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Predicting future dementia from routine clinical MRI and linked healthcare data.

Code ↔ Paper

5 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 5 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 › Preprocessing ↔ EX2_code/SVM_Code/SPM_Preprocessing.m, lines 28–92 · score 0.75 · deformation fields, SPM, Shooting, template, tissue, MNI
  2. [2] § Results › MVPA Performance › Analysis 1: All data ↔ EX2_code/SVM_Code/Step6_alt.m, lines 1–93 · score 0.62 · SVM model, p_Thres, outer folds, score, AUC, hyperparameters
  3. [3] § Methods › Nested cross-validation › Multi-variate pattern analysis ↔ EX2_code/SVM_Code/Step6_alt.m, lines 1–93 · score 0.60 · box constraint, outer fold, kernel, hyperparameters, voxel, SVM
  4. [4] § Results › MVPA Performance › Analysis 1: All data ↔ EX2_code/SVM_Code/Step4_alt.m, the whole file · a weak match · score 0.60 · SVM model, p_Thres, outer folds, score, AUC, voxels
  5. [5] § Methods › Nested cross-validation › Multi-variate pattern analysis ↔ EX2_code/SVM_Code/Step4_alt.m, the whole file · a weak match · score 0.58 · box constraint, outer fold, kernel, voxel, SVM, inner

