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Brain maintenance biomarkers from structural and functional interactions in aging and neurodegeneration.

Code ↔ Paper

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  1. [1] § Methods › Pathological aging validation dataset › ANOVA analysis ↔ abeta_anova_predict_model.m, lines 87–129 · score 0.71 · CN AD, CN MCI, MCI AD, ANOVA, HSD, Tukey
  2. [2] § Methods › The preprocessing pipeline for functional neuroimaging data ↔ nii2gii_fs.sh, the whole file · a weak match · score 0.52 · mri_vol2surf, FreeSurfer, hemispheres

Paper

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The authors' code

MATLAB · 273 lines · 7.6 KB · no license · 1 match

  1. maindir = 'E:\CR_result\abeta';
  2. cd(maindir);
  3. load('result.mat');
  5. z_ad_fre = zscore(ad_fre_group);
  6. z_ad_val = zscore(ad_val_group);
  7. z_mci_fre = zscore(mci_fre_group);
  8. z_mci_val = zscore(mci_val_group);
  9. z_cn_fre = zscore(cn_fre_group);
  10. z_cn_val = zscore(cn_val_group);
  12. r_cn=[];
  13. p_cn=[];
  14. for i = 1:400
  15. fre = cn_value(i,:);
  16. val = cn_fre(i,:);
  17. [r,p] = corr(fre',val');
  18. r_cn = cat(1,r_cn,r);
  19. p_cn = cat(1,p_cn,p);
  20. end
  21. r_mci=[];
  22. p_mci=[];
  23. for i = 1:400
  24. fre = mci_value(i,:);
  25. val = mci_fre(i,:);
  26. [r,p] = corr(fre',val');
  27. r_mci = cat(1,r_mci,r);
  28. p_mci = cat(1,p_mci,p);
  29. end
  30. r_ad=[];
  31. p_ad=[];
  32. for i = 1:400
  33. fre = ad_value(i,:);
  34. val = ad_fre(i,:);
  35. [r,p] = corr(fre',val');
  36. r_ad = cat(1,r_ad,r);
  37. p_ad = cat(1,p_ad,p);
  38. end
  39. q_cn = mafdr(p_cn);
  40. q_mci = mafdr(p_mci);
  41. q_ad = mafdr(p_ad);
  43. label_cn = find(q_cn<0.001);
  44. label_mci = find(q_mci<0.001);
  45. label_ad = find(q_ad<0.001);
  47. %%
  48. data = readtable('total.xlsx');
  49. age = data.age;
  50. gender = data.gender;
  51. edu = data.edu;
  52. % 中心化协变量
  53. age_cen = bsxfun(@minus, age, mean(age));
  54. gender_cen = bsxfun(@minus, gender, mean(gender));
  55. edu_cen = bsxfun(@minus, edu, mean(edu));
  56. X = [age_cen, gender_cen, edu_cen]; % 合并自变量
  57. % 构建回归模型
  58. residuals_total = [];
  59. for i = 1:400
  60. data = table(age_cen, gender_cen, edu_cen, value_total(i,:)', ...
  61. 'VariableNames', {'Age', 'Gender', 'Edu', 'Value'});
  62. % 进行回归（自动处理分类变量）
  63. mdl = fitlm(data, 'Value ~ Age + Gender + Edu');
  64. % 提取残差
  65. residuals = mdl.Residuals.Raw;
  66. residuals_total = cat(2,residuals_total,residuals);
  67. end
  68. residuals_total = residuals_total';
  70. group = data.cognitive_change;
  71. cn = find(group==0);
  72. mci = find(group==1);
  73. ad = find(group==2);
  75. cn_value = value_total(:,cn);
  76. mci_value = value_total(:,mci);
  77. ad_value = value_total(:,ad);
  79. cn_resi = residuals_total(:,cn);
