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Neuroanatomical signatures of chronotype in young adults.

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The authors' code

MATLAB · 304 lines · 8.9 KB · no license

  1. % If you use our method (or data included in it), please consider to cite:
  3. % Beheshti, Iman, et al. (2019). Bias-adjustment in neuroimaging-based brain age frameworks: a robust scheme. NeuroImage: Clinical, 102063.
  4. % https://www.sciencedirect.com/science/article/pii/S2213158219304103
  5. % Iman Beheshti
  6. % Email: [email hidden]
  8. clc
  9. close all
  10. clear all
  11. tic
  12. Simulation_State=1; % 1: Without bias correction 2: Cole's method 3: Proposed Method
  14. if Simulation_State==1
  15. disp('Without bias correction:')
  16. elseif Simulation_State==2
  18. disp('Cole''s method:')
  19. elseif Simulation_State==3
  20. disp('Proposed Method:')
  21. end
  23. load Data
  24. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Data sets (Training and Test ) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  25. %% Training Set , 675 HC
  26. HC_DATA_Train=Data.train.features;
  27. HC_Age_Train=Data.train.age;
  29. %% Test sets
  30. % 1: indendent 75 HC
  31. HC_DATA_Test=Data.Test.HC.Features;
  32. HC_Age_Test=Data.Test.HC.Age;
  33. % 2 : MCI
  34. MCI_DATA=Data.Test.MCI.Features;
  35. MCI_Age=Data.Test.MCI.Age;
  36. %[row, col] = find(isnan(MCI_Age));
  37. %MCI_DATA(row,:)=[];
  38. %MCI_Age(row,:)=[];
  39. % 3 : AD
  40. AD_DATA=Data.Test.AD.Features;
  41. AD_Age=Data.Test.AD.Age;
  42. %[row, col] = find(isnan(AD_Age));
  43. %AD_DATA(row,:)=[];
  44. %AD_Age(row,:)=[];
  45. %%%%%%%#######################################################################################################
  47. [ n , m ] = size (HC_DATA_Train);
  48. PredictTest = zeros(size(HC_Age_Train));
  50. K = 10 ; % nomber of folds
  51. Foldaccuracy= zeros(1,K);
  52. bestacctrainmain=zeros(1,K);
  53. cvFolds = zeros(n,1);
  55. for z = 1 :K : n
  56. for w = 1 : K
  57. cvFolds(z) = w;
  58. z = z+1;
  59. % w = w+1 ;
  60. end
  61. end
  62. cvFolds = cvFolds( 1:n ,:);
  65. for i =1:K %for each fold for i = 1 : K
  66. testIdx = (cvFolds == i); % get indices of test instances
  67. trainIdx = ~testIdx; % get indices training instances
  68. %%
  69. AgeTrain=HC_Age_Train(trainIdx,:);
  70. MainTestAge=HC_Age_Train(testIdx,:);
  71. DataTrain=HC_DATA_Train(trainIdx,:);
  72. MainTestData=HC_DATA_Train(testIdx,:);
  75. %% Regression Model
  76. Mdl = fitrsvm(DataTrain,AgeTrain,'KernelFunction','linear');
  77. XX= predict(Mdl,MainTestData);
  78. PredictTest_Before(testIdx,1)=XX;
  81. % if Simulation_State==2
  82. % p = polyfit(MainTestAge, (XX),1);
  83. % q(i)=p(1);
  84. % qq(i)=p(2);
  85. % elseif Simulation_State==3
  86. %
  87. % p = polyfit(MainTestAge, (XX-MainTestAge),1);
  88. % q(i)=p(1);
  89. % qq(i)=p(2);
  90. % end
  93. end
  96. if Simulation_State==2
  97. p = polyfit(HC_Age_Train, (PredictTest_Before),1);
  98. q=p(1);
  99. qq=p(2);
  100. elseif Simulation_State==3
  102. p = polyfit(HC_Age_Train, (PredictTest_Before-HC_Age_Train),1);
  103. q=p(1);
  104. qq=p(2);
  105. end
  107. PredictTest=[];
  108. if Simulation_State==1
  109. PredictTest=PredictTest_Before;
  110. elseif Simulation_State==2
  112. PredictTest=(PredictTest_Before - mean(qq))./mean(q);
  113. elseif Simulation_State==3
