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Functional system-specific brain aging across the Alzheimer's disease continuum.

Code ↔ Paper

10 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 10 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Functional system-specific brain age prediction model ↔ SFCN.py, lines 36–166 · score 0.83 · fully connected layer, Conv3D, batch normalization, ELU, SFCN, Max
  2. [2] § Results › Functional system PADs mediate the links between AD risk factors and cognition ↔ Mediation_analysis.py, lines 1–39 · score 0.73 · step ordinary, regression models, inferior temporal, cognitive decline, squares, PAD change rates
  3. [3] § Materials and methods › Machine learning model for predicting the MCI to AD conversion ↔ Predict_MCI2AD.py, lines 1–21 · score 0.70 · plus short term, longitudinal PAD change, Machine, conversion, Model, predicting
  4. [4] § Materials and methods › Mediation analyses of biomarkers, functional system PADs, and cognition ↔ Mediation_analysis.py, lines 1–39 · score 0.69 · step ordinary, regression models, squares, covariates, sex, education
  5. [5] § Materials and methods › Statistical analyses of functional system PADs associations ↔ Correlation.py, lines 1–88 · score 0.68 · generalized linear model, baseline PADs, utilized, PAD change rates, FDR, education
  6. [6] § Results › Associations between functional system PADs and genetic, pathological factors, and cognition ↔ Correlation.py, lines 1–88 · score 0.62 · mental health, inferior temporal, MoCA, BL, entorhinal, PHS
  7. [7] § Materials and methods › Functional system-specific brain age prediction model ↔ Predict_MCI2AD.py, lines 1–21 · score 0.59 · Machine learning, pathological biomarkers, baseline PAD, PADs change rate, conversion, genetic
  8. [8] § Materials and methods › Longitudinal analyses of PADs along the AD continuum ↔ NetworkPAD_longitudinal_analyses/emtrend.R, the whole file · a weak match · score 0.57 · Pairwise comparisons, compare models, marginal, ANOVA, MCI, Longitudinal
  9. [9] § Results › Functional system-specific brain age prediction modelling ↔ training_SFCN.py, lines 16–73 · score 0.56 · absolute errors, brain age prediction, MAEs, validated, models
  10. [10] § Results › Prediction of clinical progression from MCI to AD ↔ Predict_MCI2AD.py, lines 73–146 · score 0.51 · F1 score, ROC, AUC, accuracy, classification, metrics

