OSCR

A 3.5-minute-long reading-based fMRI localizer for the language network.

Code ↔ Paper

7 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 7 matches
  1. [1] § Methods › Definition of functional regions of interest (fROIs) ↔ analysis/scripts/constants.py, lines 1–60 · score 0.89 · LAntTemp, LIFGorb, LPostTemp, LAngG, cingulate, parietal
  2. [2] § Methods › fMRI tasks › Language network localizer tasks › Standard language localizer task ↔ evlab_langloc_speeded/evlab_langloc_speeded.m, lines 1–41 · score 0.73 · blank screen, button pressing, instructed, counterbalanced, blocked, fixation
  3. [3] § Methods › fMRI tasks › Language network localizer tasks › Speeded language localizer task ↔ evlab_langloc_speeded/evlab_langloc_speeded.m, lines 1–41 · score 0.71 · blank screen, button presses, instructions, fast, blocks, fixation
  4. [4] § Results › The speeded language localizer can reliably localize language-responsive areas in individual participants › The fROIs defined by the speeded language localizer respond at least as strongly a ↔ analysis/scripts/constants.py, lines 1–60 · score 0.60 · medial frontal, anterior temporal, DMN, parcel
  5. [5] § Methods › fMRI tasks › Multiple Demand network localizer task ↔ evlab_langloc_speeded/evlab_langloc_speeded.m, lines 259–318 · score 0.60 · red cross, button press, fixation, respond
  6. [6] § Results › The speeded language localizer can reliably localize language-responsive areas in individual participants › The activation topography is similar between the standard and speeded language loc ↔ analysis/scripts/spatial_correlation_plots.py, lines 85–183 · score 0.59 · Dice overlap coefficient, Error bars, standard error, correlation, speeded, localizer
  7. [7] § Results › The speeded language localizer can reliably localize language-responsive areas in individual participants › The activation topography is similar between the standard and speeded language loc ↔ analysis/scripts/spatial_correlation_plots.py, lines 85–183 · score 0.54 · Fisher transformed, correlation coefficient, bars, match, speeded, localizer

