OSCR

The MTL200: A surface-based, probabilistic atlas of the medial temporal lobe.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Atlas construction ↔ scripts/atlasMTLProcessSurfaceROIs.m, lines 5 to 100 · score 0.80 · ribbon constrained, native surface, surface resampling, template spaces, fsLR, pial · Report a mismatch
  2. [2] § Results › Functional connectivity ↔ scripts/atlasMTLRestNetCorrelationPlot.m, lines 64 to 107 · score 0.65 · linear mixed, dATN, interaction, DN, model, LANG · Report a mismatch
  3. [3] § Methods › MRI data preprocessing ↔ scripts/atlasMTLProcessSurfaceROIs.m, lines 5 to 100 · score 0.52 · ribbon constrained, fsLR, preprocessing, resampling, anatomical, mapping · Report a mismatch
  4. [4] § Methods › MRI data preprocessing ↔ scripts/atlasMTLReliability.m, lines 3 to 101 · score 0.51 · ribbon constrained, fsLR, preprocessing, resampling, anatomical, mapping · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

MATLAB · 320 lines · 14 KB · CC-BY-4.0 · 2 matches

  2. % Script to process hand-drawn anatomical regions drawn in the volume
  3. % then convert them to a probability map in fsLR-32k surface space.
  5. %% Step 1: Resample hand-modified ROIs to surface and .7mm volume
  6. studyDir = '/mnt/sml_share/HCP/derivatives';
  8. % Define subject list
  9. subjList = dir([studyDir '/phase1/sub-*']);
  10. subjListNames = {subjList.name};
  11. subjects = cell(length(subjListNames),1);
  12. for f = 1:length(subjListNames)
  13. subjectID = subjListNames{f}(5:end);
  14. subjects{f} = subjectID;
  15. end
  16. subjects = subjects';
  18. % Subject BIDS file path regions
  19. hemis = {'L','R'};
  20. surfaces = {'midthickness','white','pial'};
  21. segNames = {'LTP','LPRC','LERC','LPHC','RTP','RPRC','RERC','RPHC'};
  22. segColors = {[0 114 189],[217 83 25],[237 177 32], [119 172 48], [0 114 189],...
  23. [217 83 25], [237 177 32], [119 172 48]}; % Label colors by ROI type
  24. templateSpace = 'individualMTL_res-2';
  25. templateSpaceNew = 'individual_res-2';
  26. regMatPath = '/mnt/sml_share/HCP/S1200AtlasBIDS32k/from-individualMTL_to-individual_mode-image_xfm.mat';
  28. for s=1:length(subjects)
  29. subjID = subjects{s};
  30. anatDir = [studyDir '/fpp/sub-' subjID '/anat'];
  31. roiDir = [studyDir '/phase1/sub-' subjID '/roi'];
  33. % Define anatomical paths
  34. surfaceBasePath = [anatDir '/sub-' subjID '_hemi-R_space-individual_den-native_midthickness.surf.gii'];
  35. for h=1:2
  36. for su=1:3
  37. inputSurfacePaths{h}{su} = fpp.bids.changeName(surfaceBasePath,'hemi',hemis{h},surfaces{su});
  38. end
  39. surfaceROIPaths{h} = [anatDir '/sub-' subjID '_hemi-' hemis{h}...
  40. '_space-individual_den-native_desc-cortexAtlas_mask.shape.gii'];
  41. surfaceROIFsLRPaths{h} = [anatDir '/sub-' subjID '_hemi-' hemis{h}...
  42. '_space-fsLR_den-32k_desc-cortexAtlas_mask.shape.gii'];
  43. sphereRegFsLRPaths{h} = [anatDir '/sub-' subjID '_hemi-' hemis{h}...
