OSCR

Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping.

Code ↔ Paper

15 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 15 matches
  1. [1] § Methods › Experimental Paradigms › Biological Motion ↔ ibc_public/utils_paradigm.py, lines 329–388 · score 0.88 · modified upright, global upright, modified inverted, global inverted, natural upright, natural inverted
  2. [2] § Methods › Experimental Paradigms › CamCAN battery ↔ ibc_public/utils_contrasts.py, lines 69–128 · score 0.79 · Finger Tapping, Stop NoGo, Emotional Memory, Emotional Recognition, Catell, Oddball
  3. [3] § Methods › Data Analysis › Preprocessing ↔ scripts/dmri_preprocessing_tractography.py, lines 336–418 · score 0.74 · anatomical image, FSL, FreeSurfer, Segmentation, transformation, preprocessed
  4. [4] § Methods › Experimental Paradigms › Neuroimaging Analysis Replication and Prediction Study (NARPS) ↔ NARPS/protocol.m, lines 300–373 · score 0.74 · weakly reject, weakly accept, strongly reject, losses, NARPS, 20 units
  5. [5] § Methods › Experimental Paradigms › Neuroimaging Analysis Replication and Prediction Study (NARPS) ↔ NARPS/training.m, lines 302–375 · score 0.74 · weakly reject, weakly accept, strongly reject, losses, NARPS, 20 units
  6. [6] § Data Records ↔ ibc_public/utils_contrasts.py, lines 69–128 · score 0.72 · FaceBody, MathLanguage, Spatial Navigation, Reward Processing, NARPS, IBC
  7. [7] § Methods › Experimental Paradigms › Spatial Navigation ↔ SpatialNavigation/protocol/defineOptions.py, lines 43–81 · score 0.67 · Town Hall, Spatial Navigation, Church, streets, houses, location
  8. [8] § Methods › Experimental Paradigms › Mathematics and Language (MathLanguage) ↔ ibc_public/utils_paradigm.py, lines 329–388 · score 0.64 · arithmetic facts, geometry facts, auditory, Language
  9. [9] § Methods › Data Analysis › Preprocessing ↔ ibc_public/utils_relaxo.py, lines 806–868 · score 0.62 · MNI152 space, NIfTI images, Segmentation, transformation, volumes, preprocessed
  10. [10] § Methods › Experimental Paradigms › Spatial Navigation ↔ SpatialNavigation/protocol/training_fmri.py, lines 207–255 · score 0.60 · virtual environment, Spatial Navigation, balls, streets, training, phase
  11. [11] § Methods › Experimental Paradigms › Spatial Navigation ↔ SpatialNavigation/protocol/defineOptions.py, lines 43–81 · score 0.59 · Town Hall, Church, intersection, positioned, phase, Navigation
  12. [12] § Technical Validation › Behavioral Data › Scene ↔ ibc_public/utils_paradigm.py, lines 389–448 · score 0.57 · impossible scrambled, scenes, incorrect
  13. [13] § Methods › Data Analysis › Model Specification ↔ papers_scripts/F10002020/MVPA/utils_tonotopy.py, lines 183–280 · score 0.54 · nuisance regressors, HRF, GLM, Model, motion, fMRI
  14. [14] § Technical Validation › Effect of subject identity, task stimuli and phase-encoding direction on activation maps ↔ papers_scripts/scidata2020/neuroimaging_data/global_stat2.py, lines 473–525 · score 0.53 · phase encoding, contrast maps, ANOVA, FDR
  15. [15] § Methods › Data Analysis › Model Estimation ↔ ibc_public/utils_pipeline.py, lines 290–429 · score 0.52 · spatial smoothing, FWHM, regressors, GLM, model, mask

