Dog brain representations of human facial expressions: Encoding happiness and differentiating specific negative expressions.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches

[1] § STAR★Methods › Method details › Experiment 2 › Quantification and statistical analysis ↔ preprocess_functions.py, lines 1524–1578 · score 0.70 · binary mask, FSL, FD, displacement, rotation, motion
[2] § STAR★Methods › Method details › Experiment 2 › Quantification and statistical analysis ↔ tools/fsf_design.py, lines 1–50 · score 0.65 · convolving, swapped, FSL, Tool, split, filtered
[3] § STAR★Methods › Method details › Experiment 1 › Quantification and statistical analysis ↔ utils.py, lines 179–313 · score 0.57 · anatomical image, FSL, kernel, smoothing, preprocessing, fMRI
[4] § Results › Experiment 1: Happy faces elicit stronger right temporal-caudate responses than neutral faces in dogs ↔ export_static.py, lines 240–315 · score 0.52 · Sylvian gyrus, brain response, caudal, temporal, radius, dog
[5] § STAR★Methods › Method details › Experiment 1 › Quantification and statistical analysis ↔ convert_coords.ipynb, lines 394–457 · score 0.51 · Dog Brain Toolkit, FSL, smoothing, atlas, fMRI

Paper

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The authors' code

Python · 1,594 lines · 72 KB · no license · 1 match

import os
import utils
import shutil
from nilearn.plotting import plot_anat, show
import ipywidgets as widgets
import utils
import os
from importlib import reload
from nilearn import plotting
from nilearn import image as nli
import nibabel as nib
import numpy as np
from ipywidgets import HBox, VBox
import numpy as np
from nilearn.plotting import plot_anat, show
import ipywidgets as widgets
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from IPython.display import display, Markdown, clear_output
# Description: Functions to preprocess fMRI data using FSL
# Author: Raul Hernandez
def bet_app(project_dict, sub_N, initial_params=None):
global data_mask1, data_mask2, data_mask3
# Create a custom colormaps
# black
black = np.array([0, 0, 0, 0]) # RGBA for black (last value is alpha)
# red
red = np.array([1, 0, 0, 1]) # RGBA for red
# blue
blue = np.array([0, 0, 1, 1]) # RGBA for blue
# yellow
yellow = np.array([1, 1, 0, 1]) # RGBA for yellow (last value is alpha)
# create a red colormap
red_cmap = ListedColormap([black, red])
# create a blue colormap
blue_cmap = ListedColormap([black, blue])
# create a yellow colormap
yellow_cmap = ListedColormap([black, yellow])
dataset = project_dict['Dataset']
session = project_dict['Session']
task = project_dict['Task']
specie = project_dict['Specie']
datafolder = project_dict['Datafolder']
# working directory
workingdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
mean_fct_file = workingdir + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
# adding session if there is one
if session != '':
mean_fct_file += '_ses-' + session
cut_mean_fct_file = mean_fct_file + '_task-' + task + '_mean_fct.nii.gz'
mask_file = mean_fct_file + '_task-' + task + '_mean_fct_mask.nii.gz'
mask_file1 = mean_fct_file + '_task-' + task + '_mean_fct_mask1.nii.gz'
mask_file2 = mean_fct_file + '_task-' + task + '_mean_fct_mask2.nii.gz'
mask_file3 = mean_fct_file + '_task-' + task + '_mean_fct_mask3.nii.gz'
mean_fct_file += '_task-' + task + '_mean_fct.nii.gz'
masked_mean_fct_file = mean_fct_file[:-7] + '_brain.nii.gz'
# load cut_mean_fct_file
img = nib.load(cut_mean_fct_file)
# Get the data from the image
data = img.get_fdata()
# if the mask files exist, load them
if os.path.exists(mask_file1):
mask_img1 = nib.load(mask_file1)
data_mask1 = mask_img1.get_fdata()
mask_img2 = nib.load(mask_file2)
data_mask2 = mask_img2.get_fdata()
mask_img3 = nib.load(mask_file3)
data_mask3 = mask_img3.get_fdata()
#params_file = mean_fct_file[:-7] + '_bet_params.txt'
#param_dict = utils.read_params_file(params_file)
# determine min and max values for each axis
maxX,maxY,maxZ = img.shape
def plot_bet(x,y,z, betx_1, bety_1, betz_1, betx_2, bety_2, betz_2, betx_3, bety_3, betz_3, plot_mask):
global data_mask1, data_mask2, data_mask3
"""
Plot slices from the sagittal, coronal, and axial views side by side.
"""
fig, axes = plt.subplots(1, 3, figsize=(8, 5))
# Sagittal
sagittal_slice = data[x, :, :]
axes[0].imshow(sagittal_slice.T, cmap='gray', origin='lower')
axes[0].scatter(bety_1,betz_1,s=200, c='red')
# plot blue only if betx_2 matches the current slice, else plot it with alpha=0.4
if betx_2 == x:
axes[0].scatter(bety_2,betz_2,s=200, c='blue')
else:
axes[0].scatter(bety_2,betz_2,s=200, c='blue', alpha=0.4)
axes[0].scatter(bety_3,betz_3,s=200, c='yellow')
axes[0].axis('off')
# display mask if plot_mask is True
if plot_mask:
# get mask1 for sagital slice
sagital_mask1 = data_mask1[x, :, :]
# plot mask1 in red
axes[0].imshow(sagital_mask1.T, cmap=red_cmap, origin='lower', alpha=0.5)
# get mask2 for sagital slice
sagital_mask2 = data_mask2[x, :, :]
# plot mask2 in blue
axes[0].imshow(sagital_mask2.T, cmap=blue_cmap, origin='lower', alpha=0.5)
# get mask3 for sagital slice
sagital_mask3 = data_mask3[x, :, :]
# plot mask3 in yellow
axes[0].imshow(sagital_mask3.T, cmap=yellow_cmap, origin='lower', alpha=0.5)
# Coronal
coronal_slice = data[:, y, :]
axes[1].imshow(coronal_slice.T, cmap='gray', origin='lower')
# plot red only if bety_1 matches the current slice, else plot it with alpha=0.4
if bety_1 == y:
axes[1].scatter(betx_1,betz_1,s=200, c='red')
else:
axes[1].scatter(betx_1,betz_1,s=200, c='red', alpha=0.4)
axes[1].scatter(betx_2,betz_2,s=200, c='blue')
axes[1].scatter(betx_3,betz_3,s=200, c='yellow')
axes[1].axis('off')
# display mask if plot_mask is True
if plot_mask:
# get mask1 for coronal slice
coronal_mask1 = data_mask1[:, y, :]
# plot mask1 in red
axes[1].imshow(coronal_mask1.T, cmap=red_cmap, origin='lower', alpha=0.5)
# get mask2 for coronal slice
coronal_mask2 = data_mask2[:, y, :]
# plot mask2 in blue
axes[1].imshow(coronal_mask2.T, cmap=blue_cmap, origin='lower', alpha=0.5)
# get mask3 for coronal slice
coronal_mask3 = data_mask3[:, y, :]
# plot mask3 in yellow
axes[1].imshow(coronal_mask3.T, cmap=yellow_cmap, origin='lower', alpha=0.5)
# Axial
axial_slice = data[:, :, z]
axes[2].imshow(axial_slice.T, cmap='gray', origin='lower')
axes[2].scatter(betx_1,bety_1,s=200, c='red')
axes[2].scatter(betx_2,bety_2,s=200, c='blue')
# plot yellow only if betz_3 matches the current slice, else plot it with alpha=0.4
if betz_3 == z:
axes[2].scatter(betx_3,bety_3,s=200, c='yellow')
else:
axes[2].scatter(betx_3,bety_3,s=200, c='yellow', alpha=0.4)
axes[2].axis('off')
# display mask if plot_mask is True
if plot_mask:
# get mask1 for axial slice
axial_mask1 = data_mask1[:, :, z]
# plot mask1 in red
axes[2].imshow(axial_mask1.T, cmap=red_cmap, origin='lower', alpha=0.5)
# get mask2 for axial slice
axial_mask2 = data_mask2[:, :, z]
# plot mask2 in blue
axes[2].imshow(axial_mask2.T, cmap=blue_cmap, origin='lower', alpha=0.5)
# get mask3 for axial slice
axial_mask3 = data_mask3[:, :, z]
# plot mask3 in yellow
axes[2].imshow(axial_mask3.T, cmap=yellow_cmap, origin='lower', alpha=0.5)
# make a tight layout
plt.tight_layout()
# make the background black
fig.patch.set_facecolor('black')
# display the plot
plt.show()
# define function to apply BET
def apply_bet_button(betx_1, bety_1, betz_1, thr_1, betx_2, bety_2, betz_2, thr_2, betx_3, bety_3, betz_3, thr_3):
global data_mask1, data_mask2, data_mask3
# determine name of params_file based on mean_fct_file
params_file = mean_fct_file[:-7] + '_bet_params.txt'
# create a dict with the parameters
param_dict = {'betx_1':betx_1, 'bety_1':bety_1, 'betz_1':betz_1, 'thr_1':thr_1,
'betx_2':betx_2, 'bety_2':bety_2, 'betz_2':betz_2, 'thr_2':thr_2,
'betx_3':betx_3, 'bety_3':bety_3, 'betz_3':betz_3, 'thr_3':thr_3,
'output_file':mask_file, 'masked_file':masked_mean_fct_file,
'mask_file1':mask_file1, 'mask_file2':mask_file2, 'mask_file3':mask_file3,
}
# save the cutting parameters
utils.write_params_file(params_file, param_dict)
print('button pressed')
command = f"./run_bet.sh {cut_mean_fct_file} {params_file}"
os.system(command)
# update button description and status
col1.children[3].description = 'Display mask'
col1.children[3].disabled = False
# Load each of the three masks
mask_img1 = nib.load(mask_file1)
data_mask1 = mask_img1.get_fdata()
mask_img2 = nib.load(mask_file2)
data_mask2 = mask_img2.get_fdata()
mask_img3 = nib.load(mask_file3)
data_mask3 = mask_img3.get_fdata()
print('Done')
# create 4 sets of sliders, one for the slice, and 3 for the spheres marking the initial place of the BET sphere
# check if mask file 1 already exist
if os.path.exists(mask_file1):
button_description = 'Display mask'
button_disabled = False
else:
button_description = 'Mask not available'
button_disabled = True
# sliders for the slice
col1 = widgets.VBox([widgets.IntSlider(min=0, max=maxX, step=1, value=np.round(maxX/2), description='X'),
widgets.IntSlider(min=0, max=maxY, step=1, value=np.round(maxY/2), description='Y'),
widgets.IntSlider(min=0, max=maxZ, step=1, value=np.round(maxZ/2), description='Z'),
widgets.ToggleButton(value=False, description=button_description, button_style='info', disabled=button_disabled),
])
# check if param_dict exists if yes, load it
if os.path.exists(mean_fct_file[:-7] + '_bet_params.txt'):
param_dict = utils.read_params_file(mean_fct_file[:-7] + '_bet_params.txt')
else:
if initial_params is not None:
param_dict = {'betx_1':np.round(maxX/2),
'bety_1':initial_params['bety_1'],
'betz_1':initial_params['betz_1'],
'thr_1':initial_params['thr_1'],
'betx_2':np.round(maxX/2),
'bety_2':initial_params['bety_2'],
'betz_2':initial_params['betz_2'],
'thr_2':initial_params['thr_2'],
'betx_3':np.round(maxX/2),
'bety_3':initial_params['bety_3'],
'betz_3':initial_params['betz_3'],
'thr_3':initial_params['thr_3'],
}
else:
param_dict = {'betx_1':np.round(maxX/2),
'bety_1':48,
'betz_1':11,
'thr_1':0.65,
'betx_2':np.round(maxX/2),
'bety_2':29,
'betz_2':11,
'thr_2':0.6,
