OSCR

Motor cortical areas facilitate schema-mediated integration of new motor information into memory.

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 · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › fMRI acquisition and preprocessing ↔ Scripts - MRI/Preprocessing_Step1.m, lines 70–113 · score 0.90 · forward deformation field, soft tissue, native space, background, bone, mapping
  2. [2] § Methods › Statistical analyses › fMRI analyses › Multivariate analyses › ROI selection ↔ Scripts - MRI/Preprocessing_Step1.m, lines 70–113 · score 0.75 · subcortical segmentation, native space, FSL, MNI, ROI
  3. [3] § Methods › Statistical analyses › fMRI analyses › Multivariate analyses › ROI selection ↔ Scripts - MRI/Extract_corticalmasks.m, the whole file · a weak match · score 0.73 · native space, aSPL, Brainnettome, anatomically, MNI, Cortical
  4. [4] § Methods › fMRI acquisition and preprocessing ↔ Scripts - MRI/Preprocessing functions/p_segment.m, the whole file · a weak match · score 0.70 · forward deformation field, preprocessed, tissue, segmented, MNI, native
  5. [5] § Results › Neuroimaging results › Multivariate pattern analyses ↔ Scripts - MRI/MVPA_Similarity_pergroup.m, lines 43–74 · score 0.66 · angular gyrus, exploratory ROIs, aSPL, bilateral, sequences
  6. [6] § Methods › Statistical analyses › fMRI analyses › Univariate analyses › Connectivity-based analyses ↔ Scripts - MRI/PPI_Step2.m, lines 194–256 · score 0.55 · right M1, left M1, PPI, physiological, psychological, regressors
  7. [7] § Methods › Statistical analyses › fMRI analyses › Univariate analyses › Statistics ↔ Scripts - MRI/PPI_Step1.m, lines 85–134 · score 0.52 · mm radius, centred, thresholded, SPM

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 324 lines · 13 KB · no license · 2 matches