  44. '_space-individual_den-native_desc-reg2fsLR_sphere.surf.gii'];
  45. midthickFsLRPaths{h} = [anatDir '/sub-' subjID '_hemi-' hemis{h}...
  46. '_space-individual_den-32k_midthickness.surf.gii'];
  47. end
  48. inputT1Path = [anatDir '/sub-' subjID '_space-individual_res-p7_desc-preproc_T1w.nii.gz'];
  50. for r=1:length(segNames)
  51. inputPath = [roiDir '/sub-' subjID '_space-' templateSpaceNew '_desc-handDrawn' segNames{r} '_mask.nii.gz'];
  52. inputPathMTL = [roiDir '/sub-' subjID '_space-' templateSpace '_desc-handDrawn' segNames{r} '_mask.nii.gz'];
  54. % Register individualMTL to individual space
  55. fpp.fsl.moveImage(inputPathMTL,inputPathMTL,inputPath,regMatPath,'interp','nn');
  57. % Add label text file to volume
  58. inputDesc = fpp.bids.checkNameValue(inputPath,'desc');
  59. tmpLUTPath = fpp.bids.changeName(inputPath,'desc',[inputDesc...
  60. 'tmpVolumeResample21093520813502'],'lut','.txt');
  61. fid = fopen(tmpLUTPath,'w');
  62. fprintf(fid,'%s\n',segNames{r});
  63. fprintf(fid,'%d %d %d %d %d\n',[1 segColors{r} 255]);
  64. fclose(fid);
  65. fpp.wb.command('volume-label-import',inputPath,tmpLUTPath,inputPath,'-drop-unused-labels');
  67. % Resample to 32k surface
  68. outputCiftiPath = fpp.bids.changeName(inputPath,{'space','res','den'},...
  69. {'fsLR',[],'32k'},'dseg','.dlabel.nii');
  70. fpp.func.surfaceResample(inputPath,inputSurfacePaths,surfaceROIFsLRPaths,outputCiftiPath,...
  71. 'sphereRegFsLRPaths',sphereRegFsLRPaths,'midthickFsLRPaths',midthickFsLRPaths,'isLabel',1);
  73. % Resample to native surface
  74. outputCiftiPath = fpp.bids.changeName(inputPath,{'space','res','den'},...
  75. {'individual',[],'native'},'dseg','.dlabel.nii');
  76. outputGiftiPaths{1} = fpp.bids.changeName(inputPath,{'space','res','den','hemi'},...
  77. {'individual',[],'native','L'},'dseg','.label.gii');
  78. outputGiftiPaths{2} = fpp.bids.changeName(inputPath,{'space','res','den','hemi'},...
  79. {'individual',[],'native','R'},'dseg','.label.gii');
  80. fpp.func.surfaceResample(inputPath,inputSurfacePaths,surfaceROIPaths,outputCiftiPath,...
  81. 'isLabel',1,'outputGiftiPaths',outputGiftiPaths);
  83. % Resample from native surface to .7mm volume
  84. outputPath = fpp.bids.changeName(inputPath,'res','p7');
  85. for h=1:2
  86. outputVolPaths{h} = fpp.bids.changeName(outputPath,'hemi',hemis{h},[],'.nii.gz');
  87. end
  88. for h=1:2
  89. fpp.wb.command('label-to-volume-mapping',outputGiftiPaths{h},...
  90. [inputSurfacePaths{h}{1} ' ' inputT1Path],outputVolPaths{h},['-ribbon-constrained '...
  91. inputSurfacePaths{h}{2} ' ' inputSurfacePaths{h}{3}]);
  92. end
  93. fpp.fsl.maths(outputVolPaths{1},['-add ' outputVolPaths{2}],outputPath);
  94. fpp.wb.command('volume-label-import',outputPath,tmpLUTPath,outputPath,'-drop-unused-labels');
  95. fpp.util.deleteImageAndJson([outputVolPaths outputGiftiPaths]);
  96. fpp.util.system(['rm -rf ' tmpLUTPath]);
  98. disp(['Finished ' subjID ' ' segNames{r}]);
