OSCR

Larg-scale multimodal fMRI dataset of visual and haptic 3D object category learning.

Code ↔ Paper

9 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 9 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Experimental Design › fMRI data preprocessing pipeline › Task-based fMRI ↔ task-based-fMRI-preprocesing/08-Registration.ipynb, lines 1–140 · score 0.86 · MNI152_T1_2mm, high resolution, anatomical images, nonlinear, rigid, intensity
  2. [2] § Experimental Design › fMRI data preprocessing pipeline › rs-fMRI ↔ resting-state-fMRI-preprocessing/10-Denoising.ipynb, the whole file · a weak match · score 0.79 · ICA AROMA, cerebrospinal fluid, global signals, WM, CSF, filtering
  3. [3] § Experimental Design › fMRI data preprocessing pipeline › rs-fMRI ↔ resting-state-fMRI-preprocessing/11-TemporalFilterring.ipynb, the whole file · a weak match · score 0.75 · bandpass filtering, global signals, 0.01–0.1 Hz, CSF, regressed, temporal
  4. [4] § Experimental Design › fMRI data preprocessing pipeline › Task-based fMRI ↔ resting-state-fMRI-preprocessing/10-Denoising.ipynb, the whole file · a weak match · score 0.71 · ICA AROMA, motion artifacts, motion related, tissue, filter, regressors
  5. [5] § Experimental Design › Data acquisition ↔ task-based-fMRI-preprocesing/04-SliceTimeCorrection.ipynb, the whole file · a weak match · score 0.58 · multiband acceleration factor, single shot, slices, repetition, fMRI
  6. [6] § Technical Validation › Motion parameters ↔ resting-state-fMRI-preprocessing/01- Framewise_displacment_FD.ipynb, lines 1–98 · score 0.58 · fsl_motion_outliers, framewise displacement, confound, FD, threshold, head
  7. [7] § Technical Validation › Motion parameters ↔ task-based-fMRI-preprocesing/01- Framewise_displacment_FD.ipynb, lines 1–98 · score 0.58 · fsl_motion_outliers, framewise displacement, confound, FD, threshold, head
  8. [8] § Technical Validation › Stimuli ↔ PlotSimilarity_Correlation_Stress_MDS.m, lines 87–125 · score 0.58 · dissimilarity matrices, MDS, correlation, stress, dimensions, modalities
  9. [9] § Experimental Design › Data acquisition ↔ task-based-fMRI-preprocesing/08-Registration.ipynb, lines 1–140 · score 0.55 · High resolution anatomical, slices, EPI