  1. %% SPM12 Spatial Preprocessing pipeline - Schema Study - Leuven 27/01/2021 - GA/ND
  3. % Data need to be organized as follows:
  4. % ana\subj_directory\ANA: s*.img
  5. % \FUNC_RUNx: f*.img
  7. % A good habit is to save the raw data organized as mentioned above in a separate \raw_organized folder.
  8. % We therefore recommend saving the following \raw,\raw_organized and run as many ana as wished in separate \ana folder
  9. % WARNING! ALWAYS RUN NEW ANA ON RAW DATA COPIED FROM RAW_ORGANIZED FOLDER
  11. % INPUT PARAMETERS / SUBJECT INFO
  13. spm_jobman('initcfg');
  14. data.main_folder = {'F:\MRI_Analyses\Analyses'};
  15. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  17. %%%% SCHEMA MR S5704 FINAL
  19. data.subjects = {'S001','S002','S003','S004','S005','S006','S007','S008','S009','S010'...
  20. 'S011','S012','S013','S014','S015','S016','S017','S018','S019','S020',...
  21. 'S021','S022','S023','S024','S025','S026','S027','S028','S029','S030',...
  22. 'S031','S032','S033','S034', 'S035','S036','S038', 'S039', 'S040','S041', 'S042', 'S043',...
  23. 'S044', 'S045', 'S046','S047', 'S048', 'S049', 'S050', 'S051', 'S052',...
  24. 'S053','S054', 'S055','S056','S057', 'S058','S059', 'S060'};
  25. %%% note: S037 excluded due to excessive motion in scanner
  27. %%%%%%%%%%%%% make sure you've unzipped the nii files before starting
  28. %%%%%%%%%%%%% preprocessing!!!!!!
  30. for sub = 1:1:length(data.subjects)
  31. if strcmp(data.subjects{sub}, 'S010')
  32. data.sessions(sub,1:7) = { ...
  33. 'FUNC_RUN1', ...
  34. 'FUNC_RUN2',...
  35. 'FUNC_RUN4',...
  36. 'FUNC_RUN5',...
  37. 'FUNC_RUN6',...
  38. 'FUNC_RUN7',...
  39. 'FUNC_RUN8',...
  40. }';
  41. elseif strcmp(data.subjects{sub}, 'S022')
  42. data.sessions(sub,1:7) = { ...
  43. 'FUNC_RUN2',...
  44. 'FUNC_RUN3',...
  45. 'FUNC_RUN4',...
  46. 'FUNC_RUN5',...
  47. 'FUNC_RUN6',...
  48. 'FUNC_RUN7',...
  49. 'FUNC_RUN8',...
  50. }';
  51. else
  52. data.sessions(sub,1:8) = { ...
  53. 'FUNC_RUN1', ...
  54. 'FUNC_RUN2',...
  55. 'FUNC_RUN3',...
  56. 'FUNC_RUN4',...
  57. 'FUNC_RUN5',...
  58. 'FUNC_RUN6',...
  59. 'FUNC_RUN7',...
  60. 'FUNC_RUN8',...
  61. }';
  62. end
  63. end
  65. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  66. % preprocessing performed for univariate and multivariate pipeline
  67. % reorient and segment
  68. % slicetime correction
  70. %% RE-ORIENT AND SEGMENT T1
  72. % RE-ORIENT function to automatically reorient a structural image on the template
  73. % in order further proceed with the segmentation/normalisation.
  74. % very useful as segmentation/normalisation is rather sensitive on the
  75. % starting orienation of the image
  77. % SEGMENT
  78. % Data classified into different tissue types, typically with the following
  79. % order: grey matter, white matter, CSF, bone, soft tissue, background.
  81. % Within each subject's mprage folder, multiple files will be created.
  82. % Files with prefixes c1-c6 are tissue class images (one for each type of
  83. % tissue) that are in alignment with the original space (i.e., segmented
  84. % gray matter, segmented white matter, etc.). Files with the prefixes
  85. % rc1-rc6 are in a form that can be used with the Dartel toolbox.
  86. % Forward deformation field ”y_”imagename“_sn.mat” is also written and will
  87. % be used for the normalize to MNI step or to map ROI back to native space (inverse deformation field ”iy_”imagename“_sn.mat”is also
  88. % written).
  90. o_matlabbatch = [];
