def cond_rm(in_file, seed_location):
"""
Conditional Rate Map. Given an fMRI, extract timeseries, calculate Point Process
and then the Rate Map for each voxel given a seed
Parameters
----------
in_file : 4D Nifti file
seed_location : voxel to analyze as seed
Returns
-------
cond_rm_img : 3D Volume
"""
import numpy as np
import os
import nibabel as nb
from series_mod import point_process
# Treat fMRI image
img = nb.load(in_file)
# print img.shape
data = img.get_data()
(n_x, n_y, n_z, n_t) = data.shape
K = np.zeros((n_x, n_y, n_z))
# Extract each voxel
seed_data = data[seed_location[0], seed_location[1], seed_location[2], :]
# Extraction of PP signal
pp_seed_data = point_process(seed_data)
# Count how many extreme events happen. This is needed for later calculation of the CRM ratio
r = np.count_nonzero(pp_seed_data)
# As we have to compare the extreme events in the seed up to 2 time steps later,
# we roll the series 2 times, ensuring the 1st value = 0. It could happen that
# comparing with the target, 2 extreme events counted as 1 if seed[idx]=extreme
# event and seed[idx+2]=extreme event, but it is very unlikely to happen.
pp_seed_data_1 = np.roll(pp_seed_data, 1)
pp_seed_data_1[0] = 0
pp_seed_data_1 = np.logical_or(pp_seed_data, pp_seed_data_1)
pp_seed_data_2 = np.roll(pp_seed_data_1, 1)
pp_seed_data_2[0] = 0
pp_seed_data_2 = np.logical_or(pp_seed_data_1, pp_seed_data_2)
# example: 0100010010001000101001 => 0111011111101110111111
# Calculate each PP signal
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
target_data = data[i_, j_, k_, :]
pp_target_data = point_process(target_data)
# LOGIC AND (target/seed) and count(signal == 1), that will give you the X/r parameter [0,1]
K[i_, j_, k_] = np.count_nonzero(np.logical_and(pp_seed_data_2, pp_target_data)) / float(r)
# create img with K values
img_new = nb.Nifti1Image(K, header=img.get_header(), affine=img.get_affine())
# Reconstruct the 3D volume
cond_rm_img = os.path.join(os.getcwd(), in_file[:-7] + "cond_rm.nii.gz")
img_new.to_filename(cond_rm_img)
return cond_rm_img
def cluster_detection_mod2(in_file):
import numpy as np
import os
import nibabel as nb
from series_mod import point_process
# Treat fMRI image
img = nb.load(in_file)
data = img.get_data()
(n_x, n_y, n_z, n_t) = data.shape
# Get the PP data
pp_data = np.zeros((n_x, n_y, n_z, n_t))
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
voxel_data = data[i_, j_, k_, :]
pp_data[i_, j_, k_, :] = point_process(voxel_data)
cluster_graph_data_total = np.zeros((n_x, n_y, n_z, n_t))
for t_ in range(n_t):
time_slice = pp_data[:, :, :, t_]
cluster_graph_data = np.zeros((n_x, n_y, n_z))
cluster_number = 1
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
if time_slice[i_, j_, k_] == 1: # is active, check if it has active neighboours
if (
time_slice[i_ - 1, j_, k_]
or time_slice[i_ + 1, j_, k_]
or time_slice[i_, j_ - 1, k_]
or time_slice[i_, j_ + 1, k_]
or time_slice[i_, j_, k_ - 1]
or time_slice[i_, j_, k_ + 1]
or time_slice[i_ - 2, j_, k_]
or time_slice[i_ + 2, j_, k_]
or time_slice[i_, j_ - 2, k_]
or time_slice[i_, j_ + 2, k_]
or time_slice[i_, j_, k_ - 2]
or time_slice[i_, j_, k_ + 2]
