OSCR

Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer’s disease

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Neurotransmission marker properties ↔ scripts/summary/run_summary_all_conns.py, lines 46 to 102 · score 0.96 · A4B2, HT2a, mGluR5, VAChT, HT1a, HT1b · Report a mismatch
  2. [2] § Extended Data ↔ scripts/summary/run_summary_all_conns.py, lines 46 to 102 · score 0.68 · cortical expansion, AD related, microstructure, astro, endo, oligo · Report a mismatch
  3. [3] § Methods › Statistical analysis ↔ scripts/summary/summary.py, lines 313 to 340 · score 0.60 · squared error, explained variance, EV, MSE · Report a mismatch
  4. [4] § Methods › Regional biological factors › Conventional AD-related factors - Aβ, MAPT, APOE ↔ scripts/src/simulated_atrophy.py, lines 1 to 38 · score 0.56 · score normalized, misfolding rates, tau propagation, mapped, vulnerability, synthesis · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 131 lines · 7.7 KB · no license · 2 matches

  1. """
  2. Description:
  3. This module summarizes and extracts results from SIR model simulations. The module
  4. also identifies any missing simulation outputs and writes them to a text file.
  6. Key Functionalities:
  7. - Summarize the simulation results by aggregating evaluation metrics from various models.
  8. - Extract top-performing model details based on a specific connectivity index.
  9. - Identify and log any missing simulation results.
  10. - Generate summary CSV files and save aggregated metrics to disk.
  12. Usage:
  13. Run the script from the command line with the following required arguments:
  14. --result_path: Full path to the results directory (e.g., /Users/xiaoyu/Desktop/Lund/projects/SIR/results/tau/0-1Norm/hypertune/).
  15. --index_conn: Index of the connectivity measure to extract (e.g., 0).
  16. --epicenter: Epicenter identifier used in the SIR model (e.g., "ctx_lh_entorhinal").
  17. --input_data_name: Filename of the main input data file (e.g., Input_SIR_example.pt).
  18. --connectivity_file: Filename for the connectivity matrix file (e.g., Connectomes_all.pt).
  19. And the following optional arguments:
  20. --eval_metric: Evaluation metric to use (default: "pearsonr"; choices: "pearsonr", "mse").
  21. --subtype: Subtype index; if greater than or equal to 0, observed tau values are loaded for the specified subtype (default: -1).
  22. --subtype_file: CSV file containing tau values for each subtype (default: "Tau_subtypes_example.csv").
  24. Example:
  25. python run_summary_all_conns.py --result_path "/path/to/results" --index_conn 2 --epicenter "ctx_lh_entorhinal" \
  26. --input_data_name "Input_SIR_example.pt" --connectivity_file "Connectomes_all.pt"
  28. !!!!!Caution!!!!!:
  29. Before running the script, please review and update the CONNECTIVITY MEASURE LIST (`conn_list`) and the REGIONAL FACTORS
  30. DICTIONARY (`factors_dict`) in the source code if necessary, to ensure they align with your specific simulation model settings.
  32. Dependencies:
  33. - Standard libraries: os, sys, argparse, warnings
  34. - Third-party libraries: numpy, pandas, matplotlib (pyplot)
  35. - Custom modules: summary (infer_from_result_path, extract_all_conns)
  37. Created on Fri Dec 15 2023, at Lund, Sweden
  38. @author: XIAO Yu
  39. """
  41. import argparse
  42. import warnings
  44. from summary import infer_from_result_path, extract_all_conns
  46. def summary_all_conns(args):
  47. """
  48. Summarize simulation results for SIR models based on connectivity measures and subtypes.
  50. This function sets up the necessary paths using the project directory, defines the list of connectivity
  51. measures and associated regional factors, and then calls the extract_all_models() function to retrieve
  52. and aggregate evaluation metrics from simulation outputs. If any models are missing, they are logged
  53. and written to a text file.
  55. Args:
  56. args (Namespace): Parsed command-line arguments containing:
  57. - result_path (str): Relative path to the results directory.
  58. - index_conn (int): Index of the connectivity measure to extract.
  59. - epicenter (int or str): Identifier for the epicenter used in the SIR model.
  60. - eval_metric (str): Evaluation metric to use (e.g., "pearsonr" or "mse").
  61. - subtype (int): Subtype index for selecting observed tau values.
  62. - input_dir (str): Directory path for input data.
  63. - input_data_name (str): Filename for the input simulation data.
  64. - connectivity_file (str): Filename for the connectivity matrix.
  65. - subtype_file (str): Filename for the CSV file with tau subtypes.
  66. """
  67. ######################################### !Change the conn_list and factor_dict based on your own model!
  68. # Define a list of connectivity measures
  69. conn_list = ["sc", "gene_coexpression", "neurotransmission_similarity"]
  71. # Define a dictionary mapping regional factor groups to specific factors (except for "Baseline")
  72. factors_dict={"Baseline":["Baseline"], # <----------------------------------------------------- !!! DON'T change this !!!!!
  73. "AD-related": ["MAPT", "APOE"] #, "Abeta", "Cluster1", "Cluster2", "Cluster3", "Cluster4", "Cluster5", "Cluster6", "Cluster7"],
  74. #"Receptors":["5HT1a", "5HT1b", "5HT2a", "5HT4", "5HT6", "5HTT", "A4B2", "CB1",
  75. # "D1", "D2", "DAT", "GABAa", "H3", "M1", "mGluR5", "MOR", "NET", "NMDA", "VAChT"],
  76. #"Microstructure":["genepc1", "myelinmap", "thickness"],
  77. #"metabolism":["cbf", "cbv", "cmr02", "cmrglc"],
  78. #"Cortical expansion":["devexp", "evoexp", "scalingnih", "scalingpnc", "scalinghcp", "scaling"],
  79. #"Celltype":["Astro", "Endo", "Micro", "Neuro-Ex", "Neuro-In", "Oligo", "OPC"],
  80. }
  81. ######################################### !Change the conn_list and factor_dict based on your own model!
  83. # Define a list of simulation mechanism variable names (synthesis, spread, misfold, clearance)
  84. var_lists=["syn", "spread", "mis", "clear"]
  86. # Ignore FutureWarnings to keep the output clean
  87. warnings.simplefilter(action='ignore', category=FutureWarning)
  89. # Infer the input directory and data type
  90. args = infer_from_result_path(args)
  92. # Call extract_all_conns using the connectivity measure specified by index_conn in the args
  93. _, missing_results = extract_all_conns([conn_list[args.index_conn]], factors_dict, var_lists, args)
  95. # If any simulation results are missing, log and save them to a text file
  96. if len(missing_results) > 0:
  97. print("MODELS MISSING!!!")
  98. print(missing_results)
  99. with open(args.result_path + "/missing_results.txt", 'w') as file:
  100. for item in missing_results:
  101. file.write(f"{item}\n")
  102. print("Extraction done!!!!")
  104. def main():
  105. """
  106. Main function to parse command-line arguments and summarize simulation results.
  108. This function sets up the command-line interface for the summarization process. It expects
  109. arguments for the result path, connectivity index, epicenter, evaluation metric, subtype, input data name,
  110. connectivity file, and subtype file. After parsing these arguments, it calls summarize_results() to
  111. process and summarize the simulation outputs.
  112. """
  113. parser = argparse.ArgumentParser(description="Summarize SIR results of all models")
  114. # Required arguments
  115. parser.add_argument('--result_path', type=str, required=True, help='Full path to the results directory')
  116. parser.add_argument('--index_conn', type=int, help='Index of the connectivity measure to extract. Index in range(conn_list), see `conn_list` in the main function `summary_all_conns(args)` in the current file for details')
  117. parser.add_argument('--epicenter', type=str, required=True, default="ctx_lh_entorhinal", help='Epicenter used in the SIR model')
  118. parser.add_argument('--input_data_name', type=str, default="Input_SIR_example.pkl", required=True, help="Filename of the main input data file")
  119. parser.add_argument('--connectivity_file', type=str, default="Connectomes_all.pkl", required=True, help='File containing all possible connectivity matrices that could be used as `SC_len`')
  120. # Optional arguments
  121. parser.add_argument('--eval_metric', type=str, default="pearsonr", choices=["pearsonr","mse"], help='Evaludation metric to use')
  122. parser.add_argument('--subtype', type=int, default=-1, help='Subtype index; if >= 0, observed tau values are loaded for the specified subtype')
  123. parser.add_argument('--subtype_file', type=str, default="Tau_subtypes_example.csv", help='CSV file containing tau values for each subtype (shape: (n_regions, subtypes))')
  125. args = parser.parse_args()
  126. print("summarization start")
  127. summary_all_conns(args)
  129. if __name__ == "__main__":
  130. main()