  80. mci_resi = residuals_total(:,mci);
  81. ad_resi = residuals_total(:,ad);
  83. cn_resi = residuals_total(CR_label,cn);
  84. mci_resi = residuals_total(CR_label,mci);
  85. ad_resi = residuals_total(CR_label,ad);
  87. %% anova
  88. p_cn_mci = [];
  89. f_cn_mci = [];
  90. c_cn_mci = [];
  91. for i = 1:400
  92. % 示例数据（替换为你的实际数据）
  93. Group1 = cn_resi(i,:);
  94. Group2 = mci_resi(i,:);
  95. Group3 = ad_resi(i,:);
  97. % 合并数据并创建分组标签
  98. data = [Group1, Group2, Group3];
  99. groups = [repmat({'Group1'}, 1, length(Group1)), ...
  100. repmat({'Group2'}, 1, length(Group2)), ...
  101. repmat({'Group3'}, 1, length(Group3))];
  103. % 单因素方差分析
  104. [p, tbl, stats] = anova1(data, groups, 'off');
  105. [c, m, ~, gnames] = multcompare(stats, 'CType', 'tukey-kramer', 'Display', 'off');
  106. c2 = [c,[i;i;i]];
  107. p_cn_mci = cat(1,p_cn_mci,p);
  108. f_cn_mci = cat(1,f_cn_mci,tbl{2,5});
  109. c_cn_mci = cat(3,c_cn_mci,c2);
  110. end
  111. label_cn_mci_ad = find(p_cn_mci<0.05);
  112. c_cn_mci_ad = c_cn_mci(:,:,label_cn_mci_ad);
  113. B = permute(c_cn_mci_ad, [1, 3, 2]); % 调整维度顺序为3×32×6
  114. c_shaped = reshape(B, [96, 7]); % 展开为96×6
  115. label_cn_mci_ad = find(c_shaped(:,6)<0.05);
  116. c_cn_mci_ad = c_shaped(label_cn_mci_ad,:);
  117. label_cn_mci = find(c_cn_mci_ad(:,2)==2);
  118. c_cn_mci = c_cn_mci_ad(label_cn_mci,:);
  119. label_cn_ad = find(c_cn_mci_ad(:,2)==3);
  120. c_cn_ad = c_cn_mci_ad(label_cn_ad,:);
  122. parcel_cn_mci = label
  123. parcel_cn_ad =
  124. % 使用multcompare函数（基于Tukey方法）
  125. figure;
  126. %[c, m, ~, gnames] = multcompare(stats, 'CType', 'tukey-kramer', 'Display', 'off');
  127. [c, m, ~, gnames] = multcompare(stats, 'CType', 'tukey-kramer', 'Display', 'off');
  128. disp('Tukey HSD结果:');
  129. disp([gnames(c(:,1)), gnames(c(:,2)), num2cell(c(:,3:6))]);
  131. %% 分组CN MCI AD预测
  132. data = readtable("total.xlsx");
  133. group = data.cognitive_change;
  134. cn = find(group==0);
  135. mci = find(group==1);
  136. ad = find(group==2);
  138. cn_resi = residuals_total(:,cn);
  139. mci_resi = residuals_total(:,mci);
  140. ad_resi = residuals_total(:,ad);
  142. cn_abeta = abeta(:,cn);
  143. mci_abeta = abeta(:,mci);
  144. ad_abeta = abeta(:,ad);
  146. pred_cn = [];
  147. error_cn = [];
  148. % 留一法线性预测
  149. for n = 1:400
  150. X = cn_resi(n,:);
  151. Y = cn_abeta(n,:);
  153. % 初始化存储预测结果的向量
  154. predictions = zeros(size(Y));
  156. for i = 1:length(Y)
  157. % 留一法：第i个样本作为测试集，其余作为训练集
  158. trainIdx = [1:i-1, i+1:length(Y)];
  159. testIdx = i;
  161. % 训练线性回归模型
  162. mdl = fitlm(X(trainIdx), Y(trainIdx));
  164. % 预测被留出的样本
  165. predictions(testIdx) = predict(mdl, X(testIdx));
  166. end
  168. % 计算性能指标
  169. mse = mean((Y - predictions).^2);
  170. rmse = sqrt(mse);
  171. r2 = 1 - sum((Y - predictions).^2)/sum((Y - mean(Y)).^2);
  172. error = [mse,rmse,r2];