Paper

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The authors' code

MATLAB · 139 lines · 6.5 KB · MIT · 2 matches

  1. #############################################
  2. # File: Step6_alt.m
  3. #
  4. #
  5. # Purpose:
  6. # Generate outer loop training results using best hyperparameters [run on n=kFold workers]
  7. #
  8. # Author: PS Reel
  11. tic
  12. config_experiment
  14. ttest_driver = readtable(strcat(driverpath,'ttest_driver.csv'));
  16. for i = 1:kFolds
  18. worker_id = i;
  20. addpath(spmpath);
  23. spm('Defaults', 'PET');
  25. if not(isfolder(outertraintestpath))
  26. mkdir(outertraintestpath)
  27. end
  29. %prepare result logger
  30. T = array2table(nan(0,20),'VariableNames',{ 'outer_fold','worker_id','p_Thres', 'n_voxels','C_value', 'K_value','AUC','ACC', 'SENS','SPEC','PPV','NPV','NUM_OBS', 'NUM_CC','NUM_IC','CM11','CM12','CM21','CM22','HP_String'});
  31. writetable(T,strcat(outertraintestpath,num2str(worker_id),'_outertraintest.csv'),'WriteVariableNames',true );
  35. L = load(strcat(innertraintestpath,num2str(worker_id),'_bestHP.mat'));
  36. tmp_data = readtable(strcat(splitpath,'outersplitinfo.csv')); % better split this file to make it faster
  38. idx_train = use_eval(strcat('tmp_data.','fold_',num2str(worker_id),' == 1'), tmp_data);
  39. tmp_train_label = table2cell(tmp_data(idx_train,3));
  40. tmp_train_data = table2cell(tmp_data(idx_train,2));
  41. idx_test = use_eval(strcat('tmp_data.','fold_',num2str(worker_id),' == 0'), tmp_data);
  42. tmp_test_label = table2cell(tmp_data(idx_test,3));
  43. tmp_test_data = table2cell(tmp_data(idx_test,2));
  45. if not(isfolder(outertraintestpath))
  46. mkdir(outertraintestpath)
  47. end
  49. [max_value,ind]=max(L.bestHP_results.ACC);
  50. best_p_Thres = L.bestHP_results.p_Thres(ind);
  51. best_K_value = L.bestHP_results.K_value(ind);
  52. best_C_value = L.bestHP_results.C_value(ind);
  53. mask_folder = strcat(string(L.bestHP_results.outer_fold(ind)),'_',string(L.bestHP_results.inner_fold(ind)));
  55. if isfile(strcat(ttestoutpath,'/',mask_folder,'/',num2str(best_p_Thres,'%0.10f'),'_binarymask.mat'))
  56. append('Applying best mask to outer fold ',num2str(worker_id))
  58. mask_module(ttestoutpath,outertraintestpath,worker_id, tmp_train_data, tmp_train_label, mask_folder, best_p_Thres,'train')
  59. mask_module(ttestoutpath,outertraintestpath,worker_id,tmp_test_data, tmp_test_label, mask_folder, best_p_Thres ,'test')
  61. TR = load(strcat(outertraintestpath,'/',num2str(worker_id),'/',mask_folder,'/',num2str(best_p_Thres,'%0.10f'),'_traindata.mat')); %train_Vols train_Vol_Labels
  62. TE = load(strcat(outertraintestpath,'/',num2str(worker_id),'/',mask_folder,'/',num2str(best_p_Thres,'%0.10f'),'_testdata.mat')); %test_Vols test_Vol_Labels
  64. anSVMModel = fitcsvm(TR.train_Vols, TR.train_Vol_Labels, ...
  65. 'KernelFunction', 'gaussian', 'KernelScale',best_K_value, 'BoxConstraint', best_C_value);
  66. [pred_label, score] = predict(anSVMModel, TE.test_Vols); %, testTarg);
  67. [~,~, ~, AUC] = perfcurve(TE.test_Vol_Labels,abs(score(:,2)),'Above_48.5_years');
  69. data_table = table(TE.test_Vol_Labels,tmp_test_data,score(:,1));
  70. writetable(data_table,strcat(outertraintestpath,num2str(worker_id),'_predictions.csv'));
  72. perf = classperf(TE.test_Vol_Labels,pred_label);
  74. Num_correctly_classified = perf.DiagnosticTable(1,1) + perf.DiagnosticTable(2,2);
  75. Num_incorrectly_classified = perf.DiagnosticTable(1,2) + perf.DiagnosticTable(2,1);
  76. T = table([worker_id,worker_id,best_p_Thres, size(TR.train_Vols,2),best_C_value, best_K_value,AUC, perf.CorrectRate, perf.Sensitivity, perf.Specificity, perf.PositivePredictiveValue,perf.NegativePredictiveValue,perf.NumberOfObservations,Num_correctly_classified,Num_incorrectly_classified,perf.DiagnosticTable(1,1),perf.DiagnosticTable(1,2),perf.DiagnosticTable(2,1),perf.DiagnosticTable(2,2),cellstr(strcat(num2str(best_p_Thres,'%0.10f'),',',num2str(best_C_value),',',num2str(best_K_value)))]);
  77. writetable(T,strcat(outertraintestpath,num2str(worker_id),'_outertraintest.csv'),WriteMode='append');
  79. else
  80. error(append('Best mask folder not found for outer fold ',num2str(worker_id)));
  81. end
  84. end_time = toc/3600;
  85. append('Step6 outer train test processing complete for node ', num2str(worker_id), ' in ', num2str(end_time),' hours' )
  86. fileID = fopen(append(runtimelogpath,'Step6_',num2str(worker_id),'_','runtime','_',num2str(round(end_time,5)),'_','.txt'),'w');
  87. fprintf(fileID,append(num2str(round(end_time,5))));
  88. fclose(fileID);
  91. end
  93. exit
  97. function [out] = use_eval(expression_text, tmp_data) %need tmp_data variable to resolve expression_text
  98. out = eval(expression_text);
  99. end
  101. function mask_module(ttestoutpath,outertraintestpath,worker_id,Imagefilenames, Vol_Labels, mask_folder, p_Thres, dataset_type)
  102. [x,y,z,~] = size(niftiread(Imagefilenames{1})); % for scalar momentum
  103. Vols = nan(size(Imagefilenames,1),x,y,z);
  105. for i = 1:size(Imagefilenames,1)
  106. Vol_tmp1 = niftiread(Imagefilenames{i});
  107. Vols(i,:,:,:) =squeeze(Vol_tmp1(:,:,:,1));% 4D for scalar momentum Vol1 =1, Vol2 = 2
  108. end
  110. Vols = reshape(Vols,size(Imagefilenames,1),x*y*z);
  111. [~] = apply_mask(ttestoutpath,outertraintestpath,worker_id,Vols, Vol_Labels,mask_folder, p_Thres, dataset_type,1);
  112. end
  114. function [out_Vols] = apply_mask(ttestoutpath,outertraintestpath,worker_id,Vols, Vol_Labels,mask_folder, p_Thres, datasettype, save_masked_volume_flag)
  115. load(strcat(ttestoutpath,'/',mask_folder,'/',num2str(p_Thres,'%0.10f'),'_binarymask.mat'));
  117. if not(isfolder(strcat(outertraintestpath,'/', num2str(worker_id),'/', mask_folder)))
  118. mkdir(strcat(outertraintestpath,'/', num2str(worker_id)));
  119. mkdir(append(outertraintestpath,'/', num2str(worker_id),'/', mask_folder))
  120. end
  121. copyfile(strcat(strcat(ttestoutpath,'/',mask_folder,'/',num2str(p_Thres,'%0.10f'),'_binarymask.mat')), strcat(outertraintestpath,'/',num2str(worker_id),'/',mask_folder,'/',num2str(p_Thres,'%0.10f'),'_binarymask.mat'))
  122. mask = reshape(mask,1,size(mask,1)*size(mask,2)*size(mask,3));
  123. [~,ind]=find(mask);
  124. if strcmp(datasettype,'train')
  125. train_Vols = Vols(:,ind); % add normalisation here
  126. if save_masked_volume_flag == 1
  127. train_Vol_Labels = Vol_Labels;
  128. out_Vols = train_Vols;
  129. save(strcat(outertraintestpath,'/',num2str(worker_id),'/',mask_folder,'/',num2str(p_Thres,'%0.10f'),'_traindata.mat'),'train_Vols','train_Vol_Labels')
  130. end
  131. elseif strcmp(datasettype,'test')
  132. test_Vols = Vols(:,ind);
  133. if save_masked_volume_flag == 1
  134. test_Vol_Labels = Vol_Labels;
  135. out_Vols = test_Vols;
  136. save(strcat(outertraintestpath,'/',num2str(worker_id),'/',mask_folder,'/',num2str(p_Thres,'%0.10f'),'_testdata.mat'),'test_Vols','test_Vol_Labels')
  137. end
  138. end
  139. end

