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Trajectories of plasma and CSF MTBR-tau243 and phosphorylated-tau species across the Alzheimer's disease continuum.

Code ↔ Paper

2 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 2 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] § Results › PET-chronology measure ↔ SILA.m, the whole file · a weak match · score 0.82 · sampled iterative local, numerically integrate, SILA algorithm, biomarker positivity, Euler, scans
  2. [2] § Results › PET-chronology measure ↔ SILA_estimate.m, lines 1–46 · score 0.50 · SILA algorithm, Euler, smoothing, Numerical, iterative, discrete

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 114 lines · 5.6 KB · GPL-3.0 · 1 match

  1. function [tsila,tdrs] = SILA(age,value,subid,dt,val0,maxi,varargin)
  2. % SILA sampled iterative local approximation with smoothing
  3. % [tsila,tdrs] = SILA(age,value,subid,dt,val0,maxi) applies the SILA algorithm
  4. % to input data age, value, subid to approximate a value vs. time curve.
  5. % dt is the integration interval for Euler's Method used in the
  6. % approximation of the integrated curve. val0 specifies the initial
  7. % condition such that f(t=0) = val0. maxi is the maximum number of
  8. % iterations allowed before the model terminates integration.
  9. %
  10. % [tsila,tdrs] = SILA(age,value,subid,dt,val0,maxi,sk) is the same as above but
  11. % allows the user to specifiy the size of the smoothing kernel used to
  12. % smooth the rate vs. value curve. If this value is unspecified, the
  13. % algorithm will perform an initial step to select a smoothing kernel
  14. % that optimizes backwards prediction residuals based on the first and
  15. % last observations for each person
  16. %
  17. % Input Variables:
  18. % age = number vector corresponding to the age at observation
  19. % value = number vector with the observed value to be modeled over time
  20. % subid = number corresponding to a subject identifier
  21. % dt = number specifying the step size to use for numerical integration
  22. % val0 = number for the value that corresponds to t=0
  23. % maxi = number specifying the maximum number of iterations before
  24. % the model teriminates
  25. % varargin = optional input number specifying the size of the
  26. % smoothing kernel
  27. % Output Variables:
  28. % tsila = table with discrete value vs. time curve and additional
  29. % stats info
  30. % tdrs = table with the discrete values used as input to the
  31. % intergation of value vs. time with addtiional stats info
  32. %
  33. % See Also ILLA, SILA_estimate.
  34. %
  35. % Written By: Tobey J Betthauser, PhD
  36. % Univsersity of Wisconsin-Madison
  37. % Department of Medicine
  38. % Division of Geriatrics
  39. % [email hidden]
  40. %
  41. % This function was initiallty designed to estimate the amyloid vs time curve from
  42. % longitudinal PET imaging data. This function calls a subfunction to
  43. % optimize a smoothing kernel of the sampled rate vs level curve, and then
  44. % outputs the optimized model.
  45. % If using or further developing this method, please cite the following
  46. % reference: Betthauser, et al,. Multi-method investigation of factors
  47. % influencing amyloid onset and impairment in three cohorts. Brain, 2022
  49. %% create table object and identifiers for order and number of scans
  50. t = table; % create blank table
  51. t.age = age; % create age variable in table
  52. t.val = value; % create value variable in table
  53. t.subid = subid; % create subject ID variable in table
  54. t = sortrows(t,{'subid','age'}); % sort table by subject ID then age
  55. subs = unique(t.subid); % get all unique subject identifiers
  56. for i = 1:numel(subs)
  57. ids = t.subid==subs(i);
  58. ages = t.age(ids);
  59. [~,ida] = sort(ages);
  60. t.idx(ids) = ida; % for each subject create ordered observation numbers
  61. t.ns(ids) = numel(ages); % for each subject get the number of longitudinal observations
  62. end
  63. t = t(t.ns>1,:); % remove cases without longitudinal data
  65. %% Setup variables for A+/- ids and residual weighting
  66. % Emphasis will be on backwards prediction. Residuals weighted such that A+
  67. % and A- have equal say despite possible imbalance in the data.
  68. resnorm = nnz(t.val(t.idx==t.ns)>=val0)/nnz(t.val(t.idx==t.ns)<val0); %ratio of positive:negative cases used to weight residuals when estimating smoothing kernel
  69. idpos = t.val>val0; %indices for biomarker negative cases
  70. idneg = t.val<=val0; %indices for biomarker positive cases
  72. %% Run First iteration to identify the smoothing kernel
  73. switch nargin
  74. case 7
  75. % use prespecified smoothing kernel
  76. % note that the data structure for a loop was maintained, but there
  77. % is only one iteration of the loop for the case with a
  78. % pre-specified smoothing kernel
  79. sk = varargin{1};
  80. dat = struct();
  81. SSQpos = nan(numel(sk),1);
  82. SSQneg = nan(numel(sk),1);
  83. for i = 1:numel(sk)
  84. [dat.tilla{i},dat.tdrs{i}] = ILLA(t.age,t.val,t.subid,dt,val0,maxi,sk(i));
  85. temp = SILA_estimate(dat.tilla{i},t.age,t.val,t.subid,'align_event','last','truncate_aget0','no');
  86. SSQpos(i) = sum(temp.estresid(idpos).^2);
  87. SSQneg(i) = sum(temp.estresid(idneg).^2);
  88. end
  89. [~,ids] = min(SSQpos + resnorm*SSQneg);
  90. tsila = dat.tilla{ids};
  91. tdrs = dat.tdrs{ids};
  92. case 6
  93. % optimize smoothing kernel
  94. sk = 0:0.05:0.5; % these values correspond to setting span of smoothing kernel to 0-50% of the data
  95. dat = struct();
  96. SSQpos = nan(numel(sk),1);
  97. SSQneg = nan(numel(sk),1);
  98. % for each iteration of the loop, the model is estimated and
  99. % backwards prediction is performed to optimize residuals
  100. for i = 1:numel(sk)
  101. [dat.tilla{i},dat.tdrs{i}] = ILLA(t.age,t.val,t.subid,dt,val0,maxi,sk(i));
  102. temp = SILA_estimate(dat.tilla{i},t.age,t.val,t.subid,'align_event','last','truncate_aget0','no');
  103. SSQpos(i) = sum(temp.estresid(idpos).^2);
  104. SSQneg(i) = sum(temp.estresid(idneg).^2);
  105. end
  106. % the model with the best backwards prediction is selected using
  107. % weighted residuals
  108. [~,ids] = min(SSQpos + resnorm*SSQneg); % get the index of the best fit
  109. tsila = dat.tilla{ids}; % output the model table with the best fit
  110. tdrs = dat.tdrs{ids}; % output the discrete rate table from the best fit
  111. end
  113. %% Add a variable to specify the optimal smoothing kernel size to the descrete rate table
  114. tdrs.skern(:) = sk(ids);