Paper

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The authors' code

Python · 650 lines · 31 KB · BSD-3-Clause · 3 matches

  1. """
  2. Some utils too deal with peculiar protocols or peculiar ways of handling them
  4. Author: Bertrand Thirion, Ana Luisa Grilo Pinho, 2015
  5. """
  6. import numpy as np
  7. from pandas import read_csv, concat
  10. rsvp_language = ['consonant_strings', 'word_list', 'pseudoword_list',
  11. 'jabberwocky', 'simple_sentence', 'probe', 'complex_sentence']
  12. archi_social = [
  13. 'false_belief_video', 'non_speech', 'speech', 'mechanistic_audio',
  14. 'mechanistic_video', 'false_belief_audio', 'triangle_intention',
  15. 'triangle_random', ]
  17. relevant_conditions = {
  18. 'HcpEmotional': ['face', 'shape'],
  19. 'HcpGambling': ['reward', 'punishment', 'neutral'],
  20. 'HcpLanguage': ['math', 'story'],
  21. 'HcpMotor': ['left_hand', 'right_hand', 'left_foot', 'right_foot',
  22. 'cue', 'tongue'],
  23. 'HcpRelational': ['relational', 'cue', 'control'],
  24. 'HcpSocial': ['mental', 'response', 'random'],
  25. 'HcpWm': ['2back_body', '0back_body', '2back_face', '0back_face',
  26. '2back_tools', '0back_tools', '0back_place', '2back_place'],
  27. 'ArchiSocial': archi_social,
  28. 'RSVPLanguage': rsvp_language,
  29. }
  32. def post_process(df, paradigm_id):
  33. if paradigm_id == 'RSVPLanguage':
  34. targets = ['complex_sentence_objrel',
  35. 'complex_sentence_objclef',
  36. 'complex_sentence_subjrel']
  37. for target in targets:
  38. df = df.replace(target, 'complex_sentence')
  39. targets = ['simple_sentence_cvp',
  40. 'simple_sentence_adj',
  41. 'simple_sentence_coord']
  42. for target in targets:
  43. df = df.replace(target, 'simple_sentence')
  45. # df.onset *= .001
  46. # df.duration = 3 * np.ones(len(df.duration))
  47. if paradigm_id == 'HcpMotor':
  48. df = df.replace('right_foot_cue', 'cue')
  49. df = df.replace('left_foot_cue', 'cue')
  50. df = df.replace('right_hand_cue', 'cue')
  51. df = df.replace('left_hand_cue', 'cue')
  52. df = df.replace('tongue_cue', 'cue')
  54. if paradigm_id == 'Visu':
  55. df = df.replace('visage', 'face')
  56. if paradigm_id == 'Audi':
  57. df = df.replace('envir', 'environment')
  58. if paradigm_id == '':
  59. pass
  61. if paradigm_id in relevant_conditions.keys():
  62. relevant_items = relevant_conditions[paradigm_id]
  63. condition = np.array(
  64. [df.trial_type == r for r in relevant_items])\
  65. .sum(0).astype('bool')
  66. df = df[condition]
  68. if paradigm_id[:10] == 'Preference':
  69. domain = paradigm_id[10:].lower()
  70. if domain[-1] == 's':
  71. domain = domain[:-1]
  72. #
  73. linear = df[df.trial_type == domain]['score'].values.astype('float')
  74. linear[np.isnan(linear)] = np.nanmean(linear)
  75. mean = linear.mean()
  76. linear -= mean
  77. df1 = df[df.trial_type == domain]
  78. df1['modulation'] = linear
  79. df1 = df1.fillna(1)
  80. # add a regressor with constant values
  81. df2 = df[df.trial_type == domain]
  82. df2['modulation'] = np.ones_like(linear)
  83. df2.trial_type = '%s_constant' % domain
  84. # add quadratic regressor
  85. df3 = df[df.trial_type == domain]
  86. quadratic = linear ** 2
  87. quadratic -= quadratic.mean()
  88. quadratic -= (linear * np.dot(quadratic, linear) /
  89. np.dot(linear, linear))
  90. df3['modulation'] = quadratic
  91. df3.trial_type = '%s_quadratic' % domain
  92. df1 = df1.replace(domain, '%s_linear' % domain)
  93. df = concat([df1, df2, df3], axis=0, ignore_index=True)
  94. df.drop('score', axis=1)
  96. responses_we = ['response_we_east_present_space_close',
  97. 'response_we_west_present_space_far',
  98. 'response_we_center_past_space_far',
  99. 'response_we_west_present_time_close',
  100. 'response_we_east_present_time_far',
  101. 'response_we_center_past_space_close',
  102. 'response_we_center_present_space_close',
  103. 'response_we_center_present_space_far',
  104. 'response_we_center_present_time_far',
  105. 'response_we_east_present_time_cl3ose',
  106. 'response_we_center_past_time_close',
  107. 'response_we_center_past_time_far',
  108. 'response_we_east_present_space_far',
  109. 'response_we_center_future_time_far',
  110. 'response_we_center_future_time_far',
  111. 'response_we_center_future_time_close',
  112. 'response_we_west_present_space_close',
  113. 'response_we_center_present_time_close',
  114. 'response_we_center_present_time_close',
  115. 'response_we_center_future_space_far',
  116. 'response_we_center_future_space_close',
  117. 'response_we_west_present_time_far']
  119. responses_sn = ['response_sn_north_present_space_far',
  120. 'response_sn_south_present_time_close',
  121. 'response_sn_center_present_space_close',
  122. 'response_sn_south_present_time_far',
  123. 'response_sn_center_future_space_close',
  124. 'response_sn_center_past_space_close',
  125. 'response_sn_north_present_time_close',
  126. 'response_sn_center_past_space_far',
  127. 'response_sn_south_present_space_close',
  128. 'response_sn_center_present_time_far',
  129. 'response_sn_center_past_time_far',
  130. 'response_sn_center_future_space_far',
  131. 'response_sn_center_future_space_far',
  132. 'response_sn_center_future_time_close',
  133. 'response_sn_center_past_time_close',
  134. 'response_sn_north_present_time_far',
  135. 'response_sn_south_present_space_far',
  136. 'response_sn_center_present_time_close',
  137. 'response_sn_north_present_space_close',
  138. 'response_sn_center_present_space_far',
  139. 'response_sn_center_future_time_far',
  140. 'response_sn_center_future_time_far']
  142. ###
  143. if paradigm_id == 'MTTNS':
  144. for response in responses_sn:
  145. df = df.replace(response, 'response')
  147. if paradigm_id == 'MTTWE':
  148. for response in responses_we:
  149. df = df.replace(response, 'response')
  150. ###
  152. if paradigm_id == 'enumeration':
  153. for i in range(1, 9):
  154. df = df.replace('memorization_num_%d' % i, 'response_num_%d' % i)
  155. if paradigm_id == 'VSTM':