Step6_alt.m at commit bced7fe, under MIT · at the source

Overview

Authors: Parminder Singh Reel1,2, Salim Al-Wasity3,4, Craig Edwards3, Smarti Reel1,2, Esma Mansouri-Benssassi2, Szabolcs Suveges3, Muthu Rama Krishnan Mookiah5, Susan Krueger2, Emanuele Trucco5, Emily Jefferson1,6, Alexander Doney1, J. Douglas Steele3
ORCID iDs: Muthu Rama Krishnan Mookiah, Emanuele Trucco, Alexander Doney, J. Douglas Steele
  1. Division of Population Health and Genomics, University of Dundee,Dundee, UK
  2. Health Informatics Centre, University of Dundee,Dundee, UK
  3. Division of Neuroscience, University of Dundee,Dundee, UK
  4. College of Engineering, University of Wasit,Kut, Iraq
  5. School of Science and Engineering, Computing, VAMPIRE Project, University of Dundee,Dundee, UK
  6. Health Data Research UK,London, UK
Institutions: University of Dundee (United Kingdom); University of Wasit (Iraq); Health Data Research UK (United Kingdom)
Journal: Alzheimer's research & therapy, volume 18, issue 1, article 164
Dates: received 30 January 2026; accepted 8 May 2026; published online 21 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13195-026-02079-4 · PMID 42169066 · PMCID PMC13366786 · OpenAlex W7162008910
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), other (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Preprocessing
Keywords: Dementia risk prediction, Magnetic resonance imaging (MRI), Machine learning, Early detection, Population health
MeSH: Brain*, Dementia*, Electronic Health Records*, Magnetic Resonance Imaging*, Aged, Aged, 80 and over, Case-Control Studies, Female, Humans, Machine Learning, Male, Predictive Learning Models, Retrospective Studies, Scotland (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: UK Medical Research Council (MR/S010351/1)
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

Background: Early identification of individuals at risk of dementia is essential for preventive care and timely enrolment into disease-modifying interventions. However, most existing prediction approaches rely on invasive, costly, or research-only biomarkers that are not scalable within public healthcare systems. Routinely acquired National Health Service (NHS) brain magnetic resonance imaging (MRI) scans, when linked with electronic health records, represent a widely available and privacy-preserving resource for population-level dementia risk stratification. A key challenge for clinical translation is ensuring that machine-learning predictions are reliable, interpretable, and safe to apply, particularly when models are used years before clinical diagnosis.

Methods: We conducted a retrospective case–control study entirely within a secure NHS Trusted Research Environment using routine T1-weighted brain MRI scans linked to electronic health records from Tayside and Fife, Scotland. The study included 518 participants: 259 individuals who subsequently developed dementia and 259 age- and sex-matched controls. Structural brain features were derived from MRI data and analysed using a support-vector-machine classifier with nested cross-validation to minimise overfitting. Prediction confidence was quantified using distance-from-hyperplane (DFH) calibration, enabling stratification of model outputs by certainty. Primary outcomes were classification accuracy and area under the receiver-operating-characteristic curve (AUC). Secondary analyses examined DFH-stratified performance and the relationship between prediction accuracy and time from scan to first recorded dementia diagnosis.