Paper

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The authors' code

MATLAB · 343 lines · 10 KB · no license · 3 matches

  1. function evlab_langloc_speeded(subj_id, set, run)
  3. % Created By: Terri Scott ([email hidden]) 4/8/13,
  4. % edits by Greta Tuckute, summer 2021. edits by Aalok Sathe, 2022.
  6. % This is a language localizer experiment with two conditions - sentences
  7. % and nonwords. In each trial, 12 word-long sequences will be displayed one
  8. % word at a time followed by a prompt for the subject to push a button,
  9. % just to make sure they are paying attention.
  10. % The subject should be instructed to read each English word or nonword and
  11. % press a button when then image of a hand pressing a button is displayed.
  12. % Emphasis should be placed on paying attention to reading each sequence
  13. % and not to be stressed out if the sequence seems fast.
  15. % The trial timings are as follows: 100 ms of blank screen, 200 ms *
  16. % 12 words for 2400 ms of stimuli, 400 ms of the button press image, and
  17. % 100 ms of blank screen. The entire trial lasts for 3000 ms. The subject's
  18. % button press for a given trial will
  19. % be recorded if it occurs after the button press image and before the
  20. % same image of the subsequent trial.
  22. % 3 trials of a given condition will be grouped into a block. A run will
  23. % consist of 16 blocks in this sequence: Fix B1 B2 B3 B4 Fix B5 B6 B7 B8
  24. % Fix B9 B10 B11 B12 Fix B13 B14 B15 B16 Fix. Each fixation period will
  25. % last 14000 ms. Each run is 3 minutes, 34 s (214 s). 107 TRs, assuming TR=2s.
  27. % Argument definitions:
  29. % subj_id: Should be a string designating the subject.
  31. % run: The localizer is meant to be run twice. The run is the counter-balance number,
  32. % and should have the value 1 or 2 delineating the first or second run of the experiment.
  33. % The sequence of conditions are:
  34. % run = 1 : SNNS - NSNS - SNSN - NSSN
  35. % run = 2 : NSSN - SNSN - NSNS - SNNS
  36. % each 'S' or 'N' here is three 12-word-long items.
  38. % A structure 'subj_data' is created and saved to the pwd unless otherwise
  39. % specified. It will be saved as a .mat in the format:
  40. % evlab_langloc_speeded_2022_<subj_id>_fmri_run<run#>_data.mat.
  41. % If the output file already exists, it will be suffixed with the repeat #.
  43. %% Parameters to change
  44. Screen('Preference', 'SkipSyncTests', 1);
  45. % Screen('Preference','SyncTestSettings',.0004); % Run it with strict timing settings for testing
  46. Screen('Preference', 'DefaultFontSize', 200);
  47. KbName('UnifyKeyNames');
  48. screensAll = Screen('Screens');
  49. screenNum = max(screensAll); % Which screen you want to use. "1" is external monitor, "0" is this screen.
  50. my_key = '1!'; % What key gives a response.
  51. my_trigger = '=+'; % What key triggers the script from the scanner.
  52. do_suppress_warnings = 1; % You don't need to do this but I don't like the warning screen at the beginning.
  53. addpath([pwd filesep 'func']);
  54. DATA_DIR = [pwd filesep 'data']; % Where the subj_data will be saved.
  55. STIM_DIR = [pwd filesep 'new_stim']; % Where all the stimuli are.
  56. stim_font_size = 100;
  59. %% Define the output file
  60. file_to_save = ['evlab_langloc_speeded_2022_' subj_id '_fmri_run' num2str(run) '_set' num2str(set) '_data.mat'];
  62. % Error message if data file already exists.
  63. if exist([DATA_DIR filesep file_to_save],'file')
  65. all_files = dir([DATA_DIR filesep file_to_save]);
  66. all_files = {all_files.name};
  68. file_to_save = ['evlab_langloc_speeded_2022_' subj_id '_fmri_run' num2str(run) '_set' num2str(set) '_repeat' num2str(length(all_files)) '_data.mat'];
  70. end
  72. clear subj_data
  74. % Experiment settings
  75. num_of_trials = 48;
  76. num_of_fix = 5;
  77. word_time = 0.200;
  78. trial_time = 12*word_time + 0.600;
  80. %% Start experiment
  82. % Choose which stimuli set to use.
  84. % stim = load([STIM_DIR filesep 'langloc_fmri_run' num2str(run) '_stim_set' num2str(set) '.mat']); % under folder old_stim
  85. stim = load([STIM_DIR filesep 'speeded_langloc_2022_fmri_run' num2str(run) '_stim_set' num2str(set) '.mat']);
  86. stim = stim.stim;
  88. % Load variables needed later.
  90. img=imread([pwd filesep 'images' filesep 'hand-press-button-4.jpeg'], 'JPG');
  92. did_subj_respond = 0;
  93. r_count = 1;
  95. trial_times = zeros(48,1);
  96. for i = 1:num_of_trials
  97. if ismember(i,[13 25 37])
  98. trial_times(i) = trial_times(i-1) + 14.000 + trial_time;
  99. elseif i == 1
  100. trial_times(i) = 0.000;
  101. else
  102. trial_times(i) = trial_times(i-1) + trial_time;
  103. end
  104. end
  106. subj_data.id = subj_id;
  107. subj_data.did_respond = zeros(num_of_trials,1);
  108. subj_data.probe_onset = zeros(num_of_trials,1);
  109. subj_data.probe_response = zeros(num_of_trials,1);
  110. subj_data.trial_onsets = zeros(num_of_trials,1);
  111. subj_data.fix_onsets = zeros(num_of_fix,1);
  112. subj_data.condition = cell(num_of_trials,1);
  113. subj_data.stim = cell(num_of_trials,1);
  114. subj_data.run = run;
  116. % Update the conditions / stim in output structure
  117. for i = 1 : num_of_trials
  118. subj_data.stim{i}=strjoin(stim(i,2:13));
  119. subj_data.condition(i)=stim(i,14);
  120. end
  122. % Save all data to current folder.
  123. save([DATA_DIR filesep file_to_save], 'subj_data');
  125. % Screen preferences
  127. % Setting this preference to 1 suppresses the printout of warnings.
  128. oldEnableFlag = Screen('Preference', 'SuppressAllWarnings', do_suppress_warnings);
  131. % Open screen.