  156. for i in range(1, 7):
  157. df = df.replace('memorization_num_%d' % i, 'response_num_%d' % i)
  159. if paradigm_id == 'Self':
  160. df = df.replace('self_relevance_with_response', 'encode_self')
  161. df = df.replace('other_relevance_with_response', 'encode_other')
  162. df = df.replace('self_relevance_with_no_response',
  163. 'encode_self_no_response')
  164. df = df.replace('other_relevance_with_no_resp3onse',
  165. 'encode_other_no_reponse')
  166. df = df.replace('old_self_hit', 'recognition_self_hit')
  167. df = df.replace('old_self_miss', 'recognition_self_miss')
  168. df = df.replace('old_other_hit', 'recognition_other_hit')
  169. df = df.replace('old_other_miss', 'recognition_other_miss')
  170. df = df.replace('new_fa', 'false_alarm')
  171. df = df.replace('new_cr', 'correct_rejection')
  172. df = df.replace('old_self_no_response', 'recognition_self_no_response')
  173. df = df.replace('old_other_no_response',
  174. 'recognition_other_no_response')
  176. instructions = ['Ins_bouche', 'Ins_index', 'Ins_jambe',
  177. 'Ins_main', 'Ins_repos', 'Ins_yeux', ]
  178. if paradigm_id == 'Moto':
  179. for instruction in instructions:
  180. df = df.replace(instruction, 'instructions')
  181. df = df.replace('sacaade_right', 'saccade_right')
  182. df = df.replace('sacaade_left', 'saccade_left')
  183. # df = df.replace('Bfix', 'fixation')
  184. df = df[df.trial_type != 'Bfix']
  186. if paradigm_id == 'MCSE':
  187. df = df[df.trial_type != 'Bfix']
  189. if paradigm_id == 'Lec1':
  190. df = df[df.trial_type != 'Bfix']
  191. df = df[df.trial_type != 'start_random_string']
  192. df = df[df.trial_type != 'start_pseudoword']
  193. df = df[df.trial_type != 'start_word']
  195. if paradigm_id == 'Lec2':
  196. df = df[df.trial_type != 'Bfix']
  197. df = df[df.trial_type != 'Suite']
  199. if paradigm_id == 'Visu':
  200. df = df[df.trial_type != 'Bfix']
  202. if paradigm_id == 'Audi':
  203. df = df[df.trial_type != 'Bfix']
  204. df = df[df.trial_type != 'start_sound']
  205. df = df[df.trial_type != 'cut']
  206. df = df[df.trial_type != '1']
  208. if paradigm_id == 'MVIS':
  209. df = df[df.trial_type != 'grid']
  210. df = df[df.trial_type != 'Bfix']
  211. df = df[df.trial_type != 'maintenance']
  213. if paradigm_id == 'MVEB':
  214. df = df[df.trial_type != 'cross']
  215. df = df[df.trial_type != 'blank2']
  217. if paradigm_id == 'Audio':
  218. voices = ['voice_%d' % i for i in range(60)]
  219. musics = ['music_%d' % i for i in range(60)]
  220. animals = ['animal_%d' % i for i in range(60)]
  221. speeches = ['speech_%d' % i for i in range(60)]
  222. natures = ['nature_%d' % i for i in range(60)]
  223. tools = ['tools_%d' % i for i in range(60)]
  224. for voice in voices:
  225. df = df.replace(voice, 'voice')
  226. for animal in animals:
  227. df = df.replace(animal, 'animal')
  228. for music in musics:
  229. df = df.replace(music, 'music')
  230. for speech in speeches:
  231. df = df.replace(speech, 'speech')
  232. for nature in natures:
  233. df = df.replace(nature, 'nature')
  234. for tool in tools:
  235. df = df.replace(tool, 'tool')
  236. df.drop(df[df.trial_type == 'fixation'].index, inplace=True)
  237. if paradigm_id == 'Attention':
  238. df = df[df.trial_type.isin([
  239. 'spatial_incongruent', 'double_congruent', 'spatial_congruent',
  240. 'double_incongruent', 'spatialcue', 'doublecue'])]
  241. if paradigm_id == 'StopSignal':
  242. df = df[df.trial_type.isin(['go', 'stop'])]
  243. if paradigm_id in ['WardAndAllport']:
  244. df = df[df.trial_type.isin([
  245. 'planning_PA_with_intermediate',
  246. 'planning_PA_without_intermediate',
  247. 'planning_UA_with_intermediate',
  248. 'planning_UA_without_intermediate',
  249. 'move_PA_with_intermediate',
  250. 'move_PA_without_intermediate',
  251. 'move_UA_with_intermediate',
  252. 'move_UA_without_intermediate'])]
  254. df.replace('planning_PA_with_intermediate',
  255. 'planning_ambiguous_intermediate', inplace=True)
  256. df.replace('planning_PA_without_intermediate',
  257. 'planning_ambiguous_direct', inplace=True)
  258. df.replace('planning_UA_with_intermediate',
  259. 'planning_unambiguous_intermediate', inplace=True)
  260. df.replace('planning_UA_without_intermediate',
  261. 'planning_unambiguous_direct', inplace=True)
  262. df.replace('move_PA_with_intermediate',
  263. 'move_ambiguous_intermediate', inplace=True)
  264. df.replace('move_PA_without_intermediate',
  265. 'move_ambiguous_direct', inplace=True)
  266. df.replace('move_UA_with_intermediate',
  267. 'move_unambiguous_intermediate', inplace=True)
  268. df.replace('move_UA_without_intermediate', 'move_unambiguous_direct',
  269. inplace=True)
  271. if paradigm_id == 'TwoByTwo':
  272. df = df[df.trial_type.isin([
  273. 'cue_taskstay_cuestay',
  274. 'cue_taskstay_cueswitch',
  275. 'cue_taskswitch_cuestay',
  276. 'cue_taskswitch_cueswitch',
  277. 'stim_taskstay_cuestay',
  278. 'stim_taskstay_cueswitch',
  279. 'stim_taskswitch_cuestay',
  280. 'stim_taskswitch_cueswitch'])]
  281. if paradigm_id == 'Discount':
  282. df = df[df.trial_type.isin(['stim'])]
  283. df1 = df.copy()
  284. df1['modulation'] = df1['large_amount'].astype(float)
  285. df1.drop('later_delay', 1, inplace=True)
  286. df1.drop('large_amount', 1, inplace=True)
  287. df1.replace('stim', 'amount', inplace=True)
  288. df2 = df.copy()
  289. df2['modulation'] = df2['later_delay'].astype(float)
  290. df2.drop('large_amount', 1, inplace=True)
  291. df2.drop('later_delay', 1, inplace=True)
  292. df2.replace('stim', 'delay', inplace=True)
  293. df = concat([df1, df2], axis=0, ignore_index=True)
  294. if paradigm_id == 'SelectiveStopSignal':
  295. df = df[df.trial_type.isin(['go_critical', 'go_noncritical',
  296. 'ignore_noncritical',
  297. 'stop_critical'])]
  298. df = df.replace('ignore_noncritical', 'ignore')
  299. df = df.replace('stop_critical', 'stop')
  300. if paradigm_id == 'Stroop':
  301. df = df[df.trial_type.isin(['congruent', 'incongruent'])]
