def fixed_effects_analysis(subject_dic, mask_img=None, mesh=False):
""" Combine the AP and PA images """
from nibabel import load, save
from nilearn.plotting import plot_stat_map
session_ids = subject_dic['session_id']
task_ids = _session_id_to_task_id(session_ids)
paradigms = np.unique(task_ids)
if mask_img is None:
mask_img = os.path.join(subject_dic['output_dir'], "mask.nii.gz")
# Guessing paradigm from file name
for paradigm in paradigms:
# select the sessions relevant for the paradigm
session_paradigm = [
session_id for (session_id, task_id) in zip(session_ids, task_ids)
if task_id == paradigm
]
# define the relevant contrasts
contrasts = make_contrasts(paradigm).keys()
# create write_dir
if mesh is not False:
if mesh == 'fsaverage5':
write_dir = os.path.join(subject_dic['output_dir'],
'res_fsaverage5_%s_ffx' % paradigm)
elif mesh == 'individual':
write_dir = os.path.join(subject_dic['output_dir'],
'res_individual_%s_ffx' % paradigm)
else:
write_dir = os.path.join(subject_dic['output_dir'],
'res_fsaverage7_%s_ffx' % paradigm)
dirs = [
os.path.join(write_dir, stat)
for stat in ['effect_surf', 'variance_surf', 'stat_surf']
]
else:
write_dir = os.path.join(subject_dic['output_dir'],
'res_stats_%s_ffx' % paradigm)
dirs = [
os.path.join(write_dir, stat) for stat in
['effect_size_maps', 'effect_variance_maps', 'stat_maps']
]
for dir_ in dirs:
if not os.path.exists(dir_):
os.makedirs(dir_)
print(write_dir)
# iterate across contrasts
for contrast in contrasts:
print('fixed effects for contrast %s. ' % contrast)
if mesh is not False:
from nibabel.gifti import write
for side in ['lh', 'rh']:
effect_size_maps, effect_variance_maps, data_available =\
_load_summary_stats(
subject_dic['output_dir'],
np.unique(session_paradigm),
contrast,
data_available=True, side=side, mesh=mesh)
if not data_available:
raise ValueError('Missing texture stats files for '
'fixed effects computations')
ffx_effects, ffx_variance, ffx_stat = fixed_effects_surf(
effect_size_maps, effect_variance_maps)
write(
ffx_effects,
os.path.join(
write_dir,
'effect_surf/%s_%s.gii' % (contrast, side)))
write(
ffx_variance,
os.path.join(
write_dir,
'variance_surf/%s_%s.gii' % (contrast, side)))
write(
ffx_stat,
os.path.join(write_dir,
'stat_surf/%s_%s.gii' % (contrast, side)))
else:
effect_size_maps, effect_variance_maps, data_available =\
_load_summary_stats(
subject_dic['output_dir'], session_paradigm, contrast,
data_available=True)
shape = load(effect_size_maps[0]).shape
if len(shape) > 3:
if shape[3] > 1: # F contrast, skipping
continue
ffx_effects, ffx_variance, ffx_stat = fixed_effects_img(
effect_size_maps, effect_variance_maps, mask_img)
save(
ffx_effects,
os.path.join(write_dir,
'effect_size_maps/%s.nii.gz' % contrast))
save(
ffx_variance,
os.path.join(write_dir,
'effect_variance_maps/%s.nii.gz' % contrast))
save(ffx_stat,
os.path.join(write_dir, 'stat_maps/%s.nii.gz' % contrast))
plot_stat_map(ffx_stat,
bg_img=subject_dic['anat'],
display_mode='z',
dim=0,
cut_coords=7,
title=contrast,
threshold=3.0,
output_file=os.path.join(
write_dir, 'stat_maps/%s.png' % contrast))
def first_level(subject_dic,
additional_regressors=None,
compcorr=False,
smooth=None,
mesh=False,
mask_img=None):
""" Run the first-level analysis (GLM fitting + statistical maps)
in a given subject
Parameters
----------
subject_dic: dict,
exhaustive description of an individual acquisition
additional_regressors: dict or None,
additional regressors provided as an already sampled
design_matrix
dictionary keys are session_ids
compcorr: Bool, optional,
whether confound estimation and removal should be done or not
smooth: float or None, optional,
how much the data should spatially smoothed during masking
"""
start_time = time.ctime()
# experimental paradigm meta-params
motion_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz']
hrf_model = subject_dic['hrf_model']
high_pass = subject_dic['high_pass']
drift_model = subject_dic['drift_model']
tr = subject_dic['TR']
slice_time_ref = 1.
