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Selective vulnerability of monoaminergic neurons and network spread of alpha-synuclein jointly explain pathology progression in Parkinson's disease models.

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Paper

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

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The authors' code

MATLAB · 103 lines · 3.7 KB · MIT

  1. %Loading the data necessary to create this default input_struct:
  3. matpath = cd;
  4. load([matpath filesep 'brainframe_defaultHuman_datinput.mat']);
  7. %Creating the default mouse input struct
  8. %Specific fields are filled one at a time in the below script
  9. %Basic fields required for any element of brainframe to run:
  11. %Voxel or region binary flag, enter 0 or 1 respectively
  12. input_struct.voxUreg = 1;
  14. %Setting 3D brain atlas with regional voxel IDs, mouse default is slightly modified AIBS CCF
  15. input_struct.brain_atlas = human_atlas_dk;
  17. %Setting image background color, only takes 'k','w','other', where other is light gray
  18. input_struct.bgcolor = 'k';
  20. %Binary flag for saving & closing image with on axis views [1], or opening GUI for image manipulation [0]
  21. input_struct.savenclose = 0;
  23. %Field to set image file name if auto-saving
  24. input_struct.img_labels = 'yourfilename';
  26. %Field to set image file format
  27. input_struct.img_format = 'png';
  29. %Field for entering your data to be visualized over the space of the brain
  30. %This field is a vector of values for per-region data that is n-regions long
  31. %This field is either a 3D matrix the same size as or a 1D vector with the same number of elements as the brain atlas
  32. %Per region volume loss data is the default example for humans, comes from ADNI (see biliography in Help file)
  33. testdata(isnan(testdata)) = 0;
  34. input_struct.data = testdata;
  36. %Relevant For Per Voxel Visualizations Only:
  38. %Number of evenly spaced bins for heatmap visualization of per voxel data
  39. input_struct.nbin = 1;
  42. %Relevant For Per Region Visualizations:
  44. %Setting number of regions
  45. input_struct.nreg = sum(unique(input_struct.brain_atlas)>0);
  47. %Setting regions groups, here all the same group
  48. input_struct.region_groups = ones(sum(unique(input_struct.brain_atlas)>0),1);
  50. %Binary flag for whether to draw a sphere [1] or point clouds [0] for per-region visualizations
  51. input_struct.sphere = 0;
  53. %Setting number of points in per sphere visualization
  54. input_struct.sphere_npts = 35;
  56. %Index 1: binary flag for centered [1] or diffuse [0] point clouds
  57. %Index 2: Degree of centering, only turned on if [1] is in index 1
  58. input_struct.centered = [1 2];
  61. %Manipulable Fields Relevant For Both Per-Region & Per Voxel Visualizations:
  63. %Field for setting colormap, based on nbins for per-voxel & region_groups for per-region visualizations
  64. input_struct.cmap = hsv(length(unique(input_struct.region_groups)));
  66. %Multiplier for size & density of all point clouds or radius of all spheres
  67. input_struct.xfac = 1;
  69. %Point size specification (uses ptcloud(), scatter3() behaves similarly)
  70. input_struct.pointsize = 50;
  73. %Fields Relevant For Connectivity Visualizations:
  75. %Binary flag for visualizing interregional connectivity data, only visually works with per-region visualizations
  76. input_struct.iscon = 0;
  78. %Field for setting connectivity matrix, must have same number of regions as brain_atlas
  79. input_struct.conmat = conmat;
  81. %Multiplier for number of fibers from conmat
  82. input_struct.con_rescale = 1;
  84. %Fiber width setting (uses plot3() for visualizing)
  85. input_struct.con_width = 0.01;
  87. %Field for setting connections into regions groups based on ROI of origin, functions similarly to region_groups above
  88. input_struct.con_regiongroups = ones(sum(unique(input_struct.brain_atlas)>0),1);
  90. %Field for setting connectivity colormap
  91. input_struct.con_cmap = lines(length(unique(input_struct.region_groups)));
  93. %Field for setting degree of curvature of ellipse connectivity visualizations
  94. input_struct.con_arch = 0.5;
  96. %Setting the width and length of arrows on fibers indicating direction (pre to post synpatic ROI)
  97. input_struct.conarrow_WL = [1.5 2.5];
  100. %Saving this default input_struct:
  102. save([matpath filesep 'default_human.mat'],'input_struct');

brainframe_humandefault_creator.m at commit 08af074, under MIT · at the source

Overview

Authors: Samuel Teshome1, Justin Torok2, Christopher Mezias3, Ajay Gupta4, Ashish Raj2
ORCID iDs: Samuel Teshome
  1. Duke University School of Medicine, Durham, NC, United States
  2. Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
  3. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
  4. Department of Radiology, Columbia Univeristy Vagelos College of Physicians and Surgeons, New York, NY, United States
Institutions: Duke University (United States); University of California, San Francisco (United States); Cold Spring Harbor Laboratory (United States); Columbia University (United States)
Journal: Neurobiology of disease, volume 224, article 107403
Dates: published online 27 April 2026; in print 15 June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.nbd.2026.107403 · PMID 42055257 · PMCID PMC13286620 · OpenAlex W7156005449
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Machine learning
Keywords: α-synuclein, Parkinson’s disease, Connectome-based modeling, Network propagation, Monoaminergic vulnerability, Cell-type-specific expression, Alpha- synuclein, Parkinson disease, Connectome, Network diffusion, Monoaminergic neurons, Cell-type vulnerability, Monoamines
MeSH: alpha-Synuclein*, Biogenic Monoamines*, Brain*, Nerve Net*, Neurons*, Parkinson Disease*, Animals, Connectome, Disease Models, Animal, Disease Progression, Mice, Mice, Transgenic (* major topic)
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: National Institute on Aging (R01AG072753, R21AG087921); NIA NIH HHS (RF1 AG087302, R01 AG072753, R21 AG087921); Alzheimer's Disease Neuroimaging Initiative; National Institute of Neurological Disorders and Stroke (R01NS092802); NINDS NIH HHS (R01 NS092802); National Institutes of Health (RF1AG087302); Aspen Brain Institute
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