  302. if paradigm_id == 'ColumbiaCards':
  303. df = df[df.trial_type.isin(['card_flip'])]
  304. df1 = df.copy()
  305. df1.replace('card_flip', 'gain', inplace=True)
  306. df1['modulation'] = df1['gain_amount'].astype(float)
  307. df2 = df.copy()
  308. df2.replace('card_flip', 'loss', inplace=True)
  309. df2['modulation'] = df2['loss_amount'].astype(float)
  310. df3 = df.copy()
  311. df3.replace('card_flip', 'num_loss_cards', inplace=True)
  312. df3['modulation'] = df3['num_loss_cards'].astype(float)
  313. df = concat([df1, df2, df3], axis=0, ignore_index=True)
  314. df.drop('loss_amount', 1, inplace=True)
  315. df.drop('num_loss_cards', 1, inplace=True)
  316. df.drop('gain_amount', 1, inplace=True)
  317. if paradigm_id == 'DotPatterns':
  318. df.replace('cue_AX', 'cue', inplace=True)
  319. df.replace('cue_BX', 'cue', inplace=True)
  320. df.replace('cue_AY', 'cue', inplace=True)
  321. df.replace('cue_BY', 'cue', inplace=True)
  322. df = df[df.trial_type.isin([
  323. 'probe_BY', 'probe_AY', 'probe_BX', 'probe_AX', 'cue'])]
  324. df.replace('probe_AX', 'correct_cue_correct_probe', inplace=True)
  325. df.replace('probe_BX', 'incorrect_cue_correct_probe', inplace=True)
  326. df.replace('probe_AY', 'correct_cue_incorrect_probe', inplace=True)
  327. df.replace('probe_BY', 'incorrect_cue_incorrect_probe', inplace=True)
  329. if paradigm_id == 'BiologicalMotion1':
  330. df = df[df.trial_type.isin(['global_upright', 'global_inverted',
  331. 'natural_upright', 'natural_inverted'])]
  332. if paradigm_id == 'BiologicalMotion2':
  333. df = df[df.trial_type.isin(['modified_upright', 'modified_inverted',
  334. 'natural_upright', 'natural_inverted'])]
  335. if paradigm_id == 'MathLanguage':
  336. trial_types = [
  337. 'colorlessg_auditory', 'colorlessg_visual',
  338. 'wordlist_auditory', 'wordlist_visual',
  339. 'arithmetic_fact_auditory', 'arithmetic_fact_visual',
  340. 'arithmetic_principle_auditory', 'arithmetic_principle_visual',
  341. 'theory_of_mind_auditory', 'theory_of_mind_visual',
  342. 'geometry_fact_visual', 'geometry_fact_auditory',
  343. 'general_visual', 'general_auditory',
  344. 'context_visual', 'context_auditory']
  345. df = df[df.trial_type.isin(trial_types)]
  346. if paradigm_id == 'SpatialNavigation':
  347. for intersection_ in ['intersection_%d' % i for i in range(4)]:
  348. df.replace(intersection_, 'intersection', inplace=True)
  349. trial_types = ['encoding_phase', 'navigation', 'experimental',
  350. 'pointing_experimental', 'control', 'pointing_control',
  351. 'intersection']
  352. df = df[df.trial_type.isin(trial_types)]
  353. if paradigm_id == 'EmoMem':
  354. pass
  355. if paradigm_id == 'EmoReco':
  356. pass
  357. if paradigm_id == 'StopNogo':
  358. pass
  359. if paradigm_id == 'Catell':
  360. pass
  361. if paradigm_id == 'RewProc':
  362. df.drop(df[df.trial_type == 'prefix'].index, 0, inplace=True)
  363. df.drop(df[df.trial_type == 'postfix'].index, 0, inplace=True)
  364. df.replace('out_+10', 'plus_10', inplace=True)
  365. df.replace('out_+20', 'plus_20', inplace=True)
  366. df.replace('out_-10', 'minus_10', inplace=True)
  367. df.replace('out_-20', 'minus_20', inplace=True)
  368. green = [tt for tt in df.trial_type.unique() if 'green' in tt]
  369. left = [tt for tt in df.trial_type.unique() if 'left' in tt]
  370. stay = [tt for tt in df.trial_type.unique() if 'stay' in tt]
  371. switch = [tt for tt in df.trial_type.unique() if 'switch' in tt]
  372. resp = [tt for tt in df.trial_type.unique() if 'resp' in tt]
  373. df1 = df.copy()
  374. df1 = df1[df.trial_type.isin(green)]
  375. df1.trial_type = 'green'
  376. df2 = df.copy()
  377. df2 = df2[df.trial_type.isin(left)]
  378. df2.trial_type = 'left'
  379. df3 = df.copy()
  380. df3 = df3[df.trial_type.isin(switch)]
  381. df3.trial_type = 'switch'
  382. df4 = df.copy()
  383. df4 = df4[df.trial_type.isin(stay)]
  384. df4.trial_type = 'stay'
  385. df.drop(df[df.trial_type.isin(resp)].index, 0, inplace=True)
  386. df = concat([df, df1, df2, df3, df4], axis=0, ignore_index=True)
  387. if paradigm_id == 'NARPS':
  388. df.drop(df[df.trial_type == 'fix'].index, 0, inplace=True)
  389. stim = [tt for tt in df.trial_type.unique() if 'stim' in tt]
  390. resp = [tt for tt in df.trial_type.unique() if 'stim' not in tt]
  391. df1 = df.copy()
  392. df1 = df1[df.trial_type.isin(stim)]
  393. df2 = df1.copy()
  394. mod1 = np.array([float(x.split('+')[1].split('_')[0])
  395. for x in df1.trial_type.values])
  396. mod2 = np.array([float(x.split('-')[1])
  397. for x in df2.trial_type.values])
  398. df1['modulation'] = mod1 - mod1.mean() # tbc
  399. df2['modulation'] = mod2 - mod2.mean() # tbc
  400. df1.trial_type = 'gain'
  401. df2.trial_type = 'loss'
  402. df = df[df.trial_type.isin(resp)]
  403. df['modulation'] = 1
  404. df = concat([df, df1, df2], axis=0, ignore_index=True)
  405. if paradigm_id == 'FaceBody':
  406. df.drop(df[df.trial_type == 'Baseline'].index, inplace=True)
  407. df.replace('Bodies_body', 'bodies_body', inplace=True)
  408. df.replace('Bodies_limb', 'bodies_limb', inplace=True)
  409. df.replace('Characters_number', 'characters_number', inplace=True)
  410. df.replace('Characters_word', 'characters_word', inplace=True)
  411. df.replace('Faces_adult', 'faces_adult', inplace=True)
  412. df.replace('Faces_child', 'faces_child', inplace=True)
  413. df.replace('Objects_car', 'objects_car', inplace=True)
  414. df.replace('Objects_instrument', 'objects_instrument', inplace=True)
  415. df.replace('Places_corridor', 'places_corridor', inplace=True)
  416. df.replace('Places_house', 'places_house', inplace=True)
  417. if paradigm_id == 'Scene':
  418. df.drop(df[df.trial_type == 'fix'].index, inplace=True)
  419. df.drop(df[df.trial_type == 'iti'].index, inplace=True)
  420. df.replace('dot_easy_left_correct', 'possible_scrambled_left', inplace=True)
  421. df.replace('dot_easy_left_incorrect', 'possible_scrambled_left', inplace=True)
  422. df.replace('dot_easy_right_correct', 'possible_scrambled_right', inplace=True)
  423. df.replace('dot_easy_right_incorrect', 'possible_scrambled_right', inplace=True)
  424. df.replace('dot_hard_left_correct', 'impossible_scrambled_left', inplace=True)
  425. df.replace('dot_hard_left_incorrect', 'impossible_scrambled_left', inplace=True)
  426. df.replace('dot_hard_right_correct', 'impossible_scrambled_right', inplace=True)
  427. df.replace('dot_hard_right_incorrect', 'impossible_scrambled_right', inplace=True)
  428. if paradigm_id == 'Color':
  429. df.drop(df[df.trial_type == 'fix'].index, inplace=True)
  430. df.drop(df[df.trial_type == '1-back'].index, inplace=True)
  431. df.loc[df.trial_type == 'y', 'duration'] = .5
  432. if paradigm_id == 'Motion':
  433. df.drop(df[df.trial_type == 'iti_fix'].index, inplace=True)
  434. df.loc[df.trial_type == 'y', 'duration'] = .5
  435. if paradigm_id == 'OptimismBias':
  436. df.drop(df[df.trial_type == 'start'].index, inplace=True)
  437. if paradigm_id == 'HarririAomic':
  438. df.drop(df[df.trial_type.isin(['ttl', 'iti'])].index, inplace=True)
  439. if paradigm_id == 'StroopAomic':
  440. df.drop(df[df.trial_type.isin(['ttl', 'iti'])].index, inplace=True)
  441. # todo: distinguish incorrect vs female
  442. if paradigm_id == 'Emotion':
  443. to_drop = ['block_divider']
  444. df.drop(df[df.trial_type.isin(to_drop)].index, inplace=True)
  445. df.replace('NEU_image_display', 'neutral_image', inplace=True)
  446. df.replace('NEG_image_display', 'negative_image', inplace=True)
  447. df.replace('echelle_valence', 'valence_scale', inplace=True)
  448. if paradigm_id == 'MDTB':
  449. to_drop = ['iti']
  450. df.drop(df[df.trial_type.isin(to_drop)].index, inplace=True)
  451. df.replace('semantic_easy_False', 'semantic_easy', inplace=True)
  452. df.replace('semantic_easy_True', 'semantic_easy', inplace=True)
  453. df.replace('semantic_hard_False', 'semantic_hard', inplace=True)
  454. df.replace('semantic_hard_True', 'semantic_hard', inplace=True)
  455. df.replace('search_easy_False', 'search_easy', inplace=True)
  456. df.replace('search_easy_True', 'search_easy', inplace=True)
  457. df.replace('search_hard_False', 'search_hard', inplace=True)
  458. df.replace('search_hard_True', 'search_hard', inplace=True)
  459. df.replace('2back_easy_False', '2back_easy', inplace=True)
  460. df.replace('2back_easy_True', '2back_easy', inplace=True)
  461. df.replace('2back_hard_False', '2back_hard', inplace=True)
  462. df.replace('2back_hard_True', '2back_hard', inplace=True)
  463. df.replace('tom_photo_False', 'tom_photo', inplace=True)
  464. df.replace('tom_photo_True', 'tom_photo', inplace=True)
  465. df.replace('tom_belief_False', 'tom_belief', inplace=True)
  466. df.replace('tom_belief_True', 'tom_belief', inplace=True)
  467. df.replace('', '', inplace=True)
  468. if paradigm_id == 'MultiModal': # aka Leuven task
  469. to_drop = ['fix' ]
  470. df.drop(df[df.trial_type.isin(to_drop)].index, inplace=True)
  471. for x in df.trial_type.unique():
  472. y = x
  473. if 'audio' in x:
  474. y = 'audio_' + x.split('_')[2]
  475. y = y.replace('silence.wav', 'silence')
  476. if 'image' in x :
  477. y = 'image_' + x.split('_')[1]
  478. y = y.replace('humbod', 'human_body')
  479. y = y.replace('monbod', 'monkey_body')
  480. y = y.replace('monobj', 'monkey_object')
  481. y = y.replace('humobj', 'human_object')
  482. y = y.replace('monfac', 'monkey_face')
  483. y = y.replace('humfac', 'human_face')
  484. y = y.replace('sculp', 'sculpture')
  485. if x[4:10] == 'valves':
  486. y = x[:3]
  487. y = y.replace('mid', 'tactile_middle')
  488. y = y.replace('bot', 'tactile_bottom')
  489. y = y.replace('top', 'tactile_top')
  490. df.replace(x, y, inplace=True)
  491. if paradigm_id == 'Mario':
  492. to_drop = [] # fixme
  493. df.drop(df[df.trial_type.isin(to_drop)].index, inplace=True)
  494. if paradigm_id == 'StroopAomic':
  495. df.replace('correct_incongruent_word_male_face_female',
  496. 'incongruent_word_male_face_female', inplace=True)
  497. df.replace('correct_congruent_word_female_face_female',
  498. 'congruent_word_female_face_female', inplace=True)
  499. df.replace('correct_congruent_word_male_face_male',
  500. 'congruent_word_male_face_male', inplace=True)
  501. df.replace('correct_incongruent_word_female_face_male',
  502. 'incongruent_word_female_face_male', inplace=True)
  503. df.replace('incorrect_incongruent_word_male_face_female',
  504. 'incongruent_word_male_face_female', inplace=True)
  505. df.replace('incorrect_incongruent_word_female_face_male',
  506. 'incongruent_word_female_face_male', inplace=True)
  507. if paradigm_id == 'LocalizerAbstraction':
  508. df.replace('Number', 'localizer_numbers', inplace=True)
  509. df.replace('House', 'localizer_places', inplace=True)
  510. df.replace('CheckerH', 'localizer_checkerboards', inplace=True)
  511. df.replace('CheckerV', 'localizer_checkerboards', inplace=True)
  512. df.replace('Tool', 'localizer_objects', inplace=True)
  513. df.replace('Body', 'localizer_humanbody', inplace=True)
  514. df.replace('Word', 'localizer_words', inplace=True)
  515. df.replace('Face', 'localizer_faces', inplace=True)
  516. df.replace('FalseFont', 'localizer_nonsensewords', inplace=True)
  517. # df.replace('FalseFont_probe', '', inplace=True)
  518. # df.replace('Tool_probe', '', inplace=True)
  519. # df.replace('Face_probe', '', inplace=True)
  520. # df.replace('Number_probe', '', inplace=True)
  521. # df.replace('Body_probe', '', inplace=True)
  522. # df.replace('House_probe', '', inplace=True)
  523. # df.replace('Word_probe', '', inplace=True)
  524. if paradigm_id == 'Abstraction':
  525. for trial in df.trial_type.unique():
  526. parts = trial.split('_')
  527. if parts[-1] in ['geometry', 'photo', 'edge']:
  528. trial_ = parts[0] + '_' + parts[2]
  529. df.replace(trial, trial_, inplace=True)
  530. """
  531. humanbody_ = ['humanbody_geometry', '',
  532. 'humanbody_photo']
