContrast(), name="level1conest")
To do any GLM analysis, we need to also define the contrasts that we want to investigate. If we recap, we had two different conditions in this dataset:
< < < < << < > < <Therefore, we could create the following contrasts:
In [ ]:
# Condition names
condition_names = ['congruent', 'incongruent']
# Contrasts
cont01 = ['average', 'T', condition_names, [0.5, 0.5]]
cont02 = ['congruent', 'T', condition_names, [1, 0]]
cont03 = ['incongruent', 'T', condition_names, [0, 1]]
cont04 = ['cong > incong', 'T', condition_names, [1, -1]]
cont05 = ['incong > cong', 'T', condition_names, [-1, 1]]
cont06 = ['activation', 'F', [cont02, cont03]]
cont07 = ['differences', 'F', [cont04, cont05]]
contrast_list = [cont01, cont02, cont03, cont04, cont05, cont06, cont07]
In [ ]:
!cat /data/ds102/sub-01/func/sub-01_task-flanker_run-1_events.tsv
So what we need is the onset and the stimuli type, i.e. column 0 and column 5 or 7.
In [ ]:
filename = '/data/ds102/sub-01/func/sub-01_task-flanker_run-1_events.tsv'
trailinfo = np.genfromtxt(filename, delimiter='\t', dtype=None, skip_header=1)
trailinfo = [[t[0], t[7]] for t in trailinfo]
trailinfo
Out[ ]:
And finally we need to separate the onsets of the two stimuli. This can be done as follows:
In [ ]:
onset1 = []
onset2 = []
for t in trailinfo:
if 'incongruent' in t[1]:
onset2.append(t[0])
else:
onset1.append(t[0])
print onset1
print onset2
Now, let us incorporate all this in the helper function subjectinfo.
In [ ]:
def subjectinfo(subject_id):
import numpy as np
from os.path import join as opj
from nipype.interfaces.base import Bunch
condition_names = ['congruent', 'incongruent']
logfile_dir = opj('/data', 'ds102', subject_id, 'func')
for sess in ['run-1', 'run-2']:
# Read the TSV file
filename = opj(logfile_dir,
'%s_task-flanker_%s_events.tsv' % (subject_id, sess))
# Save relevant information
trailinfo = np.genfromtxt(filename, delimiter='\t',
dtype=None, skip_header=1)
trailinfo = [[t[0], t[7]] for t in trailinfo]
# Separate onset of conditions
onset1 = []
onset2 = []
for t in trailinfo:
if 'incongruent' in t[1]:
onset2.append(t[0])
else:
onset1.append(t[0])
# Svae values per session
if sess == 'run-1':
run1 = [onset1, onset2]
elif sess == 'run-2':
run2 = [onset1, onset2]
subjectinfo = []
for r in range(2):
if r == 0:
onsetTimes = run1
elif r == 1:
onsetTimes = run2
subjectinfo.insert(r,
Bunch(conditions=condition_names,
onsets=onsetTimes,
durations=[[2.0], [2.0]],
amplitudes=None,
tmod=None,
pmod=None,
regressor_names=None,
regressors=None))
return subjectinfo # this output will later be returned to infosource
# Get Subject Info - get subject specific condition information
getsubjectinfo = Node(Function(input_names=['subject_id'],
output_names=['subject_info'],
function=subjectinfo),
name='getsubjectinfo')
In [ ]:
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id',
'fwhm_id',
'contrasts'],
contrasts=contrast_list),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('fwhm_id', fwhm)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
templates = {'func': opj(output_dir, 'preproc', '{subject_id}',
'run-*_fwhm_{fwhm_id}', 's_bold_mcf_flirt.nii'),
'mc_param': opj(output_dir, 'preproc', '{subject_id}',
'run-*_bold_mcf.par')}
selectfiles = Node(SelectFiles(templates,
base_directory=experiment_dir,
sort_filelist=True),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
# Use the following DataSink output substitutions
substitutions = [('_subject_id_', '')]
subjFolders = [('_fwhm_id_%s%s' % (f, sub), '%s_fwhm%s' % (sub, f))
for f in fwhm
for sub in subject_list]
substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
In [ ]:
# Initiation of the 1st-level analysis workflow
l1analysis = Workflow(name='l1analysis')
l1analysis.base_dir = opj(experiment_dir, working_dir)
# Connect up the 1st-level analysis components
l1analysis.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('fwhm_id', 'fwhm_id')]),
(infosource, getsubjectinfo, [('subject_id',
'subject_id')]),
(getsubjectinfo, modelspec, [('subject_info',
'subject_info')]),
(infosource, level1conest, [('contrasts', 'contrasts')]),
(selectfiles, modelspec, [('func', 'functional_runs')]),
(selectfiles, modelspec, [('mc_param',
'realignment_parameters')]),
(modelspec, level1design, [('session_info',
'session_info')]),
(level1design, level1estimate, [('spm_mat_file',
'spm_mat_file')]),
(level1estimate, level1conest, [('spm_mat_file',
'spm_mat_file'),
('beta_images',
'beta_images'),
('residual_image',
'residual_image')]),
(level1conest, datasink, [('spm_mat_file',
'1stLevel.@spm_mat'),
('spmT_images', '1stLevel.@T'),
('con_images', '1stLevel.@con'),
('spmF_images', '1stLevel.@F'),
('ess_images', '1stLevel.@ess'),
]),
])
In [ ]:
# Create 1st-level analysis output graph
l1analysis.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
from IPython.display import Image
Image(filename=opj(l1analysis.base_dir, 'l1analysis', 'graph.dot.png'))
Out[ ]:
In [ ]:
l1analysis.run('MultiProc', plugin_args={'n_procs': 4})
In [ ]:
!tree /output/datasink/1stLevel/sub-01*
In [ ]:
from nilearn.plotting import plot_stat_map
anatimg = '/data/ds102/sub-01/anat/sub-01_T1w.nii.gz'
plot_stat_map(
'/output/datasink/1stLevel/sub-01_fwhm4/spmT_0001.nii', title='average - fwhm=4',
bg_img=anatimg, threshold=3, display_mode='z', cut_coords=(-30, -15, 0, 15, 30), dim=-1)
plot_stat_map(
'/output/datasink/1stLevel/sub-01_fwhm8/spmT_0001.nii', title='average - fwhm=8',
bg_img=anatimg, threshold=3, display_mode='z', cut_coords=(-30, -15, 0, 15, 30), dim=-1)
Out[ ]:
Now, let's look at the two contrasts congruent and incongruent, as well as the difference contrasts cong > incong.
In [ ]:
plot_stat_map(
'/output/datasink/1stLevel/sub-01_fwhm4/spmT_0002.nii', title='congruent - fwhm=4',
bg_img=anatimg, threshold=3, display_mode='z', cut_coords=(-30, -15, 0, 15, 30), dim=-1)
plot_stat_map(
'/output/datasink/1stLevel/sub-01_fwhm4/spmT_0003.nii', title='incongruent - fwhm=4',
bg_img=anatimg, threshold=3, display_mode='z', cut_coords=(-30, -15, 0, 15, 30), dim=-1)
plot_stat_map(
'/output/datasink/1stLevel/sub-01_fwhm4/spmT_0004.nii', title='cong > incong - fwhm=4',
bg_img=anatimg, threshold=3, display_mode='z', cut_coords=(-30, -15, 0, 15, 30), dim=-1)
Out[ ]: