def get_bids_df(bids_dir, scans_only=None, keep_defaced=False):
if isinstance(bids_dir, Path):
bids_dir = bids_dir.as_posix()
layout = BIDSLayout(bids_dir)
df_pybids = layout.as_data_frame()
if not keep_defaced:
df_pybids = df_pybids.query('~path.str.contains("defaced")')
if scans_only:
df_pybids = (df_pybids.loc[df_pybids.path.str.contains('nii.gz'), :].
query("~path.str.contains('.git')"))
df_pybids['json_path'] = \
(df_pybids.path.apply(
lambda x: Path(''.join([*x.split('.')[:-2], '.json']))))
return df_pybids
def main(sourcedata, derivatives, subject, session, tmp_dir):
sourcedata_layout = BIDSLayout(sourcedata)
sourcedata_df = sourcedata_layout.as_data_frame()
events = sourcedata_df[(sourcedata_df['suffix'] == 'events')
& (sourcedata_df['subject'] == subject) &
(sourcedata_df['session'] == session)]
derivatives_layout = BIDSLayout(os.path.join(derivatives), validate=False)
derivatives_df = derivatives_layout.as_data_frame()
bold = derivatives_df[(derivatives_df['suffix'] == 'preproc')
& (derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)]
confounds = derivatives_df[(derivatives_df['suffix'] == 'confounds')
& (derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)]
compcor = derivatives_df[(derivatives_df['suffix'] == 'compcor')
& (derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)]
mask = derivatives_layout.get(subject=subject,
session=session,
suffix='mask',
return_type='file')[0]
df = events.merge(bold,
on=['subject', 'session', 'run'],
suffixes=('_events', '_bold'))
confounds = confounds.rename(columns={'path': 'confounds'})
df = df.merge(confounds[['subject', 'session', 'run', 'confounds']])
compcor = compcor.rename(columns={'path': 'compcor'})
df = df.merge(compcor[['subject', 'session', 'run', 'compcor']])
df.sort_values('run', inplace=True)
print(df.iloc[0])
models = []
for ix, row in df.iterrows():
results_dir = os.path.join(derivatives, 'modelfitting', 'glm8',
'sub-{}'.format(row['subject']))
if 'session' in row:
results_dir = os.path.join(results_dir,
'ses-{}'.format(row['session']))
results_dir = op.join(results_dir, 'func')
os.makedirs(results_dir, exist_ok=True)
confounds = pd.read_table(row.confounds).fillna(method='bfill')
compcor = pd.read_table(row.compcor).fillna(method='bfill')
confounds = pd.concat((confounds, compcor), 1)
confounds -= confounds.mean()
confounds /= confounds.std()
pca = decomposition.PCA(n_components=6)
confounds_trans = pd.DataFrame(
pca.fit_transform(confounds),
columns=['pca_{}'.format(i) for i in range(6)])
print('Fitting {}'.format(row['path_bold']))
model = FirstLevelModel(t_r=4,
signal_scaling=False,
subject_label=int(row['run']),
mask_img=mask)
paradigm = pd.read_table(row['path_events'])
paradigm_ = paradigm.copy()
paradigm['trial_type'] = 'stimulation'
paradigm['modulation'] = 1
paradigm_['modulation'] = paradigm_.trial_type.map({
'eye_L': 1,
'eye_R': -1
})
paradigm_['trial_type'] = 'eye'
paradigm = pd.concat((paradigm, paradigm_), ignore_index=True)
model.fit(row['path_bold'], paradigm, confounds=confounds_trans)
row['run'] = int(row['run'])
row = dict(row)
left_right = model.compute_contrast('eye', output_type='z_score')
left_right.to_filename(
os.path.join(
results_dir,
'sub-{subject}_ses-{session}_task-{task_events}_run-{run:02d}_left_over_right_zmap.nii.gz'
.format(**row)))
left_right = model.compute_contrast('eye', output_type='effect_size')
left_right.to_filename(
os.path.join(
results_dir,
'sub-{subject}_ses-{session}_task-{task_events}_run-{run:02d}_left_over_right_psc.nii.gz'
.format(**row)))
stimulation = model.compute_contrast('stimulation',
output_type='effect_size')
stimulation.to_filename(
os.path.join(
results_dir,
'sub-{subject}_ses-{session}_task-{task_events}_run-{run:02d}_stimulation_psc.nii.gz'
.format(**row)))