Paper

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The authors' code

Python · 179 lines · 7.1 KB · MIT · 3 matches

  1. '''
  2. Machine learning model for predicting MCI-to-AD conversion
  3. • Model 1 (benchmark): genetic and pathological biomarkers only;
  4. • Model 2: Model 1plus baseline PADs;
  5. • Model 3: Model 2 plus short-term longitudinal PAD change rates.
  7. '''
  9. from sklearn.ensemble import RandomForestClassifier
  10. from sklearn.metrics import (
  11. accuracy_score, recall_score, f1_score, confusion_matrix,
  12. roc_curve, auc
  13. )
  14. from sklearn.model_selection import StratifiedKFold
  15. from sklearn.preprocessing import StandardScaler, LabelEncoder
  16. from imblearn.over_sampling import RandomOverSampler
  17. from scipy.stats import ttest_rel
  18. import matplotlib.pyplot as plt
  19. import pandas as pd
  20. import numpy as np
  21. import os
  23. def calculate_annual_change(group, features):
  24. change_rates = {}
  26. for feature in features:
  27. if len(group) > 1:
  28. start_value = group.iloc[0][feature]
  29. end_value = group.iloc[-1][feature]
  30. start_time = group.iloc[0]['Time']
  31. end_time = group.iloc[-1]['Time']
  33. annual_change = (end_value - start_value) / (end_time - start_time)
  34. change_rates[feature] = annual_change
  35. else:
  36. change_rates[feature] = np.nan
  38. return pd.Series(change_rates)
  40. data = pd.read_csv('adni_BA/lme_data_3group_6yr_not_3sigma.csv')
  41. data = data[data['Group'].isin(['MCI_to_MCI', 'MCI_to_AD'])]
  43. stat_data = data[data['Time'] != 0]
  44. subject_time_counts = stat_data.groupby('Subject ID')['Time'].count()
  45. subject_ids_to_keep = subject_time_counts[subject_time_counts >= 2].index
  46. stat_data = stat_data[(stat_data['Subject ID'].isin(subject_ids_to_keep))]
  47. print(stat_data.groupby('Group')['Subject ID'].nunique())
  49. columns_to_calculate = ['VIS', 'SM', 'DAN', 'VAN', 'LIM', 'FP', 'DMN']
  50. # 选取前两次检查的数据
  51. stat_data = stat_data.sort_values(['Subject ID', 'Time'])
  52. last_two_checks = stat_data.groupby('Subject ID').head(2)
  54. print(last_two_checks.groupby('Time')['Subject ID'].nunique())
  56. last_two_checks_sorted = last_two_checks.sort_values(by=['Subject ID', 'Time'])
  57. last_two_checks_sorted['is_BL'] = last_two_checks_sorted.groupby('Subject ID')['Time'].transform('min') == last_two_checks_sorted['Time']
  58. BL_data = last_two_checks_sorted[last_two_checks_sorted['is_BL']]
  59. print(BL_data)
  61. change_rates_df = stat_data.groupby('Subject ID').apply(calculate_annual_change, features=columns_to_calculate)
  62. all_individual_rates = stat_data[['Subject ID', 'Group']].drop_duplicates().merge(change_rates_df, on='Subject ID', how='left')
  63. all_individual_rates = all_individual_rates.rename(columns={col: col + '_rate' for col in change_rates_df.columns})
  65. pad_merged = pd.merge(BL_data, all_individual_rates,on=['Subject ID', 'Group'], how='inner')
  67. biomarker_df = pd.read_csv('corr/bl_rate_corr_data.csv')[['Subject ID','PHS','ABETA42', 'TAU', 'PTAU', 'HCI', 'ENTORHINAL_SUVR', 'INFERIOR_TEMPORAL_SUVR','TAU_METAROI']]
  69. merged_data = pd.merge(pad_merged,biomarker_df,on=['Subject ID'], how='inner')
  70. print(merged_data)
  71. print(merged_data.columns)
  73. def compute_metrics(X, y, suffix, return_results=True):
  74. scaler = StandardScaler()
  75. X_scaled = scaler.fit_transform(X)
  76. X = pd.DataFrame(X_scaled, columns=X.columns)
  78. label_encoder = LabelEncoder()
  79. y_encoded = label_encoder.fit_transform(y)
  81. ros = RandomOverSampler(random_state=42)
  82. X_resampled, y_resampled = ros.fit_resample(X, y_encoded)
  84. kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=39)
  86. mean_fpr = np.linspace(0, 1, 100)
  87. tprs, aucs = [], []
  88. accuracies, f1_scores = [], []
  89. fold_sensitivities, fold_specificities = [], []
  91. plt.figure(figsize=(6, 6))
  92. for i, (train_index, test_index) in enumerate(kf.split(X_resampled, y_resampled)):
  93. X_train, X_test = X_resampled.iloc[train_index], X_resampled.iloc[test_index]
  94. y_train, y_test = y_resampled[train_index], y_resampled[test_index]
  96. rf_model = RandomForestClassifier(n_estimators=100, random_state=40, class_weight='balanced')
  97. rf_model.fit(X_train, y_train)
  98. y_proba = rf_model.predict_proba(X_test)[:, 1]
  99. y_pred = rf_model.predict(X_test)
  101. acc = accuracy_score(y_test, y_pred)
  102. f1 = f1_score(y_test, y_pred)
  103. tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
  104. sensitivity = tp / (tp + fn)
  105. specificity = tn / (tn + fp)
  106. fpr, tpr, _ = roc_curve(y_test, y_proba)
  107. roc_auc = auc(fpr, tpr)
  109. accuracies.append(acc)
  110. f1_scores.append(f1)
  111. fold_sensitivities.append(sensitivity)
  112. fold_specificities.append(specificity)
  113. aucs.append(roc_auc)
  115. interp_tpr = np.interp(mean_fpr, fpr, tpr)
  116. interp_tpr[0] = 0.0
  117. tprs.append(interp_tpr)
  119. plt.plot(fpr, tpr, lw=3, alpha=0.6, label=f'Fold {i+1} (AUC = {roc_auc:.2f})')
  121. mean_tpr = np.mean(tprs, axis=0)
  122. mean_tpr[-1] = 1.0
  123. mean_auc = auc(mean_fpr, mean_tpr)
  124. std_auc = np.std(aucs)
  126. output_dir = 'figure/predict'
  127. os.makedirs(output_dir, exist_ok=True)
  128. plt.plot(mean_fpr, mean_tpr, color='blue', lw=3, linestyle='--',
  129. label=f'Mean ROC (AUC = {mean_auc:.2f} ± {std_auc:.2f})')
  130. plt.plot([0, 1], [0, 1], color='gray', linestyle='--', label='Chance', lw=3)
  131. plt.xlabel('False Positive Rate')
  132. plt.ylabel('True Positive Rate')
  133. plt.title(f'ROC - {suffix}')
  134. plt.legend(loc='lower right')
  135. plt.savefig(os.path.join(output_dir, f'roc_curve_{suffix}.pdf'), format='pdf', dpi=600)
  136. plt.close()
  138. if return_results:
  139. return {
  140. 'suffix': suffix,
  141. 'accuracy': accuracies,
  142. 'auc': aucs,
  143. 'f1': f1_scores,
  144. 'sensitivity': fold_sensitivities,
  145. 'specificity': fold_specificities
  146. }
  148. from scipy.stats import ttest_rel, wilcoxon
  150. def compare_models(result_a, result_b, metric='auc', method='ttest'):
  151. vals_a = result_a[metric]
  152. vals_b = result_b[metric]
  153. suffix_a = result_a['suffix']
  154. suffix_b = result_b['suffix']
  156. if method == 'ttest':
  157. stat, p = ttest_rel(vals_a, vals_b)
  158. elif method == 'wilcoxon':
  159. stat, p = wilcoxon(vals_a, vals_b)
  160. else:
  161. raise ValueError("method must be 'ttest' or 'wilcoxon'")
  163. print(f"Comparison [{suffix_b} vs {suffix_a}] on {metric.upper()}:")
  164. print(f"Mean {suffix_a}: {np.mean(vals_a):.3f}, Mean {suffix_b}: {np.mean(vals_b):.3f}")
  165. print(f"{method} t-value: {stat:.4f}\n p-value: {p:.4f}\n")
  167. # feature selection
  168. X1 = merged_data[columns_to_calculate]
  169. X2 = merged_data[columns_to_calculate + ['VIS_rate', 'SM_rate', 'DAN_rate', 'VAN_rate', 'LIM_rate', 'FP_rate','DMN_rate']]
  170. biomarker = ['PHS','ABETA42', 'TAU', 'PTAU', 'HCI', 'ENTORHINAL_SUVR', 'INFERIOR_TEMPORAL_SUVR','TAU_METAROI']
  171. X3 = merged_data[biomarker]
  172. X4 = merged_data[biomarker + columns_to_calculate + ['VIS_rate', 'SM_rate', 'DAN_rate', 'VAN_rate', 'LIM_rate', 'FP_rate','DMN_rate']]
  174. res_bio = compute_metrics(X3, merged_data['Group'], 'bio')
  175. res_pad = compute_metrics(X1, merged_data['Group'], 'pad_bl')
  176. res_bio_pad = compute_metrics(X4, merged_data['Group'], 'bio_pad_bl_fu')
  178. compare_models(res_bio, res_pad, metric='auc')
  179. compare_models(res_pad, res_bio_pad, metric='auc')