Paper

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The authors' code

Jupyter notebook · 494 lines · 19 KB · no license · 2 matches

  1. # %% [markdown]
  2. # # Registration
  3. # This script will creat "reg" directory in ses-01 directory and get registration infromation from functional space to anatomical space and from anatomical space to standard space. At the end, it will concatinate the results to have transformation from funrtional space to standard space.
  4. #
  5. # Linear registration: functional space >>> anatomical space (epi_reg from FSL)
  6. #
  7. # Non-Lineare registration: anatomical space >>> standard space (antsRegistration from ANT's)
  8. #
  9. # Converting ANTs output into the FSL format:
  10. #
  11. # c3d_affine_too: https://gist.github.com/MSchnei/240bc49f504ef93540d33b02f12bbe8d
  12. #
  13. # Workbench Command: https://www.humanconnectome.org/software/workbench-command
  14. #
  15. #
  16. #
  17. # -----------------------------------------------------------
  18. # Script written by Sepideh Tabrik& Mehdi Behroozi
  19. # <br>
  20. # Biopsychology,
  21. # <br>
  22. # Ruhr-University Bochum, Bochum, Germany
  23. # <br>
  24. # (2024.11)
  25. #
  26. # -----------------------------------------------------------
  28. # %% [markdown]
  29. # ## Anatomical >> Standard space registration
  31. # %%
  32. import glob
  33. import os
  34. from pathlib import Path
  36. # Define data paths
  37. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  38. reference_path = Path("/home/mehdi/fsl/data/standard")
  40. # Find subject directories
  41. sub_dirs = list(data_path.glob("sub*/ses-02"))
  43. # Process each subject directory
  44. for curr_sub in sub_dirs:
  45. subject_id = curr_sub.parts[-2]
  46. session_id = curr_sub.parts[-1]
  48. print("1) Creating 'reg' directory.")
  49. print(f"\t - Current subject is >> {subject_id}/{session_id}")
  51. reg_directory = curr_sub / "reg"
  53. # Check if the directory exists and remove if necessary
  54. if reg_directory.exists():
  55. print(f"\t {reg_directory} already exists! Removing...")
  56. os.system(f"rm -r {reg_directory}")
  58. # Create new reg directory
  59. print(f"\t Creating directory {reg_directory}...")
  60. reg_directory.mkdir(parents=True, exist_ok=True)
  62. # Copy atlas images to reg directory
  63. print("2) Copying standard images to the reg directory.")
  64. os.system(f"fslmaths {reference_path}/MNI152_T1_2mm_brain {reg_directory}/standard")
  65. os.system(f"fslmaths {reference_path}/MNI152_T1_2mm {reg_directory}/standard_head")
  66. os.system(f"fslmaths {reference_path}/MNI152_T1_2mm_brain_mask_dil {reg_directory}/standard_mask")
  68. # Copy anatomical images to reg directory
  69. print("3) Copying anatomical images to reg directory.")
  70. anat_dir = curr_sub / "anat"
  72. # Find T1w image
  73. t1w_files = list(anat_dir.glob("*T1w.nii.gz"))
  74. if not t1w_files:
  75. print("Warning: No T1w file found, skipping brain masking step.")
  76. else:
  77. t1w_image = t1w_files[0] # Get first matching file
  78. print(f"Processing T1w image: {t1w_image}")
  80. # Copy high-resolution anatomical head
  81. os.system(f"fslmaths {t1w_image} {reg_directory}/highres_head")
  83. # Copy mask and brain images
  84. mask_file = anat_dir / "mask.nii.gz"
  85. brain_file = anat_dir / "brain.nii.gz"
  87. if mask_file.exists():
  88. print(f"Copying anatomical mask: {mask_file} to reg directory...")
  89. os.system(f"fslmaths {mask_file} {reg_directory}/highres_mask")
  91. # Apply mask only if T1w exists
  92. if t1w_files:
  93. print(f"Applying mask: Multiplying {t1w_image} with {mask_file} to generate highres...")
  94. os.system(f"fslmaths {t1w_image} -mul {mask_file} {reg_directory}/highres")
  97. # Change directory to reg
  98. print("\t Changing the working directory to reg directory.")
  99. os.chdir(reg_directory)
  101. # Nonlinear registration using ANTs
  102. print("4) Running nonlinear registration using ANTs.")
  103. os.system("antsRegistration --dimensionality 3 --float 0 "
  104. "--output [highres2standard_,highres2standard.nii.gz] "
  105. "--interpolation Linear "
  106. "--winsorize-image-intensities [0.005,0.995] "
  107. "--use-histogram-matching 0 "
  108. "--initial-moving-transform [standard.nii.gz,highres.nii.gz,1] "
  109. "--transform Rigid[0.1] "
  110. "--metric MI[standard.nii.gz,highres.nii.gz,1,32,Regular,0.25] "
  111. "--convergence [1000x500x250x100,1e-6,10] "
  112. "--shrink-factors 8x4x2x1 "
  113. "--smoothing-sigmas 3x2x1x0vox "
  114. "--transform Affine[0.1] "
  115. "--metric MI[standard.nii.gz,highres.nii.gz,1,32,Regular,0.25] "
  116. "--convergence [1000x500x250x100,1e-6,10] "
  117. "--shrink-factors 8x4x2x1 "
  118. "--smoothing-sigmas 3x2x1x0vox "
  119. "--transform SyN[0.1,3,0] "
  120. "--metric CC[standard.nii.gz,highres.nii.gz,1,4] "
