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Mapping individual molecular connectomes in Alzheimer's disease.

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 · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § DATASETS › Gradient tree boosting using XGBoost ↔ Code/Step3_ConnectomeClassification_Run.R, lines 1–61 · score 0.85 · num_class, eval_metric, early_stopping_rounds, softprob, XGBoost, logistic
  2. [2] § DATASETS › Gradient tree boosting using XGBoost ↔ Code/Step6_ConnectomeAlterationVersusSUVR_Classification_Run.R, lines 1–60 · score 0.74 · eval_metric, early_stopping_rounds, XGBoost, logistic, eta, nfold
  3. [3] § DATASETS › Examining how the individual molecular connectomes changed across the AD continuum and over time ↔ Code/Step5_ConnectomeAlterationAlnalysis_Longitudinal.m, lines 42–87 · score 0.52 · linear mixed, fitting, fitlme, interaction, connectome alteration, model
  4. [4] § DATASETS › Association analysis between individual molecular connectomes and the brain's transcriptome ↔ Code/GetGeneSpecificTranscriptionNetwork.m, the whole file · a weak match · score 0.52 · perturbed transcription network, reference transcription network, gene, correlation
  5. [5] § DATASETS › Constructing individual molecular connectomes ↔ Code/Step1_GetIndividualMolecularConnectome.m, lines 57–77 · score 0.52 · perturbed tau connectome, reference tau connectome, individual molecular connectomes, individual tau connectome, sex, education
  6. [6] § RESULTS › Individual molecular connectome mapping using longitudinal PET data ↔ Code/Step1_GetIndividualMolecularConnectome.m, lines 79–99 · score 0.51 · perturbed tau connectome, reference tau connectome, individual molecular connectomes, individual tau connectome, longitudinal, sex