Results: The model predicted future dementia up to five years before first recorded NHS diagnosis with an AUC of 0.71, a performance consistent with real-world clinical imaging rather than research-optimised datasets. Model sensitivity increased for scans acquired closer to diagnosis, indicating stronger predictive signal as disease onset approached. Confidence-based stratification identified a high-confidence subgroup comprising approximately 35% of scans, within which prediction accuracy increased to around 80%. Performance was consistent across heterogeneous routine NHS scanners and imaging protocols, demonstrating robustness and generalisability to real-world clinical data rather than research-optimised acquisitions.

Conclusion: Routinely collected NHS brain MRI data can be used to predict future dementia several years before clinical diagnosis. Incorporating confidence calibration transforms a conventional machine-learning classifier into a safety-aware and clinically interpretable framework by enabling selective use of high-certainty predictions. This approach supports scalable early detection, population-level risk stratification, and targeted recruitment into preventive or disease-modifying clinical trials, with clear potential for integration into public health systems.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13195-026-02079-4.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

HicResearch/PICTURES-DementiaClassifier

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: bced7fe57a795cb877cf9c3f75dc4dcb4aea266f, 16 April 2026
Languages: MATLAB (12), R (2)
Size: 137 files, 14 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 (4 files), Image Processing Toolbox (2 files), SPM (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
15 files
At the source: github.com/HicResearch/PICTURES-DementiaClassifier

Zenodo 13921331

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file, 0 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

Zenodo 7089491

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file, 0 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

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

Tracing map

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What the map holds:

  • 3 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;
  • 5 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 availability

The data related to the results presented in this article can be accessed within the HIC TRE subject to ethical and governance approvals. The details of dementia cohort identification can be accessed online on the HDR UK Phenotype library (https://phenotypes.healthdatagateway.org/phenotypes/PH1717/version/3973/detail/). The dementia cohort identification and SVM codebase used in this study is available online at GitHub (https://github.com/HicResearch/PICTURES-DementiaClassifier).

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 14 MeSH terms, 1 funder, 37 references.

Cite

This paper

Reel, P. S., Al-Wasity, S., Edwards, C., Reel, S., Mansouri-Benssassi, E., Suveges, S., Mookiah, M. R. K., Krueger, S., Trucco, E., Jefferson, E., Doney, A., & Steele, J. D. (2026). Predicting future dementia from routine clinical MRI and linked healthcare data. Alzheimer's research & therapy, 18(1), 164. https://doi.org/10.1186/s13195-026-02079-4

BibTeX

@article{reel2026predicting,
author = {Reel, Parminder Singh and Al-Wasity, Salim and Edwards, Craig and Reel, Smarti and Mansouri-Benssassi, Esma and Suveges, Szabolcs and Mookiah, Muthu Rama Krishnan and Krueger, Susan and Trucco, Emanuele and Jefferson, Emily and Doney, Alexander and Steele, J. Douglas},
title = {{Predicting future dementia from routine clinical MRI and linked healthcare data}},
journal = {Alzheimer's research \& therapy},
year = {2026},
month = may,
volume = {18},
number = {1},
pages = {164},
publisher = {BMC},
issn = {1758-9193},
doi = {10.1186/s13195-026-02079-4},
url = {https://doi.org/10.1186/s13195-026-02079-4},
pmid = {42169066},
pmcid = {PMC13366786}
}

RIS

TY - JOUR
AU - Reel, Parminder Singh
AU - Al-Wasity, Salim
AU - Edwards, Craig
AU - Reel, Smarti
AU - Mansouri-Benssassi, Esma
AU - Suveges, Szabolcs
AU - Mookiah, Muthu Rama Krishnan
AU - Krueger, Susan
AU - Trucco, Emanuele
AU - Jefferson, Emily
AU - Doney, Alexander
AU - Steele, J. Douglas
TI - Predicting future dementia from routine clinical MRI and linked healthcare data
T2 - Alzheimer's research & therapy
J2 - Alzheimers Res Ther
PY - 2026
DA - 2026/05/21
VL - 18
IS - 1
SP - 164
SN - 1758-9193
PB - BMC
DO - 10.1186/s13195-026-02079-4
UR - https://doi.org/10.1186/s13195-026-02079-4
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Predicting future dementia from routine clinical MRI and linked healthcare data",
"container-title": "Alzheimer's research & therapy",
"author": [
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"family": "Reel",
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{
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{
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"given": "Esma"
},
{
"family": "Suveges",
"given": "Szabolcs"
},
{
"family": "Mookiah",
"given": "Muthu Rama Krishnan"
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{
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"given": "Susan"
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{
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{
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"container-title-short": "Alzheimers Res Ther",
"volume": "18",
"issue": "1",
"page": "164",
"DOI": "10.1186/s13195-026-02079-4",
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"issued": {
"date-parts": [
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2026,
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21
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]
}
}

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