'betx_3':np.round(maxX/2),
'bety_3':56,
'betz_3':2,
'thr_3':0.75,
}
# sliders for the spheres
# first sphere, has 4 values, x,y,z and threshold
col2 = widgets.VBox([widgets.IntSlider(min=0, max=maxX, step=1, value=param_dict['betx_1'], description='betx_1'),
widgets.IntSlider(min=0, max=maxY, step=1, value=param_dict['bety_1'], description='bety_1'),
widgets.IntSlider(min=0, max=maxZ, step=1, value=param_dict['betz_1'], description='betz_1'),
widgets.FloatSlider(min=0, max=1, step=0.05, value=param_dict['thr_1'], description='thr_1'),
])
# second sphere, has 4 values, x,y,z and threshold
col3 = widgets.VBox([widgets.IntSlider(min=0, max=maxX, step=1, value=param_dict['betx_2'], description='betx_2'),
widgets.IntSlider(min=0, max=maxY, step=1, value=param_dict['bety_2'], description='bety_2'),
widgets.IntSlider(min=0, max=maxZ, step=1, value=param_dict['betz_2'], description='betz_2'),
widgets.FloatSlider(min=0, max=1, step=0.05, value=param_dict['thr_2'], description='thr_2'),
])
# third sphere, has 4 values, x,y,z and threshold
col4 = widgets.VBox([widgets.IntSlider(min=0, max=maxX, step=1, value=param_dict['betx_3'], description='betx_3'),
widgets.IntSlider(min=0, max=maxY, step=1, value=param_dict['bety_3'], description='bety_3'),
widgets.IntSlider(min=0, max=maxZ, step=1, value=param_dict['betz_3'], description='betz_3'),
widgets.FloatSlider(min=0, max=1, step=0.05, value=param_dict['thr_3'], description='thr_3'),
])
# linkin the sliders to the function
bet_app_out = widgets.interactive_output(plot_bet, {'x':col1.children[0], 'y':col1.children[1], 'z':col1.children[2],
'betx_1':col2.children[0], 'bety_1':col2.children[1], 'betz_1':col2.children[2],
'betx_2':col3.children[0], 'bety_2':col3.children[1], 'betz_2':col3.children[2],
'betx_3':col4.children[0], 'bety_3':col4.children[1], 'betz_3':col4.children[2],
'plot_mask':col1.children[3],
})
# button to apply the BET
col5 = widgets.Button(description='Apply BET')
# setting the button to call the function
col5.on_click(lambda b: apply_bet_button(
col2.children[0].value, col2.children[1].value, col2.children[2].value, col2.children[3].value,
col3.children[0].value, col3.children[1].value, col3.children[2].value, col3.children[3].value,
col4.children[0].value, col4.children[1].value, col4.children[2].value, col4.children[3].value,
))
row1 = HBox([col1,col2,col3,col4])
row2 = HBox([col5])
bet_app_tab = VBox([row1,row2])
return bet_app_tab, bet_app_out
def crop_app(project_dict, sub_N):
def plot_slices(x, y, z, x_lim1, y_lim1, z_lim1, x_lim2, y_lim2, z_lim2):
"""
Plot slices from the sagittal, coronal, and axial views side by side.
"""
# get the max in x
maxX = data.shape[0]
# flip x_lim1 and x_lim2
x_lim1 = maxX - x_lim1
x_lim2 = maxX - x_lim2
fig, axes = plt.subplots(1, 3, figsize=(8, 5))
# Sagittal
sagittal_slice = data[x, :, :]
axes[0].imshow(sagittal_slice.T, cmap='gray', origin='lower')
axes[0].axis('off')
# Coronal
coronal_slice = data[:, y, :]
axes[1].imshow(coronal_slice.T, cmap='gray', origin='lower')
axes[1].axis('off')
# Axial
axial_slice = data[:, :, z]
axes[2].imshow(axial_slice.T, cmap='gray', origin='lower')
axes[2].axis('off')
# make a tight layout
plt.tight_layout()
# make the background black
fig.patch.set_facecolor('black')
# Plotting red lines
# plotting lines in sagital slice
axes[0].axhline(y=z_lim1, color='red', lw=2)
axes[0].axvline(x=y_lim1, color='red', lw=2)
# plotting lines in coronal slice
axes[1].axvline(x=x_lim1, color='red', lw=2)
axes[1].axhline(y=z_lim1, color='red', lw=2)
# plotting lines in axial slice
axes[2].axvline(x=x_lim1, color='red', lw=2)
axes[2].axhline(y=y_lim1, color='red', lw=2)
# Plotting blue lines
# plotting lines in sagital slice
axes[0].axhline(y=z_lim2, color='blue', lw=2)
axes[0].axvline(x=y_lim2, color='blue', lw=2)
# plotting lines in coronal slice
axes[1].axvline(x=x_lim2, color='blue', lw=2)
axes[1].axhline(y=z_lim2, color='blue', lw=2)
# plotting lines in axial slice
axes[2].axvline(x=x_lim2, color='blue', lw=2)
axes[2].axhline(y=y_lim2, color='blue', lw=2)
plt.show()
def apply_cut_button(x_lim1, y_lim1, z_lim1, x_lim2, y_lim2, z_lim2):
# determine name of params_file based on mean_fct_file
params_file = mean_fct_file[:-7] + '_cut_params.txt'
# make sure that x_lim1 is smaller than x_lim2, if not invert them
if x_lim1 > x_lim2:
x_lim1, x_lim2 = x_lim2, x_lim1
# make sure that y_lim1 is smaller than y_lim2, if not invert them
if y_lim1 > y_lim2:
y_lim1, y_lim2 = y_lim2, y_lim1
# make sure that z_lim1 is smaller than z_lim2, if not invert them
if z_lim1 > z_lim2:
z_lim1, z_lim2 = z_lim2, z_lim1
# create a dict with the cutting parameters
param_dict = {'x_lim1':x_lim1, 'y_lim1':y_lim1,
'z_lim1':z_lim1, 'x_lim2':x_lim2,
'y_lim2':y_lim2, 'z_lim2':z_lim2,
'output_file':cut_mean_fct_file,
}
# save the cutting parameters
utils.write_params_file(params_file, param_dict)
print('button pressed')
command = f"./remove_slices.sh {mean_fct_file} {params_file}"
os.system(command)
dataset = project_dict['Dataset']
session = project_dict['Session']
task = project_dict['Task']
specie = project_dict['Specie']
datafolder = project_dict['Datafolder']
# working directory
workingdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
mean_fct_file = workingdir + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
# adding session if there is one
if session != '':
mean_fct_file += '_ses-' + session
cut_mean_fct_file = mean_fct_file + '_task-' + task + '_mean_fct.nii.gz'
mean_fct_file += '_task-' + task + '_mean_fct_uncut.nii.gz'
# determine min and max values for each axis
img = nib.load(mean_fct_file)
# Get the data from the image
data = img.get_fdata()
slider_style = {'description_width': 'initial', 'width': '2px'}
maxX,maxY,maxZ = img.shape
col1 = widgets.VBox([widgets.IntSlider(min=0, max=maxX, step=1, value=np.round(maxX/2), description='X'),
widgets.IntSlider(min=0, max=maxY, step=1, value=np.round(maxY/2), description='Y'),
widgets.IntSlider(min=0, max=maxZ, step=1, value=np.round(maxZ/2), description='Z')])
# check if param_dict exists if yes, load it
if os.path.exists(mean_fct_file[:-7] + '_cut_params.txt'):
param_dict = utils.read_params_file(mean_fct_file[:-7] + '_cut_params.txt')
else:
param_dict = {'x_lim1':0,
'y_lim1':0,
'z_lim1':0,
'x_lim2':maxX,
'y_lim2':maxY,
'z_lim2':maxZ,
}
col2 = widgets.VBox([widgets.IntSlider(min=0, max=maxX-1, step=1, value=param_dict['x_lim1'], description='lim X'),
widgets.IntSlider(min=0, max=maxY-1, step=1, value=param_dict['y_lim1'], description='lim Y'),
widgets.IntSlider(min=0, max=maxZ-1, step=1, value=param_dict['z_lim1'], description='lim Z')])
col3 = widgets.VBox([widgets.IntSlider(min=0, max=maxX-1, step=1, value=param_dict['x_lim2'], description='lim X'),
widgets.IntSlider(min=0, max=maxY-1, step=1, value=param_dict['y_lim2'], description='lim Y'),
widgets.IntSlider(min=0, max=maxZ-1, step=1, value=param_dict['z_lim2'], description='lim Z')])
# col2 = widgets.VBox([widgets.IntSlider(min=0, max=maxX-1, step=1, value= maxX - int(param_dict['x_lim2']), description='lim X'),
# widgets.IntSlider(min=0, max=maxY-1, step=1, value=param_dict['y_lim1'], description='lim Y'),
# widgets.IntSlider(min=0, max=maxZ-1, step=1, value=param_dict['z_lim1'], description='lim Z')])
# col3 = widgets.VBox([widgets.IntSlider(min=0, max=maxX-1, step=1, value=maxX - int(param_dict['x_lim1']), description='lim X'),
# widgets.IntSlider(min=0, max=maxY-1, step=1, value=param_dict['y_lim2'], description='lim Y'),
# widgets.IntSlider(min=0, max=maxZ-1, step=1, value=param_dict['z_lim2'], description='lim Z')])
col4 = widgets.Button(description='Apply cut')
out = widgets.interactive_output(plot_slices, {'x':col1.children[0], 'y':col1.children[1], 'z':col1.children[2],
'x_lim1':col2.children[0], 'y_lim1':col2.children[1], 'z_lim1':col2.children[2],
'x_lim2':col3.children[0], 'y_lim2':col3.children[1], 'z_lim2':col3.children[2],
})
# setting the button to call the function
col4.on_click(lambda b: apply_cut_button(
col2.children[0].value, col2.children[1].value, col2.children[2].value,
col3.children[0].value, col3.children[1].value, col3.children[2].value,
))
tab_crop_app = VBox([HBox([col1,col2,col3]),col4])
return tab_crop_app,out
def check_job_status(job):
# This function will check if the job finished, failed or is still running
# Right now it will return 'Finished'
job_status = 'Finished'
return job_status
def run_process(job):
'''
Will select the variables from the schedule_table and the project_dict to run the process
Can run:
preprocess_run
get_mean_fct
crop_interface
bet_interface
mean_to_STD
run_to_STD
'''
# get the process to run
# get the other variables of the job
# user = job['User']
dataset = job['Dataset']
session = job['Session']
task = job['Task']
sub_N = job['sub_N']
specie = job['Specie']
process = job['Process']
datafolder = job['Datafolder']
job_status = job['Status']
combination = job['Combination']
smooth = job['Smooth']
atlas_type = job['Atlas_type']
base_run = 1
img_type = 'brain2mm'
run_prepro = job['Full_prepro']
variation = job['Variation']
session_and_run = job['session_and_run']
first_time = job['first_time']
use_anatomic = job['use_anatomic']
print(f"Running process: {process}")
# run the adecuate process
if process == 'Preprocess':
print('Running preprocess')
for run_N, session in zip(job['run_N'], job['Sessions']):
preprocess_run(
sub_N, run_N, dataset, task, specie,
datafolder, session, smooth,
combination, run_prepro)
elif process == 'Mean fct':
print('Running get_mean_fct')
runs_to_use = job['run_N']
sessions_to_use = job['Sessions']
session_and_run = job['session_and_run']
get_mean_fct(
sub_N, session_and_run, base_run, dataset,
task, specie, datafolder, first_time=first_time)
elif process == 'Mean to atlas':
print('Running mean_to_STD')
mean_to_STD(
sub_N, dataset, task, specie, datafolder,
atlas_type, img_type, variation=variation, use_anatomic=use_anatomic)
elif process == 'Runs to atlas':
print('Running run_to_STD')
print('Variation:', variation)
