OSCR

Neural correlates of appetitive extinction learning: an fMRI study with actively participating pigeons.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Data analysis › fMRI data analysis ↔ fMRI_processing/6-PreProcessing.ipynb, lines 179–289 · score 0.83 · temporal filtering, spatial smoothing, motion correction, Gaussian, slice, volumes
  2. [2] § Materials and methods › Data analysis › Behavioral data analysis ↔ behavioural_data/Signal_Detection_theory_pigeon.m, lines 26–42 · score 0.60 · signal detection theory, Hit rates, FA, pigeon, Behavioral
  3. [3] § Materials and methods › Data analysis › Contrasts for first-level analysis ↔ fMRI_processing/3- prep_bold.py.ipynb, the whole file · a weak match · score 0.59 · fsl motion outlier, confounds, model

Paper

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The authors' code

Jupyter notebook · 319 lines · 17 KB · no license · 1 match

  1. # %% [markdown]
  2. # # Initialisation
  3. # Here, we will remove first n volume, and copy data in result directory
  5. # %%
  6. import glob
  7. import os
  8. import subprocess
  10. dummy_scan = 10 #number of volumes for delete
  12. path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/'
  13. bold_files = glob.glob('%s/sub*/*/func.nii.gz'%(path))
  17. for cur_bold in bold_files:
  18. print(cur_bold)
  19. #cur_dir = os.path.dirname(cur_bold)
  20. # strip off .nii.gz from file name (makes code below easier)
  21. results_folder = cur_bold[:-7]
  22. #print(results_folder)
  24. # creat a result folder fro each run
  25. os.makedirs(results_folder)
  27. #copy functional data in result folder
  28. os.system("fslmaths %s %s/prefiltered_func_data -odt float"%(cur_bold,results_folder))
  30. # read number of volumes for each run and remove first five volume
  31. nVloume = subprocess.check_output("fslnvols %s"%(cur_bold), shell=True).decode('utf-8')
  32. print(nVloume)
  33. #remove the dumy scns from data
  34. os.system("fslroi %s/prefiltered_func_data %s/prefiltered_func_data %s %s"%(results_folder,results_folder,dummy_scan,int(nVloume)-int(dummy_scan)))
  35. #creat an example file
  36. os.system("fslroi %s/prefiltered_func_data %s/example_func %s %s"%(results_folder,results_folder,round((int(nVloume)-int(dummy_scan))/2),1))
  40. # %% [markdown]
  41. # # Registration
  43. # %% [markdown]
  44. # Anatomical 2 Standard space registration
  46. # %%
  47. import glob
  48. import os
  50. data_path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/'
  51. reference_path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/template'
  52. #bold_dir = glob.glob('%s/sub*/run*'%(path))
  53. sub_dir = glob.glob('%s/sub*'%(data_path))
  55. for sub in sub_dir:
  57. reg_directory = os.path.join(sub,'anat/reg')
  59. print(reg_directory)
  60. os.makedirs(reg_directory)
  62. os.system('fslmaths %s/anat/anat_brain %s/highres'%(sub,reg_directory))
  63. os.system('fslmaths %s/anat/anat %s/highres_head'%(sub,reg_directory))
  64. os.system('fslmaths %s/MYtemplate0 %s/standard'%(reference_path,reg_directory))
  65. os.system('fslmaths %s/MYtemplate0 %s/standard_head'%(reference_path,reg_directory))
  66. os.system('fslmaths %s/MYtemplate0 -bin -dilF -dilF %s/standard_mask -odt char'%(reference_path,reg_directory))
  68. # change directory to the reg
  69. os.chdir(reg_directory)
  71. #hihres 2 standard
  73. # FSL nonlinear registeration
  74. os.system('flirt -in highres -ref standard -out highres2standard -omat highres2standard.mat -cost corratio -dof 12 -searchrx -180 180 -searchry -180 180 -searchrz -180 180 -interp trilinear')
  75. os.system('fnirt --iout=highres2standard_head --in=highres_head --aff=highres2standard.mat --cout=highres2standard_warp --iout=highres2standard --jout=highres2highres_jac --config=T1_2_MNI152_2mm --ref=standard_head --refmask=standard_mask --warpres=10,10,10')
  76. os.system('applywarp -i highres -r standard -o highres2standard -w highres2standard_warp')
  77. os.system('convert_xfm -inverse -omat standard2highres.mat highres2standard.mat')
