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Brain aging patterns among nine neurological disorders: A case-control study.

Code ↔ Paper

6 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 6 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Enrichment analyses of genes related to PAD difference T-map ↔ Enrichment/AD_pos/Enrichment_GO/GONetwork.js, the whole file · a weak match · score 0.94 · carboxylic acid catabolic, inorganic ion transmembrane, cytoplasmic translation, synaptic signaling, membrane organization, protein localization
  2. [2] § Methods › Brain age prediction with age correction ↔ Brain_age_prediction_SHAP_demo.py, the whole file · a weak match · score 0.94 · maximum depth, brain age prediction, trained models, absolute error, XGBoost, hyperparameters
  3. [3] § Results › Enrichment analyses of genes related to PAD difference T-map ↔ Enrichment/BP_neg/Enrichment_GO/GONetwork.js, the whole file · a weak match · score 0.87 · carboxylic acid catabolic, small molecule biosynthetic, small molecule catabolic, protein localization, metabolic process, biological processes
  4. [4] § Results › The performance of brain age prediction ↔ Brain_age_prediction_SHAP_demo.py, the whole file · a weak match · score 0.75 · Brain age prediction, absolute error, XGBoost model, MAE, coefficient, training
  5. [5] § Methods › Gene enrichment analyses ↔ generate_gene_expression_matrix.py, the whole file · a weak match · score 0.71 · gene expression matrix, augmented Schaefer, Allen, abagen, Atlas
  6. [6] § Methods › Gene enrichment analyses ↔ PLS_bootstrap.m, the whole file · a weak match · score 0.61 · predictive variable, response variable, PLS, bootstrapping, component, weight

Paper

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The authors' code

Python · 78 lines · 2.4 KB · no license · 2 matches

  1. import numpy as np
  2. import xgboost as xgb
  3. import shap
  4. import scipy.io
  5. import pandas as pd
  6. import os
  7. from sklearn.model_selection import train_test_split
  8. from sklearn.preprocessing import MinMaxScaler
  10. # Read the train label
  11. y_train = pd.read_excel('path_to_training_HC_data.xlsx')
  12. # Read the test label
  13. y_test = pd.read_excel('path_to_diagnostic_group_data.xlsx')
  15. # Load features for brain age prediction
  16. X_train = scipy.io.loadmat('training_features_path.mat')
  17. X_test = scipy.io.loadmat('testing_features_path.mat')
  19. # Normalize the features
  20. scaler = MinMaxScaler()
  21. X_train = scaler.fit_transform(X_train)
  22. X_test = scaler.transform(X_test)
  24. X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42)
  25. # Create training data object
  26. dtrain = xgb.DMatrix(data=X_train, label=y_train)
  27. dval = xgb.DMatrix(X_val, label=y_val)
  28. dtest = xgb.DMatrix(X_test)
  29. # Define hyperparameters for the model
  30. params = {
  31. 'objective': 'reg:squarederror', # loss function
  32. 'eta': 0.05, # learning rate
  33. 'max_depth': 4, # maximum depth of a tree
  34. 'num_boost_round': 2000 # number of iterations
  35. }
  37. # training model
  38. model = xgb.train(params, dtrain, num_boost_round=params['num_boost_round'],
  39. evals=[(dtrain, 'train'), (dval, 'validation')],
  40. early_stopping_rounds=20)
  41. y_pred = model.predict(dtest)
  44. # Calculate correlation
  45. df = pd.DataFrame({'data1': y_test, 'data2': y_pred})
  46. correlation = df['data1'].corr(df['data2'])
  47. print("Correlation coefficient:", correlation)
  49. # Calculate MAE
  50. mae = np.mean(np.abs(y_test - y_pred))
  51. # Print MAE
  52. print("Mean Absolute Error (MAE):", mae)
  54. # Save model
  55. model.save_model('xgboost_model.model')
  56. # losd model
  57. loaded_model = xgb.Booster()
  58. loaded_model.load_model('xgboost_model.model')
  60. # Create SHAP interpreter
  61. background = shap.sample(X_train, 1000)
  63. explainer = shap.TreeExplainer(
  64. loaded_model,
  65. data=background,
  66. feature_perturbation="interventional"
  67. )
  69. shap_values = explainer.shap_values(X_test)
  70. # Print SHAP value
  71. print(shap_values)
  73. # Save SHAP value
  74. result_df = pd.DataFrame(shap_values)
  75. output_file_path = f':/output_path.xlsx'
  76. result_df.to_excel(output_file_path, index=False)