  99. end
  100. end
  102. %% Step 2: after ROIs have been converted to surface representations, check
  103. % if there are any discontinuities or abnormally shaped regions, and
  104. % correct drawings in the volume if needed. Generate border files.
  106. studyDir = '/mnt/sml_share/HCP/derivatives';
  108. % Define subject list
  109. subjList = dir([studyDir '/phase1/sub-*']);
  110. subjListNames = {subjList.name};
  111. subjects = cell(length(subjListNames), 1);
  112. for f = 1:length(subjListNames)
  113. subjectID = subjListNames{f}(5:end);
  114. subjects{f} = subjectID;
  115. end
  116. subjects = subjects';
  118. % Subject BIDS file path regions
  119. hemis = {'L','R'};
  120. segNames = {'LTP','LPRC','LERC','LPHC','RTP','RPRC','RERC','RPHC'};
  121. segHemis = [1 1 1 1 1 2 2 2 2 2];
  122. spaces = {'individual_den-native','fsLR_den-32k'};
  123. dens = {'native','32k'};
  125. for s=1:length(subjects)
  126. subjID = subjects{s};
  127. anatDir = [studyDir '/fpp/sub-' subjID '/anat'];
  128. roiDir = [studyDir '/phase1/sub-' subjID '/roi'];
  130. for sp=1:length(spaces)
  132. % Define anatomical paths
  133. if ~exist([anatDir 'sub-' subjID '_hemi-L_space-individual_den-32k_midthickness.surf.gii'], 'file')
  134. for h = 1:2
  135. surfacePaths{h} = [anatDir '/sub-' subjID '_hemi-' hemis{h} '_space-'...
  136. strrep(spaces{sp}, 'fsLR', 'individual') '_midthickness.surf.gii'];
  137. if strcmpi(dens{sp}, '32k')
  138. surfaceROIPaths{h} = [anatDir '/sub-' subjID '_hemi-' hemis{h}...
  139. '_space-fsLR_den-32k_desc-cortexAtlas_mask.shape.gii'];
  140. else
  141. surfaceROIPaths{h} = [anatDir '/sub-' subjID '_hemi-' hemis{h}...
  142. '_space-' spaces{sp} '_desc-cortexAtlas_mask.shape.gii'];
  143. end
  144. end
  145. end
  147. for r=1:length(segNames)
  149. % Input ROI path
  150. roiPath = [roiDir '/sub-' subjID '_space-' spaces{sp} '_desc-handDrawn' segNames{r} '_dseg.dlabel.nii'];
  152. % Define intermediate/output paths
  153. roiScalarPath = fpp.bids.changeName(roiPath,[],[],'mask','.dscalar.nii');
  154. roiLUTPath = fpp.bids.changeName(roiScalarPath,[],[],'lut','.txt');
  155. for h=1:2
  156. roiMetricPaths{h} = fpp.bids.changeName(roiPath,'hemi',hemis{h},'mask','.shape.gii');
  157. end
  158. roiBorderPath = fpp.bids.changeName(roiPath,'hemi',hemis{segHemis(r)},[],'.border');
  160. % Split into metric files to apply metric-fill-hole, then recombine
  161. fpp.wb.command('cifti-label-export-table',roiPath,'1',roiLUTPath);
  162. fpp.wb.command('cifti-label-to-roi',roiPath,[],roiScalarPath,'-map 1 -key 1');
  163. fpp.wb.command('cifti-separate',roiScalarPath,'COLUMN',[],['-metric CORTEX_LEFT '...
  164. roiMetricPaths{1} ' -metric CORTEX_RIGHT ' roiMetricPaths{2}]);
  165. fpp.wb.command('metric-fill-holes',surfacePaths{segHemis(r)},...
  166. roiMetricPaths{segHemis(r)},roiMetricPaths{segHemis(r)});
  167. fpp.wb.command('metric-remove-islands',surfacePaths{segHemis(r)},...