for run_N, session in zip(job['run_N'], job['Sessions']):
run_to_STD(
sub_N, run_N, dataset, task,
specie, datafolder, atlas_type,
img_type, session=session)
else:
print('Process not found')
def check_file_status(project_dict, sub_N, run_N, session, process, verbose=False,
model=None, dis_method=None, rsa_method=None, radius=None, rsa_model=None,
stim_N=None, stim_1_name=None, stim_2_name=None, stim_types=None,
reps=None, reps_group=None):
'''
Check which files are available for the process
returns True if the files are available, False if not
'''
# print the inputs
# print('Sub:', sub_N, 'Run:', run_N, 'Session:', session, 'Process:', process)
# get specie from the project_dict
specie = project_dict['Specie']
# get dataset from the project_dict
dataset = project_dict['Dataset']
# get task from the project_dict
task = project_dict['Task']
# get datafolder from the project_dict
datafolder = project_dict['Datafolder']
if process == 'preprocess_run': # check if the preprocess files exist
# create the filename
if session != '':
session = int(session)
filename = (datafolder + os.sep + dataset + os.sep + 'BIDS' + os.sep +
specie +'-sub-' + str(sub_N).zfill(2) + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + str(session).zfill(2) +
'_task-' + task +
'_run-' + str(run_N).zfill(2) +
'_bold.nii.gz')
filename_json = filename[:-7] + '.json'
# check if filename and filename_json exist
if os.path.exists(filename):
if verbose:
print('BIDS file exists: ' + filename)
if os.path.exists(filename_json):
if verbose:
print('json file exists: ' + filename_json)
return True, filename
else:
if verbose:
print('BIDS file does not exist: ' + filename)
return False, filename
elif process == 'get_mean_fct':
outputdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
filename = specie + '-sub-' + str(sub_N).zfill(2)
# adding session if there is one
if session != '':
session = int(session)
filename += '_ses-' + f"{session:02d}"
filename += '_task-' + task + '_run-' + str(run_N).zfill(2) + '_reoriented.nii.gz'
# check if the file exists
if os.path.exists(outputdir + os.sep + filename):
if verbose:
print('Reoriented file exists: ' + filename)
return True, filename
else:
if verbose:
print('Reoriented file does not exist: ' + filename)
return False, filename
elif process == 'mean_to_STD':
# check if the mean file exists
mean_fct_file = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
# adding session if there is one
mean_fct_file += '_task-' + task + '_mean_fct_brain.nii.gz'
# check if the file exists
if os.path.exists(mean_fct_file):
print('Mean functional file exists: ' + mean_fct_file)
return True, mean_fct_file
else:
print('Mean functional file does not exist: ' + mean_fct_file)
return False, mean_fct_file
elif process == 'run_to_STD':
preprocess_dir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
filename = (specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + session +
'_task-' + task +
'_run-' + str(run_N).zfill(2)
)
preprocessed_file = filename + '_mc.nii.gz'
# check if the file exists
if os.path.exists(preprocess_dir + os.sep + preprocessed_file):
if verbose:
print('Motion corrected file exists: ' + preprocessed_file)
return True, preprocess_dir + os.sep + preprocessed_file
else:
if verbose:
print('Motion corrected file does not exist: ' + preprocess_dir + os.sep + preprocessed_file)
return False, preprocess_dir + os.sep + preprocessed_file
elif process == 'BOLD': # check if the normalized BOLD file exist
# if session is int convert to str with 2 digits
if isinstance(session, int):
session = str(session).zfill(2)
filename = (datafolder + os.sep + dataset + os.sep + 'normalized' + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + session +
'_task-' + task +
'_run-' + str(run_N).zfill(2) +
'.nii.gz')
# check if filename exists
if os.path.exists(filename):
if verbose:
print('Normalized BOLD file exists: ' + filename)
return True, filename
else:
if verbose:
print('BOLD file does not exist: ' + filename)
return False, filename
elif process == 'feat_folder': # check if the first level GLM file exist
# model = 'basic'
# "P:\userdata\raulh87\data\EmoB\results\GLM\basic\D-sub-01\ses-01_task-EmoB_run-01.feat\stats\tstat1.nii.gz"
# if session is int convert to str with 2 digits
# check that all variables are available, if not indicate which is missing
if model is None: # model is required
print('Model is missing')
return False, 'error in model'
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'GLM' + os.sep +
model + os.sep + f"{specie}-sub-{sub_N:02d}" + os.sep + f"ses-{session}_task-{task}_run-{run_N:02d}.feat")
# check if filename exist
if os.path.exists(filename):
if verbose:
print('feat folder exists: ' + filename)
return True, filename
else:
if verbose:
print('feat folder does not exist: ' + filename)
return False, filename
# NOTE: the 'beta_map'/'beta_maps' branches below probe the *legacy* FEAT
# layout (.feat/stats/pe*). They predate step 0.5 and have no callers today
# (both check_file_status callers pass process='GLM'). If you revive them,
# route through rsa_utils.resolve_beta_map instead -- these paths report
# "missing" for any run whose .feat has been deleted after step 0.5.
# They are not fixed in place because rsa_utils imports this module, so
# importing it back here would be circular.
elif process == 'beta_map': # check if the beta map exist
# build the filename for the beta map
# make sure that all variables are available, if not indicate which is missing
list_vars = [model, stim_N]
list_vars_name = ['model', 'stim_N']
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
# build the filename for the beta map
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'GLM' + os.sep +
model + os.sep + f"{specie}-sub-{sub_N:02d}" + os.sep +
f"ses-{session}_task-{task}_run-{run_N:02d}.feat" + os.sep + "stats" + os.sep + f"pe{stim_N*2 - 1}.nii.gz")
# check if filename exist
if os.path.exists(filename):
if verbose:
print('beta map exists: ' + filename)
return True, filename
else:
if verbose:
print('beta map does not exist: ' + filename)
return False, filename
elif process == 'beta_maps': # check if the beta map exist
# build the filename for the beta map
# make sure that all variables are available, if not indicate which is missing
list_vars = [model, stim_types]
list_vars_name = ['model', 'stim_types']
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
# initialize a list to store missing files
files_missing, files_found = [], []
for stim_Nx,_ in enumerate(stim_types):
# starts at 1 and jumps of 2
stim_N = stim_Nx + 1
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'GLM' + os.sep +
model + os.sep + f"{specie}-sub-{sub_N:02d}" + os.sep +
f"ses-{session}_task-{task}_run-{run_N:02d}.feat" + os.sep + "stats" + os.sep + f"pe{stim_N*2 - 1}.nii.gz")
# add to files_found if exist
if os.path.exists(filename):
files_found.append(filename)
else:
files_missing.append(filename)
if len(files_missing) == 0:
if verbose:
print('All beta maps exist')
# return true and a list of all filenames
return True, files_found
else:
if verbose:
print('Some beta maps are missing')
print('Missing files:', files_missing)
return False, files_missing
elif process == 'pairwise_similarity_maps':
# build the filename for the pairwise similarity map
# make sure that all variables are available, if not indicate which is missing
list_vars = [model, dis_method, radius, stim_types]
list_vars_name = ['model', 'dis_method', 'radius', 'stim_types']
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
# initialize a list to store missing files
files_missing, files_found = [], []
# check if "P:\userdata\raulh87\data\EmoB\results\RSA\basic-block\D-sub-01\ses-01_task-EmoB_run-01\r-3_correlation_A-1_A-2.nii.gz"
for stim_1_N, stim_1_name in enumerate(stim_types):
for stim_2_N, stim_2_name in enumerate(stim_types):
if stim_2_N > stim_1_N: # only check for upper triangle
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'RSA' + os.sep +
model + os.sep + f"{specie}-sub-{sub_N:02d}" + os.sep +
f"ses-{session}_task-{task}_run-{run_N:02d}" + os.sep +
f"r-{radius}_{dis_method}_{stim_1_name}_{stim_2_name}.nii.gz")
# add to files_found if exist
if os.path.exists(filename):
if verbose:
print('Exists: ' + filename + 'adding...')
files_found.append(filename)
else:
if verbose:
print('Missing: ' + filename + 'adding to missing...')
files_missing.append(filename)
if len(files_missing) == 0:
if verbose:
print('All pairwise similarity maps exist')
# return true and a list of all filenames
return True, files_found
else:
if verbose:
print('Some pairwise similarity maps are missing')
print('Missing files:', files_missing)
return False, files_missing
elif process == 'pairwise_similarity_map':
# build the filename for the pairwise similarity map
# make sure that all variables are available, if not indicate which is missing
list_vars = [model, dis_method, radius, stim_1_name, stim_2_name]
list_vars_name = ['model', 'dis_method', 'radius', 'stim_1_name', 'stim_2_name']
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
# build the filename for the pairwise similarity map
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'RSA' + os.sep +
model + os.sep + f"{specie}-sub-{sub_N:02d}" + os.sep +
f"ses-{session}_task-{task}_run-{run_N:02d}" + os.sep +
f"r-{radius}_{dis_method}_{stim_1_name}_{stim_2_name}.nii.gz")
# check if filename exist
if os.path.exists(filename):
if verbose:
print('pairwise similarity map exists: ' + filename)
return True, filename
else:
if verbose:
print('pairwise similarity map does not exist: ' + filename)
return False, filename
elif process == 'model_similarity_map':
# build the filename for the model similarity map
# make sure that all variables are available, if not indicate which is missing
list_vars = [model, dis_method, rsa_method, radius, rsa_model]
list_vars_name = ['model', 'dis_method', 'rsa_method', 'radius', 'rsa_model'
]
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'RSA' + os.sep +
