def buan_bundle_profiles(model_bundle_folder,
bundle_folder,
orig_bundle_folder,
metric_folder,
group_id,
subject,
no_disks=100,
out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group_id : integer
what group subject belongs to either 0 for control or 1 for patient.
subject : string
subject id e.g. 10001.
no_disks : integer, optional
Number of disks used for dividing bundle into disks.
out_dir : string, optional
Output directory. (default current directory)
References
----------
.. [Chandio2020] Chandio, B.Q., Risacher, S.L., Pestilli, F., Bullock, D.,
Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E.
Bundle analytics, a computational framework for investigating the
shapes and profiles of brain pathways across populations.
Sci Rep 10, 17149 (2020)
"""
t = time()
dt = dict()
mb = glob(os.path.join(model_bundle_folder, "*.trk"))
print(mb)
mb.sort()
bd = glob(os.path.join(bundle_folder, "*.trk"))
bd.sort()
print(bd)
org_bd = glob(os.path.join(orig_bundle_folder, "*.trk"))
org_bd.sort()
print(org_bd)
n = len(org_bd)
n = len(mb)
for io in range(n):
mbundles = load_tractogram(mb[io],
reference='same',
bbox_valid_check=False).streamlines
bundles = load_tractogram(bd[io],
reference='same',
bbox_valid_check=False).streamlines
orig_bundles = load_tractogram(org_bd[io],
reference='same',
bbox_valid_check=False).streamlines
if len(orig_bundles) > 5:
indx = assignment_map(bundles, mbundles, no_disks)
ind = np.array(indx)
metric_files_names_dti = glob(
os.path.join(metric_folder, "*.nii.gz"))
metric_files_names_csa = glob(os.path.join(metric_folder,
"*.pam5"))
_, affine = load_nifti(metric_files_names_dti[0])
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(
orig_bundles, affine_r)
for mn in range(len(metric_files_names_dti)):
ab = os.path.split(metric_files_names_dti[mn])
metric_name = ab[1]
fm = metric_name[:-7]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
dt = dict()
logging.info("metric = " + metric_files_names_dti[mn])
metric, _ = load_nifti(metric_files_names_dti[mn])
anatomical_measures(transformed_orig_bundles, metric, dt, fm,
bm, subject, group_id, ind, out_dir)
for mn in range(len(metric_files_names_csa)):
ab = os.path.split(metric_files_names_csa[mn])
metric_name = ab[1]
fm = metric_name[:-5]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
logging.info("metric = " + metric_files_names_csa[mn])
dt = dict()
metric = load_peaks(metric_files_names_csa[mn])
peak_values(transformed_orig_bundles, metric, dt, fm, bm,
subject, group_id, ind, out_dir)
print("total time taken in minutes = ", (-t + time()) / 60)
def buan_bundle_profiles(model_bundle_folder, bundle_folder,
orig_bundle_folder, metric_folder, group_id, subject,
no_disks=100, out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group_id : integer
what group subject belongs to either 0 for control or 1 for patient
subject : string
subject id e.g. 10001
no_disks : integer, optional
Number of disks used for dividing bundle into disks. (Default 100)
out_dir : string, optional
Output directory (default input file directory)
References
----------
.. [Chandio19] Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical
analyses framework for tractometric studies, Proceedings of:
International Society of Magnetic Resonance in Medicine (ISMRM),
Montreal, Canada, 2019.
"""
t = time()
dt = dict()
mb = glob(os.path.join(model_bundle_folder, "*.trk"))
print(mb)
mb.sort()
bd = glob(os.path.join(bundle_folder, "*.trk"))
bd.sort()
print(bd)
org_bd = glob(os.path.join(orig_bundle_folder, "*.trk"))
org_bd.sort()
print(org_bd)
n = len(org_bd)
n = len(mb)
for io in range(n):
mbundles = load_tractogram(mb[io], reference='same',
bbox_valid_check=False).streamlines
bundles = load_tractogram(bd[io], reference='same',
bbox_valid_check=False).streamlines
orig_bundles = load_tractogram(org_bd[io], reference='same',
bbox_valid_check=False).streamlines
if len(orig_bundles) > 5:
indx = assignment_map(bundles, mbundles, no_disks)
ind = np.array(indx)
metric_files_names_dti = glob(os.path.join(metric_folder,
"*.nii.gz"))
metric_files_names_csa = glob(os.path.join(metric_folder,
"*.pam5"))
_, affine = load_nifti(metric_files_names_dti[0])
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(orig_bundles,
affine_r)
for mn in range(len(metric_files_names_dti)):
ab = os.path.split(metric_files_names_dti[mn])
metric_name = ab[1]
fm = metric_name[:-7]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
dt = dict()
logging.info("metric = " + metric_files_names_dti[mn])
metric, _ = load_nifti(metric_files_names_dti[mn])
anatomical_measures(transformed_orig_bundles, metric, dt, fm,
bm, subject, group_id, ind, out_dir)
for mn in range(len(metric_files_names_csa)):
ab = os.path.split(metric_files_names_csa[mn])
metric_name = ab[1]
fm = metric_name[:-5]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
logging.info("metric = " + metric_files_names_csa[mn])
dt = dict()
metric = load_peaks(metric_files_names_csa[mn])
peak_values(transformed_orig_bundles, metric, dt, fm, bm,
subject, group_id, ind, out_dir)
print("total time taken in minutes = ", (-t + time())/60)