):
if cluster_graph_data[i_, j_, k_] == 0: # if is not in any previous cluster
this_cluster = (
cluster_graph_data[i_ - 1, j_, k_]
or cluster_graph_data[i_ + 1, j_, k_]
or cluster_graph_data[i_, j_ - 1, k_]
or cluster_graph_data[i_, j_ + 1, k_]
or cluster_graph_data[i_, j_, k_ - 1]
or cluster_graph_data[i_, j_, k_ + 1]
or cluster_graph_data[i_ - 2, j_, k_]
or cluster_graph_data[i_ + 2, j_, k_]
or cluster_graph_data[i_, j_ - 2, k_]
or cluster_graph_data[i_, j_ + 2, k_]
or cluster_graph_data[i_, j_, k_ - 2]
or cluster_graph_data[i_, j_, k_ + 2]
)
if this_cluster == 0: # no neighbours in any previous cluster neither
this_cluster = cluster_number
cluster_graph_data[i_, j_, k_] = this_cluster
cluster_number = cluster_number + 1
else:
# check cluster union
merge_clusters = np.unique(
[
cluster_graph_data[i_ - 1, j_, k_],
cluster_graph_data[i_ + 1, j_, k_],
cluster_graph_data[i_, j_ - 1, k_],
cluster_graph_data[i_, j_ + 1, k_],
cluster_graph_data[i_, j_, k_ - 1],
cluster_graph_data[i_, j_, k_ + 1],
cluster_graph_data[i_ - 2, j_, k_],
cluster_graph_data[i_ + 2, j_, k_],
cluster_graph_data[i_, j_ - 2, k_],
cluster_graph_data[i_, j_ + 2, k_],
cluster_graph_data[i_, j_, k_ - 2],
cluster_graph_data[i_, j_, k_ + 2],
]
)
merge_clusters = merge_clusters[1:] # quit first value = 0
this_cluster = merge_clusters[0]
cluster_graph_data[i_, j_, k_] = this_cluster
for cluster_to_merge in merge_clusters[1:]:
cluster_graph_data[cluster_graph_data == cluster_to_merge] = this_cluster
else:
this_cluster = cluster_graph_data[i_, j_, k_]
# find neighbours and give cluster_number
if time_slice[i_ - 1, j_, k_] == 1:
cluster_graph_data[i_ - 1, j_, k_] = this_cluster
elif time_slice[i_ + 1, j_, k_] == 1:
cluster_graph_data[i_ + 1, j_, k_] = this_cluster
elif time_slice[i_, j_ - 1, k_] == 1:
cluster_graph_data[i_, j_ - 1, k_] = this_cluster
elif time_slice[i_, j_ + 1, k_] == 1:
cluster_graph_data[i_, j_ + 1, k_] = this_cluster
elif time_slice[i_, j_, k_ - 1] == 1:
cluster_graph_data[i_, j_, k_ - 1] = this_cluster
elif time_slice[i_, j_, k_ + 1] == 1:
cluster_graph_data[i_, j_, k_ + 1] = this_cluster
elif time_slice[i_ - 2, j_, k_] == 1:
cluster_graph_data[i_ - 1, j_, k_] = this_cluster
elif time_slice[i_ + 2, j_, k_] == 1:
cluster_graph_data[i_ + 1, j_, k_] = this_cluster
elif time_slice[i_, j_ - 2, k_] == 1:
cluster_graph_data[i_, j_ - 1, k_] = this_cluster
elif time_slice[i_, j_ + 2, k_] == 1:
cluster_graph_data[i_, j_ + 1, k_] = this_cluster
elif time_slice[i_, j_, k_ - 2] == 1:
cluster_graph_data[i_, j_, k_ - 1] = this_cluster
elif time_slice[i_, j_, k_ + 2] == 1:
cluster_graph_data[i_, j_, k_ + 1] = this_cluster
# find neighbours and give this_cluster
# if not == 1¡, keep the search
# if not neighbours, keep the search
cluster_graph_data_total[:, :, :, t_] = cluster_graph_data
img_new = nb.Nifti1Image(cluster_graph_data_total, header=img.get_header(), affine=img.get_affine())
# Reconstruct the 4D volume
cluster_graph_img = os.path.join(os.getcwd(), in_file[:-7] + "cluster_2N.nii.gz")
img_new.to_filename(cluster_graph_img)
return cluster_graph_img
def cluster_detection(in_file):
"""
Detects clusters after Point Processing a Brain