model + os.sep + rsa_model + os.sep + f"{specie}-sub-{sub_N:02d}" + os.sep + f"ses-{session}_task-{task}_run-{run_N:02d}" + os.sep +
f"r-{radius}_{dis_method}_{rsa_method}.nii.gz")
# check if filename exist
if os.path.exists(filename):
if verbose:
print('model similarity map exists: ' + filename)
return True, filename
else:
if verbose:
print('model similarity map does not exist: ' + filename)
return False, filename
# write check for beta map
elif process == 'mean_model_similarity_map':
# build the filename for the mean model similarity map
# make sure that all variables are available, if not indicate which is missing
list_vars = [model, dis_method, rsa_method, radius, rsa_model]
list_vars_name = ['model', 'dis_method', 'rsa_method', 'radius', 'rsa_model'
]
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'RSA' + os.sep +
model + os.sep + rsa_model + os.sep + 'mean' + os.sep +
f"r-{radius}_{dis_method}_{rsa_method}_mean.nii.gz")
# check if filename exist
if os.path.exists(filename):
if verbose:
print('mean model similarity map exists: ' + filename)
return True, filename
else:
if verbose:
print('mean model similarity map does not exist: ' + filename)
return False, filename
elif process == 'model_similarity_maps_rnd':
# checks if the permutations have been done:
# true, files_found. If all files exist, false if one or more files are missing
# false, missing_files. If one or more files are missing, list of missing files
# make sure that all variables are available, if not indicate which is missing
# reps - number of by participant repetitions
list_vars = [model, dis_method, rsa_method, radius, rsa_model, reps]
list_vars_name = ['model', 'dis_method', 'rsa_method', 'radius', 'rsa_model'
'reps']
# check that all variables are available, if not indicate which is missing
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
# initialize a list to store missing files
files_missing, files_found, file_num_missing = [], [], []
# "P:\userdata\raulh87\data\EmoB\results\RSA_rnd\basic\emotion_valence\D-sub-01\ses-01_task-EmoB_run-01\r-3_pearson_kendall_0000.nii.gz"
for rep in range(reps):
filename = (datafolder + os.sep + dataset + os.sep + 'results' + os.sep + 'RSA_rnd' + os.sep +
model + os.sep + rsa_model + os.sep + f"{specie}-sub-{sub_N:02d}" + os.sep + f"ses-{session}_task-{task}_run-{run_N:02d}" + os.sep +
f"r-{radius}_{dis_method}_{rsa_method}_{str(rep).zfill(4)}.nii.gz")
# add to files_found if exist
if os.path.exists(filename):
files_found.append(filename)
else:
files_missing.append(filename)
file_num_missing.append(rep)
if len(files_missing) == 0:
if verbose:
print('All model similarity rnd maps exist')
# return true and a list of all filenames
return True, files_found
else:
if verbose:
print('Some model similarity rnd maps are missing')
print('Missing files:', files_missing)
return False, file_num_missing
elif process == 'mean_model_similarity_maps_rnd':
# checks if the permutations have been done:
# true, files_found. If all files exist, false if one or more files are missing
# false, missing_files. If one or more files are missing, list of missing files
# make sure that all variables are available, if not indicate which is missing
# reps - number of by participant repetitions
list_vars = [model, dis_method, rsa_method, radius, rsa_model, reps, reps_group]
list_vars_name = ['model', 'dis_method', 'rsa_method', 'radius', 'rsa_model',
'reps_group']
# check that all variables are available, if not indicate which is missing
for var, var_name in zip(list_vars, list_vars_name):
if var is None:
print(f'{var_name} is missing')
return False, f'error in {var_name}'
# initialize a list to store missing files
files_missing, files_found, file_num_missing = [], [], []
# "P:\userdata\raulh87\data\EmoB\results\RSA_rnd\basic\emotion_valence\r-r-3_pearson_kendall_mean_07181.nii.gz"
for g_rep in range(reps_group):
filename = (datafolder + os.sep + dataset + os.sep +
'results' + os.sep + 'RSA_rnd' + os.sep +
model + os.sep + rsa_model + os.sep +
'r-' + str(radius) + '_' + dis_method + '_' + rsa_method + '_mean_' + str(g_rep).zfill(len(str(reps_group))) + '.nii.gz')
# add to files_found if exist
if os.path.exists(filename):
files_found.append(filename)
else:
files_missing.append(filename)
file_num_missing.append(rep)
if len(files_missing) == 0:
if verbose:
print('All model similarity rnd maps exist')
# return true and a list of all filenames
return True, files_found
else:
if verbose:
print('Some model similarity rnd maps are missing')
print('Missing files:', files_missing)
return False, file_num_missing
elif process == 'get_vectors':
# "P:\userdata\raulh87\data\EmoB\BIDS\sub-01\sub-01_ses-01_task-EmoB_run-01_events.csv"
# if session is int convert to str with 2 digits
if isinstance(session, int):
session = str(session).zfill(2)
# check if the events file exists
filename = (datafolder + os.sep + dataset + os.sep + 'BIDS' + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + session +
'_task-' + task +
'_run-' + str(run_N).zfill(2) + '_events.csv')
# check if the file exists
if os.path.exists(filename):
if verbose:
print('File exists: ' + filename)
return True, filename
else:
if verbose:
print('File does not exist: ' + filename)
return False, filename
else:
# print process (process) not found
print('Process not found: ' + process)
return False, 'error in process'
def preprocess_run(sub_N, run_N, dataset, task, specie, datafolder, session, smooth=0, combination=['-x','z','-y'], run_prepro=True):
"""
Preprocesses a single run of a single subject.
Reorients file
"""
## determine input file and output directory ##
# input directory in BIDS format
input_folder = datafolder + os.sep + dataset + os.sep + 'BIDS' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
filename = input_folder + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_ses-' + session + '_task-' + task + '_run-' + str(run_N).zfill(2) + '_bold.nii.gz'
filename_json = input_folder + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_ses-' + session + '_task-' + task + '_run-' + str(run_N).zfill(2) + '_bold.json'
# print filename
print('Input file: ' + filename)
# get TR and number of volumes
TR,volumes = utils.extract_params(filename)
# create output directory, where the fsl output will be saved (preprocessed data)
outputdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
fsl_outputdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_ses-' + session + '_task-' + task + '_run-' + str(run_N).zfill(2)
slice_timming_path = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + 'slice_timming_' + specie + '-sub-' + str(sub_N).zfill(2) + '_ses-' + session + '_task-' + task + '_run-' + str(run_N).zfill(2) + '.txt'
# check that slice_timming_path folder exist
if not os.path.exists(os.path.dirname(slice_timming_path)):
os.makedirs(os.path.dirname(slice_timming_path))
# get slice timing parameters
slice_timming = utils.get_slice_timing(filename_json, slice_timming_path)
# make sure the file was created
if not os.path.exists(slice_timming_path):
print('Slice timing file was not created: ' + slice_timming_path)
return
# check if slice_timming is not empty
if slice_timming is None:
print('No slice timing parameters found')
print('File: ' + slice_timming_path + ' empty')
return
## Filling out the design.fsf file ##
# create list of labels to fill in the design.fsf file
label_list = ['Outputdir', 'TR', 'Volumes', 'BET', 'Smooth', 'Input', 'SliceTimming']
# create dictionary to fill in the design.fsf file
to_fill_dict = dict()
for label in label_list:
to_fill_dict[label] = dict()
if label == 'Outputdir':
to_fill_dict[label]['string_to_find'] = 'set fmri(outputdir)'
to_fill_dict[label]['string_to_replace'] = ('set fmri(outputdir) "' + fsl_outputdir + '"')
elif label == 'TR':
to_fill_dict[label]['string_to_find'] = 'set fmri(tr)'
to_fill_dict[label]['string_to_replace'] = ('set fmri(tr) ' + str(TR))
elif label == 'Volumes':
to_fill_dict[label]['string_to_find'] = 'set fmri(npts)'
to_fill_dict[label]['string_to_replace'] = ('set fmri(npts) ' + str(volumes))
elif label == 'BET':
to_fill_dict[label]['string_to_find'] = 'set fmri(bet_yn)'
if specie == 'H':
to_fill_dict[label]['string_to_replace'] = ('set fmri(bet_yn) 1')
elif specie == 'D':
to_fill_dict[label]['string_to_replace'] = ('set fmri(bet_yn) 0')
elif label == 'Smooth':
to_fill_dict[label]['string_to_find'] = 'set fmri(smooth)'
to_fill_dict[label]['string_to_replace'] = ('set fmri(smooth) ' + str(smooth))
elif label == 'Input':
to_fill_dict[label]['string_to_find'] = 'set feat_files(1)'
to_fill_dict[label]['string_to_replace'] = ('set feat_files(1) "' + filename + '"')
elif label == 'SliceTimming':
to_fill_dict[label]['string_to_find'] = 'set fmri(st_file)'
to_fill_dict[label]['string_to_replace'] = ('set fmri(st_file) "' + slice_timming_path + '"')
# fill in the design.fsf file
design_path = os.path.join(os.getcwd(), 'FSL_designs' + os.sep + 'preprocess_slice_timming_from_JSON.fsf')
# design_path = os.path.join(os.getcwd(), 'FSL_designs' + os.sep + 'preprocess_no-slice-timing.fsf')
print('Design path: ' + design_path)
design_modified_path = os.path.join(os.getcwd(), 'FSL_designs' + os.sep + 'preprocess_modified.fsf')
if run_prepro:
utils.fill_fsf(to_fill_dict, design_path, design_modified_path)
# check if previous feat preprocessing directory exists, if so, delete it
if os.path.exists(fsl_outputdir + '.feat'):
shutil.rmtree(fsl_outputdir + '.feat')
# run feat
command = 'feat ' + design_modified_path
# check if system is windows, if so, do not execute command
print(command)
if os.name == 'nt':
print("The system is windows, command not executed.")
elif os.name == 'posix':
os.system(command)
else:
os.system(command)
else:
print('Preprocessing not run, skipping this step')
## reorient run ##
base_filename =(
specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + session +
'_task-' + task +
'_run-' + str(run_N).zfill(2))
# non-oriented file
non_oriented_file = base_filename + '_not-oriented.nii.gz'
