def total_significance():
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
substitutions = bids_autofind('~/ni_data/ofM.dr/bids/l1/generic/',
path_template="{bids_dir}/sub-{{subject}}/ses-{{session}}/sub-{{subject}}_ses-{{session}}_acq-{{acquisition}}_task-{{task}}_cbv_pfstat.nii.gz",
match_regex='.+/sub-(?P<sub>.+)/ses-(?P<ses>.+)/.*?_acq-(?P<acquisition>.+).*?_task-(?P<task>.+)_cbv_pfstat\.nii.gz',
)
iter_significant_signal('~/ni_data/ofM.dr/bids/l1/generic/sub-{subject}/ses-{session}/sub-{subject}_ses-{session}_acq-{acquisition}_task-{task}_cbv_pfstat.nii.gz',
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/bids/l1/generic/total_significance.csv'
)
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 vta_full(workflow_name='generic', ):
from labbookdb.report.development import animal_multiselect
from samri.pipelines import glm
from samri.pipelines.preprocess import full_prep
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
# Assuming data cobnverted to BIDS
bids_base = '~/ni_data/ofM.vta/bids'
#full_prep(bids_base, "/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
# registration_mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
# functional_match={'type':['cbv',],},
# structural_match={'acquisition':['TurboRARE']},
# actual_size=True,
# functional_registration_method='composite',
# negative_contrast_agent=True,
# out_dir='~/ni_data/ofM.vta/preprocessing',
# workflow_name=workflow_name,
# )
#glm.l1('~/ni_data/ofM.vta/preprocessing/generic',
# out_dir='~/ni_data/ofM.vta/l1',
# workflow_name=workflow_name,
# 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
path_template, substitutions = bids_autofind(
'~/ni_data/ofM.vta/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(
path_template,
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/vta/generic/total_significance.csv')
def vta_full(
workflow_name='generic',
):
from labbookdb.report.development import animal_multiselect
from samri.pipelines import glm
from samri.pipelines.preprocess import full_prep
from samri.report.snr import iter_significant_signal
from samri.utilities import bids_autofind
# Assuming data cobnverted to BIDS
bids_base = '~/ni_data/ofM.vta/bids'
#full_prep(bids_base, "/usr/share/mouse-brain-atlases/dsurqec_200micron.nii",
# registration_mask="/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii",
# functional_match={'type':['cbv',],},
# structural_match={'acquisition':['TurboRARE']},
# actual_size=True,
# functional_registration_method='composite',
# negative_contrast_agent=True,
# out_dir='~/ni_data/ofM.vta/preprocessing',
# workflow_name=workflow_name,
# )
#glm.l1('~/ni_data/ofM.vta/preprocessing/generic',
# out_dir='~/ni_data/ofM.vta/l1',
# workflow_name=workflow_name,
# 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
path_template, substitutions = bids_autofind('~/ni_data/ofM.vta/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(path_template,
substitutions=substitutions,
mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',
save_as='~/ni_data/ofM.dr/vta/generic/total_significance.csv'
)
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",
)