  168. roiMetricPaths{segHemis(r)},roiMetricPaths{segHemis(r)});
  169. fpp.wb.command('cifti-create-dense-scalar',[],[],roiScalarPath,['-left-metric '...
  170. roiMetricPaths{1} ' -roi-left ' surfaceROIPaths{1} ' -right-metric '...
  171. roiMetricPaths{2} ' -roi-right ' surfaceROIPaths{2}]);
  172. fpp.wb.command('cifti-label-import',roiScalarPath,roiLUTPath,roiPath);
  174. % Convert label to border file
  175. fpp.wb.command('cifti-label-to-border',roiPath,[],[],['-border '...
  176. surfacePaths{segHemis(r)} ' ' roiBorderPath]);
  178. fpp.util.deleteImageAndJson([{roiScalarPath,roiLUTPath} roiMetricPaths]);
  179. fpp.util.system(['rm -rf ' roiLUTPath]);
  181. disp(['Finished ' subjID ' ' segNames{r}]);
  182. end
  183. end
  184. end
  186. %% Step 3: Convert ROIs from dlabel to dscalar for roiExtract
  188. studyDir = '/mnt/sml_share/HCP/derivatives';
  190. % Define subject list
  191. subjList = dir([studyDir '/phase1/sub-*']);
  192. subjListNames = {subjList.name};
  193. subjects = cell(length(subjListNames), 1);
  194. for f = 1:length(subjListNames)
  195. subjectID = subjListNames{f}(5:end);
  196. subjects{f} = subjectID;
  197. end
  198. subjects = subjects';
  200. % Subject BIDS file path regions
  201. segNames = {'LTP','LPRC','LERC','LPHC','RTP','RPRC','RERC','RPHC'};
  202. hemis = {'L','R'};
  203. spaces = {'individual_den-native','fsLR_den-32k'};
  205. for s=1:length(subjects)
  206. subjID = subjects{s};
  207. anatDir = [studyDir '/fpp/sub-' subjID '/anat'];
  208. roiDir = [studyDir '/phase1/sub-' subjID '/roi'];
  210. for sp=1:length(spaces)
  212. for r=1:length(segNames)
  213. roiPath = [roiDir '/sub-' subjID '_space-' spaces{sp} '_desc-handDrawn' segNames{r} '_dseg.dlabel.nii'];
  214. outputPath = fpp.bids.changeName(roiPath,[],[],'mask','.dscalar.nii');
  215. fpp.wb.command('cifti-label-to-roi',roiPath,[],outputPath,'-key 1');
  216. disp(['Finished ' subjID ' ' segNames{r}]);
  217. end
  218. end
  219. end
  221. %% Step 5: Combine left/right hemisphere dscalar files
  223. studyDir = '/mnt/sml_share/HCP/derivatives';
  225. % Define subject list
  226. subjList = dir([studyDir '/phase1/sub-*']);
  227. subjListNames = {subjList.name};
  228. subjects = cell(length(subjListNames), 1);
  229. for f = 1:length(subjListNames)
  230. subjectID = subjListNames{f}(5:end);
  231. subjects{f} = subjectID;
  232. end
  233. subjects = subjects';
  235. segNames = {'TP','PRC','ERC','PHC'};
  236. spaces = {'individual_den-native','fsLR_den-32k'};
  238. for s=1:length(subjects)
  239. subjID = subjects{s};
  240. roiDir = [studyDir '/phase1/sub-' subjID '/roi'];
  242. for sp=1:length(spaces)
  244. for r=1:length(segNames)
  246. inputPath = [roiDir '/sub-' subjID '_space-' spaces{sp} '_desc-handDrawn' segNames{r} '_mask.dscalar.nii'];
  247. roiLPath = fpp.bids.changeName(inputPath,'desc',['handDrawnL' segNames{r}]);