  121. "--convergence [100x70x50x20,1e-6,10] "
  122. "--shrink-factors 8x4x2x1 "
  123. "--smoothing-sigmas 3x2x1x0vox ")
  125. # Generate slices and visualization
  126. 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 "
  127. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  128. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  129. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  130. "+ sli.png + slj.png + slk.png + sll.png highres2standard1.png ; "
  131. "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 "
  132. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  133. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  134. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  135. "+ sli.png + slj.png + slk.png + sll.png highres2standard2.png ; "
  136. "pngappend highres2standard1.png - highres2standard2.png highres2standard.png; "
  137. "rm -f sl?.png highres2standard2.png")
  139. os.system("rm highres2standard1.png")
  140. print("\n")
  143. # %% [markdown]
  144. # ### Plot Anatomical >> Atlas registration results
  146. # %%
  147. %matplotlib inline
  149. from IPython.display import display, Image
  150. import matplotlib.pyplot as plt
  151. from pathlib import Path
  152. import glob
  154. # Define data path
  155. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  157. # Find registration directories
  158. reg_dirs = list(data_path.glob("sub*/ses-02/reg"))
  160. # Process each registration directory
  161. for curr_reg in reg_dirs:
  162. subject_id = curr_reg.parts[-3] # Extract subject ID dynamically
  163. print(f"Processing subject: {subject_id}")
  165. # Ensure the image file exists before displaying
  166. image_path = curr_reg / "highres2standard.png"
  167. if image_path.exists():
  168. display(Image(filename=str(image_path)))
  169. else:
  170. print(f"Warning: {image_path} not found, skipping.")
  173. # %% [markdown]
  174. # # copy reg directory for each resting-state sessions
  176. # %%
  177. import glob
  178. import os
  179. from pathlib import Path
  181. # Define data path
  182. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  184. # Find subject directories
  185. sub_dirs = list(data_path.glob("sub*/ses-02"))
  187. # Process each subject directory
  188. for curr_sub in sub_dirs:
  189. subject_id = curr_sub.parts[-2] # Extract subject ID dynamically
  190. print(f"Processing subject: {subject_id}")
  192. # Copy reg directory to different sessions
  193. reg_src = curr_sub / "reg"
  195. sessions = curr_sub.glob("func/run*")
  196. for curr_sess in sessions:
  197. exp_name = curr_sess.parts[-1]
  198. print(f"\t Creating reg directory at {exp_name}.")
  200. reg_dst = curr_sess / "reg" # Fixed indentation issue
  201. reg_dst.mkdir(parents=True, exist_ok=True) # Ensures the directory is created safely
  203. print(f"\t Copying the reg folder to {exp_name}.")
  204. if reg_src.exists():
  205. os.system(f"cp -R {reg_src}/* {reg_dst}")
  206. else:
  207. print(f"\t Warning: Source directory {reg_src} does not exist, skipping copy.")
  209. # Create example_func file using mean function images
  210. print(f"\t Copying the mean_function as example_func file for {exp_name}.")
  211. mean_Img = curr_sess / "mean_func.nii.gz"
  212. mean_Img_Brain = curr_sess / "mean_func_brain.nii.gz"
  213. mean_Img_Brain_mask = curr_sess / "mean_func_brain_mask.nii.gz"
  215. for img_file, output_file in [(mean_Img, "example_func.nii.gz"),
  216. (mean_Img_Brain, "example_func_brain.nii.gz"),
  217. (mean_Img_Brain_mask, "example_func_brain_mask.nii.gz")]:
  218. if img_file.exists():
  219. os.system(f"cp -R {img_file} {reg_dst}/{output_file}")
  220. else:
  221. print(f"\t Warning: {img_file} not found, skipping copy.")
  223. print()
  226. # %% [markdown]
  227. # ## Linear registration of Functional data into the Anatomical space
  228. #
  229. # You must check the results to make sure the registration is done in correct way.
  231. # %%
  232. import glob
  233. import os
  234. from pathlib import Path
  236. # Define data path
  237. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  239. # Find registration directories
  240. reg_dirs = list(data_path.glob("sub*/ses-02/func/run*/reg"))
  242. # Process each registration directory
  243. for curr_reg in reg_dirs:
  244. subject_id = curr_reg.parts[-5]
  245. session_id = curr_reg.parts[-4]
  246. task_id = curr_reg.parts[-2]
  248. print(f"Current subject: {subject_id}")
  249. print(f"\tCurrent session: {session_id}")
  250. print(f"\tCurrent task: {task_id}")
  252. # Change path only if it exists
  253. if curr_reg.exists():
  254. os.chdir(curr_reg)
  255. else:
  256. print(f"\tWarning: {curr_reg} does not exist, skipping.")
  257. continue
  259. # FSL Registration: func2highres
  260. print("\tRunning FSL registration...")
  262. # Check if epi_reg is applicable
  263. epi_reg_command = "epi_reg --epi=example_func --t1=highres_head --t1brain=highres --out=example_func2highres"
  264. flirt_command = "flirt -in example_func_brain -ref highres -out example_func2highres -omat example_func2highres.mat -bins 256 -cost corratio -searchrx 0 0 -searchry 0 0 -searchrz 0 0 -dof 7 -interp trilinear"
  266. if os.system(epi_reg_command) != 0:
  267. print("\tWarning: epi_reg failed, falling back to flirt.")
  268. os.system(flirt_command)
  270. # Generate transformation matrix
  271. os.system("convert_xfm -inverse -omat highres2example_func.mat example_func2highres.mat")
  273. # Generate visualization
  274. print("\tGenerating visualization...")
  275. os.system("slicer example_func2highres highres -s 2 "
  276. "-x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png "
  277. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  278. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  279. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  280. "+ sli.png + slj.png + slk.png + sll.png example_func2highres1.png ; "
  281. "slicer highres example_func2highres -s 2 "
  282. "-x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png "
  283. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  284. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  285. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  286. "+ sli.png + slj.png + slk.png + sll.png example_func2highres2.png ; "
  287. "pngappend example_func2highres1.png - example_func2highres2.png example_func2highres.png; "
  288. "rm -f sl?.png example_func2highres2.png")
  290. os.system("rm example_func2highres1.png")
  291. print()
  294. # %% [markdown]
  295. # ### Plot example_func >> anatomical space registeration results
  297. # %%
  298. %matplotlib inline
  300. from IPython.display import display, Image
  301. import matplotlib.pyplot as plt
  302. from pathlib import Path
  304. # Define data path
  305. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  307. # Find registration directories
  308. reg_dirs = list(data_path.glob("sub*/ses-02/func/run*/reg"))
  310. # Process first 10 registration directories
  311. for curr_reg in reg_dirs:
  312. subject_id = curr_reg.parts[-5]
  313. session_id = curr_reg.parts[-4]
  314. task_id = curr_reg.parts[-2]
  316. print(f"Current subject: {subject_id}, Session: {session_id}, Task: {task_id}")
  318. # Ensure the image file exists before displaying
  319. image_path = curr_reg / "example_func2highres.png"
  320. if image_path.exists():
  321. display(Image(filename=str(image_path)))
  322. else:
  323. print(f"Warning: {image_path} not found, skipping.")
  326. # %% [markdown]
  327. # # Conver Ants file to FSL formt
  329. # %%
  330. import glob
  331. import os
  332. from pathlib import Path
  334. # Define data path
  335. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  337. # Find registration directories
  338. reg_dirs = list(data_path.glob("sub*/ses-02/func/run*/reg"))
  340. # Process each registration directory
  341. for curr_reg in reg_dirs:
  342. subject_id = curr_reg.parts[-5]
  343. session_id = curr_reg.parts[-4]
  344. task_id = curr_reg.parts[-2]
  346. print(f"Current subject: {subject_id}, Session: {session_id}, Task: {task_id}")
  348. # Change path only if it exists
  349. if curr_reg.exists():
  350. os.chdir(curr_reg)
  351. else:
  352. print(f"\tWarning: {curr_reg} does not exist, skipping.")
  353. continue
  355. # Convert Transform Files
  356. print("\tConverting transformation files...")
  357. os.system("c3d_affine_tool -itk highres2standard_0GenericAffine.mat -o ants_affine_world.txt")
  358. os.system("wb_command -convert-affine -from-world ants_affine_world.txt -to-flirt ants_affine_flirt.txt standard.nii.gz highres.nii.gz")
  359. os.system("convert_xfm -inverse ants_affine_flirt.txt -omat ants_affine_inv_flirt.txt")
  361. # Apply X and Y flips to warp fields
  362. print("\tApplying flips to warp fields...")
  363. os.system("wb_command -volume-math '-x' ants_warponly_negative.nii.gz -var x highres2standard_1Warp.nii.gz")
  364. os.system("wb_command -volume-merge ants_warponly_world.nii.gz -volume ants_warponly_negative.nii.gz -subvolume 1 -up-to 2 -volume highres2standard_1Warp.nii.gz -subvolume 3")
  365. os.system("wb_command -convert-warpfield -from-world ants_warponly_world.nii.gz -to-fnirt ants_warponly_fnirt.nii.gz standard.nii.gz")
  367. # Compose transformation
  368. print("\tComposing transformation...")
  369. os.system("convertwarp --ref=standard.nii.gz --premat=ants_affine_inv_flirt.txt --warp1=ants_warponly_fnirt.nii.gz --out=highres2standard_warp.nii.gz")
  371. # Resample for sanity check
  372. print("\tResampling for sanity check...")
  373. os.system("applywarp --ref=standard.nii.gz --in=highres.nii.gz --warp=highres2standard_warp.nii.gz --out=highres2standard.nii.gz")
  375. # Generate visualization
  376. print("\tGenerating visualization...")