  78. os.system('slicer highres2standard standard -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png highres2standard1.png ; slicer standard highres2standard -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png highres2standard2.png ; pngappend highres2standard1.png - highres2standard2.png highres2standard.png; rm -f sl?.png highres2standard2.png')
  79. os.system('rm highres2standard1.png')
  81. # %% [markdown]
  82. # # Plot Anatomical 2 Atlas registration reults
  84. # %%
  85. %matplotlib inline
  86. %pylab inline
  87. #import matplotlib.pyplot as plt
  88. #import matplotlib.image as mpimg
  90. from IPython.display import display, Image
  91. import glob
  92. import os
  94. data_path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/'
  95. reg_dir = glob.glob('%s/sub*/anat/reg'%(data_path))
  96. for curr_reg in reg_dir:
  98. #print(curr_reg)
  99. print(curr_reg[-9:-4])
  100. plt.show()
  101. display(Image(filename=os.path.join(curr_reg, 'highres2standard.png' )))
  103. # %% [markdown]
  104. # # Functional 2 anatomical space registration
  105. # <br>
  106. # ANt is working better than FSL nonlinear registration.
  107. # Dow you will find both methods.
  109. # %%
  110. import glob
  111. import os
  113. data_path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/'
  114. func_dir = glob.glob('%s/sub*/*/func'%(data_path))
  116. for curr_func_dir in func_dir[14:15]:
  118. reg_dir = curr_func_dir[0:43]+'/anat/reg'
  119. print(curr_func_dir)
  120. print(reg_dir)
  121. extended_func_dir = curr_func_dir[:-5]
  123. #copy reg directory to each run directory
  124. os.system('cp -R %s %s'%(reg_dir,curr_func_dir))
  125. os.system('cp %s/example_func.nii.gz %s/reg'%(curr_func_dir,curr_func_dir))
  127. # change path
  128. os.chdir(curr_func_dir + '/reg')
  130. #FSL
  131. # register func 2 extended func
  132. os.system('fslmaths %s/expanded_func %s/reg/initial_highres'%(extended_func_dir,curr_func_dir))
  133. os.system('flirt -in example_func -ref initial_highres -out example_func2initial_highres -omat example_func2initial_highres.mat -cost corratio -dof 6 -searchrx -180 180 -searchry -180 180 -searchrz -180 180 -interp trilinear')
  134. os.system('convert_xfm -inverse -omat initial_highres2example_func.mat example_func2initial_highres.mat')
  135. os.system('slicer example_func2initial_highres initial_highres -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png example_func2initial_highres1.png ; /usr/local/fsl/bin/slicer initial_highres example_func2initial_highres -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png example_func2initial_highres2.png ; /usr/local/fsl/bin/pngappend example_func2initial_highres1.png - example_func2initial_highres2.png example_func2initial_highres.png; /bin/rm -f sl?.png example_func2initial_highres2.png')
  136. os.system('rm example_func2initial_highres1.png')
  137. # initial_highres to highres
  138. os.system('flirt -in initial_highres -ref highres -out initial_highres2highres -omat initial_highres2highres.mat -cost corratio -dof 3 -searchrx -180 180 -searchry -180 180 -searchrz -180 180 -interp trilinear')
  139. os.system('convert_xfm -inverse -omat highres2initial_highres.mat initial_highres2highres.mat')
  140. os.system('slicer initial_highres2highres highres -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png initial_highres2highres1.png ; /usr/local/fsl/bin/slicer highres initial_highres2highres -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png initial_highres2highres2.png ; /usr/local/fsl/bin/pngappend initial_highres2highres1.png - initial_highres2highres2.png initial_highres2highres.png; /bin/rm -f sl?.png initial_highres2highres2.png')
  141. os.system('rm initial_highres2highres1.png')
  142. # example_func 2 highres
  143. os.system('convert_xfm -omat example_func2highres.mat -concat initial_highres2highres.mat example_func2initial_highres.mat')
  144. os.system('flirt -ref highres -in example_func -out example_func2highres -applyxfm -init example_func2highres.mat -interp trilinear')
  145. os.system('convert_xfm -inverse -omat highres2example_func.mat example_func2highres.mat')