  91. for nSub =1:length(data.subjects)
  92. fprintf(['AUTO-ORIENT SUBJECT ', data.subjects{nSub},'\n']);
  93. anat_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT',filesep);
  94. anat = p_get_files(anat_folder,'nii','DBIEX'); % Get nifti files that begin with DBIEX (original)
  95. %anat = p_get_files(anat_folder,'img','s'); % FOR ME TO PLAY
  96. p_reorient(anat);
  97. fprintf(['DONE AUTO-ORIENT T1 SUBJECT ', data.subjects{nSub},'\n']);
  98. o_matlabbatch{end+1} = p_segment(anat); % Still retrieving nifti files that begin with DBIEX (but these have not been auto-oriented; not renamed)
  99. end
  101. spm_jobman('run',o_matlabbatch);
  102. fprintf(['DONE SEGMENTATION T1 SUBJECT ', data.subjects{nSub},'\n']);
  104. % Copy reoriented T1 to FSL folder - to be used for subcortical segmentation in FSL
  105. for nSub=1:length(data.subjects)
  106. anat_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT',filesep);
  107. anat = p_get_files(anat_folder,'nii','DBIEX');
  108. new_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT/FSL', filesep);
  109. mkdir(new_folder);
  110. copyfile([anat], [char(new_folder) 'long_T1.nii']);
  111. cd(new_folder)
  112. end
  113. cd ([char(data.main_folder)])
  115. %%
  116. % VERIFY PARAMETERS OUTLINED IN p_slicetime, INCLUDING NUMBER OF SLICES,
  117. % TR, REFERENCE SLICE, ORDER OF ACQUISITION, ETC. THESE ARE LIKELY TO
  118. % CHANGE MORE FREQUENTLY THAN PARAMETERS FOR OTHER FUNCTIONS
  120. % Within each subject's primary (functional) data folder, new files with
  121. % the prefix 'a_' will be created. This file should then be called in
  122. % subsequent processing (i.e., coregister).
  124. o_matlabbatch = [];
  125. for nSub=1:length(data.subjects)
  126. num_runs(nSub) = 0;
  127. for ii = 1:1:length(data.sessions(nSub,:))
  128. if strcmp(data.sessions(nSub,ii), {'NaN'}) == 0
  129. num_runs(nSub) = num_runs(nSub) + 1;
  130. end
  131. end
  132. clear ii;
  133. for nRun = 1:num_runs(nSub)
  134. slicetime_folder = fullfile(data.main_folder{1}, data.subjects{nSub},data.sessions{nSub,nRun}, filesep);
  135. slicetime = p_get_files(slicetime_folder,'nii','vol');
  136. o_matlabbatch{end+1} = p_mvcs_slicetime_task(slicetime);
  137. end
  138. end
  139. spm_jobman('run',o_matlabbatch);
  140. clear nSub; clear num_runs; clear nRun;
  142. %% REALIGN FUNCTIONAL (across runs) - estimate & write
  143. % Within each subject's primary (functional) data folder, a text file with
  144. % the prefix rp will be created with realignment parameters. A nifti file
  145. % with the prefix 'mean' will also be created with the mean resliced (functional) image.
  146. % New realigned files (i.e., niftis with prefix 'r' will be written with parameters specified in
  147. % p_realign).
  150. for nSub=1:length(data.subjects)
  151. o_matlabbatch = [];
  152. num_runs(nSub) = 0;
  153. for ii = 1:1:length(data.sessions(nSub,:))
  154. if strcmp(data.sessions(nSub,ii), {'NaN'}) == 0
  155. num_runs(nSub) = num_runs(nSub) + 1;
  156. end
  157. end
  158. clear ii;
  161. for nRun = 1:num_runs(nSub)
  162. realign_folder = fullfile(data.main_folder{1}, data.subjects{nSub},data.sessions{nSub,nRun}, filesep);
  163. realign = p_get_files(realign_folder, 'nii','a_');
  164. realign = strcat(realign, ',1');
  165. o_matlabbatch{1}.spm.spatial.realign.estwrite.data{nRun} = cellstr(realign);
  166. % o_matlabbatch{end+1}.spm.spatial.realign.estwrite.data = {cellstr(realign)};
  167. end