run_summary_all_conns.py at commit a23d516, no license · at the source

Overview

Authors: Yu Xiao1, Nicola Spotorno2, Lijun An1, Vincent Bazinet3, Justine Y. Hansen3, Olof Strandberg2, Golia Shafiei4, Harry H. Behjat2, Thomas Funck5,6, Gemma Salvadó2,7, Erik Stomrud2,8, Ruben Smith2,8, Sebastian Palmqvist2,8, Rik Ossenkoppele2,9,10, Niklas Mattsson-Carlgren2,8, Nicola Palomero-Gallagher6,11, Alain Dagher12, Bratislav Misic3, Oskar Hansson2, Jacob W. Vogel1
ORCID iDs: Yu Xiao, Lijun An, Gemma Salvadó, Ruben Smith, Sebastian Palmqvist, Rik Ossenkoppele, Niklas Mattsson-Carlgren, Bratislav Misic, Oskar Hansson, Jacob W. Vogel
  1. Department of Clinical Sciences Malmö, SciLifeLab, Lund Univerisity, Lund, Sweden
  2. Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univerisity, Lund, Sweden
  3. McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
  4. Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
  5. Center for the Developing Brain, Child Mind Institute, New York, New York, USA
  6. Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, 52425, Germany
  7. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
  8. Memory Clinic, Skåne University Hospital, Region Skåne, Malmö, Sweden
  9. Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
  10. Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
  11. C. & O. Vogt Institute for Brain Research, Heinrich-Heine-University, Dusseldorf, 40225, Germany
  12. Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
Institutions: Science for Life Laboratory (Sweden); Lund University (Sweden); McGill University (Canada); University of Pennsylvania (United States); Child Mind Institute (United States); Forschungszentrum Jülich (Germany); Barcelonaβeta Brain Research Center (Spain); Pasqual Maragall Foundation (Spain); Skåne University Hospital (Sweden); Region Skåne (Sweden); Vrije Universiteit Amsterdam (Netherlands); Amsterdam UMC Location Vrije Universiteit Amsterdam (Netherlands); Amsterdam Neuroscience (Netherlands); Heinrich Heine University Düsseldorf (Germany)
Journal: Research Square (preprint)
Dates: published online 23 March 2026
Type: Preprint · Language: English
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-8687892/v1 · OpenAlex W7140088722
Open access: green, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: PET / SPECT (modality), human (organism), Alzheimer's / dementia (population)
Methods: Machine learning, Preprocessing, Connectivity, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Alzheimer’s disease, Disease progression, Tau-PET, Brain connectivity, Model
Topic: Alzheimer's disease research and treatments (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (101096455)
Citations: not cited yet (Europe PMC); 200 references in the paper

Abstract

In Alzheimer’s disease (AD), tau pathology accumulates gradually throughout the brain, with clinical decline reflecting tau progression. A comprehensive understanding of, first, whether tau propagation is predominantly governed by connectome-based diffusion, regional vulnerability, or an interplay of both, and second, which types of brain connectivity or regional factors best explain tau propagation, remains crucial for advancing our understanding of AD progression. Here, we apply multi-scale, biologically informed disease progression simulations to human data, to disentangle the influence of local mechanisms on global tau progression patterns in AD. We find that whether tau reaches a brain region (presence) and how much tau accumulates there (load) are governed by different mechanisms. Tau presence patterns are highly consistent across the population, and can be largely explained through synaptic spread through white-matter networks and excitatory-inhibitory dynamics. Meanwhile tau load differs across people, and is driven by a combination of synaptic spread and intrinsic or extrinsic regional properties, including regional β-amyloid load, MAPT gene expression and regional blood flow. Finally, while distinct tau patterns in the population could each be explained by established AD mechanisms, our models highlight a role of distinct brain networks (parietal networks in MTL-sparing AD tau subtype) and neurotransmitter systems (cholinergic system in posterior subtype). Together, this work suggests that network dynamics likely determine the sequence of regional tau progression, while individual-specific tissue-vulnerability factors influence regional tau load.

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 4 matches between paragraphs and lines of code.

netneurolab/hansenmanynetworks

License: none: the authors keep all their rights
State: the link is dead, verified on 30 September 2026
Evidence: found in the paper
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 30 September 2026: the link is dead
  • 30 September 2026: the link is dead
At the source: github.com/netneurolab/hansenmanynetworks/tree/v1.0.0

DeMONLab-BioFINDER/Xiao_Tau_simulation_SIR

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a23d516efc8990f01368255c37ddca5da6f4af9d, 1 April 2026
Languages: Python (14)
Size: 34 files, 14 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (8 files), pandas (6 files), SciPy (5 files), scikit-learn (4 files), Matplotlib (3 files), seaborn (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
15 files
At the source: github.com/DeMONLab-BioFINDER/Xiao_Tau_simulation_SIR

ucl-pond

License: none: the authors keep all their rights
State: the link answers, verified on 31 October 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 2 checks, the latest on 31 October 2026: the link answers (HTTP 200)
  • 31 October 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: github.com/ucl-pond

Code availability

Python3.9 was used to run all models and analyses. Scripts for the revised Susceptible-Infectious-Recovery model can be found at https://github.com/DeMONLab-BioFINDER/Xiao_Tau_simulation_SIR. Implementations of the SuStaIn algorithm are available on the UCL-POND GitHub page: https://github.com/ucl-pond.