  146. os.system('slicer example_func2highres highres -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png example_func2highres1.png ; /usr/local/fsl/bin/slicer highres example_func2highres -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png example_func2highres2.png ; /usr/local/fsl/bin/pngappend example_func2highres1.png - example_func2highres2.png example_func2highres.png; /bin/rm -f sl?.png example_func2highres2.png')
  147. os.system('rm example_func2highres1.png')
  148. #example_func 2 standard
  149. os.system('convert_xfm -omat example_func2standard.mat -concat highres2standard.mat example_func2highres.mat')
  150. os.system('convertwarp --ref=standard --premat=example_func2highres.mat --warp1=highres2standard_warp --out=example_func2standard_warp')
  151. os.system('applywarp --ref=standard --in=example_func --out=example_func2standard --warp=example_func2standard_warp')
  152. os.system('convert_xfm -inverse -omat standard2example_func.mat example_func2standard.mat')
  153. os.system('slicer example_func2standard standard -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png example_func2standard1.png ; /usr/local/fsl/bin/slicer standard example_func2standard -s 2 -x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png -y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png -z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; /usr/local/fsl/bin/pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png + sli.png + slj.png + slk.png + sll.png example_func2standard2.png ; /usr/local/fsl/bin/pngappend example_func2standard1.png - example_func2standard2.png example_func2standard.png; /bin/rm -f sl?.png example_func2standard2.png')
  155. # %% [markdown]
  156. # # Plot registration results
  158. # %%
  159. %matplotlib inline
  160. %pylab inline
  161. #import matplotlib.pyplot as plt
  162. #import matplotlib.image as mpimg
  164. from IPython.display import display, Image
  165. import glob
  166. import os
  168. data_path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/'
  169. reg_dir = glob.glob('%s/sub*/*/func/reg'%(data_path))
  170. for curr_img in reg_dir[14:15]:
  172. print(curr_img)
  173. #img = mpimg.imread(os.path.join(reg_dir, 'example_func2standard.png' ))
  174. #imgplot = plt.imshow(img)
  175. plt.show()
  176. display(Image(filename=os.path.join(curr_img, 'example_func2standard.png' )))
  179. # %% [markdown]
  180. # # Preprocessing
  182. # %%
  183. import glob
  184. import os
  185. import subprocess
  187. data_path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/'
  188. func_dir = glob.glob('%s/sub*/*/func'%(data_path))
  190. MotionCorrection = True
  192. SliceTimeCorrection = True
  193. if SliceTimeCorrection:
  194. TR = 4 #second
  195. acquisitionType = 'odd'
  197. SpatialSmoothing = True
  198. if SpatialSmoothing:
  199. smooth = 8 #mm
  201. TemporalFiltering = True
  202. if TemporalFiltering:
  203. paradigm_hp = 80 #second
  205. for curr_bold in func_dir:
  207. print(curr_bold)
  208. # change directory to the reg
  209. os.chdir(curr_bold)
  211. #motion correction
  212. if MotionCorrection:
  213. print('motion correction is running ...')
  214. os.system("mcflirt -in prefiltered_func_data -out prefiltered_func_data_mcf -mats -plots -reffile example_func -rmsrel -rmsabs -spline_final")
  215. os.system("mkdir -p mc ; mv -f prefiltered_func_data_mcf.mat prefiltered_func_data_mcf.par prefiltered_func_data_mcf_abs.rms prefiltered_func_data_mcf_abs_mean.rms prefiltered_func_data_mcf_rel.rms prefiltered_func_data_mcf_rel_mean.rms mc")
  216. os.system("fsl_tsplot -i %s/prefiltered_func_data_mcf.par -t 'MCFLIRT estimated rotations (radians)' -u 1 --start=1 --finish=3 -a x,y,z -w 640 -h 144 -o %s/rot.png"%('mc','mc'))
  217. os.system("fsl_tsplot -i %s/prefiltered_func_data_mcf.par -t 'MCFLIRT estimated translations (mm)' -u 1 --start=4 --finish=6 -a x,y,z -w 640 -h 144 -o %s/trans.png"%('mc','mc'))
  218. os.system("fsl_tsplot -i %s/prefiltered_func_data_mcf_abs.rms,prefiltered_func_data_mcf_rel.rms -t 'MCFLIRT estimated mean displacement (mm)' -u 1 -w 640 -h 144 -a absolute,relative -o %s/disp.png"%('mc','mc'))
  219. else:
  220. print('no motion correction')
  221. os.system("fslmaths prefiltered_func_data prefiltered_func_data_mcf -odt float")
  223. #slice time correction
  224. if SliceTimeCorrection:
  225. print('slice time correction is running ...')
  226. os.system("slicetimer -i prefiltered_func_data_mcf --out=prefiltered_func_data_st -r %s --%s"%(TR,acquisitionType))
  227. os.system("fslmaths prefiltered_func_data_st -Tmean mean_func")
  228. else:
  229. print('no slice time correction')
  230. os.system("fslmaths prefiltered_func_data_mcf prefiltered_func_data_st -odt float")
  231. os.system("fslmaths prefiltered_func_data_st -Tmean mean_func")
  233. # brain extraction
  234. os.system("bet2 mean_func mask -f 0.3 -n -m; immv mask_mask mask")
  235. os.system("fslmaths prefiltered_func_data_st -mas mask prefiltered_func_data_bet")
  237. #spatial smootiong
  238. if SpatialSmoothing:
  239. print('spatial smooting is running ...')
  240. output = subprocess.check_output("fslstats prefiltered_func_data_bet -p 2 -p 98", shell=True).decode('utf-8')
  241. output_split = output.split()
  242. int2 = float(output_split[0])
  243. int98 = float(output_split[1])
  244. print(int2,int98)#0.000000 954.826904
  245. brain_thresh =10
  246. intensity_threshold = int2 + ( brain_thresh * ( int98 - int2 ) / 100.0)
  247. os.system("fslmaths prefiltered_func_data_bet -thr %s -Tmin -bin mask -odt char"%(intensity_threshold))
  248. median_intensity = subprocess.check_output("fslstats prefiltered_func_data_st -k mask -p 50", shell=True).decode('utf-8')
  249. median_intensity = float(median_intensity)
  250. print(median_intensity)#705.560059
  251. os.system("fslmaths mask -dilF mask")
  252. os.system("fslmaths prefiltered_func_data_st -mas mask prefiltered_func_data_thresh")
  253. os.system("fslmaths prefiltered_func_data_thresh -Tmean mean_func")
  254. susan_int = (median_intensity - int2) * 0.75
  255. smoothsigma = smooth / 2.355
  256. os.system("susan prefiltered_func_data_thresh %s %s 3 1 1 mean_func %s prefiltered_func_data_smooth"%(susan_int,smoothsigma,susan_int))
  257. else:
  258. print('no spatial smoothing')
  259. os.system("fslmaths prefiltered_func_data_bet prefiltered_func_data_smooth -odt float")
  261. #Normalization
  262. normmean = 10000
  263. scaling = normmean / median_intensity
  264. os.system("fslmaths prefiltered_func_data_smooth -mas mask prefiltered_func_data_smooth")
  265. os.system("fslmaths prefiltered_func_data_smooth -mul %s prefiltered_func_data_intnorm"%(scaling))
  267. # temporal filtering
  268. if TemporalFiltering:
  269. print('temporal filtering is running...')
  270. #highpass temporal filtering (Gaussian-weighted least-squares straight line fitting, with sigma=hp_sigma_sec)
  271. os.system("fslmaths prefiltered_func_data_intnorm -Tmean tempMean")
  272. hp_sigma_sec = paradigm_hp / 2.0
  273. hp_sigma_vol = hp_sigma_sec / TR
  274. lp_sigma_vol = -1
  276. # if you want to do lowpass filtering, you cn using these lines:
  277. #Gaussian lowpass temporal filtering, with sigma=lp_sigma_sec
  278. #lp_sigma_sec = 2.8
  279. #lp_sigma_vol = lp_sigma_sec / TR
  280. os.system("fslmaths prefiltered_func_data_intnorm -bptf %s %s -add tempMean prefiltered_func_data_tempfilt"%(hp_sigma_vol, lp_sigma_vol))
  281. os.system("imrm tempMean")
  282. os.system("fslmaths prefiltered_func_data_tempfilt filtered_func_data")
  283. else:
  284. print('no temporal filtering')
  285. os.system("fslmaths prefiltered_func_data_intnorm filtered_func_data -odt float")
  288. os.system("fslmaths filtered_func_data -Tmean mean_func")
  289. os.system("rm -rf prefiltered_func_data*")
  294. # %% [markdown]
  295. # # Plot Estimated Motion Parameters
  297. # %%
  298. %matplotlib inline
  299. %pylab inline
  300. #import matplotlib.pyplot as plt
  301. #import matplotlib.image as mpimg
  303. from IPython.display import display, Image
  304. import glob
  305. import os
  307. data_path = '/mnt/d/Data/Pigeon/analysis/ColorDisc/'
  308. func_dir = glob.glob('%s/sub*/*/func/mc'%(data_path))
  309. for mc_dir in func_dir:
  311. #print(reg_dir)
  312. #img = mpimg.imread(os.path.join(reg_dir, 'example_func2standard.png' ))
  313. #imgplot = plt.imshow(img)
  314. plt.show()
  315. display(Image(filename=os.path.join(mc_dir, 'rot.png' )))
  316. display(Image(filename=os.path.join(mc_dir, 'trans.png' )))
  318. # %%