as described in http://journal.frontiersin.org/article/10.3389/fphys.2012.00015/abstract
Parameters
----------
in_file : 4D Nifti file
Returns
-------
cluster_graph_img : 4D Nifti file with an id for each cluster in each timestep
"""
import numpy as np
import os
import nibabel as nb
from series_mod import point_process
# Treat fMRI image
img = nb.load(in_file)
data = img.get_data()
(n_x, n_y, n_z, n_t) = data.shape
# Get the PP data
pp_data = np.zeros((n_x, n_y, n_z, n_t))
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
voxel_data = data[i_, j_, k_, :]
pp_data[i_, j_, k_, :] = point_process(voxel_data)
cluster_graph_data_total = np.zeros((n_x, n_y, n_z, n_t))
for t_ in range(n_t):
time_slice = pp_data[:, :, :, t_]
cluster_graph_data = np.zeros((n_x, n_y, n_z))
cluster_number = 1
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
if time_slice[i_, j_, k_] == 1: # is active, check if it has active neighboours
if (
time_slice[i_ - 1, j_, k_]
or time_slice[i_ + 1, j_, k_]
or time_slice[i_, j_ - 1, k_]
or time_slice[i_, j_ + 1, k_]
or time_slice[i_, j_, k_ - 1]
or time_slice[i_, j_, k_ + 1]
):
if cluster_graph_data[i_, j_, k_] == 0: # if is not in any previous cluster
this_cluster = (
cluster_graph_data[i_ - 1, j_, k_]
or cluster_graph_data[i_ + 1, j_, k_]
or cluster_graph_data[i_, j_ - 1, k_]
or cluster_graph_data[i_, j_ + 1, k_]
or cluster_graph_data[i_, j_, k_ - 1]
or cluster_graph_data[i_, j_, k_ + 1]
)
if this_cluster == 0: # no neighbours in any previous cluster neither
this_cluster = cluster_number
cluster_graph_data[i_, j_, k_] = this_cluster
cluster_number = cluster_number + 1
else:
# check cluster union
merge_clusters = np.unique(
[
cluster_graph_data[i_ - 1, j_, k_],
cluster_graph_data[i_ + 1, j_, k_],
cluster_graph_data[i_, j_ - 1, k_],
cluster_graph_data[i_, j_ + 1, k_],
cluster_graph_data[i_, j_, k_ - 1],
cluster_graph_data[i_, j_, k_ + 1],
]
)
merge_clusters = merge_clusters[1:] # quit first value = 0
this_cluster = merge_clusters[0]
cluster_graph_data[i_, j_, k_] = this_cluster
for cluster_to_merge in merge_clusters[1:]:
cluster_graph_data[cluster_graph_data == cluster_to_merge] = this_cluster
else:
this_cluster = cluster_graph_data[i_, j_, k_]
# find neighbours and give cluster_number
if time_slice[i_ - 1, j_, k_] == 1:
cluster_graph_data[i_ - 1, j_, k_] = this_cluster
elif time_slice[i_ + 1, j_, k_] == 1:
cluster_graph_data[i_ + 1, j_, k_] = this_cluster
elif time_slice[i_, j_ - 1, k_] == 1:
cluster_graph_data[i_, j_ - 1, k_] = this_cluster
elif time_slice[i_, j_ + 1, k_] == 1:
cluster_graph_data[i_, j_ + 1, k_] = this_cluster
elif time_slice[i_, j_, k_ - 1] == 1:
cluster_graph_data[i_, j_, k_ - 1] = this_cluster
elif time_slice[i_, j_, k_ + 1] == 1:
cluster_graph_data[i_, j_, k_ + 1] = this_cluster
# find neighbours and give this_cluster
# if not == 1¡, keep the search
# if not neighbours, keep the search
cluster_graph_data_total[:, :, :, t_] = cluster_graph_data
img_new = nb.Nifti1Image(cluster_graph_data_total, header=img.get_header(), affine=img.get_affine())
# Reconstruct the 4D volume
cluster_graph_img = os.path.join(os.getcwd(), in_file[:-7] + "cluster_1N.nii.gz")