  168. % o_matlabbatch{end+1}.spm.spatial.realign.estwrite.data = {cellstr(realign)};
  170. o_matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.quality = 0.9;
  171. o_matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.sep = 4;
  172. o_matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.fwhm = 5;
  173. o_matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.rtm = 1;
  174. o_matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.interp = 2;
  175. o_matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.wrap = [0 0 0];
  176. o_matlabbatch{1}.spm.spatial.realign.estwrite.eoptions.weight = '';
  177. o_matlabbatch{1}.spm.spatial.realign.estwrite.roptions.which = [2 1];
  178. o_matlabbatch{1}.spm.spatial.realign.estwrite.roptions.interp = 4;
  179. o_matlabbatch{1}.spm.spatial.realign.estwrite.roptions.wrap = [0 0 0];
  180. o_matlabbatch{1}.spm.spatial.realign.estwrite.roptions.mask = 1;
  181. o_matlabbatch{1}.spm.spatial.realign.estwrite.roptions.prefix = 'r_';
  183. spm_jobman('run',o_matlabbatch);
  184. clear realign; clear realign_folder; clear num_runs;
  185. end
  187. clear nSub; clear nRun;
  189. % COREGISTRATION
  190. % Within each subject's primary (anat) data folder, an mstructural file
  191. % will be created (anatomical coregistered). No reslicing of mean and
  192. % functional (estimate only)
  194. o_matlabbatch = [];
  195. for nSub=1:length(data.subjects)
  196. num_runs(nSub) = 0;
  197. for ii = 1:1:length(data.sessions(nSub,:))
  198. if strcmp(data.sessions(nSub,ii), {'NaN'}) == 0
  199. num_runs(nSub) = num_runs(nSub) + 1;
  200. end
  201. end
  202. clear ii;
  204. anat_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT',filesep);
  205. anat = p_get_files(anat_folder,'nii','DBIEX'); % Get nifti files that begin with DBIEX (original)
  206. mean_folder = fullfile(data.main_folder{1}, data.subjects{nSub},data.sessions{nSub,1}, filesep);
  207. mean = p_get_files(mean_folder,'nii','mean');
  209. other = [];
  210. for nRun = 1:num_runs(nSub)
  211. realign_folder = fullfile(data.main_folder{1}, data.subjects{nSub},data.sessions{nSub, nRun}, filesep);
  212. filesTmp = p_get_files(realign_folder, 'nii','r_a_');
  213. filesTmp = strcat(filesTmp, ',1');
  214. other = strvcat(other, filesTmp);
  215. end
  216. o_matlabbatch{end+1} = p_coregister(anat,mean,other); % anatomical is the reference; mean functional is the source and other is all other functionals are to be converted
  217. end
  218. spm_jobman('run',o_matlabbatch);
  219. clear nSub; clear nRun;
  222. % NORMALIZATION STRUCTURAL AND FUNCTIONAL TO MNI
  223. % Normalize will be done with DARTEL for the final analysis (i.e., when the full data set is available)
  225. % NORM WRITE ANAT
  226. o_matlabbatch = [];
  227. for nSub=1:length(data.subjects)
  228. anat_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT',filesep);
  229. i_files2normalise = p_get_files(anat_folder,'nii','DBIEX'); % Get nifti files that begin with DBIEX (original)
  230. i_flowfield = p_get_files(anat_folder,'nii','y');
  231. o_matlabbatch{end+1} = p_normalise2mni_anat_nodartel(i_flowfield,i_files2normalise);
  232. end
  233. spm_jobman('run',o_matlabbatch);
  234. fprintf(['DONE NORMALIZE ANAT SUBJECT ', data.subjects{nSub},'\n']);
  235. clear nSub;
  237. % Within each subject's anatomical data folder, a new nifti file
  238. % will be created with the prefix 'w' which represents the anatomical scan
  239. % normalized to the MNI template.
  241. % NORM WRITE FUNC