Brain_age_prediction_SHAP_demo.py at commit ee25729, no license · at the source

Overview

Authors: Chuang Liang1,2, Godfrey Pearlson3, Juan Bustillo4, Peter Kochunov5, Jiayu Chen6, Xiangrong Zhang7, Rongtao Jiang8, Kent E Hutchison9, Jing Sui8, Zening Fu6, Xiao Yang10, Yuhui Du11, Daoqiang Zhang1,2, Shile Qi1,2, Vince D Calhoun6
ORCID iDs: Xiao Yang, Shile Qi
  1. Department of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
  2. Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
  3. Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut, United States of America
  4. Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico, United States of America
  5. Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
  6. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States of America
  7. Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  8. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  9. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado, United States of America
  10. Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
  11. School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China
Institutions: Nanjing University of Aeronautics and Astronautics (China); University of New Mexico (United States); University of Maryland, Baltimore (United States); Georgia Institute of Technology (United States); Emory University (United States); Georgia State University (United States); Center for Translational Research in Neuroimaging and Data Science (United States); Nanjing Brain Hospital (China); Nanjing Medical University (China); Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning; University of Colorado Boulder (United States); West China Hospital of Sichuan University (China); Shanxi University (China)
Journal: PLoS medicine, volume 23, issue 7, article e1004860
Dates: received 28 November 2025; accepted 15 June 2026; published online 21 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pmed.1004860 · PMID 42479667 · PMCID PMC13387544 · OpenAlex W7169827624
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), Alzheimer's / dementia (population), depression (population), autism (population), ADHD (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
MeSH: Aging*, Brain*, Nervous System Diseases*, Adult, Case-Control Studies, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Key Research and Development Plan of Jiangsu Province, China (BE2023668); National Natural Science Foundation of China (62376124)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

Background: The difference between neuroimaging-predicted brain age and chronological age, the predicted age difference (PAD), has been studied as a potential biomarker reflecting individual brain health. Although previous large-scale studies have shown that brain age deviations occur across multiple disorders, cross-disorder comparisons of PAD within a unified framework, together with identification of the neuroimaging features associated with these differences and their related gene expression profiles, remain limited. Our aims are to systematically compare brain aging across multiple common brain disorders and explore the brain patterns and biological processes underlying these differences.

Methods and findings: In this study, structural MRI data from 45,900 healthy controls (HCs) and 2,698 patients with developmental disorders (attention-deficit/hyperactivity disorder [ADHD] and autism spectrum disorder [ASD]), addiction (alcohol use disorder [AUD], tobacco use disorder [TUD], and AUD&TUD-A&TUD), dementia (Alzheimer’s disease [AD], and mild cognitive impairment [MCI]) or other psychiatric disorders (schizophrenia [SZ], bipolar disorder [BP], and major depressive disorder [MDD]), were collected to generate PAD, along with transcriptome data. Then, we calculated the PAD difference between patient and HC as Cohen’s d effect sizes, derived from a linear model that accounted for age, age2, sex, and site, and further identified the interpretable brain patterns associated with the PAD difference for each diagnostic group. Finally, enrichment analyses was conducted to identify the biological function of genes relatively over- or underexpressed in association with these patterns. Results showed that while PAD was consistently greater across disorders, different brain disorders showed different degrees of abnormality, the highest effects in dementia (AD: d = 0.97, 95% confidence interval (CI) [0.82,1.13]; p < 0.001 and MCI: d = 0.45, 95% CI [0.34,0.56]; p < 0.001), followed by addiction (A&TUD: d = 0.84, 95% CI [0.44,1.23]; p < 0.001, TUD: d = 0.72, 95% CI [0.49,0.96]; p < 0.001, and AUD d = 0.62, 95% CI [0.39,0.84]; p < 0.001) and psychiatric disorders (SZ: d = 0.53, 95% CI [0.30,0.76]; p < 0.001, BP: d = 0.46, 95% CI [0.22,0.69]; p < 0.001 and MDD: d = 0.28, 95% CI [0.11,0.46]; p < 0.001), but not different from expected in developmental disorders (ASD: d = 0.06, 95% CI [−0.04,0.16]; p = 0.36) and ADHD: d = 0.01, 95% CI [−0.14,0.15]; p = 0.98). Furthermore, higher PAD values in patient groups were linked to specific spatial brain patterns, including the frontotemporal network in psychiatric disorders, default mode network-salience network-putamen-thalamus in addiction and fronto-occipital network in dementia. Prefrontal cortex involvement was common across disorders, and disorder-specific brain patterns associated genes were enriched in different biological processes. A limitation of our study is that psychiatric disorders and addiction have high comorbidity, and these potential confounders were not considered.

Conclusions: In summary, the different brain aging patterns, each based around specific underlying circuits, may serve as neuroimaging biomarkers for understanding the neural aging mechanisms in commonly occurring brain disorders. Future studies should test whether these disorder-specific brain aging patterns can serve as useful biomarkers to guide critical clinical decision-making.