img_new.to_filename(cluster_graph_img)
return cluster_graph_img
def cond_rm(in_file, seed_location):
"""
Conditional Rate Map. Given an fMRI, extract timeseries, calculate Point Process
and then the Rate Map for each voxel given a seed
Parameters
----------
in_file : 4D Nifti file
seed_location : voxel to analyze as seed
Returns
-------
cond_rm_img : 3D Volume
"""
import numpy as np
import os
import nibabel as nb
from series_mod import point_process
# Treat fMRI image
img = nb.load(in_file)
#print img.shape
data = img.get_data()
(n_x, n_y, n_z, n_t) = data.shape
K = np.zeros((n_x, n_y, n_z))
# Extract each voxel
seed_data = data[seed_location[0], seed_location[1], seed_location[2], :]
# Extraction of PP signal
pp_seed_data = point_process(seed_data)
# Count how many extreme events happen. This is needed for later calculation of the CRM ratio
r = np.count_nonzero(pp_seed_data)
# As we have to compare the extreme events in the seed up to 2 time steps later,
# we roll the series 2 times, ensuring the 1st value = 0. It could happen that
# comparing with the target, 2 extreme events counted as 1 if seed[idx]=extreme
# event and seed[idx+2]=extreme event, but it is very unlikely to happen.
pp_seed_data_1 = np.roll(pp_seed_data, 1)
pp_seed_data_1[0] = 0
pp_seed_data_1 = np.logical_or(pp_seed_data, pp_seed_data_1)
pp_seed_data_2 = np.roll(pp_seed_data_1, 1)
pp_seed_data_2[0] = 0
pp_seed_data_2 = np.logical_or(pp_seed_data_1, pp_seed_data_2)
# example: 0100010010001000101001 => 0111011111101110111111
# Calculate each PP signal
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
target_data = data[i_, j_, k_, :]
pp_target_data = point_process(target_data)
# LOGIC AND (target/seed) and count(signal == 1), that will give you the X/r parameter [0,1]
K[i_, j_, k_] = np.count_nonzero(
np.logical_and(pp_seed_data_2, pp_target_data)) / float(r)
#create img with K values
img_new = nb.Nifti1Image(K,
header=img.get_header(),
affine=img.get_affine())
# Reconstruct the 3D volume
cond_rm_img = os.path.join(os.getcwd(), in_file[:-7] + 'cond_rm.nii.gz')
img_new.to_filename(cond_rm_img)
return cond_rm_img
def cluster_detection_mod2(in_file):
import numpy as np
import os
import nibabel as nb
from series_mod import point_process
# Treat fMRI image
img = nb.load(in_file)
data = img.get_data()
(n_x, n_y, n_z, n_t) = data.shape
# Get the PP data
pp_data = np.zeros((n_x, n_y, n_z, n_t))
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
voxel_data = data[i_, j_, k_, :]
pp_data[i_, j_, k_, :] = point_process(voxel_data)
cluster_graph_data_total = np.zeros((n_x, n_y, n_z, n_t))
for t_ in range(n_t):
time_slice = pp_data[:, :, :, t_]
cluster_graph_data = np.zeros((n_x, n_y, n_z))
cluster_number = 1
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
if time_slice[
i_, j_,
k_] == 1: # is active, check if it has active neighboours
if time_slice[i_-1,j_,k_] or time_slice[i_+1,j_,k_] \
or time_slice[i_,j_-1,k_] or time_slice[i_,j_+1,k_] \
or time_slice[i_,j_,k_-1] or time_slice[i_,j_,k_+1] \
or time_slice[i_-2,j_,k_] or time_slice[i_+2,j_,k_] \
or time_slice[i_,j_-2,k_] or time_slice[i_,j_+2,k_] \