  173. error_cn = cat(1,error_cn,error);
  174. pred_cn = cat(2,pred_cn,predictions);
  175. end
  176. baseline_rmse = [];
  177. for i = 1:400
  178. % 计算均值模型的RMSE
  179. Y = cn_abeta(i,:)'; % 确保为列向量
  180. baseline_pred = mean(Y)*ones(size(Y));
  181. baseline = sqrt(mean((Y - baseline_pred).^2));
  182. baseline_rmse = cat(1,baseline_rmse,baseline);
  183. end
  185. rmse_diff = error_cn(:,2)-baseline_rmse;
  186. label_pred_cn = find(rmse_diff<0);
  187. rmse_pred_cn = 1./error_cn(label_pred_cn,2);
  189. pred_mci = [];
  190. error_mci = [];
  191. % 留一法线性预测
  192. for n = 1:400
  193. X = mci_resi(n,:);
  194. Y = mci_abeta(n,:);
  196. % 初始化存储预测结果的向量
  197. predictions = zeros(size(Y));
  199. for i = 1:length(Y)
  200. % 留一法：第i个样本作为测试集，其余作为训练集
  201. trainIdx = [1:i-1, i+1:length(Y)];
  202. testIdx = i;
  204. % 训练线性回归模型
  205. mdl = fitlm(X(trainIdx), Y(trainIdx));
  207. % 预测被留出的样本
  208. predictions(testIdx) = predict(mdl, X(testIdx));
  209. end
  211. % 计算性能指标
  212. mse = mean((Y - predictions).^2);
  213. rmse = sqrt(mse);
  214. r2 = 1 - sum((Y - predictions).^2)/sum((Y - mean(Y)).^2);
  215. error = [mse,rmse,r2];
  216. error_mci = cat(1,error_mci,error);
  217. pred_mci = cat(2,pred_mci,predictions);
  218. end
  219. baseline_rmse_mci = [];
  220. for i = 1:400
  221. % 计算均值模型的RMSE
  222. Y = mci_abeta(i,:)'; % 确保为列向量
  223. baseline_pred = mean(Y)*ones(size(Y));
  224. baseline = sqrt(mean((Y - baseline_pred).^2));
  225. baseline_rmse_mci = cat(1,baseline_rmse_mci,baseline);
  226. end
  228. rmse_diff = error_mci(:,2)-baseline_rmse_mci;
  229. label_pred_mci = find(rmse_diff<0);
  230. rmse_pred_mci = 1./error_cn(label_pred_mci,2);
  232. pred_ad = [];
  233. error_ad = [];
  234. % 留一法线性预测
  235. for n = 1:400
  236. X = ad_resi(n,:);
  237. Y = ad_abeta(n,:);
  239. % 初始化存储预测结果的向量
  240. predictions = zeros(size(Y));
  242. for i = 1:length(Y)
  243. % 留一法：第i个样本作为测试集，其余作为训练集
  244. trainIdx = [1:i-1, i+1:length(Y)];
  245. testIdx = i;
  247. % 训练线性回归模型
  248. mdl = fitlm(X(trainIdx), Y(trainIdx));
  250. % 预测被留出的样本
  251. predictions(testIdx) = predict(mdl, X(testIdx));
  252. end
  254. % 计算性能指标
  255. mse = mean((Y - predictions).^2);
  256. rmse = sqrt(mse);
  257. r2 = 1 - sum((Y - predictions).^2)/sum((Y - mean(Y)).^2);
  258. error = [mse,rmse,r2];
  259. error_ad = cat(1,error_ad,error);
  260. pred_ad = cat(1,pred_ad,predictions);
  261. end
  262. baseline_rmse_ad = [];
  263. for i = 1:400
  264. % 计算均值模型的RMSE
  265. Y = mci_abeta(i,:)'; % 确保为列向量
  266. baseline_pred = mean(Y)*ones(size(Y));
  267. baseline = sqrt(mean((Y - baseline_pred).^2));
  268. baseline_rmse_ad = cat(1,baseline_rmse_ad,baseline);
  269. end
  271. rmse_diff = error_ad(:,2)-baseline_rmse_ad;
  272. label_pred_ad = find(rmse_diff<0);
  273. rmse_pred_ad = 1./error_cn(label_pred_ad,2);