  242. o_matlabbatch = [];
  243. for nSub=1:length(data.subjects)
  244. num_runs(nSub) = 0;
  245. for ii = 1:1:length(data.sessions(nSub,:))
  246. if strcmp(data.sessions(nSub,ii), {'NaN'}) == 0
  247. num_runs(nSub) = num_runs(nSub) + 1;
  248. end
  249. end
  250. clear ii;
  252. i_files2normalise = [];
  253. for nRun = 1:num_runs(nSub)
  254. realign_folder = fullfile(data.main_folder{1}, data.subjects{nSub},data.sessions{nSub,nRun}, filesep);
  255. filesTmp = p_get_files(realign_folder, 'nii','r_a_');
  256. filesTmp = strcat(filesTmp, ',1');
  257. i_files2normalise = strvcat(i_files2normalise, filesTmp);
  258. end
  259. anat_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT',filesep);
  260. i_flowfield = p_get_files(anat_folder,'nii','y');
  261. o_matlabbatch{end+1} = p_normalise2mni_func_nodartel(i_flowfield,i_files2normalise);
  262. end
  263. spm_jobman('run',o_matlabbatch);
  264. fprintf(['DONE NORMALIZE FUNC SUBJECT ', data.subjects{nSub},'\n']);
  265. clear nSub;
  267. % Within each subject's functional data folder, a new nifti file
  268. % will be created with the prefix 'w' which represents the functional
  269. % scans normalized to the MNI template.
  271. %% SMOOTH FUNCTIONAL IMAGES
  272. % %% No smoothing will be applied for the final analysis as NORMALIZE DARTEL includes smoothing
  273. o_matlabbatch = [];
  274. for nSub=1:length(data.subjects)
  275. num_runs(nSub) = 0;
  276. for ii = 1:1:length(data.sessions(nSub,:))
  277. if strcmp(data.sessions(nSub,ii), {'NaN'}) == 0
  278. num_runs(nSub) = num_runs(nSub) + 1;
  279. end
  280. end
  281. clear ii;
  283. i_files2smooth = [];
  284. for nRun = 1:num_runs(nSub)
  285. realign_folder = fullfile(data.main_folder{1}, data.subjects{nSub},data.sessions{nSub,nRun}, filesep);
  286. filesTmp = p_get_files(realign_folder, 'nii','wr_a_vol');
  287. filesTmp = strcat(filesTmp, ',1');
  288. i_files2smooth = strvcat(i_files2smooth, filesTmp);
  289. end
  290. o_matlabbatch{end+1} = p_smooth(i_files2smooth);
  291. end
  292. spm_jobman('run',o_matlabbatch);
  293. fprintf(['DONE SMOOTH FUNC SUBJECT ', data.subjects{nSub},'\n']);
  294. clear nSub;
  296. % CREATE A BRAIN MASK FOR EACH INDIVIDUAL IN NATIVE SPACE; CONSTRAINS THE
  297. % PREPROCESSING IN DANTE' PIPELINE
  299. % MAIN SUBJECT LOOP
  300. o_matlabbatch = [];
  301. for nSub = 1:length(data.subjects) % for each subject
  302. % for nSub = 1
  303. segmented_anat = [];
  304. anat_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT',filesep);
  305. for ii = 1:1:3 % creating mask with GM, WM and CSF (not other tissues)
  306. filesTmp = p_get_files(anat_folder,'nii',['c' num2str(ii)]); % Get nifti files that begin with DBIEX (original)
  307. segmented_anat = strvcat(segmented_anat, filesTmp);
  308. end
  309. output = 'brain_mask';
  310. o_matlabbatch{end+1} = p_mvcs_CreateBrainMask_ND(segmented_anat, anat_folder, output);
  311. end
  312. spm_jobman('run',o_matlabbatch);
  313. clear nSub; clear nRun;
  315. %
  316. % DELETE UNNCESSARY FILES CREATED FROM STEP ABOVE
  317. for nSub = 1:length(data.subjects) % for each subject
  318. % for nSub = 1
  319. anat_folder = fullfile(data.main_folder{1}, data.subjects{nSub},'ANAT',filesep);
  320. filesTmp = p_get_files(anat_folder,'nii','ic1'); % Get nifti files that begin with ic
  321. delete(filesTmp); clear filesTmp;
  322. filesTmp = p_get_files(anat_folder,'nii','sic1'); % Get nifti files that begin with sic
  323. delete(filesTmp); clear filesTmp
  324. end

