def typical_resp(data_path='~/ni_data/ofM.dr/',
l1_dir='l1',
workflow_name='composite'):
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="best_responders_old",
include={
'subject':
["5689", "5690", "5691", "5700", "6262", "6255", "5694", "5706"],
'trial': ["CogB"],
},
groupby="session",
keep_work=True,
mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
)
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="best_responders",
include={
'subject':
["5699", "5687", "5691", "5694", "4005", "6255", "5706"],
'trial': ["CogB"],
},
groupby="session",
keep_work=True,
mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
)
def typical_resp(data_path='~/ni_data/ofM.dr/bids/', l1_dir='l1', workflow_name='generic'):
from samri.pipelines import preprocess, glm
glm.l2_common_effect(path.join(data_path,l1_dir,workflow_name),
workflow_name="best_responders",
include={
'subject':["6262","6255","5694","5706",'5704','6455','6459','5691',"5689","5690","5700","6451","6460","6456","6461","6462"],
'task':["CogB"],
},
groupby="session",
keep_work=True,
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
)
def typical_resp(data_path='~/ni_data/ofM.dr/bids/',
l1_dir='l1',
workflow_name='generic'):
from samri.pipelines import preprocess, glm
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="best_responders",
include={
'subject': [
"6262", "6255", "5694", "5706", '5704', '6455', '6459', '5691',
"5689", "5690", "5700", "6451", "6460", "6456", "6461", "6462"
],
'task': ["CogB"],
},
groupby="session",
keep_work=True,
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
)
def dr_cont():
from labbookdb.report.development import animal_multiselect
from samri.pipelines import glm
from samri.pipelines.preprocess import bruker
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
# Assuming data cobnverted to BIDS
bids_base = '~/ni_data/ofM.dr/bids'
# Preprocess
#animal_list = animal_multiselect(cage_treatments=['cFluDW','cFluDW_','cFluIP'])
# Animal list selection needs fixing in LabbookDB database, so we add the following animals manually
#animal_list.extend(['4001','4002','4003','4004','4005','4006','4007','4008','4009','4011','4012','4013','6557'])
# Determining Responders by Significance
_, substitutions = bids_autofind(
'~/ni_data/ofM.dr/l1/generic/',
path_template=
"{bids_dir}/sub-{{subject}}/ses-{{session}}/sub-{{subject}}_ses-{{session}}_task-{{task}}_acq-{{acquisition}}_cbv_pfstat.nii.gz",
match_regex=
'.+/sub-(?P<sub>.+)/ses-(?P<ses>.+)/.*?_task-(?P<task>.+).*?_acq-(?P<acquisition>.+)_cbv_pfstat\.nii.gz',
)
print(substitutions)
iter_significant_signal(
'~/ni_data/ofM.dr/l1/generic/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{{acquisition}}_cbv_pfstat.nii.gz',
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/l1/generic/total_significance.csv')
# Determining Responders by a priori pattern
glm.l2_common_effect(
'~/ni_data/ofM.dr/l1/',
workflow_name="a_priori_responders",
include={
'subject': [
'4001', '4005', '4006', '4007', '4008', '4009', '4011', '4012',
'4013'
],
},
groupby="session",
keep_work=True,
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
)
def cbv_composite(
data_path="~/ni_data/ofM.dr/",
workflow_name='composite',
preprocessing_dir="preprocessing",
l1_dir="l1",
):
#preprocessing.bruker(data_path,
# #exclude_measurements=['20151027_121613_4013_1_1'],
# functional_match={'trial':['CogB','JogB']},
# structural_match={'acquisition':['TurboRARE','TurboRARElowcov']},
# #subjects=["4007","4008","4011","4012","5687","5688","5695","5689","5690","5691","5703","5704","5706"],
# #subjects=["4007","4008","5687","5688","5704",
# # "5692","6262","5694","5700","6255","5699"],
# #subjects=["4001","4011","5703","5706",],
# workflow_name=workflow_name,
# lowpass_sigma=2,
# highpass_sigma=225,
# very_nasty_bruker_delay_hack=True,
# negative_contrast_agent=True,
# functional_blur_xy=.4,
# functional_registration_method="composite",
# keep_work=True,
# template="~/ni_data/templates/DSURQEc_200micron_average.nii",
# registration_mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
# actual_size=True,
# verbose=True,
# )
#glm.l1(path.join(data_path,preprocessing_dir,workflow_name),