abeta_anova_predict_model.m at commit ae40d1c, no license · at the source

Overview

Authors: Yumeng Li1,2,3, Xinyue Zhang1,2,3, Xin Li1,2,3, Zhanjun Zhang1,2,3,4,5
ORCID iDs: Yumeng Li, Xinyue Zhang, Xin Li, Zhanjun Zhang
  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  2. Beijing Key Laboratory of Cognitive Intelligence for Elderly Brain Health, Faculty of Psychology, Beijing Normal University, Beijing, China
  3. Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
  4. Institute for Advanced Study, Beijing Normal University, Beijing, China
  5. Innovation Institute of Integrated Traditional Chinese and Western Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong China
Institutions: Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning; Shandong First Medical University (China)
Journal: Nature communications, volume 17, issue 1, article 6908
Dates: received 29 May 2025; accepted 28 April 2026; published online 27 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73071-7 · PMID 42204140 · PMCID PMC13389195 · OpenAlex W7162547362
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging, Preprocessing
Keywords: Alzheimer's disease, Predictive markers, Cognitive ageing
MeSH: Aging*, Brain*, Neurodegenerative Diseases*, Aged, Aged, 80 and over, Amyloid beta-Peptides, Biomarkers, Cognitive Dysfunction, Female, Humans, Magnetic Resonance Imaging, Male, Memory, Processing Speed (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 112 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Rainmon2020/Cognitive-Reserve

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: ae40d1cc1ebb0f313f32ddd4e1a3034e67c9f91a, 18 March 2026
Languages: Python (2), MATLAB (2), Shell (1)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), NumPy (2 files), SciPy (2 files), FreeSurfer (1 file), NiBabel (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
6 files
At the source: github.com/Rainmon2020/Cognitive-Reserve

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-73071-7.

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Data

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Read it in the paper: doi.org/10.1038/s41467-026-73071-7.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 14 MeSH terms, 101 references.

Cite

This paper

Li, Y., Zhang, X., Li, X., & Zhang, Z. (2026). Brain maintenance biomarkers from structural and functional interactions in aging and neurodegeneration. Nature communications, 17(1), 6908. https://doi.org/10.1038/s41467-026-73071-7

BibTeX

@article{li2026brain,
author = {Li, Yumeng and Zhang, Xinyue and Li, Xin and Zhang, Zhanjun},
title = {{Brain maintenance biomarkers from structural and functional interactions in aging and neurodegeneration}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6908},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73071-7},
url = {https://doi.org/10.1038/s41467-026-73071-7},
pmid = {42204140},
pmcid = {PMC13389195}
}

RIS

TY - JOUR
AU - Li, Yumeng
AU - Zhang, Xinyue
AU - Li, Xin
AU - Zhang, Zhanjun
TI - Brain maintenance biomarkers from structural and functional interactions in aging and neurodegeneration
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/27
VL - 17
IS - 1
SP - 6908
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73071-7
UR - https://doi.org/10.1038/s41467-026-73071-7
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-73071-7",
"type": "article-journal",
"title": "Brain maintenance biomarkers from structural and functional interactions in aging and neurodegeneration",
"container-title": "Nature communications",
"author": [
{
"family": "Li",
"given": "Yumeng"
},
{
"family": "Zhang",
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{
"family": "Li",
"given": "Xin"
},
{
"family": "Zhang",
"given": "Zhanjun"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6908",
"DOI": "10.1038/s41467-026-73071-7",
"PMID": "42204140",
"PMCID": "PMC13389195",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73071-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
27
]
]
}
}

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