Predict_MCI2AD.py at commit b01de5d, under MIT · at the source

Overview

Authors: Yanxi Huo1,2,3, Weijie Huang4, Zhenzhao Liu1, Haojie Chen1,2,3, Tianyu Bai1,2,3, Yichen Wang1,2,3, Kexin Wang1,2,3, Daoqiang Zhang4, Jian Cheng5, Yu Sun6, Guolin Ma6, Cui Zhao7, Zhanjun Zhang1,2, Ni Shu1,2,3
ORCID iDs: Yanxi Huo, Haojie Chen, Daoqiang Zhang, Zhanjun Zhang, Ni Shu
  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, Beijing Normal University,Beijing, China
  3. Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University,Beijing, China
  4. College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education,Nanjing, China
  5. School of Computer Science and Engineering, Beihang University,Beijing, China
  6. Department of Radiology, China-Japan Friendship Hospital,Beijing, China
  7. Department of Geriatrics, Affiliated Hospital of Chengde Medical University,Chengde, China
Institutions: Beijing Normal University (China); Nanjing University of Aeronautics and Astronautics (China); Beihang University (China); China-Japan Friendship Hospital (China); Chengde Medical University (China)
Journal: Translational psychiatry, volume 16, issue 1, article 336
Dates: received 14 October 2025; accepted 30 April 2026; published online 13 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04081-8 · PMID 42129136 · PMCID PMC13338047 · OpenAlex W4412939354
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.2139/ssrn.5373730
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning
Keywords: Predictive markers, Diseases
MeSH: Aging*, Alzheimer Disease*, Brain*, Cognitive Dysfunction*, Aged, Aged, 80 and over, Biomarkers, Disease Progression, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Retrospective Studies (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (82301608, 32271145, 81871425, 210510238); Beijing Natural Science Foundation (L252087)
Citations: not cited yet (Europe PMC); 51 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