# workflow_name=workflow_name,
# # include={"subjects":["5689","5690","5691"]},
# habituation="confound",
# mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
# keep_work=True,
# )
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="composite_subjects",
groupby="subject",
keep_work=True,
mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
)
def dr_composite():
from samri.pipelines import glm, preprocess
preprocessing.bruker("~/ni_data/ofM.dr/",
exclude_measurements=['20151027_121613_4013_1_1'],
workflow_name="composite",
very_nasty_bruker_delay_hack=True,
negative_contrast_agent=True,
functional_blur_xy=4,
functional_registration_method="composite")
glm.l1("~/ni_data/ofM.dr/preprocessing/composite",
workflow_name="composite",
include={"subjects": [i for i in range(4001, 4010)] + [4011, 4012]},
habituation="confound",
mask="~/ni_data/templates/ds_QBI_chr_bin.nii.gz",
keep_work=True)
glm.l1(
"~/ni_data/ofM.dr/preprocessing/composite",
workflow_name="composite_dr",
include={"subjects": [i for i in range(4001, 4010)] + [4011, 4012]},
habituation="confound",
mask="~/ni_data/templates/roi/f_dr_chr_bin.nii.gz",
)
glm.l2_common_effect("~/ni_data/ofM.dr/l1/composite",
workflow_name="subjectwise_composite",
groupby="subject")
glm.l2_common_effect("~/ni_data/ofM.dr/l1/composite",
workflow_name="sessionwise_composite",
groupby="session",
exclude={
"subjects": [
"4001", "4002", "4003", "4004", "4005",
"4006", "4009", "4011", "4013"
]
})
glm.l2_common_effect(
"~/ni_data/ofM.dr/l1/composite",
workflow_name="sessionwise_composite_w4011",
groupby="session",
exclude={
"subjects":
["4001", "4002", "4003", "4004", "4005", "4006", "4009", "4013"]
})
preprocess_base = '{}/l1/'.format(scratch_dir)
masks = {
'generic': '/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
'masked': '/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
}
for key in masks:
# We filter by run, since the primary contrast is replaced by the statistic contrast in level2.
# We exclude animal 4006, as its slice positioning significantly diminishes statistic coverage.
glm.l2_common_effect(path.join(preprocess_base, key),
workflow_name=key,
mask=masks[key],
groupby='none',
keep_work=False,
n_jobs_percentage=.33,
exclude={'subject': ['4006'], },
include={'run': ['0'], },
out_base='{}/l2'.format(scratch_dir),
)
glm.l2_common_effect(path.join(preprocess_base, key),
workflow_name=key,
mask=masks[key],
groupby='none',
keep_work=False,
n_jobs_percentage=.33,
exclude={'subject': ['4006'], },
include={'run': ['1'], },
out_base='{}/l2'.format(scratch_dir),
)
def dr_full():
from labbookdb.report.development import animal_multiselect
from samri.pipelines import glm
from samri.pipelines.preprocess import bruker
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
# Assuming data cobnverted to BIDS
bids_base = '~/ni_data/ofM.dr/bids'
# Preprocess
animal_list = animal_multiselect(
cage_treatments=['cFluDW', 'cFluDW_', 'cFluIP'])
# Animal list selection needs fixing in LabbookDB database, so we add the following animals manually
animal_list.extend([
'4001', '4002', '4003', '4004', '4005', '4006', '4007', '4008', '4009',
'4011', '4012', '4013', '6557'
])
full_prep(
bids_base,
"~/ni_data/templates/dsurqec_200micron.nii",
registration_mask="~/ni_data/templates/dsurqec_200micron_mask.nii",
functional_match={
'type': [
'cbv',
],
},
structural_match={
'acquisition': ['TurboRARE', 'TurboRARElowcov'],
},
subjects=animal_list,
actual_size=True,
functional_registration_method="composite",
negative_contrast_agent=True,
out_dir='~/ni_data/ofM.dr/preprocessing',
)
#bruker(bids_base, "~/ni_data/templates/dsurqec_200micron.nii",