  133. [wPtr,~]=Screen('OpenWindow',screenNum,1);
  135. white=WhiteIndex(wPtr);
  136. scrColor = white;
  137. Screen('FillRect',wPtr,scrColor);
  138. Screen(wPtr, 'Flip');
  140. HideCursor;
  142. Screen('Preference', 'TextRenderer', 1);
  143. Screen('TextFont', wPtr, '-misc-fixed-medium-o-normal--13-120-75-75-c-70-iso8859-1');
  144. Screen('TextSize', wPtr , stim_font_size);
  145. DrawFormattedText(wPtr,'Waiting for scanner...','center','center');
  146. Screen(wPtr, 'Flip');
  148. % Pre-draw button press image
  149. textureIndex=Screen('MakeTexture', wPtr, double(img));
  151. % Get trigger from scanner.
  153. TRIGGER_KEY = [KbName('=+'), KbName('+')]; %,KbName('=')]; % ** AK changed this 081612 % if this doesn't work, change to '=+'
  154. while 1
  155. [keyIsDown, seconds, keyCode] = KbCheck(-3);
  157. if ismember(find(keyCode,1),TRIGGER_KEY) %keyCode(KbName(TRIGGER_KEY))
  158. break
  159. end
  160. WaitSecs('YieldSecs', 0.0001); % Wait for yieldInterval to prevent system overload.
  161. end
  163. subj_data.run_onset = GetSecs;
  165. %% Runs
  167. try
  169. % Fixation
  171. %Screen('TextSize', wPtr , 100); A.S. changed this to the below
  172. Screen('TextSize', wPtr , stim_font_size);
  173. DrawFormattedText(wPtr,'+','center','center');
  174. Screen(wPtr, 'Flip');
  176. subj_data.fix_onsets(1) = GetSecs;
  178. % Calculate trial onsets:
  180. subj_data.i_trial_onsets = (subj_data.run_onset+14.000) + trial_times;
  182. while GetSecs<14.000+subj_data.run_onset
  183. WaitSecs('YieldSecs', 0.0001);
  184. end
  186. % Start trials
  188. for i = 1:num_of_trials
  190. subj_data.trial_onsets(i) = GetSecs;
  191. stim_seq = stim(i,2:13);
  193. % White screen for 100 ms
  195. white=WhiteIndex(wPtr);
  196. Screen('FillRect',wPtr,scrColor);
  197. Screen(wPtr, 'Flip');
  199. while GetSecs<0.100+subj_data.i_trial_onsets(i)
  200. if did_subj_respond == 0
  201. did_subj_respond = getKeyResponse;
  202. end
  203. WaitSecs('YieldSecs', 0.0001);
  204. end
  206. % Sequence presentation 12 * 450 ms
  208. Screen('TextSize', wPtr , stim_font_size);
  210. for j = 1:12
  212. DrawFormattedText(wPtr,stim_seq{j},'center','center');
  213. Screen(wPtr, 'Flip');
  215. while GetSecs<word_time*j + 0.100 + subj_data.i_trial_onsets(i)
  216. if did_subj_respond == 0
  217. did_subj_respond = getKeyResponse;
  218. end
  219. WaitSecs('YieldSecs', 0.0001);
  220. end
  221. end
  223. % Present image for word_time (200 ms)
  225. subj_data.did_respond(r_count) = did_subj_respond;
  227. if i ~= 1
  228. r_count = r_count + 1;
  229. end
  231. did_subj_respond = 0;
  233. Screen('DrawTexture', wPtr, textureIndex);
  234. Screen(wPtr, 'Flip');
  236. subj_data.probe_onset(i) = GetSecs;
  238. while GetSecs<0.400+(12*word_time)+0.100+subj_data.i_trial_onsets(i)
  239. if did_subj_respond == 0
  240. did_subj_respond = getKeyResponse;
  241. end
  242. WaitSecs('YieldSecs', 0.0001);
  243. end
  245. % White screen for 100 ms
  247. Screen('FillRect',wPtr,scrColor);
  248. Screen(wPtr, 'Flip');
  250. while GetSecs<0.100+0.400+(12*word_time)+0.100+subj_data.i_trial_onsets(i)
  251. if did_subj_respond == 0
  252. did_subj_respond = getKeyResponse;
  253. end
  254. WaitSecs('YieldSecs', 0.0001);
  255. end
  257. if ismember(i,[12,24,36,48]) % Fixation occurs after every 12 trials or 4 blocks
  259. % Fixation
  261. %Screen('TextSize', wPtr , 100); % AS 20220331
  262. Screen('TextSize', wPtr , stim_font_size);
  263. DrawFormattedText(wPtr,'+','center','center');
  264. Screen(wPtr, 'Flip');
  266. subj_data.fix_onsets(i/12+1) = GetSecs;
  268. % 14s is fixation time; 100ms blank; 400ms is the button press img
  269. while GetSecs<14.000+0.100+0.400+(12*word_time)+0.100+subj_data.i_trial_onsets(i)
  270. if did_subj_respond == 0
  271. did_subj_respond = getKeyResponse;
  272. end
  273. WaitSecs('YieldSecs', 0.0001);
  274. end
  276. end
  277. end
  279. subj_data.runtime = GetSecs - subj_data.run_onset;
  281. subj_data.did_respond(r_count) = did_subj_respond;
  283. Screen('CloseAll');
  284. ShowCursor
  286. % Get reaction time.
  287. subj_data.rt = zeros(num_of_trials,1);
  288. responses = find(subj_data.did_respond);
  289. subj_data.rt(responses) = subj_data.probe_response(responses) - subj_data.probe_onset(responses);
  291. % Save all data to current folder.
  292. save([DATA_DIR filesep file_to_save], 'subj_data');
  294. catch err
  296. subj_data.did_respond(r_count) = did_subj_respond;
  298. % Get reaction time.
  299. subj_data.rt = zeros(num_of_trials,1);
  300. responses = find(subj_data.did_respond);
  301. subj_data.rt(responses) = subj_data.probe_response(responses) - subj_data.probe_onset(responses);
  303. subj_data.set=set;
  305. % Save all data to current folder.
  306. save([DATA_DIR filesep file_to_save], 'subj_data','err');
  308. % Fixation with red cross after trials end
  309. Screen('TextSize', wPtr , stim_font_size);
  310. DrawFormattedText(wPtr,'+','center','center', [255 0 0]);
  311. Screen(wPtr, 'Flip');
  313. Screen('CloseAll');
  314. ShowCursor
  315. end
  317. % At the end of your code, it is a good idea to restore the old level.
  318. Screen('Preference','SuppressAllWarnings',oldEnableFlag);
  322. %%
  323. % % % % % % % %
  324. % SUBFUNCTION %
  325. % % % % % % % %
  327. function out = getKeyResponse
  328. KEY1=KbName(my_key);
  330. [keyIsDown,x,keyCode]=KbCheck;
  331. if keyIsDown
  332. response=find(keyCode);
  333. if response==KEY1
  334. out = 1;
  335. subj_data.probe_response(r_count) = x;
  336. else
  337. out = 0;
  338. end
  339. else
  340. out = 0;
  341. end
  342. end
  343. end

