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Mechanistically driven transnidal hemodynamic manipulations enhance simulated endovascular transvenous treatments for brain AVMs.

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
  1. [1] § Methods › AVM electrical network model ↔ pseudocode.py, lines 30–93 · score 0.77 · Euclidean distance, adjacent columns, closest, horizontal, nearest, unconnected
  2. [2] § Methods › Hemodynamic simulations ↔ pseudocode.py, lines 96–143 · score 0.74 · external pressure sources, steady state, Kirchhoff, loop, EMF, laws
  3. [3] § Methods › AVM electrical network model ↔ pseudocode.py, lines 30–93 · score 0.68 · plexiform vessels, AVM nidus, fistulous, nidus architectures, interconnected, variability
  4. [4] § Methods › AVM hemodynamics ↔ pseudocode.py, lines 96–143 · score 0.60 · Hagen Poiseuille, blood flow, law, resistance, network, vasculature
  5. [5] § Methods › Simulations of TRENSH and its variations › TRENSH at different systemic arterial hypotension levels and CVPs ↔ pseudocode.py, lines 146–177 · score 0.54 · post injection, directed graph, baseline, filled, physiologically, intranidal

Paper

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The authors' code

Python · 177 lines · 6.9 KB · no license · 5 matches

  1. """This file contains Python-style pseudocode for the brain arteriovenous malformation simulation. It is not meant to be compiled/run."""
  3. def main():
  5. for _ in range(NUM_NIDI):
  7. # Generate nidus architecture
  8. compartments = random_normal_int(3, 6)
  9. columns = random_normal_int(3, 7)
  10. nidus = create_nidus(compartments, columns)
  12. # Define feeders and drainers
  13. feeders = get_feeders(nidus)
  15. # For each occlusion scenario
  16. for occluded_feeder in [None] + feeders:
  18. nidus = apply_occlusion(nidus, occluded_feeder)
  20. # Simulate all injection scenarios
  21. for scenario in INJECTION_SCENARIOS:
  23. flows, pressures, graph = simulate(nidus, scenario)
  25. # Evaluate physiological outcomes
  26. stats = get_stats(graph)
  27. export(stats)
  30. def create_nidus(compartments: int, columns: int) -> Graph:
  31. """Generates a synthetic AVM nidus as a vascular graph.
  33. The generated architecture consists of interconnected compartments and columns
  34. of vessels, including plexiform and a central fistulous path. Intercompartmental
  35. vessels are also added to mimic anatomical variability.
  37. Args:
  38. compartments: Number of horizontal vascular compartments in the nidus.
  39. columns: Number of vertical columns (proxy for spatial progression from feeder to drainer).
  41. Returns:
  42. An undirected graph representing the AVM nidus architecture.
  43. """
  44. # Initialize an empty undirected graph with predefined feeders and drainers
  45. nidus = initialize_graph()
  47. # Create nodes in a grid of [columns × compartments]
  48. layout = create_node_layout(compartments, columns)
  50. # Add intranidal plexiform vessels between adjacent columns within each compartment
  51. for c in range(columns - 1):
  52. for k in range(compartments):
  53. A = layout[c][k] # Nodes in compartment k, column c
  54. B = layout[c + 1][k] # Nodes in same compartment, next column
  55. randomly_pair_nodes(nidus, A, B, vessel_type="plexiform")
  57. # Connect each leftmost node to its nearest arterial feeder (by Euclidean distance)
  58. for node in get_leftmost_nodes(layout):
  59. feeder = get_closest_feeder(node)
  60. add_edge(nidus, feeder, node, vessel_type="plexiform")
  62. # Connect each rightmost node to its nearest draining vein
  63. for node in get_rightmost_nodes(layout):
  64. drainer = get_closest_drainer(node)
  65. add_edge(nidus, node, drainer, vessel_type="plexiform")
  67. # Add any unconnected feeder or drainer to its closest nidus node
  68. for feeder in remaining_unconnected_feeders():
  69. nearest = get_closest_nidus_node(feeder)
  70. add_edge(nidus, feeder, nearest, vessel_type="plexiform")
  72. for drainer in remaining_unconnected_drainers():
  73. nearest = get_closest_nidus_node(drainer)
  74. add_edge(nidus, nearest, drainer, vessel_type="plexiform")
  76. # Add intercompartmental vessels
  77. for _ in range(2 * compartments * columns):
  78. src_col, src_comp = random_column_and_compartment()
  79. target_col = sample_nearby_column(src_col)
  80. target_comp = choose_different_compartment(src_comp)
  81. src_node = random.choice(layout[src_col][src_comp])
  82. target_node = random.choice(layout[target_col][target_comp])
  83. add_edge(nidus, src_node, target_node, vessel_type="plexiform")
  85. # Add a continuous intranidal fistulous path from AF2 to DV2 through the center compartment
  86. mid_comp = compartments // 2
  87. path_nodes = [choose_node(layout[c][mid_comp]) for c in range(columns)]
  88. add_edge(nidus, "AF2", path_nodes[0], vessel_type="fistulous")
  89. for i in range(len(path_nodes) - 1):
  90. add_edge(nidus, path_nodes[i], path_nodes[i + 1], vessel_type="fistulous")
  91. add_edge(nidus, path_nodes[-1], "DV2", vessel_type="fistulous")
  93. return nidus
  96. def simulate(nidus: Graph, scenario: Scenario) -> tuple[list[float], list[float], Graph]:
  97. """Solves for vessel flow and pressure under a given set of EMF inputs.
  99. Builds a system of linear equations based on Kirchhoff’s laws to model steady-state
  100. blood flow through the vascular network, then computes pressure gradients using
  101. Hagen-Poiseuille’s law. Returns a directed graph with physiological flow directions.
  103. Args:
  104. nidus: The vascular network to simulate, represented as a graph.
  105. scenario: A physiological condition defining EMFs and boundary pressures.
  107. Returns:
  108. flows: A list of computed flow values for each vessel (mL/min).
  109. pressures: Corresponding pressure drops across each vessel (mmHg).
  110. graph: A directed version of the input graph with flow and pressure attributes.
  111. """
  112. # Get list of all vessels as directed edges
  113. vessels = get_all_vessels(nidus)
  115. # Get external pressure sources (EMFs) for this scenario
  116. pressure_inputs = scenario.get_external_pressures()
  118. # Initialize a system of linear equations to solve for flow in each vessel
  119. equations = []
  121. # Add one equation per node - conservation of flow
  122. # ∑ Q_in - ∑ Q_out = 0
  123. for node in nidus.nodes:
  124. equations.append(flow_conservation_equation(node, vessels))
  126. # Add one equation per loop (cycle) - conservation of pressure
  127. # ∑ R_j Q_j = ∑ EMF_i
  128. for cycle in get_cycle_basis(nidus):
  129. equations.append(pressure_loop_equation(cycle, pressure_inputs))
  131. # Solve the linear system for flows
  132. flows = solve_linear_system(equations)
  134. # Compute pressure drop in each vessel: ΔP = R × Q
  135. pressures = []
  136. for vessel, flow in zip(vessels, flows):
  137. resistance = nidus.get_resistance(vessel)
  138. pressures.append(resistance * flow)
  140. # Build a directed graph with flow and pressure attributes
  141. graph = create_flow_graph(nidus, vessels, flows, pressures)
  143. return flows, pressures, graph
  146. def get_stats(graph: Graph) -> dict:
  147. """Extracts physiological and structural metrics from a flow simulation.
  149. Aggregates flow, pressure, and anatomical statistics across different vessel types.
  150. If an injection was performed, calculates the extent of intranidal filling.
  152. Args:
  153. graph: Directed graph output from `simulate()` with flow and pressure attributes.
  155. Returns:
  156. A dictionary of physiological and architectural metrics.
  157. """
  159. stats = dict()
  161. # Tally flow and pressure stats for each edge
  162. for edge in graph.edges():
  163. update_stats(stats, edge)
  165. # Estimate rupture risk using log-pressure scaling
  166. # risk = log(P / Pmin) / log(Pmax / Pmin)
  167. add_rupture_risk_to_stats(stats, graph)
  169. # Forward BFS from injection location within nidus
  170. reached_nodes = bfs(graph, start_from=graph.get_injection_location())
  172. # Post-injection analysis using flow directions from baseline graph
  173. reached_nodes = bfs(graph.without_injection(), start_from=reached_nodes)
  175. stats["filling"] = reached_nodes / graph.get_num_intranidal_vessels()
  177. return stats