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:

  • 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;
  • 4 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

The structural and functional connectomes used in this study are openly available from 87 and can be accessed via GitHub (https://github.com/netneurolab/hansenmanynetworks/tree/v1.0.0). The neuromaps software toolbox, utilized for brain mapping analyses, is available at https://github.com/netneurolab/neuromaps, with documentation provided on GitHub Pages (https://netneurolab.github.io/neuromaps/). Further details can be found in the associated publication 89. Cortical layer thickness data are publicly available through BigBrainWarp (https://bigbrainwarp.readthedocs.io/en/latest/). Gene expression data used in this study were obtained from the Allen Human Brain Atlas and are openly accessible at https://human.brain-map.org.

The BioFINDER data are not publicly available, but access requests of anonymized data can be made to the study’s steering group . Access to the data will be granted in compliance with European Union legislation on the General Data Protection Regulation (GDPR) and decisions by the Ethical Review Board of Sweden and Region Skåne. Data transfer will be regulated under a material transfer agreement.

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, dates, 20 authors, 5 keywords, 1 funder, 195 references.

Cite

This paper

Xiao, Y., Spotorno, N., An, L., Bazinet, V., Hansen, J. Y., Strandberg, O., Shafiei, G., Behjat, H. H., Funck, T., Salvadó, G., Stomrud, E., Smith, R., Palmqvist, S., Ossenkoppele, R., Mattsson-Carlgren, N., Palomero-Gallagher, N., Dagher, A., Misic, B., Hansson, O., & Vogel, J. W. (2026). Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer’s disease. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-8687892/v1

BibTeX

@article{xiao2026brain,
author = {Xiao, Yu and Spotorno, Nicola and An, Lijun and Bazinet, Vincent and Hansen, Justine Y. and Strandberg, Olof and Shafiei, Golia and Behjat, Harry H. and Funck, Thomas and Salvadó, Gemma and Stomrud, Erik and Smith, Ruben and Palmqvist, Sebastian and Ossenkoppele, Rik and Mattsson-Carlgren, Niklas and Palomero-Gallagher, Nicola and Dagher, Alain and Misic, Bratislav and Hansson, Oskar and Vogel, Jacob W.},
title = {{Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer’s disease}},
journal = {Research Square (preprint)},
year = {2026},
month = mar,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-8687892/v1},
url = {https://doi.org/10.21203/rs.3.rs-8687892/v1}
}

RIS

TY - JOUR
AU - Xiao, Yu
AU - Spotorno, Nicola
AU - An, Lijun
AU - Bazinet, Vincent
AU - Hansen, Justine Y.
AU - Strandberg, Olof
AU - Shafiei, Golia
AU - Behjat, Harry H.
AU - Funck, Thomas
AU - Salvadó, Gemma
AU - Stomrud, Erik
AU - Smith, Ruben
AU - Palmqvist, Sebastian
AU - Ossenkoppele, Rik
AU - Mattsson-Carlgren, Niklas
AU - Palomero-Gallagher, Nicola
AU - Dagher, Alain
AU - Misic, Bratislav
AU - Hansson, Oskar
AU - Vogel, Jacob W.
TI - Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer’s disease
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/03/23
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-8687892/v1
UR - https://doi.org/10.21203/rs.3.rs-8687892/v1
LA - en
ER -