  533. animals_ = ['animals_geometry', 'animals_edge', 'animals_photo']
  534. faces_ = ['faces_geometry', 'faces_edge', 'faces_photo']
  535. flora_ =['flora_geometry', 'flora_edge', 'flora_photo']
  536. objects_ = ['objects_geometry', 'objects_edge', 'objects_photo']
  537. places_ = ['places_geometry', 'places_edge', 'places_photo']
  539. df.replace('flora_tree_edge', '', inplace=True)
  540. df.replace('flora_flower_edge', '', inplace=True)
  541. df.replace('flora_cherry_edge', '', inplace=True)
  542. df.replace('flora_carrot_edge', '', inplace=True)
  543. df.replace('flora_flower_geometry', '', inplace=True)
  544. df.replace('flora_carrot_geometry', '', inplace=True)
  545. df.replace('flora_tree_geometry', '', inplace=True)
  546. df.replace('flora_cherry_geometry', '', inplace=True)
  547. df.replace('flora_cherry_photo', '', inplace=True)
  548. df.replace('flora_carrot_photo', '', inplace=True)
  549. df.replace('flora_flower_photo', '', inplace=True)
  550. df.replace('flora_tree_photo', '', inplace=True)
  552. df.replace('humanbody_standing_edge', 'humanbody_edge', inplace=True)
  553. df.replace('humanbody_walking_edge', 'humanbody_edge', inplace=True)
  554. df.replace('humanbody_hand_edge', 'humanbody_edge', inplace=True)
  555. df.replace('humanbody_legs_edge', 'humanbody_edge', inplace=True)
  556. df.replace('humanbody_standing_geometry', '', inplace=True)
  557. df.replace('humanbody_walking_geometry', '', inplace=True)
  558. df.replace('humanbody_hand_geometry', '', inplace=True)
  559. df.replace('humanbody_legs_geometry', '', inplace=True)
  560. df.replace('humanbody_hand_photo', '', inplace=True)
  561. df.replace('humanbody_walking_photo', '', inplace=True)
  562. df.replace('humanbody_standing_photo', '', inplace=True)
  563. df.replace('humanbody_legs_photo', '', inplace=True)
  566. df.replace('faces_face_edge', '', inplace=True)
  567. df.replace('faces_cat_edge', '', inplace=True)
  568. df.replace('faces_eyes_edge', '', inplace=True)
  569. df.replace('faces_eyes_geometry', '', inplace=True)
  570. df.replace('faces_cat_geometry', '', inplace=True)
  571. df.replace('faces_face2_geometry', '', inplace=True)
  572. df.replace('faces_face_geometry', '', inplace=True)
  573. df.replace('faces_face2_photo', '', inplace=True)
  574. df.replace('faces_cat_photo', '', inplace=True)
  575. df.replace('faces_eyes_photo', '', inplace=True)
  576. df.replace('faces_face_photo', '', inplace=True)
  578. df.replace('places_windmill_edge', '', inplace=True)
  579. df.replace('places_house_edge', '', inplace=True)
  580. df.replace('places_road_edge', '', inplace=True)
  581. df.replace('places_mountain_edge', '', inplace=True)
  582. df.replace('places_windmill_geometry', '', inplace=True)
  583. df.replace('places_road_geometry', '', inplace=True)
  584. df.replace('places_mountain_geometry', '', inplace=True)
  585. df.replace('places_house_geometry', '', inplace=True)
  586. df.replace('places_house_photo', '', inplace=True)
  587. df.replace('places_road_photo', '', inplace=True)
  588. df.replace('places_windmill_photo', '', inplace=True)
  589. df.replace('places_mountain_photo', '', inplace=True)
  591. df.replace('objects_key_edge', '', inplace=True)
  592. df.replace('objects_camera_edge', '', inplace=True)
  593. df.replace('objects_watch_edge', '', inplace=True)
  594. df.replace('objects_truck_edge', '', inplace=True)
  595. df.replace('objects_camera_geometry', '', inplace=True)
  596. df.replace('objects_truck_geometry', '', inplace=True)
  597. df.replace('objects_key_geometry', '', inplace=True)
  598. df.replace('objects_watch_geometry', '', inplace=True)
  599. df.replace('objects_watch_photo', '', inplace=True)
  600. df.replace('objects_camera_photo', '', inplace=True)
  601. df.replace('objects_key_photo', '', inplace=True)
  602. df.replace('objects_truck_photo', '', inplace=True)
  604. df.replace('animals_girafe_edge', '', inplace=True)
  605. df.replace('animals_bird_edge', '', inplace=True)
  606. df.replace('animals_butterfly_edge', '', inplace=True)
  607. df.replace('animals_fish_edge', '', inplace=True)
  608. df.replace('animals_butterfly_geometry', '', inplace=True)
  609. df.replace('animals_bird_geometry', '', inplace=True)
  610. df.replace('animals_girafe_geometry', '', inplace=True)
  611. df.replace('animals_fish_geometry', '', inplace=True)
  612. df.replace('animals_fish_photo', '', inplace=True)
  613. df.replace('animals_butterfly_photo', '', inplace=True)
  614. df.replace('animals_girafe_photo', '', inplace=True)
  615. df.replace('animals_bird_photo', '', inplace=True)
  616. """
  618. return df
  621. def make_paradigm(onset_file, paradigm_id=None):
  622. """ Temporary fix """
  623. # if paradigm_id in ['WedgeClock', 'WedgeAnti', 'ContRing', 'ExpRing']:
  624. # return None
  625. df = read_csv(onset_file, index_col=None, sep='\t', na_values=['NaN'],
  626. keep_default_na=False)
  627. if 'onset' not in df.keys() and 'Onsets' in df.keys():
  628. df['onset'] = df['Onsets']
  629. df.drop('Onsets', 1, inplace=True)
  630. if 'duration' not in df.keys() and 'Durations' in df.keys():
  631. df['duration'] = df['Durations']
  632. df.drop('Durations', 1, inplace=True)
  633. if 'trial_type' not in df.keys() and 'Conditions' in df.keys():
  634. df['trial_type'] = df['Conditions']
  635. df.drop('Conditions', 1, inplace=True)
  636. if 'onset' not in df.keys() and 'Onset' in df.keys():
  637. df['onset'] = df['Onset']
  638. df.drop('Onset', 1, inplace=True)
  639. if 'duration' not in df.keys() and 'Duration' in df.keys():
  640. df['duration'] = df['Duration']
  641. df.drop('Duration', 1, inplace=True)
  642. if 'trial_type' not in df.keys() and 'Condition' in df.keys():
  643. df['trial_type'] = df['Condition']
  644. df.drop('Condition', 1, inplace=True)
  645. if 'trial_type' not in df.keys() and 'name' in df.keys():
  646. df['trial_type'] = df['name']
  647. df.drop('name', 1, inplace=True)
  648. df = post_process(df, paradigm_id)
  649. df['name'] = df['trial_type']
  650. return df