if not mesh and (mask_img is None):
mask_img = masking(subject_dic['func'], subject_dic['output_dir'])
if additional_regressors is None:
additional_regressors = dict([
(session_id, None) for session_id in subject_dic['session_id']
])
for session_id, fmri_path, onset, motion_path in zip(
subject_dic['session_id'], subject_dic['func'],
subject_dic['onset'], subject_dic['realignment_parameters']):
task_id = _session_id_to_task_id([session_id])[0]
if mesh is not False:
from nibabel.gifti import read
n_scans = np.array(
[darrays.data for darrays in read(fmri_path).darrays]).shape[0]
else:
n_scans = nib.load(fmri_path).shape[3]
# motion parameters
motion = np.loadtxt(motion_path)
# define the time stamps for different images
frametimes = np.linspace(slice_time_ref,
(n_scans - 1 + slice_time_ref) * tr, n_scans)
if task_id == 'audio':
mask = np.array([1, 0, 1, 1, 0, 1, 1, 0, 1, 1])
n_cycles = 28
cycle_duration = 20
t_r = 2
cycle = np.arange(0, cycle_duration, t_r)[mask > 0]
frametimes = np.tile(cycle, n_cycles) +\
np.repeat(np.arange(n_cycles) * cycle_duration, mask.sum())
frametimes = frametimes[:-2] # for some reason...
if mesh is not False:
compcorr = False # XXX Fixme
if compcorr:
confounds = high_variance_confounds(fmri_path, mask_img=mask_img)
confounds = np.hstack((confounds, motion))
confound_names = ['conf_%d' % i for i in range(5)] + motion_names
else:
confounds = motion
confound_names = motion_names
if onset is None:
warnings.warn('Onset file not provided. Trying to guess it')
task = os.path.basename(fmri_path).split('task')[-1][4:]
onset = os.path.join(
os.path.split(os.path.dirname(fmri_path))[0], 'model001',
'onsets', 'task' + task + '_run001', 'task%s.csv' % task)
if not os.path.exists(onset):
warnings.warn('non-existant onset file. proceeding without it')
paradigm = None
else:
paradigm = make_paradigm(onset, task_id)
# handle manually supplied regressors
add_reg_names = []
if additional_regressors[session_id] is None:
add_regs = confounds
else:
df = read_csv(additional_regressors[session_id])
add_regs = []
for regressor in df:
add_reg_names.append(regressor)
add_regs.append(df[regressor])
add_regs = np.array(add_regs).T
add_regs = np.hstack((add_regs, confounds))
add_reg_names += confound_names
# create the design matrix
design_matrix = make_first_level_design_matrix(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
high_pass=high_pass,
add_regs=add_regs,
add_reg_names=add_reg_names)
_, dmtx, names = check_design_matrix(design_matrix)
# create the relevant contrasts
contrasts = make_contrasts(task_id, names)
if mesh == 'fsaverage5':
# this is low-resolution data
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_fsaverage5_%s' % session_id)
elif mesh == 'fsaverage7':
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_fsaverage7_%s' % session_id)
elif mesh == 'individual':
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_individual_%s' % session_id)
else:
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_stats_%s' % session_id)
if not os.path.exists(subject_session_output_dir):
os.makedirs(subject_session_output_dir)
np.savez(os.path.join(subject_session_output_dir, 'design_matrix.npz'),
design_matrix=design_matrix)
if mesh is not False:
run_surface_glm(design_matrix, contrasts, fmri_path,
subject_session_output_dir)
else:
z_maps, fmri_glm = run_glm(design_matrix,
contrasts,
fmri_path,
mask_img,
subject_dic,
subject_session_output_dir,
tr=tr,
slice_time_ref=slice_time_ref,
smoothing_fwhm=smooth)
# do stats report
anat_img = nib.load(subject_dic['anat'])
stats_report_filename = os.path.join(subject_session_output_dir,
'report_stats.html')
report = make_glm_report(
fmri_glm,
contrasts,
threshold=3.0,
bg_img=anat_img,
cluster_threshold=15,
title="GLM for subject %s" % session_id,
)
report.save_as_html(stats_report_filename)