# registration_mask="~/ni_data/templates/dsurqec_200micron_mask.nii",
# functional_match={'type':['bold',],},
# structural_match={'acquisition':['TurboRARE','TurboRARElowcov'],},
# subjects=animal_list,
# actual_size=True,
# functional_registration_method="composite",
# negative_contrast_agent=False,
# out_dir='~/ni_data/ofM.dr/preprocessing',
# )
# Model fitting
glm.l1(
'~/ni_data/ofM.dr/preprocessing/generic',
out_dir='~/ni_data/ofM.dr/l1',
workflow_name='generic',
habituation="confound",
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
# We need the workdir to extract the betas
keep_work=True,
)
# Determining Responders by Significance
substitutions = bids_autofind(
'~/ni_data/ofM.dr/l1/generic/',
path_template=
"{bids_dir}/sub-{{subject}}/ses-{{session}}/sub-{{subject}}_ses-{{session}}_task-{{task}}_acq-{{acquisition}}_cbv_pfstat.nii.gz",
match_regex=
'.+/sub-(?P<sub>.+)/ses-(?P<ses>.+)/.*?_task-(?P<task>.+).*?_acq-(?P<acquisition>.+)_cbv_pfstat\.nii.gz',
)
iter_significant_signal(
'~/ni_data/ofM.dr/l1/generic/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_task-{task}_acq-{{acquisition}}_cbv_pfstat.nii.gz',
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/l1/generic/total_significance.csv')
# Determining Responders by a priori pattern
glm.l2_common_effect(
'~/ni_data/ofM.dr/l1/',
workflow_name="a_priori_responders",
include={
'subject': [
'4001', '4005', '4006', '4007', '4008', '4009', '4011', '4012',
'4013'
],
},
groupby="session",
keep_work=True,
mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
)
def cbv_composite(
data_path,
workflow_name,
preprocessing_dir="preprocessing",
l1_dir="l1",
):
preprocessing.bruker(
data_path,
#exclude_measurements=['20151027_121613_4013_1_1'],
functional_scan_types=["EPI_CBV_chr_longSOA", "EPI_CBV_jb_long"],
#subjects=["4007","4008","4011","4012","5687","5688","5695","5689","5690","5691","5703","5704","5706"],
subjects=["4007", "4008", "4011", "5687", "5688", "5704"],
#subjects=["4007","4008","4009","4011","4012","5689","5690","5691","5703","5704","5706"],
workflow_name=workflow_name,
lowpass_sigma=2,
highpass_sigma=225,
very_nasty_bruker_delay_hack=True,
negative_contrast_agent=True,
functional_blur_xy=.4,
functional_registration_method="composite",
keep_work=True,
template="~/ni_data/templates/DSURQEc_200micron_average.nii",
registration_mask="~/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
actual_size=True,
)
glm.l1(
path.join(data_path, preprocessing_dir, workflow_name),
workflow_name=workflow_name,
# include={"subjects":["5689","5690","5691"]},
habituation="confound",
mask="/home/chymera/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
keep_work=True,
)
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="composite_sessions_best_responders",
exclude={
"scans": ["EPI_BOLD_"],
"subjects": [
"4001", "4002", "4003", "4004", "4006", "4008", "4009", "5674",
"5703", "5704", "5706", "4005", "5687"
]
},
groupby="session",
keep_work=True,
mask="/home/chymera/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
)
glm.l2_common_effect(
path.join(data_path, l1_dir, workflow_name),
workflow_name="composite_sessions_responders",
exclude={
"scans": ["EPI_BOLD_"],
"subjects": [
"4001", "4002", "4003", "4004", "4006", "4008", "4009", "5674",
"5703", "5704", "5706"
]
},
groupby="session",
keep_work=True,
mask="/home/chymera/ni_data/templates/DSURQEc_200micron_mask.nii.gz",
)
def abi_connectivity_map(
identifier,
exclude_experiments=[],
keep_work=False,
mask='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
prepare_root='/var/tmp/{user}/samri/abi_connectivity/',
prepare_subdirs='sub-{experiment}/ses-1/anat/sub-{experiment}_ses-1_desc-cope.nii.gz',
save_as_cope='',
save_as_zstat='',
save_as_tstat='',
tmp_dir='/var/tmp/{user}/samri/abi_connectivity/l2',
abi_data_root='/usr/share/ABI-connectivity-data/',
invert_lr_experiments=[],
):
"""Create statistical summary maps (any subset of COPE, t-statistics, and z-statistics) for an ABI connectivity identifier.