utils_paradigm.py at commit 2d9a685, under BSD-3-Clause · at the source

Overview

Authors: Ana Fernanda Ponce1, Himanshu Aggarwal1, Swetha Shankar1, Juan Jesús Torre1, Ana Luísa Pinho1,2,3, Alexis Thual1, Chantal Ginisty4, Yann Lecomte4, Valérie Berland4, Lucile Beriot4, Laurence Laurier4, Véronique Joly-Testault4, Gaëlle Médiouni-Cloarec4, Lucie Hertz-Pannier4, Christine Doublé4, Bernadette Martins4, Marie Amalric5, Stanislas Dehaene5,6, Nadine Diersch7, Thomas Wolbers7
and 16 other authorsMeredith A Shafto8, John P O’Doherty9, Vincent Man9, Raymond J Dolan10, Russell A Poldrack11, Anthony Stigliani11, Kalanit Grill-Spector11,12, Danielle Douglas13, Andy C H Lee13, David B Keator14, Steven G Potkin14, Dorita H F Chang15, Nikolaus F Troje16, Bo-Cheng Kuo17,18, Duncan E Astle17, Bertrand Thirion1
ORCID iDs: Ana Fernanda Ponce, Ana Luísa Pinho, Raymond J Dolan, Russell A Poldrack, Kalanit Grill-Spector, Dorita H F Chang, Bertrand Thirion
18 affiliations
  1. Université Paris-Saclay, Inria, CEA, Palaiseau, 91120 France
  2. Department of Computer Science, Western University, London, Ontario N6A 5B7 Canada
  3. Brain and Mind Institute, Western University, London Ontario, N6A 3K7 Canada
  4. CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Gif-sur-Yvette, 91191 France
  5. Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191 Gif-sur-Yvette, France
  6. Collège de France, Paris, 75005 France
  7. Aging, Cognition & Technology Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, 39120 Germany
  8. Department of Psychology, University of Cambridge, Cambridge, CB2 3EB UK
  9. Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125 USA
  10. Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG UK
  11. Department of Psychology, Stanford University, Stanford, California 94305 USA
  12. Stanford Neurosciences Institute, Stanford University, Stanford, California 94305 USA
  13. Psychology (Scarborough), University of Toronto, Toronto Ontario, M1C 1A4 Canada
  14. Department of Psychiatry and Human Behavior, University of California, Irvine, California 92697 USA
  15. Department of Psychology, The University of Hong Kong, Stanford, Hong Kong
  16. Department of Biology, Centre for Vision Research, 4700 Keele Street, Toronto, ON M3J 1P3 Canada
  17. Cognition and Brain Sciences Unit, Cambridge, CB2 7EF UK
  18. Department of Psychology, National Taiwan University, Taipei, 10617 Taiwan
Institutions: Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Université Paris-Saclay (France); Western University (Canada); Inserm (France); CEA Paris-Saclay (France); Collège de France (France); German Center for Neurodegenerative Diseases (Germany); University of Cambridge (United Kingdom); California Institute of Technology (United States); Wellcome Centre for Human Neuroimaging (United Kingdom); University College London (United Kingdom); Stanford University (United States); University of Toronto (Canada); University of Toronto Scarborough (Canada); University of California, Irvine (United States); University of Hong Kong (Hong Kong SAR China); National Taiwan University (Taiwan); MRC Cognition and Brain Sciences Unit (United Kingdom)
Journal: Scientific data, volume 13, issue 1, article 593
Dates: received 12 March 2025; accepted 9 February 2026; published online 5 March 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-06869-1 · PMID 41781395 · PMCID PMC13076999 · OpenAlex W7133631049
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Statistics, Connectivity, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Computational neuroscience
MeSH: Brain*, Brain Mapping*, Cognition*, Magnetic Resonance Imaging*, Humans (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) (720270, 785907)
Citations: not cited yet (Europe PMC); 78 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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VPNL/fLoc

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Size: 1,616 files, 21 scripts
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Found in: the text, “Face Body”
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individual-brain-charting/public_protocols

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individual-brain-charting/public_analysis_code

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At the source: github.com/individual-brain-charting/public_analysis_code/

individual-brain-charting/api

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At the source: github.com/individual-brain-charting/api

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Recorded: type, language, journal, volume, issue, pages, dates, 36 authors, 1 keyword, 5 MeSH terms, 1 funder, 78 references.