  377. os.system("slicer highres2standard standard -s 2 "
  378. "-x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png "
  379. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  380. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  381. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  382. "+ sli.png + slj.png + slk.png + sll.png highres2standard1.png ; "
  383. "slicer standard highres2standard -s 2 "
  384. "-x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png "
  385. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  386. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  387. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  388. "+ sli.png + slj.png + slk.png + sll.png highres2standard2.png ; "
  389. "pngappend highres2standard1.png - highres2standard2.png highres2standard.png; "
  390. "rm -f sl?.png highres2standard2.png")
  392. os.system("rm highres2standard1.png")
  394. # Apply transformation to functional image
  395. print("\tApplying transformation to functional image...")
  396. os.system("convertwarp --ref=standard --premat=example_func2highres.mat --warp1=highres2standard_warp --out=example_func2standard_warp")
  397. os.system("applywarp --ref=standard --in=example_func_brain --out=example_func2standard --warp=example_func2standard_warp")
  399. # Generate visualization for functional transformation
  400. print("\tGenerating visualization for functional transformation...")
  401. os.system("slicer example_func2standard standard -s 2 "
  402. "-x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png "
  403. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  404. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  405. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  406. "+ sli.png + slj.png + slk.png + sll.png example_func2standard1.png ; "
  407. "slicer standard example_func2standard -s 2 "
  408. "-x 0.35 sla.png -x 0.45 slb.png -x 0.55 slc.png -x 0.65 sld.png "
  409. "-y 0.35 sle.png -y 0.45 slf.png -y 0.55 slg.png -y 0.65 slh.png "
  410. "-z 0.35 sli.png -z 0.45 slj.png -z 0.55 slk.png -z 0.65 sll.png ; "
  411. "pngappend sla.png + slb.png + slc.png + sld.png + sle.png + slf.png + slg.png + slh.png "
  412. "+ sli.png + slj.png + slk.png + sll.png example_func2standard2.png ; "
  413. "pngappend example_func2standard1.png - example_func2standard2.png example_func2standard.png; "
  414. "rm -f sl?.png example_func2standard2.png")
  416. print()
  419. # %% [markdown]
  420. # # plot registration results: functional >> Atlas space
  422. # %%
  423. %matplotlib inline
  425. from IPython.display import display, Image
  426. import matplotlib.pyplot as plt
  427. from pathlib import Path
  429. # Define data path
  430. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  432. # Find registration directories
  433. reg_dirs = list(data_path.glob("sub*/ses-02/func/run*/reg"))
  435. # Process each registration directory
  436. for curr_reg in reg_dirs:
  437. subject_id = curr_reg.parts[-5]
  438. session_id = curr_reg.parts[-4]
  439. task_id = curr_reg.parts[-2]
  441. print(f"Current subject: {subject_id}, Session: {session_id}, Task: {task_id}")
  443. # Ensure the image file exists before displaying
  444. image_path = curr_reg / "example_func2standard.png"
  445. if image_path.exists():
  446. display(Image(filename=str(image_path)))
  447. else:
  448. print(f"Warning: {image_path} not found, skipping.")
  451. # %% [markdown]
  452. # # Invert the warp from MNI 2 functional space
  454. # %%
  455. import glob
  456. import os
  457. from pathlib import Path
  459. # Define data path
  460. data_path = Path("/mnt/d/Data/Human/ObjectCategorization/analysis2")
  462. # Find registration directories
  463. sub_dirs = list(data_path.glob("sub*/ses-02/func/run*/reg"))
  465. # Process each registration directory
  466. for curr_reg in sub_dirs:
  467. print(f"Processing: {curr_reg}")
  469. # Change directory only if it exists
  470. if curr_reg.exists():
  471. os.chdir(curr_reg)
  472. else:
  473. print(f"\tWarning: {curr_reg} does not exist, skipping.")
  474. continue
  476. # Define warp file paths
  477. warp_file = curr_reg / "example_func2standard_warp.nii.gz"
  478. ref_image = curr_reg / "example_func_brain.nii.gz"
  479. output_warp = curr_reg / "standard2example_func_warp.nii.gz"
  480. output_image = curr_reg / "standard2example_func.nii.gz"
  482. # Ensure warp file exists before running transformations
  483. if warp_file.exists():
  484. print("\tRunning inverse warp...")
  485. os.system(f"invwarp --ref={ref_image} --warp={warp_file} --out={output_warp}")
  487. print("\tApplying warp transformation...")
  488. os.system(f"applywarp --ref={ref_image} --in=standard --out={output_image} --warp={output_warp}")
  489. else:
  490. print(f"\tWarning: Warp file {warp_file} not found, skipping transformations.")
  493. # %%