SILA.m at commit f70a425, under GPL-3.0 · at the source

Overview

Authors: Lyduine E. Collij1,2,3, Gemma Salvadó1, Kanta Horie4,5,6, Nicolas R. Barthélemy4,5, Tobey J. Betthauser7,8, Olof Strandberg1, Ruben Smith1,9, Sebastian Palmqvist1,9, Suzanne E. Schindler5,10, Rik Ossenkoppele1,11,12, Shorena Janelidze1, Niklas Mattsson-Carlgren1,9,13, Randall J. Bateman4,5, Oskar Hansson1
ORCID iDs: Lyduine E. Collij, Gemma Salvadó, Kanta Horie, Nicolas R. Barthélemy, Tobey J. Betthauser, Ruben Smith, Suzanne E. Schindler, Rik Ossenkoppele, Shorena Janelidze, Niklas Mattsson-Carlgren, Randall J. Bateman, Oskar Hansson
13 affiliations
  1. Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University,Lund, Sweden
  2. Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc,Amsterdam, The Netherlands
  3. Brain Imaging, Amsterdam Neuroscience,Amsterdam, The Netherlands
  4. Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine,St. Louis, MO USA
  5. Department of Neurology, Washington University School of Medicine,St. Louis, MO USA
  6. Eisai, Inc.,Nutley, NJ USA
  7. Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison,Madison, WI USA
  8. Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison,Madison, WI USA
  9. Memory Clinic, Skåne University Hospital,Malmö, Sweden
  10. Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine,St. Louis, MO USA
  11. Neurology, Alzheimercenter Amsterdam, Amsterdam UMC, location VUmc,Amsterdam, The Netherlands
  12. Neurodegeneration, Amsterdam Neuroscience,Amsterdam, The Netherlands
  13. Wallenberg Center for Molecular Medicine, Lund University,Lund, Sweden
Institutions: Lund University (Sweden); Amsterdam University Medical Centers (Netherlands); Amsterdam Neuroscience (Netherlands); Washington University in St. Louis (United States); Eisai (United States) (United States); University of Wisconsin–Madison (United States); Skåne University Hospital (Sweden)
Journal: Nature communications, volume 17, issue 1, article 3400
Dates: received 29 August 2025; accepted 27 March 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71732-1 · PMID 41957377 · PMCID PMC13065743 · OpenAlex W7152541314
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: PET / SPECT (modality), human (organism), Alzheimer's / dementia (population)
Methods: Preprocessing, Statistics, fMRI & imaging
Keywords: Prognostic markers, Neuroscience
MeSH: Alzheimer Disease*, tau Proteins*, Aged, Amyloid beta-Peptides, Biomarkers, Disease Progression, Female, Humans, Male, Phosphorylation, Positron-Emission Tomography (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: MSCA Postdoctoral fellowship (#101108819) and Alzheimer Association Research Fellowship (#23AARF-1029663) grants
Citations: cited by 2 papers (Europe PMC); 48 references in the paper