Cite

This paper

Ponce, A. F., Aggarwal, H., Shankar, S., Torre, J. J., Pinho, A. L., Thual, A., Ginisty, C., Lecomte, Y., Berland, V., Beriot, L., Laurier, L., Joly-Testault, V., Médiouni-Cloarec, G., Hertz-Pannier, L., Doublé, C., Martins, B., Amalric, M., Dehaene, S., Diersch, N., . . . Thirion, B. (2026). Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping. Scientific data, 13(1), 593. https://doi.org/10.1038/s41597-026-06869-1

BibTeX

@article{ponce2026individual,
author = {Ponce, Ana Fernanda and Aggarwal, Himanshu and Shankar, Swetha and Torre, Juan Jesús and Pinho, Ana Luísa and Thual, Alexis and Ginisty, Chantal and Lecomte, Yann and Berland, Valérie and Beriot, Lucile and Laurier, Laurence and Joly-Testault, Véronique and Médiouni-Cloarec, Gaëlle and Hertz-Pannier, Lucie and Doublé, Christine and Martins, Bernadette and Amalric, Marie and Dehaene, Stanislas and Diersch, Nadine and Wolbers, Thomas and Shafto, Meredith A and O’Doherty, John P and Man, Vincent and Dolan, Raymond J and Poldrack, Russell A and Stigliani, Anthony and Grill-Spector, Kalanit and Douglas, Danielle and Lee, Andy C H and Keator, David B and Potkin, Steven G and Chang, Dorita H F and Troje, Nikolaus F and Kuo, Bo-Cheng and Astle, Duncan E and Thirion, Bertrand},
title = {{Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping}},
journal = {Scientific data},
year = {2026},
month = mar,
volume = {13},
number = {1},
pages = {593},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-06869-1},
url = {https://doi.org/10.1038/s41597-026-06869-1},
pmid = {41781395},
pmcid = {PMC13076999}
}

RIS

TY - JOUR
AU - Ponce, Ana Fernanda
AU - Aggarwal, Himanshu
AU - Shankar, Swetha
AU - Torre, Juan Jesús
AU - Pinho, Ana Luísa
AU - Thual, Alexis
AU - Ginisty, Chantal
AU - Lecomte, Yann
AU - Berland, Valérie
AU - Beriot, Lucile
AU - Laurier, Laurence
AU - Joly-Testault, Véronique
AU - Médiouni-Cloarec, Gaëlle
AU - Hertz-Pannier, Lucie
AU - Doublé, Christine
AU - Martins, Bernadette
AU - Amalric, Marie
AU - Dehaene, Stanislas
AU - Diersch, Nadine
AU - Wolbers, Thomas
AU - Shafto, Meredith A
AU - O’Doherty, John P
AU - Man, Vincent
AU - Dolan, Raymond J
AU - Poldrack, Russell A
AU - Stigliani, Anthony
AU - Grill-Spector, Kalanit
AU - Douglas, Danielle
AU - Lee, Andy C H
AU - Keator, David B
AU - Potkin, Steven G
AU - Chang, Dorita H F
AU - Troje, Nikolaus F
AU - Kuo, Bo-Cheng
AU - Astle, Duncan E
AU - Thirion, Bertrand
TI - Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/03/05
VL - 13
IS - 1
SP - 593
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-06869-1
UR - https://doi.org/10.1038/s41597-026-06869-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41597-026-06869-1",
"type": "article-journal",
"title": "Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping",
"container-title": "Scientific data",
"author": [
{
"family": "Ponce",
"given": "Ana Fernanda"
},
{
"family": "Aggarwal",
"given": "Himanshu"
},
{
"family": "Shankar",
"given": "Swetha"
},
{
"family": "Torre",
"given": "Juan Jesús"
},
{
"family": "Pinho",
"given": "Ana Luísa"
},
{
"family": "Thual",
"given": "Alexis"
},
{
"family": "Ginisty",
"given": "Chantal"
},
{
"family": "Lecomte",
"given": "Yann"
},
{
"family": "Berland",
"given": "Valérie"
},
{
"family": "Beriot",
"given": "Lucile"
},
{
"family": "Laurier",
"given": "Laurence"
},
{
"family": "Joly-Testault",
"given": "Véronique"
},
{
"family": "Médiouni-Cloarec",
"given": "Gaëlle"
},
{
"family": "Hertz-Pannier",
"given": "Lucie"
},
{
"family": "Doublé",
"given": "Christine"
},
{
"family": "Martins",
"given": "Bernadette"
},
{
"family": "Amalric",
"given": "Marie"
},
{
"family": "Dehaene",
"given": "Stanislas"
},
{
"family": "Diersch",
"given": "Nadine"
},
{
"family": "Wolbers",
"given": "Thomas"
},
{
"family": "Shafto",
"given": "Meredith A"
},
{
"family": "O’Doherty",
"given": "John P"
},
{
"family": "Man",
"given": "Vincent"
},
{
"family": "Dolan",
"given": "Raymond J"
},
{
"family": "Poldrack",
"given": "Russell A"
},
{
"family": "Stigliani",
"given": "Anthony"
},
{
"family": "Grill-Spector",
"given": "Kalanit"
},
{
"family": "Douglas",
"given": "Danielle"
},
{
"family": "Lee",
"given": "Andy C H"
},
{
"family": "Keator",
"given": "David B"
},
{
"family": "Potkin",
"given": "Steven G"
},
{
"family": "Chang",
"given": "Dorita H F"
},
{
"family": "Troje",
"given": "Nikolaus F"
},
{
"family": "Kuo",
"given": "Bo-Cheng"
},
{
"family": "Astle",
"given": "Duncan E"
},
{
"family": "Thirion",
"given": "Bertrand"
}
],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "593",
"DOI": "10.1038/s41597-026-06869-1",
"PMID": "41781395",
"PMCID": "PMC13076999",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-06869-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
5
]
]
}
}

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