08-Registration.ipynb at commit c67a3d2, no license · at the source

Overview

Authors: Sepideh Tabrik1,2, Sama Rahnemayan3,4, Martin Tegenthoff1, Mehdi Behroozi5,6
ORCID iDs: Mehdi Behroozi
  1. Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr University Bochum,44789 Bochum, Germany
  2. Department of Information Technology, University of Applied Sciences and Arts Dortmund,Dortmund, Germany
  3. Department of Neurosurgery, Medical University of Vienna,Vienna, Austria
  4. High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna,Vienna, Austria
  5. Institute of Cognitive Neuroscience, Department of Biopsychology, Faculty of Psychology, Ruhr University Bochum,44780 Bochum, Germany
  6. Cognitive Neurobiology, Research Center “One Health” Ruhr, University Alliance Ruhr, Faculty of Biology and Biotechnology, Ruhr-University Bochum,Universitätsstraße 150, 44801 Bochum, Germany
Institutions: Ruhr University Bochum (Germany); Dortmund University of Applied Sciences and Arts (Germany); Medical University of Vienna (Austria)
Journal: Scientific data, volume 13, issue 1, article 1064
Dates: received 2 July 2025; accepted 30 June 2026; published online 16 July 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07814-y · PMID 42463676 · PMCID PMC13376767 · OpenAlex W7168814765
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Preprocessing
Keywords: Cognitive neuroscience, Short-term memory
MeSH: Learning*, Touch Perception*, Visual Perception*, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Journal subjects: Data Descriptor
Funding: Deutsche Forschungsgemeinschaft (DFG) (SFB 1280, SFB1280 project #316803389)
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