Preprocessing_Step1.m, no license · at the source

Overview

Authors: Serena Reverberi1,2, Nina Dolfen3, Bradley Ross King4, Geneviève Albouy1,4
ORCID iDs: Geneviève Albouy
  1. Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
  2. Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
  3. Faculty of Psychology and Educational Sciences, Department of Experimental Psychology, Ghent University, Ghent, Belgium
  4. Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, UT, United States
Institutions: Clinique Romande de Réadaptation (Switzerland); École Polytechnique Fédérale de Lausanne (Switzerland); KU Leuven (Belgium); Ghent University (Belgium); University of Utah (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1203
Dates: received 7 August 2025; accepted 13 March 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1203 · PMID 41993141 · PMCID PMC13081739 · OpenAlex W7139909836
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), cognitive (subfield)
Methods: Connectivity, Statistics, fMRI & imaging, Machine learning
Keywords: motor learning, schema, integration, fMRI, memory consolidation
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Fonds Wetenschappelijk Onderzoek (11C6221N, G099516N, G0B1419N, 1524218N, G0D7918N); KU Leuven (C12/18/007)
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

New information is rapidly learned when it is compatible with pre-existing knowledge, that is, with a previously acquired schematic representation of the learned information. The influence of pre-established schema on learning has been extensively studied in the declarative memory domain, where it was shown that schema-compatible information could be rapidly assimilated into neocortical storage, bypassing the slow hippocampo-neocortical memory transfer process. Schema-mediated learning was recently examined in the motor memory domain; however, its neural substrates remain unknown. The goal of this study was to address this knowledge gap using both univariate and multivariate analyses of functional magnetic resonance imaging (fMRI) data acquired in 60 young healthy participants during the practice of a motor sequence that was either compatible or incompatible with a previously acquired cognitive–motor schema. Consistent with the literature, our behavioural results suggest that performance of sequential movements was enhanced when practice occurred in a context that was compatible with the previously acquired schema. Brain imaging results show that practice in a schema-compatible context specifically recruited the left primary motor cortex and resulted in a decrease in connectivity between the bilateral motor cortex and a set of task-relevant brain regions including the hippocampus, striatum, and cerebellum. Temporally fine-grained MRI analyses suggest that multivoxel activation patterns in the primary motor and the premotor cortices were modulated by schema-compatibility, with greater pattern similarity detected for sequence elements corresponding to and surrounding novel sequential movements under schema-compatible compared with schema-incompatible conditions. Altogether, these results suggest that motor cortical regions facilitate schema-mediated integration of novel movements into memory.

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 7 matches between paragraphs and lines of code.

OSF xnrg8

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (35)
Size: 204 files, 35 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (15 files), Statistics and Machine Learning Toolbox (4 files), CoSMoMVPA (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
36 files
At the source: osf.io/xnrg8

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;
  • 35 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

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

Data and Code Availability

Raw data, analyzed data corresponding to the figures presented in the text, and the scripts used to produce them are publicly available on OSF (https://osf.io/xnrg8).

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 2 funders, 86 references.

Cite

This paper

Reverberi, S., Dolfen, N., King, B. R., & Albouy, G. (2026). Motor cortical areas facilitate schema-mediated integration of new motor information into memory. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1203. https://doi.org/10.1162/imag.a.1203

BibTeX

@article{reverberi2026motor,
author = {Reverberi, Serena and Dolfen, Nina and King, Bradley Ross and Albouy, Geneviève},
title = {{Motor cortical areas facilitate schema-mediated integration of new motor information into memory}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1203},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1203},
url = {https://doi.org/10.1162/imag.a.1203},
pmid = {41993141},
pmcid = {PMC13081739}
}

RIS

TY - JOUR
AU - Reverberi, Serena
AU - Dolfen, Nina
AU - King, Bradley Ross
AU - Albouy, Geneviève
TI - Motor cortical areas facilitate schema-mediated integration of new motor information into memory
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/13
VL - 4
SP - IMAG.a.1203
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1203
UR - https://doi.org/10.1162/imag.a.1203
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1203",
"type": "article-journal",
"title": "Motor cortical areas facilitate schema-mediated integration of new motor information into memory",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Reverberi",
"given": "Serena"
},
{
"family": "Dolfen",
"given": "Nina"
},
{
"family": "King",
"given": "Bradley Ross"
},
{
"family": "Albouy",
"given": "Geneviève"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1203",
"DOI": "10.1162/imag.a.1203",
"PMID": "41993141",
"PMCID": "PMC13081739",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1203",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
13
]
]
}
}

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