Paper

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The authors' code

MATLAB · 99 lines · 6.6 KB · no license · 2 matches

  1. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  2. %%%%%%%%%%%%%%% Step 1: Get Individual Molecular Connectome %%%%%%%%%%%%%%%
  3. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  5. % Here, we use the NULL Tau-PET as an example
  7. clear;
  8. clc;
  9. close all;
  11. OutputPath = [ pwd, filesep, '..', filesep, 'NullIndividualConnectome' ];
  12. mkdir( OutputPath );
  14. load( [ pwd, filesep, '..', filesep, 'NullPETData', filesep, 'Tau.mat' ] );
  15. Tau.Tau = [ Tau.SUVR_LH, Tau.SUVR_RH ];
  17. %% Get Included PET Scans
  18. IncludedScan = find( ( ~isnan( Tau.AmyloidGroup ) )&( ~isnan( Tau.Group ) )&...
  19. ( ~isnan( Tau.Age ) )&( ~isnan( Tau.Sex ) )&( ~isnan( Tau.Education ) ) );
  21. IncludedScan_NC_AN = IncludedScan( ( Tau.AmyloidGroup( IncludedScan ) == 0 )&( Tau.Group( IncludedScan ) == 0 ) );
  22. IncludedScan_NC_AP = IncludedScan( ( Tau.AmyloidGroup( IncludedScan ) == 1 )&( Tau.Group( IncludedScan ) == 0 ) );
  23. IncludedScan_MCI_AP = IncludedScan( ( Tau.AmyloidGroup( IncludedScan ) == 1 )&( Tau.Group( IncludedScan ) == 1 ) );
  24. IncludedScan_AD_AP = IncludedScan( ( Tau.AmyloidGroup( IncludedScan ) == 1 )&( Tau.Group( IncludedScan ) == 2 ) );
  26. IncludedScan_AP = [ IncludedScan_NC_AP; IncludedScan_MCI_AP; IncludedScan_AD_AP ];
  27. Tau_AP = Tau.Tau( IncludedScan_AP, : );
  28. Age_AP = Tau.Age( IncludedScan_AP );
  29. Sex_AP = Tau.Sex( IncludedScan_AP );
  30. Education_AP = Tau.Education( IncludedScan_AP );
  31. GroupIndex_AP = [ zeros( numel( IncludedScan_NC_AP ), 1 ); ...
  32. zeros( numel( IncludedScan_MCI_AP ), 1 ) + 1; ...
  33. zeros( numel( IncludedScan_AD_AP ), 1 ) + 2 ];
  35. [ ScanOrder_NC_AN, TimeLag_NC_AN, FirstScanAge_NC_AN ] = GetScanOrder( Tau.RID( IncludedScan_NC_AN ), Tau.ScanDate( IncludedScan_NC_AN ), Tau.Age( IncludedScan_NC_AN ) );
  36. [ ScanOrder_NC_AP, TimeLag_NC_AP, FirstScanAge_NC_AP ] = GetScanOrder( Tau.RID( IncludedScan_NC_AP ), Tau.ScanDate( IncludedScan_NC_AP ), Tau.Age( IncludedScan_NC_AP ) );
  37. [ ScanOrder_MCI_AP, TimeLag_MCI_AP, FirstScanAge_MCI_AP ] = GetScanOrder( Tau.RID( IncludedScan_MCI_AP ), Tau.ScanDate( IncludedScan_MCI_AP ), Tau.Age( IncludedScan_MCI_AP ) );
  38. [ ScanOrder_AD_AP, TimeLag_AD_AP, FirstScanAge_AD_AP ] = GetScanOrder( Tau.RID( IncludedScan_AD_AP ), Tau.ScanDate( IncludedScan_AD_AP ), Tau.Age( IncludedScan_AD_AP ) );
  40. ScanOrder_AP = [ ScanOrder_NC_AP; ScanOrder_MCI_AP; ScanOrder_AD_AP ];
  41. TimeLag_AP = [ TimeLag_NC_AP; TimeLag_MCI_AP; TimeLag_AD_AP ];
  42. FirstScanAge_AP = [ FirstScanAge_NC_AP; FirstScanAge_MCI_AP; FirstScanAge_AD_AP ];
  44. %% Get Reference Tau Connectome
  45. IncludedScan_Reference = IncludedScan_NC_AN( ScanOrder_NC_AN == 1 );
  46. Tau_Reference = Tau.Tau( IncludedScan_Reference, : );
  47. Age_Reference = Tau.Age( IncludedScan_Reference );
  48. Sex_Reference = Tau.Sex( IncludedScan_Reference );
  49. Education_Reference = Tau.Education( IncludedScan_Reference );
  51. ReferenceTauConnectome = partialcorr( Tau_Reference, [ Age_Reference - mean( Age_Reference ), ...
  52. Sex_Reference, Education_Reference - mean( Education_Reference ) ] );
  53. DiagMask = diag( ones( numel( Tau_Reference( 1, : ) ), 1 ) );
  55. save( [ OutputPath, filesep, 'ReferenceTauConnectome.mat' ], 'IncludedScan_Reference', 'ReferenceTauConnectome', 'DiagMask' );
  57. %% Get Individual Tau Connectome
  58. % These connectome matrices will be used in baseline analysis.
  60. IndividualTauConnectome_AP = NaN( numel( IncludedScan_AP ), numel( squareform( ReferenceTauConnectome - DiagMask ) ) );
  61. for Scan = 1:numel( IncludedScan_AP )
  62. PerturbedTauConnectome = partialcorr( [ Tau_Reference; Tau_AP( Scan, : ) ], ...
  63. [ [ Age_Reference; Age_AP( Scan ) ] - mean( Age_Reference ), ...
  64. [ Sex_Reference; Sex_AP( Scan ) ], ...
  65. [ Education_Reference; Education_AP( Scan ) ] - mean( Education_Reference ) ] );
  66. IndividualTauConnectome_AP( Scan, : ) = squareform( PerturbedTauConnectome - ReferenceTauConnectome )...
  67. ./squareform( 1 - ReferenceTauConnectome.^2 )*( numel( IncludedScan_Reference ) - 1 );
  68. end
  70. IndividualTauConnectome_NC_AP = IndividualTauConnectome_AP( GroupIndex_AP == 0, : );
  71. IndividualTauConnectome_MCI_AP = IndividualTauConnectome_AP( GroupIndex_AP == 1, : );
  72. IndividualTauConnectome_AD_AP = IndividualTauConnectome_AP( GroupIndex_AP == 2, : );
  74. save( [ OutputPath, filesep, 'IndividualTauConnectome_AP.mat' ], 'IncludedScan_AP', 'IndividualTauConnectome_AP', 'ScanOrder_AP', 'TimeLag_AP', 'GroupIndex_AP' );
  75. save( [ OutputPath, filesep, 'IndividualTauConnectome_NC_AP.mat' ], 'IncludedScan_NC_AP', 'IndividualTauConnectome_NC_AP', 'ScanOrder_NC_AP', 'TimeLag_NC_AP' );
  76. save( [ OutputPath, filesep, 'IndividualTauConnectome_MCI_AP.mat' ], 'IncludedScan_MCI_AP', 'IndividualTauConnectome_MCI_AP', 'ScanOrder_MCI_AP', 'TimeLag_MCI_AP' );
  77. save( [ OutputPath, filesep, 'IndividualTauConnectome_AD_AP.mat' ], 'IncludedScan_AD_AP', 'IndividualTauConnectome_AD_AP', 'ScanOrder_AD_AP', 'TimeLag_AD_AP' );
  79. %% Get Individual Tau Connectome Using FirstScanAge
  80. % These connectome matrices will be used in longitudinal analysis.
  82. IndividualTauConnectome_AP = NaN( numel( IncludedScan_AP ), numel( squareform( ReferenceTauConnectome - DiagMask ) ) );
  83. for Scan = 1:numel( IncludedScan_AP )
  84. PerturbedTauConnectome = partialcorr( [ Tau_Reference; Tau_AP( Scan, : ) ], ...
  85. [ [ Age_Reference; FirstScanAge_AP( Scan ) ] - mean( Age_Reference ), ...
  86. [ Sex_Reference; Sex_AP( Scan ) ], ...
  87. [ Education_Reference; Education_AP( Scan ) ] - mean( Education_Reference ) ] );
  88. IndividualTauConnectome_AP( Scan, : ) = squareform( PerturbedTauConnectome - ReferenceTauConnectome )...
  89. ./squareform( 1 - ReferenceTauConnectome.^2 )*( numel( IncludedScan_Reference ) - 1 );
  90. end
  92. IndividualTauConnectome_NC_AP = IndividualTauConnectome_AP( GroupIndex_AP == 0, : );
  93. IndividualTauConnectome_MCI_AP = IndividualTauConnectome_AP( GroupIndex_AP == 1, : );
  94. IndividualTauConnectome_AD_AP = IndividualTauConnectome_AP( GroupIndex_AP == 2, : );
  96. save( [ OutputPath, filesep, 'IndividualTauConnectomeUsingFirstScanAge_AP.mat' ], 'IncludedScan_AP', 'IndividualTauConnectome_AP', 'ScanOrder_AP', 'TimeLag_AP', 'GroupIndex_AP' );
  97. save( [ OutputPath, filesep, 'IndividualTauConnectomeUsingFirstScanAge_NC_AP.mat' ], 'IncludedScan_NC_AP', 'IndividualTauConnectome_NC_AP', 'ScanOrder_NC_AP', 'TimeLag_NC_AP' );
  98. save( [ OutputPath, filesep, 'IndividualTauConnectomeUsingFirstScanAge_MCI_AP.mat' ], 'IncludedScan_MCI_AP', 'IndividualTauConnectome_MCI_AP', 'ScanOrder_MCI_AP', 'TimeLag_MCI_AP' );
  99. save( [ OutputPath, filesep, 'IndividualTauConnectomeUsingFirstScanAge_AD_AP.mat' ], 'IncludedScan_AD_AP', 'IndividualTauConnectome_AD_AP', 'ScanOrder_AD_AP', 'TimeLag_AD_AP' );