  115. Offset=mean(q).*HC_Age_Train+mean(qq);
  116. for t=1:size(PredictTest_Before,1)
  117. PredictTest(t,1)=PredictTest_Before(t,1)-Offset(t,1);
  118. end
  119. end
  123. MAEtest(1,1)=sum(abs(PredictTest -HC_Age_Train))/numel(HC_Age_Train);
  124. RMSEtest(1,1)= (mean((PredictTest -HC_Age_Train).^2))^0.5;
  125. MEANHCs(1,1)=mean((PredictTest -HC_Age_Train));
  126. [RTest, Pvalue] = corr(HC_Age_Train,PredictTest);
  127. R2_Train(1,1)=RTest.*RTest;
  130. subplot(2,2,1);plot( HC_Age_Train,PredictTest-HC_Age_Train, 'go','MarkerSize',8 )
  131. xlabel('Real age (years)','FontSize', 20)
  132. ylabel('Delta brain age (years)','FontSize', 20)
  133. coeff = polyfit(HC_Age_Train,PredictTest-HC_Age_Train,1);
  134. xline = linspace( min(min(HC_Age_Train)), max(max(HC_Age_Train)), 2000);
  135. yline = coeff(1)*xline+coeff(2);
  136. hold on
  137. plot(xline,yline,'g-')
  138. hold on
  141. [p,S] = polyfit(HC_Age_Train,PredictTest-HC_Age_Train,1);
  142. [y_fit,delta] = polyval(p,HC_Age_Train,S);
  143. %plot(Age_HC2,PredictTest,'bo')
  144. hold on
  145. plot(HC_Age_Train,y_fit,'g-')
  146. %grid on
  147. grid minor
  148. subplot(2,2,1);plot(HC_Age_Train,y_fit+2*delta,'g--',HC_Age_Train,y_fit-2*delta,'g--','LineWidth',2)
  149. title('Linear Fit of Data with 95% Prediction Interval, Training set : HC','FontSize', 15)
  150. %legend('Data','Linear Fit','95% Prediction Interval')
  156. %%############################################## . ON INDEPENDENT TEST SETS #######################################
  158. %$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%1 :INDEPENDENT HC
  160. Mdl_onTest_HC = fitrsvm(HC_DATA_Train,HC_Age_Train,'KernelFunction','linear');
  161. PredictTest_HC_Before= predict(Mdl_onTest_HC,HC_DATA_Test);
  163. PredictTest_HC=[];
  165. if Simulation_State==1
  166. PredictTest_HC=PredictTest_HC_Before;
  167. elseif Simulation_State==2
  169. PredictTest_HC=(PredictTest_HC_Before - mean(qq))./mean(q);
  170. elseif Simulation_State==3
  172. Offset=mean(q).*HC_Age_Test+mean(qq);
  173. for t=1:size(PredictTest_HC_Before,1)
  174. PredictTest_HC(t,1)=PredictTest_HC_Before(t,1)-Offset(t,1);
  175. end
  177. end
  182. Mean_HC_Final(1,1)=mean(PredictTest_HC-HC_Age_Test);
  183. MAE_HC_Final(1,1)=sum(abs(PredictTest_HC-HC_Age_Test))/numel(HC_Age_Test);
  184. RMSE_HC_Final(1,:)= (mean((PredictTest_HC-HC_Age_Test).^2))^0.5;
  185. [R_HC_Final, Pvalue_HC_Final] = corr(PredictTest_HC,HC_Age_Test);
  186. R2_HC_Final(1,:)=R_HC_Final.*R_HC_Final;
  187. % hold on
  188. subplot(2,2,2);plot( HC_Age_Test,PredictTest_HC-HC_Age_Test, 'bo' )
  189. xlabel('Real age (years)','FontSize', 20)
  190. ylabel('Delta brain age (years)','FontSize', 20)
  191. coeff = polyfit(HC_Age_Test,PredictTest_HC-HC_Age_Test,1);
  192. xline = linspace( min(min(HC_Age_Test)), max(max(HC_Age_Test)), 2000);
  193. yline = coeff(1)*xline+coeff(2);
  194. hold on
  195. plot(xline,yline,'b-')
  196. hold on
  201. [p,S] = polyfit(HC_Age_Test,PredictTest_HC-HC_Age_Test,1);
  202. [y_fit,delta] = polyval(p,HC_Age_Test,S);
  203. hold on
  204. plot(HC_Age_Test,y_fit,'b-')
  205. grid minor
  206. subplot(2,2,2);plot(HC_Age_Test,y_fit+2*delta,'b--',HC_Age_Test,y_fit-2*delta,'b--','LineWidth',2)
  207. title('Linear Fit of Data with 95% Prediction Interval, Independent Test set: HC ','FontSize', 15)
  208. hold on
  209. subplot(2,2,3);plot( HC_Age_Test,PredictTest_HC-HC_Age_Test, 'bo' )