or time_slice[i_,j_,k_-2] or time_slice[i_,j_,k_+2]:
if cluster_graph_data[
i_, j_,
k_] == 0: # if is not in any previous cluster
this_cluster = (cluster_graph_data[i_-1,j_,k_] or cluster_graph_data[i_+1,j_,k_] \
or cluster_graph_data[i_,j_-1,k_] or cluster_graph_data[i_,j_+1,k_] \
or cluster_graph_data[i_,j_,k_-1] or cluster_graph_data[i_,j_,k_+1] \
or cluster_graph_data[i_-2,j_,k_] or cluster_graph_data[i_+2,j_,k_] \
or cluster_graph_data[i_,j_-2,k_] or cluster_graph_data[i_,j_+2,k_] \
or cluster_graph_data[i_,j_,k_-2] or cluster_graph_data[i_,j_,k_+2])
if this_cluster == 0: #no neighbours in any previous cluster neither
this_cluster = cluster_number
cluster_graph_data[i_, j_,
k_] = this_cluster
cluster_number = cluster_number + 1
else:
#check cluster union
merge_clusters = np.unique([cluster_graph_data[i_-1,j_,k_], cluster_graph_data[i_+1,j_,k_] \
, cluster_graph_data[i_,j_-1,k_], cluster_graph_data[i_,j_+1,k_] \
, cluster_graph_data[i_,j_,k_-1], cluster_graph_data[i_,j_,k_+1] \
, cluster_graph_data[i_-2,j_,k_] , cluster_graph_data[i_+2,j_,k_] \
, cluster_graph_data[i_,j_-2,k_] , cluster_graph_data[i_,j_+2,k_] \
, cluster_graph_data[i_,j_,k_-2] , cluster_graph_data[i_,j_,k_+2]])
merge_clusters = merge_clusters[
1:] #quit first value = 0
this_cluster = merge_clusters[0]
cluster_graph_data[i_, j_,
k_] = this_cluster
for cluster_to_merge in merge_clusters[1:]:
cluster_graph_data[
cluster_graph_data ==
cluster_to_merge] = this_cluster
else:
this_cluster = cluster_graph_data[i_, j_, k_]
#find neighbours and give cluster_number
if time_slice[i_ - 1, j_, k_] == 1:
cluster_graph_data[i_ - 1, j_,
k_] = this_cluster
elif time_slice[i_ + 1, j_, k_] == 1:
cluster_graph_data[i_ + 1, j_,
k_] = this_cluster
elif time_slice[i_, j_ - 1, k_] == 1:
cluster_graph_data[i_, j_ - 1,
k_] = this_cluster
elif time_slice[i_, j_ + 1, k_] == 1:
cluster_graph_data[i_, j_ + 1,
k_] = this_cluster
elif time_slice[i_, j_, k_ - 1] == 1:
cluster_graph_data[i_, j_,
k_ - 1] = this_cluster
elif time_slice[i_, j_, k_ + 1] == 1:
cluster_graph_data[i_, j_,
k_ + 1] = this_cluster
elif time_slice[i_ - 2, j_, k_] == 1:
cluster_graph_data[i_ - 1, j_,
k_] = this_cluster
elif time_slice[i_ + 2, j_, k_] == 1:
cluster_graph_data[i_ + 1, j_,
k_] = this_cluster
elif time_slice[i_, j_ - 2, k_] == 1:
cluster_graph_data[i_, j_ - 1,
k_] = this_cluster
elif time_slice[i_, j_ + 2, k_] == 1:
cluster_graph_data[i_, j_ + 1,
k_] = this_cluster
elif time_slice[i_, j_, k_ - 2] == 1:
cluster_graph_data[i_, j_,
k_ - 1] = this_cluster
elif time_slice[i_, j_, k_ + 2] == 1:
cluster_graph_data[i_, j_,
k_ + 1] = this_cluster
#find neighbours and give this_cluster
# if not == 1¡, keep the search
# if not neighbours, keep the search
cluster_graph_data_total[:, :, :, t_] = cluster_graph_data
img_new = nb.Nifti1Image(cluster_graph_data_total,
header=img.get_header(),
affine=img.get_affine())
# Reconstruct the 4D volume
cluster_graph_img = os.path.join(os.getcwd(),
in_file[:-7] + 'cluster_2N.nii.gz')
img_new.to_filename(cluster_graph_img)
return cluster_graph_img
def cluster_detection(in_file):
"""
Detects clusters after Point Processing a Brain