# oriented file
reoriented_file = base_filename + '_reoriented.nii.gz'
preprocessed_file = fsl_outputdir + '.feat' + os.sep + 'filtered_func_data.nii.gz'
# check if the system is windows
if os.name == 'nt':
print('A copy should have been created, but this is Windows')
print(non_oriented_file + ' a copy of this file here:')
print('FSL output directory: ' + fsl_outputdir)
else:
#copy preprocessed_file to non_oriented_file
shutil.copyfile(preprocessed_file, outputdir + os.sep + non_oriented_file)
print(non_oriented_file + ' created')
print('FSL output directory: ' + fsl_outputdir)
# check if system is windows, if so, do not execute command
if os.name == 'nt': # Windows
print("system is windows, command not executed.")
print("orientation to use: ",combination)
print("non-oriented file: " + outputdir + os.sep + non_oriented_file)
print("oriented file: " + outputdir + os.sep + reoriented_file)
utils.reorient_file(outputdir + os.sep + non_oriented_file, outputdir + os.sep + reoriented_file, combination)
def get_mean_fct(sub_N, session_and_run, base_run, dataset, task, specie, datafolder, first_time=True):
"""
Calculates the mean functional image for a subject and a task.
The mean functional image is calculated by averaging the mean images of each run.
The mean image of each run is calculated by averaging all volumes of the run.
The mean image of each run is calculated by averaging all volumes of the run.
The motion is corrected for each run using the first volume of the first run as reference.
The motion parameters are saved in a .par file.
The mean image of each run
"""
# Check if the system is windows
if os.name == 'nt':
print('The system is Windows, this is a test, no actual system or FSL commands will be run')
# output directory where the fsl output will be saved (preprocessed data)
outputdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
# movement directory
movementdir = datafolder + os.sep + dataset + os.sep + 'movement'
# create movement directory if it does not exist
if not os.path.exists(movementdir):
os.makedirs(movementdir)
# get initial run from session_and_run[0] = ['ses-01_run-1', 'ses-02_run-2']
initial_run = int(session_and_run[0].split('_')[1].split('-')[1])
initial_session = session_and_run[0].split('_')[0].split('-')[1]
## obtain volume to be used as base to correct all others ##
filename = (specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + initial_session +
'_task-' + task +
'_run-' + str(initial_run).zfill(2) +
'_reoriented.nii.gz')
if first_time: # get the volume
# get the first volume of the first run to use as base volume
command = f"fslroi {outputdir + os.sep + filename} {outputdir + os.sep + 'base_vol.nii.gz'} 0 1"
# print commmand
print(command)
# if the system is not windows, run the command
if os.name != 'nt':
os.system(command)
else:
# will use average created before
print('Using existing base volume')
# check if base_vol exists
if not os.path.exists(outputdir + os.sep + 'base_vol.nii.gz'):
print('base_vol.nii.gz does not exist, run the code with first_time = True')
print('path: ' + outputdir + os.sep + 'base_vol.nii.gz')
raise ValueError('base_vol.nii.gz does not exist, run the code with first_time = True')
## ----- ##
# This string will be used to generate the mean image
mean_images = ''
## calculate motion for each run and generate par file ##
for n, file_ending in enumerate(session_and_run):
print('processing ' + file_ending)
session = file_ending.split('_')[0].split('-')[1]
run_N = int(file_ending.split('_')[1].split('-')[1])
print('processing ' + str(n+1) + ' of ' + str(len(session_and_run)) + ' runs')
filename = (specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + session +
'_task-' + task +
'_run-' + str(run_N).zfill(2))
# '_reoriented.nii.gz')
# average the reoriented file to create a mean image
print('calculating mean image...')
command = f"fslmaths {outputdir + os.sep + filename + '_reoriented.nii.gz'} -Tmean {outputdir + os.sep + filename + '_mean_unaligned.nii.gz'}"
# print commmand
print(command)
# if the system is not windows, run the command
if os.name != 'nt':
os.system(command)
# command to calculate transformation matrix from mean image to base volume
command = f"flirt -in {outputdir + os.sep + filename + '_mean_unaligned.nii.gz'} -ref {outputdir + os.sep + 'base_vol.nii.gz'} -out {outputdir + os.sep + filename + '_mean_aligned_tmp.nii.gz'} -omat {outputdir + os.sep + filename + '2base_vol.mat'}"
# print commmand
print(command)
if os.name != 'nt':
os.system(command)
# apply the transformation matrix to the 4D file
command = f"applyxfm4D {outputdir + os.sep + filename + '_reoriented.nii.gz'} {outputdir + os.sep + 'base_vol.nii.gz'} {outputdir + os.sep + filename + '_mc.nii.gz'} {outputdir + os.sep + filename + '2base_vol.mat'} -singlematrix"
# print commmand
print(command)
# if the system is not windows, run the command
if os.name != 'nt':
os.system(command)
# apply the transformation matrix to the file
print('calculating mean image...')
command = f"fslmaths {outputdir + os.sep + filename + '_mc.nii.gz'} -Tmean {outputdir + os.sep + filename + '_mean.nii.gz'}"
# print commmand
print(command)
# if the system is not windows, run the command
if os.name != 'nt':
os.system(command)
# if the system is not windows, run the command
# print file saved
print('aligned file saved as ' + filename + '_mc.nii.gz')
# add filename to mean_images
mean_images += outputdir + os.sep + filename + '_mean.nii.gz' + ' '
if first_time: # if yes, calculate mean image
mean_fct_file = (outputdir + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) +
'_task-' + task + '_mean_fct_uncut.nii.gz'
)
# append mean images to a single 4D image
command = f"fslmerge -t {mean_fct_file} {mean_images}"
# print commmand
print(command)
# if the system is not windows, run the command
if os.name != 'nt':
os.system(command)
print('mean fct file saved as ' + mean_fct_file)
# calculate mean image
command = f"fslmaths {mean_fct_file} -Tmean {mean_fct_file}"
# print commmand
print(command)
# if the system is not windows, run the command
if os.name != 'nt':
os.system(command)
print('done')
def mean_to_STD(sub_N, dataset, task, specie, datafolder, atlas_type, img_type='brain2mm', variation='STD0', use_anatomic=False):
"""
This function will take the mean functional image of a subject and transform it to the space of the atlas.
sub_N: subject number
dataset: dataset name
task: task name
specie: specie name
datafolder: path to the data folder
atlas_type: type of atlas to use
img_type: type of image to use, default is 'brain2mm'
"""
# working directory
workingdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
mean_fct_file = workingdir + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
cut_mean_fct_file = mean_fct_file + '_task-' + task + '_mean_fct.nii.gz'
mean_fct_file += '_task-' + task + '_mean_fct.nii.gz'
masked_mean_fct_file = mean_fct_file[:-7] + '_brain.nii.gz'
mean_fct_file_STD = mean_fct_file[:-7] + '_STD.nii.gz'
mean_fct2STD_mat = mean_fct_file[:-7] + '2STD.mat'
#T1w_brain_file = datafolder + os.sep + dataset + os.sep + 'BIDS' + os.sep + 'sub-' + str(sub_N).zfill(2) + os.sep + 'sub-' + str(sub_N).zfill(2) + '_T1w_brain.nii.gz'
T1w_brain_file = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_T1w_brain.nii.gz'
T1w_brain_file_unoriented = datafolder + os.sep + dataset + os.sep + 'BIDS' + os.sep + 'sub-' + str(sub_N).zfill(2) + os.sep + 'sub-' + str(sub_N).zfill(2) + '_T1w_brain.nii.gz'
T1w_brain_STD_file = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_T1w_brain_STD.nii.gz'
T1w_brain2STD_mat = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + specie + '-sub-' + str(sub_N).zfill(2)+ '_T1w_brain2STD.mat'
mean_fct_T1w_file = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + specie + '-sub-' + str(sub_N).zfill(2)+ '_mean_fct_T1w.nii.gz'
mean_fct2T1w_mat = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + os.sep + specie + '-sub-' + str(sub_N).zfill(2)+ '_mean_fct2T1w.mat'
if specie == 'D':
specieS = 'Dog'
elif specie == 'H':
specieS = 'Hum'
# generate path to atlas. The atlas is in the same folder as the script
atlas_file = os.getcwd() + os.sep + "Atlas" + os.sep + specieS + os.sep + atlas_type + os.sep + img_type + ".nii.gz"
# check which variation to use
# A - mean
# B - anatomic
# C - STD
if use_anatomic:
# mean_to_anatomic(sub_N, dataset, task, specie, datafolder) # A -> B
# anatomic_to_STD(sub_N, dataset, task, specie, datafolder, atlas_type, img_type=img_type) # B -> C
# apply fslreorient2std to the T1w_brain file
print('Reorienting T1w_brain file to standard orientation')
command = f"fslreorient2std {T1w_brain_file_unoriented} {T1w_brain_file}"
print(command)
#if system is not windows, run the command
if os.name != 'nt':
os.system(command)
print('Transforming masked mean functional image to T1w_brain A -> B')
# masked_mean_fct to T1w_brain A -> B
command = f"flirt -in {masked_mean_fct_file} -ref {T1w_brain_file} -out {mean_fct_T1w_file} -omat {mean_fct2T1w_mat} -bins 256 -cost corratio -searchrx -30 30 -searchry -30 30 -searchrz -30 30 -dof 7 -interp trilinear"
# if the system is windows, don't run the command, just write it down
print(command)
if os.name == 'nt': # Windows
print("System is Windows, command not executed")
else:
os.system(command)
# T1w_brain to atlas B -> C T1w_brain_STD_file
print('Transforming T1w_brain to atlas space B -> C')
command = f"flirt -in {T1w_brain_file} -ref {atlas_file} -out {T1w_brain_STD_file} -omat {T1w_brain2STD_mat} -bins 256 -cost corratio -searchrx -45 45 -searchry -45 45 -searchrz -45 45 -dof 12 -interp trilinear"
# flirt -in ${dataFolder}/data/${sub}/masks/${brain_nMeanfct}.nii.gz -ref ${atlasFile} -out ${dataFolder}/data/${sub}/masks/brainSTD.nii.gz -omat ${dataFolder}/data/${sub}/masks/brain2STD.mat -bins 256 -cost corratio -searchrx -45 45 -searchry -45 45 -searchrz -45 45 -dof 12 -interp trilinear
print(command)
if os.name != 'nt': # Windows
os.system(command)