  248. roiRPath = fpp.bids.changeName(inputPath,'desc',['handDrawnR' segNames{r}]);
  250. fpp.wb.command('cifti-math','a+b',[],inputPath,['-var a ' roiLPath ' -var b ' roiRPath]);
  252. disp(['Finished ' subjID ' ' segNames{r}]);
  253. end
  254. end
  255. end
  257. %% Step 6: Create and threshold probability map across participants of surface-based hand-drawn ROIs
  259. studyDir = '/mnt/sml_share/HCP/derivatives';
  260. atlasDir = [studyDir '/fpp/group'];
  262. % Define subject list
  263. subjList = dir([studyDir '/phase1/sub-*']);
  264. subjListNames = {subjList.name};
  265. subjects = cell(length(subjListNames), 1);
  266. for f = 1:length(subjListNames)
  267. subjectID = subjListNames{f}(5:end);
  268. subjects{f} = subjectID;
  269. end
  270. subjects = subjects';
  272. hemis = {'L','R'};
  273. segNames = {'LTP','LPRC','LERC','LPHC','RTP','RPRC','RERC','RPHC'};
  275. fwhmSm = 3; % Gradient smoothing kernel FWHM in mm
  276. sigmaSm = fwhmSm/2.355; % Standard deviation of Gaussian kernel
  277. probThresh = .7;
  278. probThreshStr = 'P7';
  280. for r=1:length(segNames)
  281. outputPath = [atlasDir '/atlasMTL/space-fsLR_den-32k_desc-handDrawn' segNames{r} '_probseg.dscalar.nii'];
  282. outputPathSm = [atlasDir '/atlasMTL/space-fsLR_den-32k_desc-handDrawn' segNames{r} 'Sm' int2str(fwhmSm) '_probseg.dscalar.nii'];
  283. outputPathThr = [atlasDir '/atlasMTL/space-fsLR_den-32k_desc-handDrawn' segNames{r} 'Thr' probThreshStr '_mask.dscalar.nii'];
  285. % Sum ROIs across subjects to define probability map
  286. mathString = '(';
  287. varString = '';
  288. for s=1:length(subjects)
  289. subjID = subjects{s};
  290. roiDir = [studyDir '/phase1/sub-' subjID '/roi'];
  291. inputPath = [roiDir '/sub-' subjID '_space-fsLR_den-32k_desc-handDrawn' segNames{r} '_mask.dscalar.nii'];
  293. if s==1
  294. mathString = [mathString 'r' int2str(s)];
  295. else
  296. mathString = [mathString ' + r' int2str(s)];
  297. end
  298. varString = [varString ' -var r' int2str(s) ' ' inputPath];
  299. end
  300. mathString = [mathString ')/' int2str(length(subjects))];
  301. fpp.wb.command('cifti-math',mathString,[],outputPath,varString);
  302. disp(['Wrote ' segNames{r}]);
  303. end
  305. %% OPTIONAL: Smooth probmap and threshold probmap
  306. for s=1:length(subjects)
  307. for h=1:2
  308. anatDir = [studyDir '/fpp/sub-hcp100206/anat'];
  309. surfacePaths{h} = [anatDir '/' fpp.bids.changeName('',{'sub','hemi','space','den'},...
  310. {'hcp100206',hemis{h},'individual','32k'},'midthickness','.surf.gii')];
  311. end
  312. fpp.wb.command('cifti-smoothing',outputPath,[num2str(sigmaSm) ' '...
  313. num2str(sigmaSm) ' COLUMN'],outputPathSm,['-left-surface '...
  314. surfacePaths{1} ' -right-surface ' surfacePaths{2}]);
  316. % Threshold probmap
  317. mathString = ['r > ' num2str(probThresh)];
  318. varString = [' -var r ' outputPathSm];
  319. fpp.wb.command('cifti-math',mathString,[],outputPathThr,varString);
  320. end