How do humans recognize objects in their environment using either vision or touch, even when only one sense is available? Humans routinely integrate visual and haptic sensory systems for object recognition, yet the mechanisms behind cross-modal transfer of category information are not fully understood. In this study, we collected behavioral and fMRI data from 134 participants. Fifty participants (25 per group; 13 females each) performed a similarity rating task with novel 3D objects (“digital embryos”) using either visual or haptic modalities. The remaining 84 participants completed a 3D object category learning experiment in unimodal (Visual-Visual: n = 19, 11 females; Haptic-Haptic: n = 22, 10 females) or cross-modal (Visual-Haptic: n = 22, 11 females; Haptic-Visual: n = 21, 10 females) conditions. Alongside task-based fMRI, we acquired resting-state fMRI data at four time points: pre-training, post-training, pre-test, and post-test. High-resolution T1-weighted images were also obtained for each MRI session. These data support investigations into visual and haptic representations of the environment, representational similarity across modalities, cross-modal transfer of category information for object recognition, and neural plasticity in brain networks.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

Sepideh-Tabrik/Similarity-Rating-Task

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ded9f64b08f0ff67e00753b999fd4648507a2a6c, 15 May 2025
Languages: MATLAB (1)
Size: 20 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/Sepideh-Tabrik/Similarity-Rating-Task

Sepideh-Tabrik/Neural-mechanisms-underlying-cross-modal-3D-category-learning

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c67a3d2286492d113a66a496ab0cee3b80178d21, 26 May 2026
Languages: Jupyter (26), MATLAB (5)
Size: 34 files, 31 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 26 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (20 files), Matplotlib (6 files), AFNI (4 files), ANTs (2 files), FreeSurfer (2 files), Violinplot-Matlab (2 files), Connectome Workbench (2 files), Statistics and Machine Learning Toolbox (1 file), NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
32 files
At the source: github.com/Sepideh-Tabrik/Neural-mechanisms-underlying-cross-modal-3D-category-learning

Zenodo 15646449

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 5 files
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

Code availability

The stimuli and data preprocessing scripts for the similarity rating task are available on GitHub at https://github.com/Sepideh-Tabrik/Similarity-Rating-Task. The code for neuroimaging data processing is accessible at https://github.com/Sepideh-Tabrik/Neural-mechanisms-underlying-cross-modal-3D-category-learning.

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

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 32 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The datasets described in this manuscript are openly available at the following repositories: raw neuroimaging data (10.60517/0874db71-e087-444b-96eb-41b97dc49a7d), preprocessed neuroimaging data (10.60517/80bf4025-2594-4a46-93b2-b729867d6d48), and similarity rating experiment data (https://data.mendeley.com/datasets/8k7ys2xw4h/1).

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 7 MeSH terms, 1 funder, 38 references.

Cite

This paper

Tabrik, S., Rahnemayan, S., Tegenthoff, M., & Behroozi, M. (2026). Larg-scale multimodal fMRI dataset of visual and haptic 3D object category learning. Scientific data, 13(1), 1064. https://doi.org/10.1038/s41597-026-07814-y

BibTeX

@article{tabrik2026larg,
author = {Tabrik, Sepideh and Rahnemayan, Sama and Tegenthoff, Martin and Behroozi, Mehdi},
title = {{Larg-scale multimodal fMRI dataset of visual and haptic 3D object category learning}},
journal = {Scientific data},
year = {2026},
month = jul,
volume = {13},
number = {1},
pages = {1064},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07814-y},
url = {https://doi.org/10.1038/s41597-026-07814-y},
pmid = {42463676},
pmcid = {PMC13376767}
}

RIS

TY - JOUR
AU - Tabrik, Sepideh
AU - Rahnemayan, Sama
AU - Tegenthoff, Martin
AU - Behroozi, Mehdi
TI - Larg-scale multimodal fMRI dataset of visual and haptic 3D object category learning
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/07/16
VL - 13
IS - 1
SP - 1064
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07814-y
UR - https://doi.org/10.1038/s41597-026-07814-y
LA - en
ER -

CSL-JSON

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"issued": {
"date-parts": [
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2026,
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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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