  210. subplot(2,2,3);plot(xline,yline,'b-')
  215. %%%%%%%%%%%#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%%%%%%%%%%%%%%%%%%%%%#######%%%% . TEST ON MCI
  217. Mdl_onTest_MCI = fitrsvm(HC_DATA_Train,HC_Age_Train,'KernelFunction','linear');
  218. PredictTest_MCI_Before= predict(Mdl_onTest_MCI,MCI_DATA);
  220. PredictTest_MCI=[];
  222. if Simulation_State==1
  223. PredictTest_MCI=PredictTest_MCI_Before;
  224. elseif Simulation_State==2
  225. PredictTest_MCI=(PredictTest_MCI_Before - mean(qq))./mean(q);
  226. elseif Simulation_State==3
  227. Offset=mean(q).*MCI_Age+mean(qq);
  228. for t=1:size(PredictTest_MCI_Before,1)
  229. PredictTest_MCI(t,1)=PredictTest_MCI_Before(t,1)-Offset(t,1);
  230. end
  231. end
  236. Mean_MCI_Final(1,1)=mean(PredictTest_MCI-MCI_Age);
  237. MAE_MCI_Final(1,1)=sum(abs(PredictTest_MCI-MCI_Age))/numel(MCI_Age);
  238. RMSE_MCI_Final(1,:)= (mean((PredictTest_MCI-MCI_Age).^2))^0.5;
  239. [R_MCI_Final, Pvalue_MCI_Final] = corr(PredictTest_MCI,MCI_Age);
  240. R2_MCI_Final(1,:)=R_MCI_Final.*R_MCI_Final;
  241. hold on
  242. subplot(2,2,3);plot( MCI_Age,PredictTest_MCI-MCI_Age, 'ko' )
  243. title('Linear Fit of Data with 95% Prediction Interval, Independent Test sets: MCI & AD ','FontSize', 15)
  246. xlabel('Real age (years)','FontSize', 20)
  247. ylabel('Delta brain age (years)','FontSize', 20)
  248. coeff = polyfit(MCI_Age,PredictTest_MCI-MCI_Age,1);
  249. xline = linspace( min(min(MCI_Age)), max(max(MCI_Age)), 2000);
  250. yline = coeff(1)*xline+coeff(2);
  251. grid on
  252. hold on
  253. plot(xline,yline,'k-')
  254. hold on
  257. %%$$$$$$$$$$$$$$$$$$$$$$$$$$$%%%%%%%%%%@#######################################@@@@ 3 AD
  259. Mdl_onTest_AD = fitrsvm(HC_DATA_Train,HC_Age_Train,'KernelFunction','linear');
  260. PredictTest_AD_Before= predict(Mdl_onTest_AD,AD_DATA);
  261. PredictTest_AD=[];
  263. if Simulation_State==1
  264. PredictTest_AD=PredictTest_AD_Before;
  265. elseif Simulation_State==2
  266. PredictTest_AD=(PredictTest_AD_Before - mean(qq))./mean(q);
  267. elseif Simulation_State==3
  268. Offset=mean(q).*AD_Age+mean(qq);
  269. for t=1:size(PredictTest_AD_Before,1)
  270. PredictTest_AD(t,1)=PredictTest_AD_Before(t,1)-Offset(t,1);
  271. end
  272. end
  276. Mean_AD_Final(1,1)=mean(PredictTest_AD-AD_Age);
  277. MAE_AD_Final(1,1)=sum(abs(PredictTest_AD-AD_Age))/numel(AD_Age);
  278. RMSE_AD_Final(1,:)= (mean((PredictTest_AD-AD_Age).^2))^0.5;
  279. [R_AD_Final, Pvalue_AD_Final] = corr(PredictTest_AD,AD_Age);
  280. R2_AD_Final(1,:)=R_AD_Final.*R_AD_Final;
  281. hold on
  282. subplot(2,2,3);plot( AD_Age,PredictTest_AD-AD_Age, 'ro' )
  284. xlabel('Real age (years)','FontSize', 20)
  285. ylabel('Delta brain age (years)','FontSize', 20)
  286. coeff = polyfit(AD_Age,PredictTest_AD-AD_Age,1);
  287. xline = linspace( min(min(AD_Age)), max(max(AD_Age)), 2000);
  288. yline = coeff(1)*xline+coeff(2);
  289. hold on
  290. plot(xline,yline,'r-')
  291. hold on
  297. Group = {'Training set : HC';'Test set : HC';'Test set : MCI';'Test set : AD'};
  298. MAE = [MAEtest;MAE_HC_Final;MAE_MCI_Final;MAE_AD_Final];
  299. RMSE = [RMSEtest;RMSE_HC_Final;RMSE_MCI_Final;RMSE_AD_Final];
  300. R2 = [R2_Train;R2_HC_Final;R2_MCI_Final;R2_AD_Final];
  301. Mean_Delta_Age = [MEANHCs;Mean_HC_Final;Mean_MCI_Final;Mean_AD_Final];
  302. Results = table(Group,MAE,RMSE,R2,Mean_Delta_Age)
  304. toc