6-PreProcessing.ipynb at commit d441d87, no license · at the source

Overview

Authors: Mehdi Behroozi1,2, Alaleh Sadraee1,3, Xavier Helluy1,4, Erhan Genç5, Meng Gao6, Onur Güntürkün1,7
ORCID iDs: Onur Güntürkün
  1. Department of Biopsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, Germany
  2. Cognitive Neurobiology, Research Center “One Health” Ruhr, Faculty of Biology and Biotechnology, University Alliance Ruhr, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany
  3. International Graduate School of Neuroscience, Ruhr University Bochum, Bochum, Germany
  4. Department of Neurophysiology, Faculty of Medicine, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, Germany
  5. Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
  6. Developmental Computational Psychiatry Lab, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University Tübingen, Calwerstraße 14, 72076 Tübingen, Germany
  7. Research Center One Health Ruhr, Faculty of Psychology, University Research Alliance Ruhr, Ruhr University Bochum, Bochum, Germany
Institutions: Ruhr University Bochum (Germany); Leibniz Research Centre for Working Environment and Human Factors (Germany); University of Tübingen (Germany)
Journal: Scientific reports, volume 16, issue 1, article 16455
Dates: received 6 April 2025; accepted 20 May 2026; published online 27 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-54678-8 · PMID 42203897 · PMCID PMC13216286 · OpenAlex W7162486487
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), other (organism), cognitive (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Bird, Prefrontal, Hippocampus, Go/NoGo Paradigm, Brain Asymmetry, Multiple Memory Systems, Neuroscience, Psychology
MeSH: Brain*, Columbidae*, Extinction, Psychological*, Learning*, Magnetic Resonance Imaging*, Animals, Brain Mapping, Conditioning, Operant, Nerve Net, Reward (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Ruhr-Universität Bochum; European Research Council (101021354)
Citations: not cited yet (Europe PMC); 106 references in the paper

Abstract

Extinction learning is an important learning process that enables adaptive and flexible behavior. Human neuroimaging studies show that the neural basis of extinction learning consists of a neural network that includes the hippocampus, amygdala, and subcomponents of the prefrontal cortex, but also extends beyond them. The limitations of applying fMRI to actively participating animals have so far restricted the identification of the entire extinction network in non-human animals. Here, we present the first fMRI study of extinction in awake and actively participating pigeons, using a Go/NoGo operant paradigm with a water reward. Our study revealed an extensive and largely left hemispheric telencephalic network of sensory, limbic, executive, and motor areas that slowly ceased to be active during the process of extinction learning. We propose that the beginning of extinction ignites a neuronal updating of the associated consequences of own actions within a large telencephalic neural network until a new association is established which competes with the previously acquired operant response.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-54678-8.

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

Repository

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mehdibehroozi/pigeon_fMRI

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d441d87e9ed844028e6e327e6e0f86adad1f94ab, 14 August 2020
Languages: MATLAB (10), Jupyter (7)
Size: 20 files, 17 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 7 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (4 files), Statistics and Machine Learning Toolbox (3 files), Matplotlib (2 files), Nilearn (2 files), AFNI (1 file), ANTs (1 file), Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
18 files
At the source: github.com/mehdibehroozi/pigeon_fMRI

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

Tracing map

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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;
  • 17 scripts, each with its path and the digest of its content;
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Data

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

Data availability

Related data processing codes are available at https://github.com/mehdibehroozi/pigeon_fMRI.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 8 keywords, 10 MeSH terms, 2 funders, 102 references.

Cite

This paper

Behroozi, M., Sadraee, A., Helluy, X., Genç, E., Gao, M., & Güntürkün, O. (2026). Neural correlates of appetitive extinction learning: an fMRI study with actively participating pigeons. Scientific reports, 16(1), 16455. https://doi.org/10.1038/s41598-026-54678-8

BibTeX

@article{behroozi2026neural,
author = {Behroozi, Mehdi and Sadraee, Alaleh and Helluy, Xavier and Genç, Erhan and Gao, Meng and Güntürkün, Onur},
title = {{Neural correlates of appetitive extinction learning: an fMRI study with actively participating pigeons}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {16455},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-54678-8},
url = {https://doi.org/10.1038/s41598-026-54678-8},
pmid = {42203897},
pmcid = {PMC13216286}
}

RIS

TY - JOUR
AU - Behroozi, Mehdi
AU - Sadraee, Alaleh
AU - Helluy, Xavier
AU - Genç, Erhan
AU - Gao, Meng
AU - Güntürkün, Onur
TI - Neural correlates of appetitive extinction learning: an fMRI study with actively participating pigeons
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/27
VL - 16
IS - 1
SP - 16455
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-54678-8
UR - https://doi.org/10.1038/s41598-026-54678-8
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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