print('Calculating transformation matrix from mean functional image to atlas A->B, B->C = A->C')
# adding the matrices... #A->B + B->C = A->C
command = f"convert_xfm -omat {mean_fct2STD_mat} -concat {T1w_brain2STD_mat} {mean_fct2T1w_mat}"
print(command)
if os.name == 'nt': # Windows
print("System is Windows, command not executed")
else:
os.system(command)
# apply the transformation matrix to the mean functional image
print('Applying transformation matrix to masked mean functional image')
command = f"flirt -in {masked_mean_fct_file} -ref {atlas_file} -out {mean_fct_file_STD} -applyxfm -init {mean_fct2STD_mat} -interp trilinear"
else: # use mean functional image directly
print('Transforming masked mean functional image directly to atlas space')
print('variation: ' + variation)
if variation == 'STD0':
command = f"flirt -in {masked_mean_fct_file} -ref {atlas_file} -out {mean_fct_file_STD} -omat {mean_fct2STD_mat} -bins 256 -cost corratio -searchrx -90 90 -searchry -90 90 -searchrz -90 90 -dof 12 -interp trilinear"
elif variation == 'STD1':
command = f"flirt -in {masked_mean_fct_file} -ref {atlas_file} -out {mean_fct_file_STD} -omat {mean_fct2STD_mat} -bins 256 -cost corratio -searchrx -30 30 -searchry -30 30 -searchrz -30 30 -dof 12 -interp trilinear"
else: #error, variation not found
print('Variation not found')
#generate error message
return
# if the system is windows, don't run the command, just write it down
print(command)
if os.name == 'nt': # Windows
print("System is Windows, command not executed")
else:
os.system(command)
def mean_to_anatomic(sub_N, dataset, task, specie, datafolder):
'''
input:
\BIDS\sub-01_T1w_brain.nii.gz: anatomic file in BIDS format, skull removed, reoriented and labeled
output:
'''
workingdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
T1w_brain_file = datafolder + os.sep + dataset + os.sep + 'BIDS' + os.sep + 'sub-' + str(sub_N).zfill(2) + '_T1w_brain.nii.gz'
mean_fct_T1w_file = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_mean_fct_T1w.nii.gz'
mean_fct2T1w_mat = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_mean_fct2T1w.mat'
mean_fct_file = workingdir + os.sep + specie + '-sub-' + str(sub_N).zfill(2) # like: D-sub-01_task-EmoB_run-01_mean_fct.nii.gz
mean_fct_file += '_task-' + task + '_mean_fct.nii.gz'
masked_mean_fct_file = mean_fct_file[:-7] + '_brain.nii.gz'
# mean_fct_file_STD = mean_fct_file[:-7] + '_STD.nii.gz'
# mean_fct2STD_mat = mean_fct_file[:-7] + '2STD.mat'
if specie == 'D':
specieS = 'Dog'
elif specie == 'H':
specieS = 'Hum'
# masked_mean_fct to T1w_brain
command = f"flirt -in {masked_mean_fct_file} -ref {T1w_brain_file} -out {mean_fct_T1w_file} -omat {mean_fct2T1w_mat} -bins 256 -cost corratio -searchrx -30 30 -searchry -30 30 -searchrz -30 30 -dof 7 -interp trilinear"
# if the system is windows, don't run the command, just write it down
print(command)
if os.name == 'nt': # Windows
print("System is Windows, command not executed")
else:
os.system(command)
def anatomic_to_STD(sub_N, dataset, task, specie, datafolder, atlas_type, img_type='brain2mm'):
'''
input:
\BIDS\sub-01_T1w_brain.nii.gz: anatomic file in BIDS format, skull removed, reoriented and labeled
output:
'''
workingdir = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
T1w_brain_file = datafolder + os.sep + dataset + os.sep + 'BIDS' + os.sep + 'sub-' + str(sub_N).zfill(2) + '_T1w_brain.nii.gz'
T1w_brain_STD_file = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_T1w_brain_STD.nii.gz'
T1w_brain2STD_mat = datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep + specie + '-sub-' + str(sub_N).zfill(2) + '_T1w_brain2STD.mat'
if specie == 'D':
specieS = 'Dog'
elif specie == 'H':
specieS = 'Hum'
# generate path to atlas. The atlas is in the same folder as the script
atlas_file = os.getcwd() + os.sep + "Atlas" + os.sep + specieS + os.sep + atlas_type + os.sep + img_type + ".nii.gz"
# T1w_brain to atlas
command = f"flirt -in {T1w_brain_file} -ref {atlas_file} -out {T1w_brain_STD_file} -omat {T1w_brain2STD_mat} -bins 256 -cost corratio -searchrx -30 30 -searchry -30 30 -searchrz -30 30 -dof 7 -interp trilinear"
# if the system is windows, don't run the command, just write it down
print(command)
if os.name == 'nt': # Windows
print("System is Windows, command not executed")
else:
os.system(command)
def run_to_STD(sub_N, run_N, dataset, task, specie, datafolder, atlas_type, img_type='brain2mm', session=''):
"""
This function will take a semi-processed run of a participant
cut it, apply BET and transform it to the space of the atlas.
sub_N: subject number
run_N: run number
dataset: dataset name
task: task name
specie: specie name
datafolder: path to the data folder
atlas_type: type of atlas to use
img_type: type of image to use, default is 'brain2mm'
session: session number (in case there is one)
"""
# if the system is windows
if os.name == 'nt':
print('The system is Windows, FSL functions ans bash scripts will not be executed')
# working directories
std_dir = datafolder + os.sep + dataset + os.sep + 'normalized' + os.sep + specie + '-sub-' + str(sub_N).zfill(2)
preprocess_dir = (datafolder + os.sep + dataset + os.sep + 'preprocessing' + os.sep +
specie + '-sub-' + str(sub_N).zfill(2)
)
# cutting parameters file
params_file = (preprocess_dir + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) +
'_task-' + task + '_mean_fct_uncut_cut_params.txt'
)
filename = (specie + '-sub-' + str(sub_N).zfill(2) +
'_ses-' + session +
'_task-' + task +
'_run-' + str(run_N).zfill(2))
# name for reoriented and motion corrected file
preprocessed_file = filename + '_mc.nii.gz'
cut_file = filename + '_reoriented_mc_cut.nii.gz'
# updating output params_file
params_dict = utils.read_params_file(params_file)
params_dict['output_file'] = preprocess_dir + os.sep + cut_file
params_file_current = preprocess_dir + os.sep + filename + '_cut_params.txt'
# params_file_current = params_file[:-30] + '_run-' + str(run_N).zfill(2) + '_cut_params.txt'
# add mask file to parameters
params_dict['mask_file'] = (preprocess_dir + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) +
'_task-' + task + '_mean_fct_mask.nii.gz')
# save the cutting parameters
utils.write_params_file(params_file_current, params_dict)
# cut preprocessed file and apply BET
command = f"./remove_slices.sh {preprocess_dir + os.sep + preprocessed_file} {params_file_current}"
print(command)
if os.name != 'nt': # Windows
os.system(command)
# apply transformation to STD
mean_fct2STD_mat = (
preprocess_dir + os.sep +
specie + '-sub-' + str(sub_N).zfill(2) +
'_task-' + task + '_mean_fct2STD.mat'
)
# determine folder for the atlas
if specie == 'D':
specieS = 'Dog'
elif specie == 'H':
specieS = 'Hum'
atlas_file = os.getcwd() + os.sep + "Atlas" + os.sep + specieS + os.sep + atlas_type + os.sep + img_type + ".nii.gz"
# if normalized directory does not exist, create it
if not os.path.exists(std_dir):
os.makedirs(std_dir)
print('Directory ' + std_dir + ' created')
else:
print('Directory ' + std_dir + ' already exists')
command = f"flirt -in {preprocess_dir + os.sep + cut_file} -ref {atlas_file} -applyxfm -init {mean_fct2STD_mat} -out {std_dir + os.sep + filename + '.nii.gz'} -interp trilinear"
print(command)
if os.name != 'nt': # Windows
os.system(command)
print('done')
from pathlib import Path
import numpy as np
from scipy.signal import detrend as _sp_detrend
def fwd(par_file, radius=50.0, threshold=0.5, detrend_type="linear-demean", output_file=None, add_movement_params=True):
"""
Compute framewise displacement (FD) and return a binary mask of frames above threshold.
If written to disk, the file includes the 6 motion parameters followed by one
additional censoring column per volume that exceeded the FD threshold.
Parameters
----------
par_file : str or Path
Path to FSL .par file with 6 motion parameters (rotations in radians first).
radius : float, optional
Radius in mm used to convert rotations to displacement (default is 50).
threshold : float, optional
Threshold in mm for FD (default is 0.5).
detrend_type : str, optional
Detrending method: 'linear-demean', 'linear-nodemean', or 'none' (default is 'linear-demean').
output_file : str or Path, optional
If provided, saves a .txt file with the 6 motion parameters followed by one
censoring column for each excluded volume.
add_movement_params : bool, optional
If True, includes motion parameters in the output file (default is True).
Returns
-------
numpy.ndarray
Array of 1s and 0s where 1 = frame above or equal to threshold, 0 = below.
"""
par_file = Path(par_file)
motion = np.loadtxt(par_file, ndmin=2, dtype=float)
if motion.shape[1] != 6:
raise ValueError(f"Expected 6 motion columns, got {motion.shape[1]}")
motion_detrended = _detrend_columns(motion, detrend_type)
motion_detrended[:, :3] *= radius # convert radians to mm
d_motion = np.vstack([np.zeros((1, 6)), np.diff(motion_detrended, axis=0)])
fd = np.sum(np.abs(d_motion), axis=1)
mask = (fd >= threshold).astype(np.int8)
if output_file:
if add_movement_params:
excluded_idx = np.flatnonzero(mask)
censor_columns = np.zeros((motion.shape[0], excluded_idx.size), dtype=float)
if excluded_idx.size > 0:
censor_columns[excluded_idx, np.arange(excluded_idx.size)] = 1.0
output_data = np.hstack([motion, censor_columns])
np.savetxt(output_file, output_data, fmt="%.6f", delimiter="\t")
print(f"Motion parameters and {excluded_idx.size} censoring columns saved to {output_file}")
else:
if mask.size == 0:
np.savetxt(output_file, np.empty((0, 0)), fmt="%d", delimiter="\t")
else:
np.savetxt(output_file, mask, fmt="%d", delimiter="\t")
print(f"FD mask saved to {output_file}")
return mask
def _detrend_columns(arr, kind="linear-demean"):
if kind == "none":
return arr.copy()
out = arr.copy()
demean = kind == "linear-demean"
if kind in {"linear-demean", "linear-nodemean"}:
for i in range(arr.shape[1]):
col = arr[:, i]
mu = col.mean() if demean else 0.0
out[:, i] = _sp_detrend(col) + mu
else:
raise NotImplementedError("Only 'linear-demean', 'linear-nodemean', and 'none' are supported.")
return out