Bias_Correction_Main.m at commit 691f988, no license · at the source

Overview

Authors: Iman Beheshti1, Odelia Elkana2
ORCID iDs: Iman Beheshti, Odelia Elkana
  1. Department Electrical Engineering Technology, Red River College Polytechnic,W406-160 Princess St, R3B 1K9 Winnipeg, MB Canada
  2. Behavioral Sciences, The Academic College of Tel Aviv-Yaffo,P.O.B 8401, Tel Aviv, 61083 Israel
Institutions: Red River College (Canada); Academic College of Tel Aviv-Yafo (Israel)
Journal: Brain imaging and behavior, volume 20, issue 3, article 80
Dates: received 17 October 2025; accepted 13 April 2026; published online 25 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s11682-026-01153-7 · PMID 42033536 · PMCID PMC13110214 · OpenAlex W7155632507
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Statistics, Spectral & time-frequency, Preprocessing
Keywords: Chronotype, Circadian rhythm, Brain structure, Cortical thickness, Gray matter, Sleep health, Young adults, Neuroimaging
MeSH: Brain*, Chronotype*, Circadian Rhythm*, Adolescent, Adult, Female, Gray Matter, Humans, Magnetic Resonance Imaging, Male, Sleep, Surveys and Questionnaires, White Matter, Young Adult (* major topic)
Topic: Circadian rhythm and melatonin (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: Academic College of Tel Aviv - Jaffo
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