atlasMTLProcessSurfaceROIs.m at commit 3caaf99, under CC-BY-4.0 · at the source

Overview

Authors: Ian Faul1, Adya Agarwal1, Stephen Graziose1, Andrew Nwacha1, Janis Park1, Anisah Sahibul1, Katie Tong1, Ben Deen1
ORCID iDs: Ian Faul, Andrew Nwacha, Janis Park, Ben Deen
  1. Department of Psychology, Tulane University, New Orleans, LA, United States
Institutions: Tulane University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1447
Dates: received 11 December 2025; accepted 13 October 2026; published online 9 November 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1447 · PMID 43011773 · PMCID PMC13749375 · OpenAlex W7222993897
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.64898/2025.12.04.692420
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: medial temporal lobe, atlas, MRI, long-term memory, temporopolar
MeSH: Atlases as Topic*, Temporal Lobe*, Adult, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Medical Image Segmentation Techniques (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: National Science Foundation; Louisiana Board of Regents (LEQSF(2024-27)-RD-A-25); National Institutes of Health; McDonnell Center for Systems Neuroscience; National Institute of Mental Health (U54MH091657)
Citations: not cited yet (Europe PMC); 103 references in the paper

Abstract

The human medial temporal lobe (MTL) includes a number of cortical regions surrounding the hippocampus implicated in long-term memory and high-level perception, including entorhinal cortex (ERC), perirhinal cortex (PRC), parahippocampal cortex (PHC), and the medial aspect of temporopolar cortex (TPC). A critical prerequisite to studying the function of these areas is to define their anatomical boundaries. While several atlases or segmentations methods for automated boundary definition exist, they have limitations: (1) they are derived from small sample sizes; (2) most do not include TPC; and (3) they are volumetric rather than surface-based and are therefore not straightforward to use for surface-based analysis of MRI data. Here we present the MTL200: a surface-based, probabilistic atlas of MTL regions ERC, PRC, PHC, and TPC. The atlas is derived from hand drawings of regions based on gross anatomical features corresponding to cytoarchitectonic boundaries, using anatomical MRI data from 200 human participants. This work provides a valuable tool for researchers studying the anatomy and function of MTL regions, particularly the poorly characterized area TPC.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

SocialMemoryLab/MTL200

License: CC-BY-4.0
State: the link answers, verified on 12 November 2026
Evidence: files inventoried
Commit: 3caaf99b2049e92c4c63698de9d12605d40439ef, 11 December 2025
Languages: MATLAB (13)
Size: 69 files, 13 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 12 November 2026: the link answers
  • 12 November 2026: the link answers
14 files
At the source: github.com/SocialMemoryLab/MTL200

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

Tracing map

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

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 4 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 atlas and code used for its generation are available at https://github.com/SocialMemoryLab/MTL200. Data used to generate the atlas come from the Human Connectome Project 1200 subjects data release (https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release).

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, 12 November 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 5 keywords, 10 MeSH terms, 5 funders, 102 references.

Cite

This paper

Faul, I., Agarwal, A., Graziose, S., Nwacha, A., Park, J., Sahibul, A., Tong, K., & Deen, B. (2026). The MTL200: A surface-based, probabilistic atlas of the medial temporal lobe. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1447. https://doi.org/10.1162/imag.a.1447

BibTeX

@article{faul2026mtl200,
author = {Faul, Ian and Agarwal, Adya and Graziose, Stephen and Nwacha, Andrew and Park, Janis and Sahibul, Anisah and Tong, Katie and Deen, Ben},
title = {{The MTL200: A surface-based, probabilistic atlas of the medial temporal lobe}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = nov,
volume = {4},
pages = {IMAG.a.1447},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1447},
url = {https://doi.org/10.1162/imag.a.1447},
pmid = {43011773},
pmcid = {PMC13749375}
}

RIS

TY - JOUR
AU - Faul, Ian
AU - Agarwal, Adya
AU - Graziose, Stephen
AU - Nwacha, Andrew
AU - Park, Janis
AU - Sahibul, Anisah
AU - Tong, Katie
AU - Deen, Ben
TI - The MTL200: A surface-based, probabilistic atlas of the medial temporal lobe
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/11/09
VL - 4
SP - IMAG.a.1447
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1447
UR - https://doi.org/10.1162/imag.a.1447
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1447",
"type": "article-journal",
"title": "The MTL200: A surface-based, probabilistic atlas of the medial temporal lobe",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Faul",
"given": "Ian"
},
{
"family": "Agarwal",
"given": "Adya"
},
{
"family": "Graziose",
"given": "Stephen"
},
{
"family": "Nwacha",
"given": "Andrew"
},
{
"family": "Park",
"given": "Janis"
},
{
"family": "Sahibul",
"given": "Anisah"
},
{
"family": "Tong",
"given": "Katie"
},
{
"family": "Deen",
"given": "Ben"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1447",
"DOI": "10.1162/imag.a.1447",
"PMID": "43011773",
"PMCID": "PMC13749375",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1447",
"language": "en",
"issued": {
"date-parts": [
[
2026,
11,
9
]
]
}
}

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