preprocess_functions.py at commit 55ed729, no license · at the source

Overview

Authors: Raúl Hernández-Pérez^1,2,3, Luis Concha⁴, Attila Andics^2,3, Rodolfo Bernal-Gamboa⁵, Laura V. Cuaya^1,2

ORCID iDs: Raúl Hernández-Pérez, Laura V. Cuaya

Institutions: Eötvös Loránd University (Hungary); University of Vienna (Austria); Universidad Nacional Autónoma de México (Mexico)

Journal: iScience, volume 29, issue 8, article 116900

Dates: received 25 August 2025; accepted 7 July 2026; published online 10 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.116900 · PMID 42666977 · PMCID PMC13523845 · OpenAlex W7202098964

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism), other (organism), cognitive (subfield)

Methods: Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging

Keywords: dogs, social perception, human facial expressions, affective neuroscience, functional magnetic resonance imaging, fMRI

Topic: Human-Animal Interaction Studies (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Austrian Science Fund (10.55776, ESP602); European Research Council; ERC; Horizon 2020 (950159); Hungarian Academy of Sciences; MTA-ELTE; Lendület; Neuroethology of Communication Research Group (LP2017-13/2017); National Brain Programme 3.0 (NAP2022-I-3/2022); R.H.-P; L.V.C; Consejo Nacional de Humanidades, Ciencias y Tecnologías (409258, 407590, 181508, 1782); Universidad Nacional Autónoma de México (IB201712, IG200117, IN204720)

Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

Dogs can distinguish human facial expressions, particularly happiness, yet brain processes remain unclear. Using fMRI, we conducted two experiments in awake pet dogs. In Experiment 1 (n = 8), happy faces elicited a stronger response than neutral faces in a right temporal cluster extending to the caudate nucleus, including the rostral Sylvian gyrus. In Experiment 2 (n = 12), dogs viewed faces expressing happiness, anger, fear, or sadness. Using the Experiment 1 cluster as a region of interest, a machine-learning classifier distinguished happiness from each negative facial expression, but not between negative pairs, showing differential BOLD responsiveness to happy faces. Whole-brain representational similarity analyses revealed activity patterns differentiating angry vs. fearful faces (right mid ectosylvian and left splenial gyri) and sad vs. fearful faces (right rostral suprasylvian gyrus) but not angry vs. sad faces. This provides direct evidence that dog brains can distinguish between two negative facial expressions.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

rhernandez00/dog_brain_toolkit

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 55ed729dab520cc0786c3c97e8a26d3f232abf5f, 22 September 2026

Languages: Python (66), MATLAB (53), Jupyter (12), Shell (4), JavaScript (1)

Size: 4,135 files, 136 scripts

Software Heritage: not archived

Found in: the references

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (32 files), pandas (30 files), NiBabel (28 files), Tools for NIfTI and ANALYZE image (MATLAB) (18 files), PyTorch (16 files), FSL (10 files), Matplotlib (7 files), SciPy (6 files), Nilearn (5 files), Plotly (3 files), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), scikit-learn (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