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

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Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

liangchuang11/Brain-age-prediction

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ee2572914f39531653729796caec22c4bc41fd28, 17 March 2026
Languages: Python (3), MATLAB (2)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), abagen (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file), SHAP (1 file), XGBoost (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/liangchuang11/Brain-age-prediction.git

Zenodo 20743298

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), abagen (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file), SHAP (1 file), XGBoost (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
6 files
At the source: doi.org/10.5281/zenodo.20743298

liangchuang11/brain_age_de-identified-minimal_data

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8db42d2bd6719f0051844e564a3d7434546159c2, 24 May 2026
Languages: JavaScript (144)
Size: 822 files, 144 scripts
Software Heritage: not archived
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
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144 files
At the source: github.com/liangchuang11/brain_age_de-identified-minimal_data.git

Zenodo 20743260

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
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Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.20743260

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

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Data Availability

The main code used in this study is available at https://github.com/liangchuang11/Brain-age-prediction.git and has been archived with a citable DOI on Zenodo: https://doi.org/10.5281/zenodo.20743298. The de-identified minimal data required to replicate the findings of this study have been made publicly available without restriction on the GitHub repository: https://github.com/liangchuang11/brain_age_de-identified-minimal_data.git and archived with a DOI on Zenodo: https://doi.org/10.5281/zenodo.20743260. The multimodal image data of HC, developmental disorders (ADHD and ASD) and dementia (AD and MCI) used in the present study are publicly available from the following consortia: HCP (https://www.humanconnectome.org/), GSP (https://www.neuroinfo.org/gsp/), UKB (https://www.fmrib.ox.ac.uk/ukbiobank), ADHD-200 (https://fcon_1000.projects.nitrc.org/indi/adhd200/), ABIDE (https://fcon_1000.projects.nitrc.org/indi/abide/) and ADNI (https://adni.loni.usc.edu/) consortia. Each consortium has its own data access policies and application procedures, which are detailed on their respective websites. The psychiatric disorders (SZ, BP and MDD) and addiction (AUD and TUD) data are protected and are not publicly available due to data privacy and IRB restrictions. Data access requests may be submitted via https://trendscenter.org/contact-us/ or by email to for researchers who meet the criteria for access to data.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 12 MeSH terms, 2 funders, 69 references.

Cite

This paper

Liang, C., Pearlson, G., Bustillo, J., Kochunov, P., Chen, J., Zhang, X., Jiang, R., Hutchison, K. E., Sui, J., Fu, Z., Yang, X., Du, Y., Zhang, D., Qi, S., & Calhoun, V. D. (2026). Brain aging patterns among nine neurological disorders: A case-control study. PLoS medicine, 23(7), e1004860. https://doi.org/10.1371/journal.pmed.1004860

BibTeX

@article{liang2026brain,
author = {Liang, Chuang and Pearlson, Godfrey and Bustillo, Juan and Kochunov, Peter and Chen, Jiayu and Zhang, Xiangrong and Jiang, Rongtao and Hutchison, Kent E and Sui, Jing and Fu, Zening and Yang, Xiao and Du, Yuhui and Zhang, Daoqiang and Qi, Shile and Calhoun, Vince D},
title = {{Brain aging patterns among nine neurological disorders: A case-control study}},
journal = {PLoS medicine},
year = {2026},
month = jul,
volume = {23},
number = {7},
pages = {e1004860},
publisher = {PLOS},
issn = {1549-1277},
doi = {10.1371/journal.pmed.1004860},
url = {https://doi.org/10.1371/journal.pmed.1004860},
pmid = {42479667},
pmcid = {PMC13387544}
}

RIS

TY - JOUR
AU - Liang, Chuang
AU - Pearlson, Godfrey
AU - Bustillo, Juan
AU - Kochunov, Peter
AU - Chen, Jiayu
AU - Zhang, Xiangrong
AU - Jiang, Rongtao
AU - Hutchison, Kent E
AU - Sui, Jing
AU - Fu, Zening
AU - Yang, Xiao
AU - Du, Yuhui
AU - Zhang, Daoqiang
AU - Qi, Shile
AU - Calhoun, Vince D
TI - Brain aging patterns among nine neurological disorders: A case-control study
T2 - PLoS medicine
J2 - PLoS Med
PY - 2026
DA - 2026/07/21
VL - 23
IS - 7
SP - e1004860
SN - 1549-1277
PB - PLOS
DO - 10.1371/journal.pmed.1004860
UR - https://doi.org/10.1371/journal.pmed.1004860
LA - en
ER -

CSL-JSON

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