Abstract

To efficiently implement plasma and cerebrospinal fluid (CSF) biomarkers for staging and prognosis of Alzheimer disease (AD), we must understand their dynamics across disease progression. We analyzed participants from the Swedish BioFINDER-2 study with mass spectrometry measurements of plasma and CSF tau species, including eMTBR-tau243/MTBR-tau243 and phosphorylation occupancies (%p-tau). Disease duration was estimated using Aβ-PET and tau-PET with the SILA algorithm. Bootstrapped LOESS models showed that %p-tau217 changes earliest, increasing just before Aβ-PET positivity. Other p-tau species changed later, with smaller dynamic ranges and earlier ceiling effects. %p-tau205 and MTBR-tau243 changes aligned with tau-PET positivity onset, while MTBR-tau243—especially plasma eMTBR-tau243—tracked cortical tau burden in later stages. Non-phosphorylated mid-region tau may serve as a late-stage biomarker. Taken together, concurrent assessments of plasma or CSF %p-tau217, %p-tau205, and (e)MTBR-tau243 provides information about different biological events in the disease cascade, which can benefit clinical trials and patient management in clinical practice.

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 2 matches between paragraphs and lines of code.

Betthauser-Neuro-Lab/SILA-AD-Biomarker

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: f70a42571bcaddf454f04f4573b614d060e6f572, 2 August 2024
Languages: MATLAB (9)
Size: 14 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, continuous integration
Not found: CITATION.cff, environment file, tests, documentation
Tools: Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
11 files
At the source: github.com/Betthauser-Neuro-Lab/SILA-AD-Biomarker

Code availability

The SILA algorithm is freely available at GitHub (https://github.com/Betthauser-Neuro-Lab/SILA-AD-Biomarker). The R code used for analysis of this work can be made available upon request.

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

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;
  • 9 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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

Anonymized data can be made available upon reasonable request from a qualified academic investigator for the sole purpose of replicating procedures and results presented in the article and as long as data transfer is in agreement with EU legislation on the general data protection regulation which should be regulated in a material transfer agreement. Source data are provided with this paper.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 2 keywords, 11 MeSH terms, 1 funder, 47 references.

Cite

This paper

Collij, L. E., Salvadó, G., Horie, K., Barthélemy, N. R., Betthauser, T. J., Strandberg, O., Smith, R., Palmqvist, S., Schindler, S. E., Ossenkoppele, R., Janelidze, S., Mattsson-Carlgren, N., Bateman, R. J., & Hansson, O. (2026). Trajectories of plasma and CSF MTBR-tau243 and phosphorylated-tau species across the Alzheimer's disease continuum. Nature communications, 17(1), 3400. https://doi.org/10.1038/s41467-026-71732-1

BibTeX

@article{collij2026trajectories,
author = {Collij, Lyduine E. and Salvadó, Gemma and Horie, Kanta and Barthélemy, Nicolas R. and Betthauser, Tobey J. and Strandberg, Olof and Smith, Ruben and Palmqvist, Sebastian and Schindler, Suzanne E. and Ossenkoppele, Rik and Janelidze, Shorena and Mattsson-Carlgren, Niklas and Bateman, Randall J. and Hansson, Oskar},
title = {{Trajectories of plasma and CSF MTBR-tau243 and phosphorylated-tau species across the Alzheimer's disease continuum}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {3400},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71732-1},
url = {https://doi.org/10.1038/s41467-026-71732-1},
pmid = {41957377},
pmcid = {PMC13065743}
}

RIS

TY - JOUR
AU - Collij, Lyduine E.
AU - Salvadó, Gemma
AU - Horie, Kanta
AU - Barthélemy, Nicolas R.
AU - Betthauser, Tobey J.
AU - Strandberg, Olof
AU - Smith, Ruben
AU - Palmqvist, Sebastian
AU - Schindler, Suzanne E.
AU - Ossenkoppele, Rik
AU - Janelidze, Shorena
AU - Mattsson-Carlgren, Niklas
AU - Bateman, Randall J.
AU - Hansson, Oskar
TI - Trajectories of plasma and CSF MTBR-tau243 and phosphorylated-tau species across the Alzheimer's disease continuum
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/09
VL - 17
IS - 1
SP - 3400
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71732-1
UR - https://doi.org/10.1038/s41467-026-71732-1
LA - en
ER -

CSL-JSON

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