evlab_langloc_speeded.m at commit 4c820ec, no license · at the source

Overview

Authors: Greta Tuckute1,2, Elizabeth Jiachen Lee1, Aalok Sathe1, Evelina Fedorenko1
ORCID iDs: Greta Tuckute, Aalok Sathe, Evelina Fedorenko
  1. Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
  2. Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, Boston, MA, United States
Institutions: Harvard University (United States); McGovern Institute for Brain Research (United States); Massachusetts Institute of Technology (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1246
Dates: received 10 July 2024; accepted 14 April 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1246 · PMID 42212229 · PMCID PMC13214572 · OpenAlex W4400344713
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.07.02.601683
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging, Spectral & time-frequency
Keywords: language comprehension, language network, speeded reading, rapid serial visual presentation, efficient functional localization, Multiple Demand network
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Simons Foundation; Massachusetts Institute of Technology; National Institutes of Health (DC016607, NS121471); McGovern Institute for Brain Research, Massachusetts Institute of Technology; National Institute of Neurological Disorders and Stroke (NS121471); NIH Blueprint for Neuroscience Research (DC016607)
Citations: cited by 2 papers (Europe PMC); 145 references in the paper

Abstract

The field of human cognitive neuroscience is increasingly acknowledging inter-individual differences in the precise locations of functional areas and the corresponding need for individual-level analyses in functional magnetic resonance imaging (fMRI) studies. One approach to identifying functional areas and networks within individual brains is based on robust and extensively validated ‘localizer’ paradigms—contrasts of conditions that aim to isolate some mental process of interest. Here, we present a new version of a localizer for the fronto-temporal language-selective network. This localizer is similar to a commonly used localizer based on the reading of sentences and nonword sequences but uses speeded presentation (200 ms per word/nonword). Based on a direct comparison between the standard version (450 ms per word/nonword) and the speeded version of the language localizer in 24 participants, we show that a single run of the speeded localizer (3.5 minutes) is highly effective at identifying the language-selective areas: indeed, it is more effective than the standard localizer given that it leads to an increased response to the critical (sentence) condition and a decreased response to the control (nonwords) condition. This localizer may therefore become the version of choice for identifying the language network in neurotypical adults or special populations (as long as they are proficient readers), especially when time is of essence.