pseudocode.py at commit 1717109, no license · at the source

Overview

Authors: Tarik F Massoud1,2, Bryce C Vu3, Kellen Vo Vu4, Jeremy J Heit1,5, Siddhant Suri Dhawan6
ORCID iDs: Tarik F Massoud, Bryce C Vu, Kellen Vo Vu, Jeremy J Heit, Siddhant Suri Dhawan
  1. Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University School of Medicine, Stanford, CA USA
  2. Division of Interventional Neuroradiology, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
  3. Arizona State University, Tempe, AZ USA
  4. MD Program, Weill Cornell Medicine, New York, NY USA
  5. Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA USA
  6. Department of Bioengineering, Stanford University Schools of Engineering and Medicine, Stanford, CA USA
Institutions: University of California, Los Angeles (United States); Stanford Medicine (United States); Stanford University (United States); Arizona State University (United States); Weill Cornell Medicine (United States)
Journal: Communications medicine, volume 6, issue 1, article 312
Dates: received 20 May 2025; accepted 12 March 2026; published online 3 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s43856-026-01555-0 · PMID 41927722 · PMCID PMC13216285 · OpenAlex W7148505863
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), stroke (population)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: Blood flow, Stroke
Topic: Vascular Malformations Diagnosis and Treatment (Neurology, Medicine), according to OpenAlex
Funding: National Institutes of Health (1r01hl052352-01a1)
Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Background: Treatment strategies for plexiform brain arteriovenous malformations (bAVMs) have evolved to include minimally invasive transvenous embolization. Since its conceptualization as “transvenous retrograde nidus sclerotherapy under controlled hypotension” (TRENSH), this approach has shown curative potential for highly selected small bAVMs when using adhesive embolic agents. Further innovation is required to extend safety and efficacy to larger, more complex bAVMs. We conceived and evaluated a set of theoretical hemodynamic manipulations within the venous outflow of bAVMs designed to augment nidus retropermeation during simulated TRENSH.

Methods: We used two complementary experimental platforms. First, we developed a computational bAVM model to simulate hemodynamic variations during TRENSH, including: (1) degrees of controlled arterial hypotension; (2) effects of temporary balloon occlusion of arterial feeders; (3) differing draining vein (DV) retrograde injection pressures; (4) use of alternative DVs for retroinjection; (5) elevation of central venous pressure (CVP) during injection; and (6) cardiac-cycle–synchronized diastolic retroinjection. Second, we used a carotid–jugular fistula rete mirabile AVM model in six pigs to evaluate combinations of induced hypotension and venous hypertension, simulating raised CVP to enhance retropermeation during TRENSH-like maneuvers.

Results: Here we show that CVP elevation, retrograde injection through dominant DVs, maximally safe transvenous injection pressures, and a distinct strategy of synchronized diastolic-phase DV retroinjection each increase nidus retropermeation in experimental simulations.

Conclusions: These theoretical TRENSH-derived venous manipulation strategies may offer adjunctive benefit for future transvenous treatment of large bAVMs. The findings provide a conceptual foundation for further validation studies to determine their translational feasibility and potential incorporation into advanced clinical TRENSH paradigms.

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 5 matches between paragraphs and lines of code.

kellenvu/massoud-avm

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 171710945771b041c2eeebc36a92553615908510, 6 April 2025
Languages: Python (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
At the source: github.com/kellenvu/massoud-avm

Code availability

The analysis code for algorithms of theoretical AVM model simulations is available at: https://github.com/kellenvu/massoud-avm.

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;
  • 1 script, 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

Datasets cited

Data availability

The computational model datasets needed to reproduce the simulation figures are available in Figshare (10.6084/m9.figshare.29143385). The raw outputs from each individual computational simulation are available from the corresponding author upon reasonable request. The data for the pig AVM model simulations are as presented in the “Results” section and the Supplementary Information.

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 2, 28 September 2026

  • Funding: added National Institutes of Health: 1r01hl052352-01a1

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 57 references.

Cite

This paper

Massoud, T. F., Vu, B. C., Vu, K. V., Heit, J. J., & Dhawan, S. S. (2026). Mechanistically driven transnidal hemodynamic manipulations enhance simulated endovascular transvenous treatments for brain AVMs. Communications medicine, 6(1), 312. https://doi.org/10.1038/s43856-026-01555-0

BibTeX

@article{massoud2026mechanistically,
author = {Massoud, Tarik F and Vu, Bryce C and Vu, Kellen Vo and Heit, Jeremy J and Dhawan, Siddhant Suri},
title = {{Mechanistically driven transnidal hemodynamic manipulations enhance simulated endovascular transvenous treatments for brain AVMs}},
journal = {Communications medicine},
year = {2026},
month = apr,
volume = {6},
number = {1},
pages = {312},
publisher = {Nature Publishing Group},
issn = {2730-664X},
doi = {10.1038/s43856-026-01555-0},
url = {https://doi.org/10.1038/s43856-026-01555-0},
pmid = {41927722},
pmcid = {PMC13216285}
}

RIS

TY - JOUR
AU - Massoud, Tarik F
AU - Vu, Bryce C
AU - Vu, Kellen Vo
AU - Heit, Jeremy J
AU - Dhawan, Siddhant Suri
TI - Mechanistically driven transnidal hemodynamic manipulations enhance simulated endovascular transvenous treatments for brain AVMs
T2 - Communications medicine
J2 - Commun Med (Lond)
PY - 2026
DA - 2026/04/03
VL - 6
IS - 1
SP - 312
SN - 2730-664X
PB - Nature Publishing Group
DO - 10.1038/s43856-026-01555-0
UR - https://doi.org/10.1038/s43856-026-01555-0
LA - en
ER -

CSL-JSON

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