as described in http://journal.frontiersin.org/article/10.3389/fphys.2012.00015/abstract
Parameters
----------
in_file : 4D Nifti file
Returns
-------
cluster_graph_img : 4D Nifti file with an id for each cluster in each timestep
"""
import numpy as np
import os
import nibabel as nb
from series_mod import point_process
# Treat fMRI image
img = nb.load(in_file)
data = img.get_data()
(n_x, n_y, n_z, n_t) = data.shape
# Get the PP data
pp_data = np.zeros((n_x, n_y, n_z, n_t))
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
voxel_data = data[i_, j_, k_, :]
pp_data[i_, j_, k_, :] = point_process(voxel_data)
cluster_graph_data_total = np.zeros((n_x, n_y, n_z, n_t))
for t_ in range(n_t):
time_slice = pp_data[:, :, :, t_]
cluster_graph_data = np.zeros((n_x, n_y, n_z))
cluster_number = 1
for i_ in range(n_x):
for j_ in range(n_y):
for k_ in range(n_z):
if time_slice[
i_, j_,
k_] == 1: # is active, check if it has active neighboours
if time_slice[i_-1,j_,k_] or time_slice[i_+1,j_,k_] \
or time_slice[i_,j_-1,k_] or time_slice[i_,j_+1,k_] \
or time_slice[i_,j_,k_-1] or time_slice[i_,j_,k_+1]:
if cluster_graph_data[
i_, j_,
k_] == 0: # if is not in any previous cluster
this_cluster = (cluster_graph_data[i_-1,j_,k_] or cluster_graph_data[i_+1,j_,k_] \
or cluster_graph_data[i_,j_-1,k_] or cluster_graph_data[i_,j_+1,k_] \
or cluster_graph_data[i_,j_,k_-1] or cluster_graph_data[i_,j_,k_+1])
if this_cluster == 0: #no neighbours in any previous cluster neither
this_cluster = cluster_number
cluster_graph_data[i_, j_,
k_] = this_cluster
cluster_number = cluster_number + 1
else:
#check cluster union
merge_clusters = np.unique([cluster_graph_data[i_-1,j_,k_], cluster_graph_data[i_+1,j_,k_] \
, cluster_graph_data[i_,j_-1,k_], cluster_graph_data[i_,j_+1,k_] \
, cluster_graph_data[i_,j_,k_-1], cluster_graph_data[i_,j_,k_+1]])
merge_clusters = merge_clusters[
1:] #quit first value = 0
this_cluster = merge_clusters[0]
cluster_graph_data[i_, j_,
k_] = this_cluster
for cluster_to_merge in merge_clusters[1:]:
cluster_graph_data[
cluster_graph_data ==
cluster_to_merge] = this_cluster
else:
this_cluster = cluster_graph_data[i_, j_, k_]
#find neighbours and give cluster_number
if time_slice[i_ - 1, j_, k_] == 1:
cluster_graph_data[i_ - 1, j_,
k_] = this_cluster
elif time_slice[i_ + 1, j_, k_] == 1:
cluster_graph_data[i_ + 1, j_,
k_] = this_cluster
elif time_slice[i_, j_ - 1, k_] == 1:
cluster_graph_data[i_, j_ - 1,
k_] = this_cluster
elif time_slice[i_, j_ + 1, k_] == 1:
cluster_graph_data[i_, j_ + 1,
k_] = this_cluster
elif time_slice[i_, j_, k_ - 1] == 1:
cluster_graph_data[i_, j_,
k_ - 1] = this_cluster
elif time_slice[i_, j_, k_ + 1] == 1:
cluster_graph_data[i_, j_,
k_ + 1] = this_cluster
#find neighbours and give this_cluster
# if not == 1¡, keep the search
# if not neighbours, keep the search
cluster_graph_data_total[:, :, :, t_] = cluster_graph_data
img_new = nb.Nifti1Image(cluster_graph_data_total,
header=img.get_header(),
affine=img.get_affine())
# Reconstruct the 4D volume
cluster_graph_img = os.path.join(os.getcwd(),
in_file[:-7] + 'cluster_1N.nii.gz')
img_new.to_filename(cluster_graph_img)
return cluster_graph_img