stimulation = model.compute_contrast('stimulation',
output_type='z_score')
stimulation.to_filename(
os.path.join(
results_dir,
'sub-{subject}_ses-{session}_task-{task_events}_run-{run:02d}_stimulation_zmap.nii.gz'
.format(**row)))
models.append(model)
second_level_model = SecondLevelModel(mask_img=mask)
second_level_model.fit(models)
left_right_group = second_level_model.compute_contrast(
first_level_contrast='eye', output_type='z_score')
left_right_group.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_left_over_right_zmap.nii.gz'.format(
row['subject'], row['session'])))
left_right_group = second_level_model.compute_contrast(
first_level_contrast='eye', output_type='effect_size')
left_right_group.to_filename(
os.path.join(
results_dir,
'sub-{}_ses-{}_left_over_right_effect_size.nii.gz'.format(
row['subject'], row['session'])))
stimulation_group = second_level_model.compute_contrast(
first_level_contrast='stimulation', output_type='z_score')
left_right_group.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_stimulation_zmap.nii.gz'.format(
row['subject'], row['session'])))
stimulation_group = second_level_model.compute_contrast(
first_level_contrast='stimulation', output_type='effect_size')
left_right_group.to_filename(
os.path.join(
results_dir, 'sub-{}_ses-{}_stimulation_effect_size.nii.gz'.format(
row['subject'], row['session'])))
def main(sourcedata,
derivatives,
subject,
session):
derivatives_layout = BIDSLayout(op.join(derivatives), validate=False)
derivatives_df = derivatives_layout.as_data_frame()
confounds = derivatives_df[(derivatives_df['suffix'] == 'confounds') &
(derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)].set_index(['subject', 'session', 'task', 'run'])
compcor = derivatives_df[(derivatives_df['suffix'] == 'compcor') &
(derivatives_df['subject'] == subject) &
(derivatives_df['session'] == session)].set_index(['subject', 'session', 'task', 'run'])
fns = glob.glob(op.join(derivatives, 'sampled_giis/sub-{subject}/ses-{session}/func/sub-{subject}_ses-{session}_*.gii'.format(**locals())))
# Events are the same for all subjects
events = pd.read_table(op.join(sourcedata,
'sub-tk/ses-odc2/func/sub-tk_ses-odc2_task-checkerboard_acq-07_run-02_events.tsv'))
df = pd.read_pickle(op.join(derivatives,
'depth_sampled_surfaces/sub-{subject}/sub-{subject}_ses-{session}_depth_sampled_data.pkl.gz'.format(**locals())))
print(df.head())
df = df.loc[:, 'psc']
df.index = df.index.droplevel('acq')
frametimes = np.linspace(0, 66*4, 66, endpoint=False)
X = make_first_level_design_matrix(frametimes, events, drift_order=None, drift_model=None)
ts = []
for ix, d in df.loc[:, ['V1l', 'V1r']].groupby(['subject', 'session', 'task', 'run']):
print(ix)
conf = pd.read_table(confounds.loc[ix].path).fillna(method='bfill')
comc = pd.read_table(compcor.loc[ix].path).fillna(method='bfill')
conf = pd.concat((conf, comc), 1)
conf -= conf.mean()
conf /= conf.std()
pca = decomposition.PCA(n_components=6)
confounds_trans = pd.DataFrame(pca.fit_transform(conf),
columns=['pca_{}'.format(i) for i in range(6)])
confounds_trans.index = X.index
X_ = pd.concat((X, confounds_trans), axis=1)
labels, results = run_glm(d, X, noise_model='ols')
results = results[0.0]
ts.append(pd.DataFrame([results.theta[0], results.theta[1], results.t(0), results.t(1)],
index=pd.MultiIndex.from_product([[e] for e in ix] + [['psc', 't'], ['eye_L', 'eye_R']],
names=['subject',
'session',
'task',
'run',
'type',
'contrast']),
columns=d.columns))
ts = pd.concat(ts, axis=0)
results_dir = op.join(derivatives, 'surfacewise_glms', 'sub-{subject}', 'ses-{session}', 'func').format(**locals())
if not op.exists(results_dir):
os.makedirs(results_dir)
ts.to_pickle(op.join(results_dir, 'sub-{subject}_ses-{session}_desc-laminarglms.pkl').format(**locals()))