CSL-JSON

{
"id": "10.21203/rs.3.rs-8687892/v1",
"type": "article",
"title": "Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer’s disease",
"container-title": "Research Square (preprint)",
"author": [
{
"family": "Xiao",
"given": "Yu"
},
{
"family": "Spotorno",
"given": "Nicola"
},
{
"family": "An",
"given": "Lijun"
},
{
"family": "Bazinet",
"given": "Vincent"
},
{
"family": "Hansen",
"given": "Justine Y."
},
{
"family": "Strandberg",
"given": "Olof"
},
{
"family": "Shafiei",
"given": "Golia"
},
{
"family": "Behjat",
"given": "Harry H."
},
{
"family": "Funck",
"given": "Thomas"
},
{
"family": "Salvadó",
"given": "Gemma"
},
{
"family": "Stomrud",
"given": "Erik"
},
{
"family": "Smith",
"given": "Ruben"
},
{
"family": "Palmqvist",
"given": "Sebastian"
},
{
"family": "Ossenkoppele",
"given": "Rik"
},
{
"family": "Mattsson-Carlgren",
"given": "Niklas"
},
{
"family": "Palomero-Gallagher",
"given": "Nicola"
},
{
"family": "Dagher",
"given": "Alain"
},
{
"family": "Misic",
"given": "Bratislav"
},
{
"family": "Hansson",
"given": "Oskar"
},
{
"family": "Vogel",
"given": "Jacob W."
}
],
"container-title-short": "Res Sq",
"DOI": "10.21203/rs.3.rs-8687892/v1",
"ISSN": "2693-5015",
"publisher": "Research Square",
"URL": "https://doi.org/10.21203/rs.3.rs-8687892/v1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
23
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1101/2025.04.17.648358 [code]
Brain network dynamics determine tau presence while regional vulnerability governs tau load in Alzheimer’s disease
Journal: bioRxiv (preprint)
In common: seaborn, scikit-learn, pandas, 3 other tools, bigbrainwarp.readthedocs.io, PET / SPECT, Alzheimer's / dementia, 157 references, 10 authors
[2] doi:10.64898/2026.01.27.701320
Dynamical A <i>β</i> -Tau-Neurodegeneration Model Predicts Alzheimer’s Disease Mechanisms and Biomarker Progression
Journal: bioRxiv (preprint)
In common: Alzheimer's / dementia, 25 references, 5 authors
[3] doi:10.1101/2025.04.15.648728 [code]
Hemispheric Asymmetry of Tau Pathology is Related to Asymmetric Amyloid Deposition in Alzheimer’s Disease
Journal: bioRxiv (preprint)
In common: seaborn, scikit-learn, pandas, 3 other tools, PET / SPECT, Alzheimer's / dementia, 13 references, 6 authors
[4] doi:10.1038/s41467-025-63564-2 [code]
Hemispheric asymmetry of tau pathology is related to asymmetric amyloid deposition in Alzheimer's Disease.
Journal: Nature communications
In common: seaborn, scikit-learn, pandas, 3 other tools, PET / SPECT, Alzheimer's / dementia, 13 references, 6 authors
[5] doi:10.1093/brain/awaf279 [code]
Connectivity as a universal predictor of tau progression in atypical Alzheimer's disease.
Journal: Brain : a journal of neurology
In common: PET / SPECT, Alzheimer's / dementia, 24 references, 2 authors
[6] doi:10.1038/s41467-022-34129-4 [code]
Amyloid-associated increases in soluble tau relate to tau aggregation rates and cognitive decline in early Alzheimer's disease.
Journal: Nature communications
In common: PET / SPECT, Alzheimer's / dementia, 11 references, 4 authors
[7] doi:10.1371/journal.pbio.3003241 [code]
Personalised regional modelling predicts tau progression in the human brain.
Journal: PLoS biology
In common: PET / SPECT, Alzheimer's / dementia, 20 references, author Jacob W Vogel
[8] doi:10.1038/s41467-024-49300-2 [code]
Tau follows principal axes of functional and structural brain organization in Alzheimer's disease.
Journal: Nature communications
In common: seaborn, scikit-learn, pandas, 3 other tools, PET / SPECT, Alzheimer's / dementia, 21 references
[9] doi:10.1038/s41467-026-71732-1 [code]
Trajectories of plasma and CSF MTBR-tau243 and phosphorylated-tau species across the Alzheimer's disease continuum.
Journal: Nature communications
In common: PET / SPECT, Alzheimer's / dementia, 6 references, 5 authors
[10] doi:10.1038/s41593-024-01737-w [code]
Proteomic changes in Alzheimer's disease associated with progressive Aβ plaque and tau tangle pathologies.
Journal: Nature neuroscience
In common: Alzheimer's / dementia, 9 references, 4 authors

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

OSCR: the badge of this paper

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.21203/rs.3.rs-8687892/v1.svg)](https://openscicode.org/badge/doi/10.21203/rs.3.rs-8687892/v1)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.21203/rs.3.rs-8687892/v1.svg
   :target: https://openscicode.org/badge/doi/10.21203/rs.3.rs-8687892/v1
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.21203/rs.3.rs-8687892/v1"><img src="https://openscicode.org/badge/doi/10.21203/rs.3.rs-8687892/v1.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.