Despite extensive evidence linking chronotype to behavioral and physiological outcomes, its structural neuroanatomical correlates, especially in healthy young adults, remain insufficiently characterized, and multimodal structural investigations integrating Voxel-based morphometry (VBM), cortical thickness (CT), and brain-age metrics are still limited. We examined whether chronotype—preference for early or late sleep–wake timing—is associated with structural brain variation in 136 healthy young adults (68 early chronotypes [EC], 68 late chronotypes [LC]) using high-resolution MRI. Early and late chronotypes were defined using the Morningness–Eveningness subscale of the Chronotype Questionnaire (ChQ-ME): early chronotype (EC) scores 11–21 and late chronotype (LC) scores 22–32. The VBM analyses were conducted to assess gray and white matter morphology, complemented by CT analyses and estimation of brain-predicted age difference (Brain-PAD) as an index of biological brain aging. Sensitivity analyses additionally modeled ChQ-ME as a continuous predictor. Primary voxel-wise VBM analyses did not identify between-group differences in gray or white matter morphology that survived family-wise error (FWE) correction (p < 0.05). In pre-specified exploratory analyses (voxel-wise p < 0.001, uncorrected; cluster-level false discovery rate [FDR] correction q < 0.05), late chronotype (LC) participants showed an exploratory left cerebellar/occipital cluster with lower gray matter volume. Region-wise CT differences were nominal (p < 0.05) and did not survive FDR correction across regions. No significant differences in Brain-PAD were observed. In healthy young adults, chronotype-related structural differences were not detectable under conservative voxel-wise FWE correction; however, pre-specified exploratory analyses suggested a regionally specific cerebellar gray matter pattern and nominal CT trends. These findings motivate larger and longitudinal studies with objective sleep–wake timing measures to clarify whether sleep timing is linked to early structural variation and to test its potential modifiability.

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

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Beheshtiiman2/Bias-Correction-in-Brain-Age-Estimation-Frameworks

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 691f988837a297583337b352e3d7ad2bf2f4f188, 12 January 2025
Languages: MATLAB (1)
Size: 3 files, 1 script
Software Heritage: not archived
Found in: the text, “Brain age estimation framework”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: github.com/Beheshtiiman2/Bias-Correction-in-Brain-Age-Estimation-Frameworks

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.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

This study is a secondary analysis of anonymized, publicly available data obtained from the OpenNeuro repository (https://openneuro.org/datasets/ds003826/versions/3.0.1/file-display/dataset_description.json; accessed March 1, 2025). No new data were collected by the authors. The original data were collected as part of two separate studies, both of which received ethical approval from the Research Ethics Committee at the Institute of Applied Psychology, Jagiellonian University, and the Bioethics Commission at the Polish Military Institute of Aviation Medicine. All participants gave written informed consent prior to participation in the original studies. Data collection complied with the ethical standards of the respective institutions and was conducted in accordance with the Declaration of Helsinki.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 8 keywords, 14 MeSH terms, 1 funder, 38 references.

Cite

This paper

Beheshti, I., & Elkana, O. (2026). Neuroanatomical signatures of chronotype in young adults. Brain imaging and behavior, 20(3), 80. https://doi.org/10.1007/s11682-026-01153-7

BibTeX

@article{beheshti2026neuroanatomical,
author = {Beheshti, Iman and Elkana, Odelia},
title = {{Neuroanatomical signatures of chronotype in young adults}},
journal = {Brain imaging and behavior},
year = {2026},
month = apr,
volume = {20},
number = {3},
pages = {80},
publisher = {Springer Science+Business Media},
issn = {1931-7557},
doi = {10.1007/s11682-026-01153-7},
url = {https://doi.org/10.1007/s11682-026-01153-7},
pmid = {42033536},
pmcid = {PMC13110214}
}

RIS

TY - JOUR
AU - Beheshti, Iman
AU - Elkana, Odelia
TI - Neuroanatomical signatures of chronotype in young adults
T2 - Brain imaging and behavior
J2 - Brain Imaging Behav
PY - 2026
DA - 2026/04/25
VL - 20
IS - 3
SP - 80
SN - 1931-7557
PB - Springer Science+Business Media
DO - 10.1007/s11682-026-01153-7
UR - https://doi.org/10.1007/s11682-026-01153-7
LA - en
ER -

CSL-JSON

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"id": "10.1007/s11682-026-01153-7",
"type": "article-journal",
"title": "Neuroanatomical signatures of chronotype in young adults",
"container-title": "Brain imaging and behavior",
"author": [
{
"family": "Beheshti",
"given": "Iman"
},
{
"family": "Elkana",
"given": "Odelia"
}
],
"container-title-short": "Brain Imaging Behav",
"volume": "20",
"issue": "3",
"page": "80",
"DOI": "10.1007/s11682-026-01153-7",
"PMID": "42033536",
"PMCID": "PMC13110214",
"ISSN": "1931-7557",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s11682-026-01153-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
25
]
]
}
}

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