Step1_GetIndividualMolecularConnectome.m at commit c52a91b, no license · at the source

Overview

Authors: Zhilei Xu1, Mite Mijalkov1, Jiawei Sun1, Yu‐Wei Chang2, Arianna Sala3,4, Giovanni Volpe2, Mario Severino5, Mattia Veronese5,6, Sara Garcia‐Ptacek7,8, Joana B Pereira1, for the Alzheimer's Disease Neuroimaging Initiative
ORCID iDs: Zhilei Xu, Sara Garcia‐Ptacek
  1. Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Solna, Sweden
  2. Department of Physics, University of Gothenburg, Gothenburg, Sweden
  3. Coma Science Group, GIGA Consciousness, University of Liege, Liege, Belgium
  4. Centre du Cerveau2, University Hospital of Liege, Liege, Belgium
  5. Department of Information Engineering, University of Padua, Padua, Italy
  6. Department of Neuroimaging, King's College London, Strand, London, UK
  7. Department of Neurobiology, Division of Clinical Geriatrics, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
  8. Theme Inflammation and Aging, Karolinska University Hospital, Solna, Sweden
Institutions: Karolinska Institutet (Sweden); University of Gothenburg (Sweden); University of Liège (Belgium); Centre Hospitalier Universitaire de Liège (Belgium); University of Padua (Italy); King's College London (United Kingdom); Karolinska University Hospital (Sweden)
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 3, article e71310
Dates: received 7 April 2025; accepted 30 December 2025; published online 26 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/alz.71310 · PMID 41888480 · PMCID PMC13093537 · OpenAlex W7141417187
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), PET / SPECT (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Keywords: amyloid, molecular connectome, positron emission tomography, tau, transcriptome
MeSH: Alzheimer Disease*, Brain*, Connectome*, Amyloid beta-Peptides, Disease Progression, Female, Humans, Male, Positron-Emission Tomography, tau Proteins, Transcriptome (* major topic)
Topic: Bioinformatics and Genomic Networks (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Swedish Research Council (2022-01108, 2025-03210); U. S. Department of Defense (W81XWH‐12‐2‐0012, W81XWH-12-2-0012); Blomquist family; Lars Hierta Memorial Foundation; Omanian Ministry of Research, Innovation and Digitalization; Stiftelsen Lars Hiertas Minne; Hjärnfonden (FO2025‐0059); European Union - NextGenerationEU and the Romanian Government; National Institutes of Health Grant; StratNeuro; Romania's National Recovery and Resilience Plan (760250/28.12.2023, PNRR-C9-I8-CF109/31.07.2023); KI Research Incubator; Strategic Research Area Neuroscience; Swedish Alzheimer Foundation; Dementia Foundation; KI Consolidator Grant; King Gustaf V and Queen Victoria's Foundatio; Vetenskapsrådet (2022‐01108, 2025‐03210); Swedish Brain Foundation (FO2025-0059); Karolinska Institute Consolidator Position grant; Alzheimer Foundation (AF-1032782); Gamla Tjänarinnor Foundation; Blomqvist Foundation (2-3980/2025); Gun och Bertil Stohnes Stiftelse; KI Foundations; Kung Gustav:S & Viktorias Stiftelse
Citations: cited by 1 paper (Europe PMC); 75 references in the paper