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 7 matches between paragraphs and lines of code.

evlab.mit.edu/resources-all/download-localizer-tasks

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Brief overview”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: evlab.mit.edu/resources-all/download-localizer-tasks

el849/speeded_language_localizer

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 4c820ec2f76d953d604919c96bd37923929c789c, 14 February 2026
Languages: Python (6), MATLAB (3), R (2)
Size: 155 files, 11 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), Matplotlib (4 files), SciPy (4 files), Psychtoolbox (3 files), lme4 (2 files), lmerTest (2 files), tidyverse (2 files), h5py (1 file), NiBabel (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
12 files
At the source: github.com/el849/speeded_language_localizer/

WuggyCode/wuggy

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 25de4ac1a608913bcdd633f312f3ac7dd2fead4a, 17 April 2026
Languages: Python (11)
Size: 30 files, 11 scripts
Software Heritage: not archived
Found in: the text, “Standard language localizer materials”
Holds: README, license file, environment (requirements.txt, setup.py), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: statsmodels (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/WuggyCode/wuggy

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

Tracing map

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

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • 7 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 and Code Availability

The scripts for running the speeded language localizer as well as the associated analyses can be found here: https://github.com/el849/speeded_language_localizer/. The data can be found on OSF: https://osf.io/2vskh/.

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 2, 28 September 2026

  • Funding: added Simons Foundation; Massachusetts Institute of Technology; National Institutes of Health: DC016607, NS121471; McGovern Institute for Brain Research, Massachusetts Institute of Technology; National Institute of Neurological Disorders and Stroke: NS121471; NIH Blueprint for Neuroscience Research: DC016607

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 140 references.

Cite

This paper

Tuckute, G., Lee, E. J., Sathe, A., & Fedorenko, E. (2026). A 3.5-minute-long reading-based fMRI localizer for the language network. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1246. https://doi.org/10.1162/imag.a.1246

BibTeX

@article{tuckute20263,
author = {Tuckute, Greta and Lee, Elizabeth Jiachen and Sathe, Aalok and Fedorenko, Evelina},
title = {{A 3.5-minute-long reading-based fMRI localizer for the language network}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1246},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1246},
url = {https://doi.org/10.1162/imag.a.1246},
pmid = {42212229},
pmcid = {PMC13214572}
}

RIS

TY - JOUR
AU - Tuckute, Greta
AU - Lee, Elizabeth Jiachen
AU - Sathe, Aalok
AU - Fedorenko, Evelina
TI - A 3.5-minute-long reading-based fMRI localizer for the language network
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/05/26
VL - 4
SP - IMAG.a.1246
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1246
UR - https://doi.org/10.1162/imag.a.1246
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1246",
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"title": "A 3.5-minute-long reading-based fMRI localizer for the language network",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
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"given": "Greta"
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"given": "Evelina"
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"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1246",
"DOI": "10.1162/imag.a.1246",
"PMID": "42212229",
"PMCID": "PMC13214572",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1246",
"language": "en",
"issued": {
"date-parts": [
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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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In common: h5py, NiBabel, seaborn, 4 other tools, 34 references, author Evelina Fedorenko
[4] doi:10.1038/s41467-026-72916-5 [code]
Precision fMRI reveals that the language network exhibits adult-like left-hemispheric lateralization by 4 years of age.
Journal: Nature communications
In common: lmerTest, lme4, tidyverse, fMRI, 30 references, author Evelina Fedorenko
[5] doi:10.1523/jneurosci.0638-25.2026 [code]
The Extended Language Network: Language-Responsive Brain Areas Whose Contributions to Language Remain To Be Discovered.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: fMRI, 30 references
[6] doi:10.1016/j.neuron.2026.04.011 [code]
Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex.
Journal: Neuron
In common: NiBabel, SciPy, Matplotlib, 1 other tool, fMRI, 14 references
[7] doi:10.1038/s41597-026-06869-1 [code]
Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping.
Journal: Scientific data
In common: Psychtoolbox, NiBabel, seaborn, 4 other tools, fMRI, 7 references
[8] doi:10.1162/imag.a.1347 [code]
Neural and behavioural correlates of theory of mind reasoning in five-year-old children born preterm.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: lmerTest, lme4, NiBabel, 5 other tools, fMRI, 5 references
[9] doi:10.1038/s42003-026-10040-2 [code]
Functional dissociation of language and theory of mind in the developing superior temporal lobe.
Journal: Communications biology
In common: lmerTest, lme4, tidyverse, 8 references
[10] doi:10.1016/j.isci.2026.116704
Domain-general and language-specific brain networks are differentially associated with verbal intelligence during development.
Journal: iScience
In common: 10 references

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