137 files

Atlas/transform_mask.sh, Shell, 23 lines
Quality_check/NIfTI_toolbox/affine.m, MATLAB, 554 lines
Quality_check/NIfTI_toolbox/bipolar.m, MATLAB, 94 lines
Quality_check/NIfTI_toolbox/bresenham_line3d.m, MATLAB, 189 lines
Quality_check/NIfTI_toolbox/clip_nii.m, MATLAB, 115 lines
Quality_check/NIfTI_toolbox/collapse_nii_scan.m, MATLAB, 260 lines
Quality_check/NIfTI_toolbox/expand_nii_scan.m, MATLAB, 48 lines
Quality_check/NIfTI_toolbox/extra_nii_hdr.m, MATLAB, 255 lines
Quality_check/NIfTI_toolbox/flip_lr.m, MATLAB, 84 lines
Quality_check/NIfTI_toolbox/get_nii_frame.m, MATLAB, 164 lines
Quality_check/NIfTI_toolbox/load_nii.m, MATLAB, 199 lines
Quality_check/NIfTI_toolbox/load_nii_ext.m, MATLAB, 207 lines
Quality_check/NIfTI_toolbox/load_nii_hdr.m, MATLAB, 280 lines
Quality_check/NIfTI_toolbox/load_nii_img.m, MATLAB, 392 lines
Quality_check/NIfTI_toolbox/load_untouch0_nii_hdr.m, MATLAB, 200 lines
Quality_check/NIfTI_toolbox/load_untouch_header_only.m, MATLAB, 187 lines
Quality_check/NIfTI_toolbox/load_untouch_nii.m, MATLAB, 191 lines
Quality_check/NIfTI_toolbox/load_untouch_nii_hdr.m, MATLAB, 217 lines
Quality_check/NIfTI_toolbox/load_untouch_nii_img.m, MATLAB, 468 lines
Quality_check/NIfTI_toolbox/make_ana.m, MATLAB, 210 lines
Quality_check/NIfTI_toolbox/make_nii.m, MATLAB, 256 lines
Quality_check/NIfTI_toolbox/mat_into_hdr.m, MATLAB, 83 lines
Quality_check/NIfTI_toolbox/pad_nii.m, MATLAB, 142 lines
Quality_check/NIfTI_toolbox/reslice_nii.m, MATLAB, 321 lines
Quality_check/NIfTI_toolbox/rri_file_menu.m, MATLAB, 179 lines
Quality_check/NIfTI_toolbox/rri_orient.m, MATLAB, 106 lines
Quality_check/NIfTI_toolbox/rri_orient_ui.m, MATLAB, 251 lines
Quality_check/NIfTI_toolbox/rri_select_file.m, MATLAB, 636 lines
Quality_check/NIfTI_toolbox/rri_xhair.m, MATLAB, 92 lines
Quality_check/NIfTI_toolbox/rri_zoom_menu.m, MATLAB, 33 lines
Quality_check/NIfTI_toolbox/save_nii.m, MATLAB, 286 lines
Quality_check/NIfTI_toolbox/save_nii_ext.m, MATLAB, 38 lines
Quality_check/NIfTI_toolbox/save_nii_hdr.m, MATLAB, 227 lines
Quality_check/NIfTI_toolbox/save_untouch0_nii_hdr.m, MATLAB, 219 lines
Quality_check/NIfTI_toolbox/save_untouch_header_only.m, MATLAB, 71 lines
Quality_check/NIfTI_toolbox/save_untouch_nii.m, MATLAB, 232 lines
Quality_check/NIfTI_toolbox/save_untouch_nii_hdr.m, MATLAB, 207 lines
Quality_check/NIfTI_toolbox/save_untouch_slice.m, MATLAB, 580 lines
Quality_check/NIfTI_toolbox/unxform_nii.m, MATLAB, 40 lines
Quality_check/NIfTI_toolbox/verify_nii_ext.m, MATLAB, 45 lines
Quality_check/NIfTI_toolbox/view_nii.m, MATLAB, 4,873 lines
Quality_check/NIfTI_toolbox/view_nii_menu.m, MATLAB, 480 lines
Quality_check/NIfTI_toolbox/xform_nii.m, MATLAB, 521 lines
Quality_check/functions/bramila_bold2perc.m, MATLAB, 25 lines
Quality_check/functions/bramila_dvars.m, MATLAB, 109 lines
Quality_check/functions/bramila_framewiseDisplacement.m, MATLAB, 280 lines
Quality_check/functions/createTxtColumns.m, MATLAB, 22 lines
Quality_check/functions/fuctionToTxt.m, MATLAB, 82 lines
Quality_check/functions/load_niiFolder.m, MATLAB, 27 lines
Quality_check/functions/load_nii_hdr.m, MATLAB, 280 lines
Quality_check/functions/load_untouch_nii.m, MATLAB, 191 lines
Quality_check/functions/load_untouch_nii_hdr.m, MATLAB, 217 lines
Quality_check/functions/writeTxt.m, MATLAB, 35 lines
Quality_check/run_quality_check.m, MATLAB, 117 lines
affine_correction.ipynb, Jupyter, 55 lines
convert_coords.ipynb, Jupyter, 943 lines, 1 match
docs/app.js, JavaScript, 178 lines
export_static.py, Python, 620 lines, 1 match
interface.ipynb, Jupyter, 897 lines
job_status.py, Python, 91 lines
old/crop_and_bet.ipynb, Jupyter, 394 lines
old/preprocess.ipynb, Jupyter, 1,529 lines
old/running.ipynb, Jupyter, 73 lines
preprocess_CAPS-Knee.ipynb, Jupyter, 550 lines
preprocess_functions.py, Python, 1,594 lines, 1 match
remove_slices.sh, Shell, 103 lines
resample_atlas.sh, Shell, 26 lines
rsa_model_builder.py, Python, 2,318 lines
rsa_utils.py, Python, 4,180 lines
run_GLM.py, Python, 239 lines
run_bet.sh, Shell, 69 lines
run_jobs.py, Python, 206 lines
schedule_rsa.py, Python, 103 lines
scheduler/__init__.py, Python, 1 line
scheduler/dag.py, Python, 271 lines
scheduler/jobs.py, Python, 207 lines
scheduler/paths.py, Python, 31 lines
searchlight.py, Python, 1,094 lines
searchlight_difference.py, Python, 257 lines
step11.py, Python, 51 lines
sync_rsa_to_drive.py, Python, 225 lines
tests/test_colab_regression.py, Python, 216 lines
tests/test_colab_regression_inference.py, Python, 124 lines
tests/test_generate_fsf.py, Python, 68 lines
tests/test_regression_pipeline.py, Python, 240 lines
tools/build_all_categories_groupings.py, Python, 177 lines
tools/build_models_manifest.py, Python, 151 lines
tools/build_rsa_models.py, Python, 274 lines
tools/bulk_check.py, Python, 518 lines
tools/check_space.py, Python, 208 lines
tools/colab_gpu/colab_rsa.ipynb, Jupyter, 82 lines
tools/colab_gpu/colab_rsa4.ipynb, Jupyter, 53 lines
tools/colab_gpu/colab_rsa_group.ipynb, Jupyter, 246 lines
tools/colab_gpu/colab_rsa_regression.ipynb, Jupyter, 119 lines
tools/colab_gpu/colab_rsa_regression_inference.ipynb, Jupyter, 103 lines
tools/colab_gpu/gpu_group.py, Python, 2,068 lines
tools/colab_gpu/gpu_regression.py, Python, 279 lines
tools/colab_gpu/gpu_rsa.py, Python, 1,095 lines
tools/colab_gpu/memory_estimate.py, Python, 199 lines
tools/colab_gpu/refs/build_refs.py, Python, 107 lines
tools/colab_gpu/refs/check_refs_mask.py, Python, 161 lines
tools/colab_gpu/run_colab.py, Python, 191 lines
tools/colab_gpu/run_colab_group.py, Python, 321 lines
tools/colab_gpu/run_colab_regression.py, Python, 408 lines
tools/colab_gpu/run_colab_regression_inference.py, Python, 199 lines
tools/colab_gpu/test_model_subsets.py, Python, 84 lines
tools/colab_gpu/validate_gpu.py, Python, 297 lines
tools/colab_gpu/validate_group.py, Python, 647 lines
tools/create_group_package.py, Python, 221 lines
tools/create_package.py, Python, 571 lines
tools/create_regression_inference_package.py, Python, 57 lines
tools/create_regression_package.py, Python, 164 lines
tools/dashboard.py, Python, 133 lines
tools/fsf_design.py, Python, 515 lines, 1 match
tools/glm_designer.py, Python, 746 lines
tools/hypothesis_explorer.py, Python, 3,881 lines
tools/make_mask.py, Python, 232 lines
tools/make_qr.py, Python, 50 lines
tools/models_manifest.py, Python, 398 lines
tools/pipeline_console.py, Python, 1,357 lines
tools/pipeline_dashboard.py, Python, 1,457 lines
tools/purge_step1_from_package.py, Python, 124 lines
tools/queue_scan.py, Python, 198 lines
tools/schedule_steps.py, Python, 164 lines
tools/set_live.py, Python, 36 lines
tools/unpack_results.py, Python, 461 lines
tools/zmap_summary.py, Python, 1,294 lines
utils.py, Python, 1,005 lines, 1 match
viz/__init__.py, Python, 26 lines
viz/datasource.py, Python, 183 lines
viz/hypothesis_tree.py, Python, 320 lines
viz/niftiutil.py, Python, 457 lines
viz/planner_app.py, Python, 110 lines
viz/scheduler_app.py, Python, 344 lines
viz/stimuli.py, Python, 66 lines
viz/viewer_app.py, Python, 337 lines
README.md, Text, 56 lines

At the source: github.com/rhernandez00/dog_brain_toolkit

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
136 scripts, each with its path and the digest of its content;
5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data and code availability

The data associated with this manuscript have been deposited at Zenodo, and the custom preprocessing and analysis code used in this study is available through the Dog Brain Toolkit on GitHub. Both resources are publicly available, and their DOIs are listed in the key resources table. Any additional information required to reanalyze the data reported in this work is available from the lead contact upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 3, 28 September 2026

Authors: added Raúl Hernández-Pérez (0000-0003-3971-6236); Laura V. Cuaya (0000-0002-1073-7300); removed Raúl Hernández-Pérez; Laura V. Cuaya

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 13 funders, 90 references.

Cite

This paper

Hernández-Pérez, R., Concha, L., Andics, A., Bernal-Gamboa, R., & Cuaya, L. V. (2026). Dog brain representations of human facial expressions: Encoding happiness and differentiating specific negative expressions. iScience, 29(8), 116900. https://doi.org/10.1016/j.isci.2026.116900

BibTeX

@article{hernandezperez2026dog, author = {Hernández-Pérez, Raúl and Concha, Luis and Andics, Attila and Bernal-Gamboa, Rodolfo and Cuaya, Laura V.}, title = {{Dog brain representations of human facial expressions: Encoding happiness and differentiating specific negative expressions}}, journal = {iScience}, year = {2026}, month = aug, volume = {29}, number = {8}, pages = {116900}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.116900}, url = {https://doi.org/10.1016/j.isci.2026.116900}, pmid = {42666977}, pmcid = {PMC13523845} }

RIS

TY - JOUR AU - Hernández-Pérez, Raúl AU - Concha, Luis AU - Andics, Attila AU - Bernal-Gamboa, Rodolfo AU - Cuaya, Laura V. TI - Dog brain representations of human facial expressions: Encoding happiness and differentiating specific negative expressions T2 - iScience J2 - iScience PY - 2026 DA - 2026/08/10 VL - 29 IS - 8 SP - 116900 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.116900 UR - https://doi.org/10.1016/j.isci.2026.116900 LA - en ER -

CSL-JSON

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