Abstract

INTRODUCTION: Mapping individual differences is crucial to improve personalized medicine approaches in Alzheimer's disease (AD), which is characterized by strong inter‐individual variability in the accumulation patterns of tau and amyloid beta pathology.

METHODS: We assess the progression of AD across the disease continuum by building individual molecular connectomes using longitudinal positron emission tomography (PET) data.

RESULTS: We demonstrate that these connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects. Alterations in the connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional PET measures. We introduce a novel gene‐specific transcription network analysis that linked individual tau and amyloid connectomes to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.

DISCUSSION: Individual molecular connectome mapping provides a novel and sensitive framework to monitor AD progression.

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

Repository

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

zhileixu/IndividualMolecularConnectomes

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: c52a91b9918ae12a17520c16cb5119beca0cbe45, 28 June 2024
Languages: MATLAB (13), R (4)
Size: 37 files, 17 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), XGBoost (4 files), FreeSurfer (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
18 files
At the source: github.com/zhileixu/IndividualMolecularConnectomes

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

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.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 scripts, each with its path and the digest of its content;
  • 6 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

Datasets cited

Data availability statement

The ADNI cohort is available through the ADNI data portal (https://adni.loni.usc.edu/), subject to approval by the ADNI team. The HABS cohort is available through the XNAT data portal (https://central.xnat.org/), subject to approval by the HABS team. The preprocessed AHBA dataset is available at https://doi.org/10.6084/m9.figshare.6852911. The human pathway gene sets dataset is available at http://download.baderlab.org/EM_Genesets/. The code to reproduce the results reported in this article is available at https://github.com/zhileixu/IndividualMolecularConnectomes.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 11 MeSH terms, 26 funders, 75 references.

Cite

This paper

Xu, Z., Mijalkov, M., Sun, J., Chang, Y., Sala, A., Volpe, G., Severino, M., Veronese, M., Garcia‐Ptacek, S., Pereira, J. B., & for the Alzheimer's Disease Neuroimaging Initiative. (2026). Mapping individual molecular connectomes in Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(3), e71310. https://doi.org/10.1002/alz.71310

BibTeX

@article{xu2026mapping,
author = {Xu, Zhilei and Mijalkov, Mite and Sun, Jiawei and Chang, Yu‐Wei and Sala, Arianna and Volpe, Giovanni and Severino, Mario and Veronese, Mattia and Garcia‐Ptacek, Sara and Pereira, Joana B and {for the Alzheimer's Disease Neuroimaging Initiative}},
title = {{Mapping individual molecular connectomes in Alzheimer's disease}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e71310},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/alz.71310},
url = {https://doi.org/10.1002/alz.71310},
pmid = {41888480},
pmcid = {PMC13093537}
}

RIS

TY - JOUR
AU - Xu, Zhilei
AU - Mijalkov, Mite
AU - Sun, Jiawei
AU - Chang, Yu‐Wei
AU - Sala, Arianna
AU - Volpe, Giovanni
AU - Severino, Mario
AU - Veronese, Mattia
AU - Garcia‐Ptacek, Sara
AU - Pereira, Joana B
AU - for the Alzheimer's Disease Neuroimaging Initiative
TI - Mapping individual molecular connectomes in Alzheimer's disease
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/03/01
VL - 22
IS - 3
SP - e71310
SN - 1552-5260
PB - Wiley
DO - 10.1002/alz.71310
UR - https://doi.org/10.1002/alz.71310
LA - en
ER -

CSL-JSON

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