Parameters
----------
identifier : str
Experiment set identifier which corresponds to the data set paths from the ABI-connectivity-data package.
exclude_experiments : list of str, optional
List of strings, each string 9 characters long, identifying which experiments should be excluded from the modelling.
keep_work : bool, optional
Whether to keep the work files (including the prepared ABI experiment data, the work directory, results directory, and crash directory of the model).
mask : string, optional
Path to a NIfTI file containing ones and zeroes and specifying the mask for the modelling workflow.
It is important that this data is in the same template space as the ABI-connectivity-data package, which is the DSURQEC space [1]_ .
prepare_root : string, optional
Python-formattable string, under which the prepared (e.g. flipped) data from ABI experiments is to be saved.
Generally this should be a temporal and new path, containing either "{user}" or located under the user's home directory, in order to avoid race conditions between users.
It will be deleted without request for confirmation if this function is executed with `keep_work=False` (which is the default).
prepare_subdirs : string, optional
Python-formattable string, containing "{experiment}" according to which the prepared (e.g. flipped) data from ABI experiments is to be organized inside the `prepare_root` directory.
save_as_cope : str, optional
Path under which to save the COPE result of the modelling.
save_as_tstat : str, optional
Path under which to save the t-statistic result of the modelling.
save_as_zstat : str, optional
Path under which to save the z-statistic result of the modelling.
tmp_dir : string, optional
Temporary directory inside which to execute the modelling workflow.
Generally this should be a temporal and new path, containing either "{user}" or located under the user's home directory, in order to avoid race conditions between users.
abi_data_root : str, optional
Root path for the ABI-connectivity-data package installation on the current machine.
invert_lr_experiments : list of str, optional
List of strings, each string 9 characters long, identifying which experiments need to be inverted with respect to the left-right orientation.
Notes
-----
If neither of the `save_as_cope`, `save_as_tstat`, and `save_as_zstat` parameters are specified, all of the results are saved in the current work directory as `{identifier}_{statistic}.nii.gz`.
References
----------
.. [1] H. I. Ioanas and M. Marks and M. F. Yanik and M. Rudin "An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging" https://doi.org/10.1101/619650
"""
# Prepend user name to SAMRI temp directories to prevent users from overwriting each other's work.
current_user = getpass.getuser()
prepare_root = prepare_root.format(user=current_user)
tmp_dir = tmp_dir.format(user=current_user)
reposit_path = path.join(prepare_root, '{identifier}', prepare_subdirs)
prepare_abi_connectivity_maps(
identifier,
abi_data_root=abi_data_root,
reposit_path=reposit_path,
invert_lr_experiments=invert_lr_experiments,
)
prepare_root = prepare_root.format(identifier=identifier)
glm.l2_common_effect(
prepare_root,
workflow_name=identifier,
mask=mask,
n_jobs_percentage=.33,
out_base=tmp_dir,
exclude={'subject': exclude_experiments},
run_mode='fe',
keep_work=keep_work,
)
if save_as_zstat:
zstat_path = path.join(tmp_dir, identifier, '_zstat.nii.gz')
shutil.copyfile(zstat_path, save_as_zstat)
if save_as_tstat:
tstat_path = path.join(tmp_dir, identifier, '_tstat.nii.gz')
shutil.copyfile(tstat_path, save_as_tstat)
if save_as_cope:
cope_path = path.join(tmp_dir, identifier, '_cope.nii.gz')
shutil.copyfile(cope_path, save_as_cope)
if not any([save_as_zstat, save_as_tstat, save_as_cope]):
zstat_path = path.join(tmp_dir, identifier, '_zstat.nii.gz')
shutil.copyfile(zstat_path, '{identifier}_zstat.nii.gz')
tstat_path = path.join(tmp_dir, identifier, '_tstat.nii.gz')
shutil.copyfile(tstat_path, '{identifier}_tstat.nii.gz')
cope_path = path.join(tmp_dir, identifier, '_cope.nii.gz')
shutil.copyfile(cope_path, '{identifier}_cope.nii.gz')
if not keep_work:
results_dir = path.join(tmp_dir, identifier)
shutil.rmtree(results_dir)
shutil.rmtree(prepare_root)