The trans-synaptic propagation of α-synuclein aggregates is a defining feature of Parkinson’s disease, yet how specific cellular phenotypes interact with the brain’s structural connectome to govern disease progression remains a fundamental question in neurobiology. Here, we address this question using a connectome-based modeling framework that simulates synucinopathy spread along the mouse structural connectome, to examine how monoaminergic cell-type vulnerability and network-based transmission jointly shape pathology progression. ‘Nexopathy in silico’ (NexIS), along with the MISS algorithm for spatial gene expression mapping, provided a framework to examine how regional and cellular context impact synucleinopathy spread. In particular we focus on monoaminergic neurons, since inadequate clearance of pathologic α-synuclein species in monoaminergic neurons is a prominent hypothesis implicated in PD. We delineate the specific roles of monoaminergic cell types and directional transmission of α-synuclein arising from axonal transport polarity in shaping α-synuclein pathology in mouse models.

Our analysis reveals that hindbrain noradrenergic (HBNOR) and midbrain dopaminergic (MBDOP) cell-type distributions are the primary mediators of network-wide transmission, with the former outperforming endogenous regional Snca expression as predictors of long-term pathology. Crucially, we demonstrate a synergistic effect between cell-type vulnerability and retrograde-biased transport, finding that both factors are required to accurately recapitulate empirical spatiotemporal patterns at 6 and 12 months post-seeding. This work constitutes one of the most mechanistically complete models of synucleinopathy to date, providing a comprehensive theoretical bridge that links microscale molecular motor regulation to macroscale regional vulnerability. Our findings suggest that the interaction between noradrenergic cell-type distribution and retrograde transport serves as the dominant driver of late-stage progression, offering highly specific cellular and mechanistic targets for therapeutic intervention.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

Raj-Lab-UCSF/Brainframe

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 08af07485c85974d89097c1db400eb7bb32f39ff, 19 September 2023
Languages: MATLAB (8)
Size: 35 files, 8 scripts
Software Heritage: not archived
Found in: the text, “Visualization”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/Raj-Lab-UCSF/Brainframe

Raj-Lab-UCSF/NexIS

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b99fbf79a6e1c796049ea556c33a7f01b152a00e, 13 October 2022
Languages: MATLAB (58), Jupyter (1), Python (1)
Size: 73 files, 60 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, tests, 1 notebook
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Optimization Toolbox (16 files), Statistics and Machine Learning Toolbox (12 files), NumPy (2 files), SciPy (2 files), pandas (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
62 files
At the source: github.com/Raj-Lab-UCSF/NexIS

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 68 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 source data on the mouse models, gene expression, and mesoscale mouse connectome as well as the computer code to run the models are available at our repository at https://github.com/Raj-Lab-UCSF/NexIS.

Reproduced under the paper's license (CC BY-NC), 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, pages, dates, 5 authors, 13 keywords, 12 MeSH terms, 7 funders, 52 references.

Cite

This paper

Teshome, S., Torok, J., Mezias, C., Gupta, A., & Raj, A. (2026). Selective vulnerability of monoaminergic neurons and network spread of alpha-synuclein jointly explain pathology progression in Parkinson's disease models. Neurobiology of disease, 224, 107403. https://doi.org/10.1016/j.nbd.2026.107403

BibTeX

@article{teshome2026selective,
author = {Teshome, Samuel and Torok, Justin and Mezias, Christopher and Gupta, Ajay and Raj, Ashish},
title = {{Selective vulnerability of monoaminergic neurons and network spread of alpha-synuclein jointly explain pathology progression in Parkinson's disease models}},
journal = {Neurobiology of disease},
year = {2026},
month = apr,
volume = {224},
pages = {107403},
publisher = {Elsevier BV},
issn = {0969-9961},
doi = {10.1016/j.nbd.2026.107403},
url = {https://doi.org/10.1016/j.nbd.2026.107403},
pmid = {42055257},
pmcid = {PMC13286620}
}

RIS

TY - JOUR
AU - Teshome, Samuel
AU - Torok, Justin
AU - Mezias, Christopher
AU - Gupta, Ajay
AU - Raj, Ashish
TI - Selective vulnerability of monoaminergic neurons and network spread of alpha-synuclein jointly explain pathology progression in Parkinson's disease models
T2 - Neurobiology of disease
J2 - Neurobiol Dis
PY - 2026
DA - 2026/04/27
VL - 224
SP - 107403
SN - 0969-9961
PB - Elsevier BV
DO - 10.1016/j.nbd.2026.107403
UR - https://doi.org/10.1016/j.nbd.2026.107403
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.nbd.2026.107403",
"type": "article-journal",
"title": "Selective vulnerability of monoaminergic neurons and network spread of alpha-synuclein jointly explain pathology progression in Parkinson's disease models",
"container-title": "Neurobiology of disease",
"author": [
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"family": "Teshome",
"given": "Samuel"
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{
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{
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"container-title-short": "Neurobiol Dis",
"volume": "224",
"page": "107403",
"DOI": "10.1016/j.nbd.2026.107403",
"PMID": "42055257",
"PMCID": "PMC13286620",
"ISSN": "0969-9961",
"publisher": "Elsevier BV",
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"language": "en",
"issued": {
"date-parts": [
[
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]
}
}

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