xuanmer/NetworkBrainAge

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 049a0b1d8530df6a1a6226b516297a8b9ea62fe1, 27 May 2025
Languages: Python (8)
Size: 41 files, 8 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), Keras (4 files), NiBabel (4 files), pandas (4 files), TensorFlow (4 files), Matplotlib (3 files), scikit-learn (3 files), AFNI (2 files), Nipype (2 files), SciPy (2 files), FreeSurfer (1 file), QSIPrep (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
10 files
At the source: github.com/xuanmer/NetworkBrainAge

xuanmer/StatisticalAnalysis

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b01de5d2ab8e47393a29cf75fac6e83ae2ef308c, 26 May 2025
Languages: Python (4), R (2)
Size: 9 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), pandas (4 files), scikit-learn (4 files), Matplotlib (3 files), SciPy (3 files), statsmodels (3 files), emmeans (2 files), lmerTest (2 files), seaborn (2 files), tidyverse (2 files), imbalanced-learn (1 file), Pingouin (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/xuanmer/StatisticalAnalysis

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41398-026-04081-8.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 scripts, each with its path and the digest of its content;
  • 10 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

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Read it in the paper: doi.org/10.1038/s41398-026-04081-8.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 2 keywords, 15 MeSH terms, 2 funders, 49 references.

Cite

This paper

Huo, Y., Huang, W., Liu, Z., Chen, H., Bai, T., Wang, Y., Wang, K., Zhang, D., Cheng, J., Sun, Y., Ma, G., Zhao, C., Zhang, Z., & Shu, N. (2026). Functional system-specific brain aging across the Alzheimer's disease continuum. Translational psychiatry, 16(1), 336. https://doi.org/10.1038/s41398-026-04081-8

BibTeX

@article{huo2026functional,
author = {Huo, Yanxi and Huang, Weijie and Liu, Zhenzhao and Chen, Haojie and Bai, Tianyu and Wang, Yichen and Wang, Kexin and Zhang, Daoqiang and Cheng, Jian and Sun, Yu and Ma, Guolin and Zhao, Cui and Zhang, Zhanjun and Shu, Ni},
title = {{Functional system-specific brain aging across the Alzheimer's disease continuum}},
journal = {Translational psychiatry},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {336},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04081-8},
url = {https://doi.org/10.1038/s41398-026-04081-8},
pmid = {42129136},
pmcid = {PMC13338047}
}

RIS

TY - JOUR
AU - Huo, Yanxi
AU - Huang, Weijie
AU - Liu, Zhenzhao
AU - Chen, Haojie
AU - Bai, Tianyu
AU - Wang, Yichen
AU - Wang, Kexin
AU - Zhang, Daoqiang
AU - Cheng, Jian
AU - Sun, Yu
AU - Ma, Guolin
AU - Zhao, Cui
AU - Zhang, Zhanjun
AU - Shu, Ni
TI - Functional system-specific brain aging across the Alzheimer's disease continuum
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/05/13
VL - 16
IS - 1
SP - 336
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04081-8
UR - https://doi.org/10.1038/s41398-026-04081-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-04081-8",
"type": "article-journal",
"title": "Functional system-specific brain aging across the Alzheimer's disease continuum",
"container-title": "Translational psychiatry",
"author": [
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"family": "Huo",
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{
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{
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{
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"given": "Yichen"
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{
"family": "Wang",
"given": "Kexin"
},
{
"family": "Zhang",
"given": "Daoqiang"
},
{
"family": "Cheng",
"given": "Jian"
},
{
"family": "Sun",
"given": "Yu"
},
{
"family": "Ma",
"given": "Guolin"
},
{
"family": "Zhao",
"given": "Cui"
},
{
"family": "Zhang",
"given": "Zhanjun"
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{
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"given": "Ni"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "336",
"DOI": "10.1038/s41398-026-04081-8",
"PMID": "42129136",
"PMCID": "PMC13338047",
"ISSN": "2158-3188",
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"language": "en",
"issued": {
"date-parts": [
[
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5,
13
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]
}
}

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