OSCR

Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology.

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 › Finite element models of electric currents › Assembled geometry and meshing ↔ scripts/sip_mesher_ds.py, lines 333–369 · score 0.92 · max edge length, minimum edge length, internal change rate, surface change rate, layer elements, max error
  2. [2] § METHODS › Cable models of nerve fibers ↔ src/neuron/MOD_Files/gaines_internode_FLUTSTIN.mod, lines 1–67 · score 0.55 · McIntyre, internodal, Grill, Richardson, mammalian, NEURON
  3. [3] § METHODS › Cable models of nerve fibers ↔ src/neuron/MOD_Files/gaines_internode_MYSA.mod, lines 1–67 · score 0.55 · McIntyre, internodal, Grill, Richardson, mammalian, NEURON
  4. [4] § METHODS › Cable models of nerve fibers ↔ src/neuron/run_controls.py, lines 49–177 · score 0.54 · end excitation, threshold searches, PyFibers, amplitudes, activation

Paper

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The authors' code

Python · 402 lines · 13 KB · GPL-2.0 · 1 match

  1. """Created on Tue Sep 7 16:44:25 2021.
  3. @author: dpm42
  4. """
  6. import json
  7. import math
  8. import os
  10. from scanip_api3 import *
  12. # temp
  15. # end temp
  16. sipconfig = App.GetInstance().GetInputValue()
  18. sipconfig = r'D:\work\threed\config\system/sipmeshconfig.json' if sipconfig == "" else sipconfig
  20. print(sipconfig)
  22. with open(sipconfig) as f:
  23. config = json.load(f)
  25. masks = ['i', 'p', 'n']
  27. matpriority = {
  28. 1: 'endoneurium',
  29. 2: 'perineurium',
  30. 3: 'epineurium',
  31. 4: "conductor",
  32. 5: "recess",
  33. 6: "insulator",
  34. 7: "fill",
  35. 8: "medium",
  36. }
  38. # %% import and generate masks
  39. doc = App.GetInstance().ImportStackOfImages(
  40. App.GetInstance().SearchForImages(config['n_imgs']),
  41. config['um_per_px'] / 1000,
  42. config['um_per_px'] / 1000,
  43. config['um_per_slice'] / 1000,
  44. CommonImportConstraints().SetWindowLevel(0.0, 0.0),
  45. )
  47. doc.ImportBackgroundFromStackOfImages(
  48. App.GetInstance().SearchForImages(config['p_imgs']),
  49. config['um_per_px'] / 1000,
  50. config['um_per_px'] / 1000,
  51. config['um_per_slice'] / 1000,
  52. CommonImportConstraints().SetWindowLevel(0.0, 0.0),
  53. )
  55. doc.ImportBackgroundFromStackOfImages(
  56. App.GetInstance().SearchForImages(config['i_imgs']),
  57. config['um_per_px'] / 1000,
  58. config['um_per_px'] / 1000,
  59. config['um_per_slice'] / 1000,
  60. CommonImportConstraints().SetWindowLevel(0.0, 0.0),
  61. )
  63. doc.ImportBackgroundFromStackOfImages(
  64. App.GetInstance().SearchForImages(config['pcap_imgs']),
  65. config['um_per_px'] / 1000,
  66. config['um_per_px'] / 1000,
  67. config['um_per_slice'] / 1000,
  68. CommonImportConstraints().SetWindowLevel(0.0, 0.0),
  69. )
  71. doc.GetBackgroundByName("Stack").SetName('n')
  73. doc.GetBackgroundByName("Stack (2)").SetName('p')
  75. doc.GetBackgroundByName("Stack (3)").SetName('i')
  77. doc.GetBackgroundByName("Stack (4)").SetName('pcap')
  79. # 2022-02-16 16:47:38 - Combine backgrounds
  80. doc.ReplaceBackgroundUsingCombineBackgroundsOperation(
  81. doc.GetBackgroundByName("p"), [doc.GetBackgroundByName("pcap")], doc.GetBackgroundByName("p"), Doc.Maximum
  82. )
  84. for mask in masks:
  85. doc.GetBackgroundByName(mask).Activate()
  87. App.GetDocument().CopyBackgroundToMask()
  89. # 2021-10-15 15:24:22 - Flip
  90. # doc.FlipData(Doc.AxisY) nope
  92. for position, mask in enumerate(masks):
  93. # 2021-10-15 15:25:38 - Mask activation
  94. doc.GetGenericMaskByName(mask).Activate()
  96. # 2021-10-15 15:25:38 - Movement of mask
  97. doc.MoveMaskTo(doc.GetActiveGenericMask(), len(masks) - position)
  99. # 2021-10-15 15:27:55 - Mask activation
  100. doc.GetGenericMaskByName("n").Activate()
  102. # 2021-10-15 15:27:56 - Island removal filter
  103. doc.ApplyIslandRemovalFilter(1000)
  105. # 2021-10-15 15:29:38 - Fill gaps
  106. doc.ApplyFillGaps(Doc.MostContactSurface, [doc.GetMaskByName("p"), doc.GetMaskByName("i")], True, 1000)
  108. # fix for peri too close to epi after end caps
  109. sep_nerve = config['sep_nerve']
  111. pixelval = math.floor(config['sep_nerve'] / config['um_per_px'])
  113. partialval = config['sep_nerve'] / config['um_per_px'] - pixelval
  115. # 2022-05-18 11:27:33 - Mask activation
  116. App.GetDocument().GetGenericMaskByName("p").Activate()
  118. # 2022-05-18 11:27:45 - Mask duplication
  119. App.GetDocument().GetActiveGenericMask().Duplicate()
  121. # 2022-05-18 11:27:46 - Mask activation
  122. App.GetDocument().GetGenericMaskByName("Copy of p").Activate()
  124. # 2022-05-18 11:28:22 - Morphological filter
  125. App.GetDocument().ApplyDilateFilter(Doc.TargetMask, pixelval, pixelval, pixelval, partialval)
  127. # 2022-05-18 11:31:02 - Voxel Boolean
  128. App.GetDocument().ReplaceMaskUsingBooleanExpression(
  129. "(n OR \"Copy of p\")",
  130. App.GetDocument().GetMaskByName("n"),
  131. App.GetDocument().GetSliceIndices(Doc.OrientationXY),
  132. Doc.OrientationXY,
  133. )
  135. # 2022-05-18 11:32:04 - Mask activation
  136. App.GetDocument().GetGenericMaskByName("Copy of p").Activate()
  138. # 2022-05-18 11:32:05 - Mask removal
  139. App.GetDocument().RemoveMask(App.GetDocument().GetMaskByName("Copy of p"))
  141. # Get the dimensions of the image volume
  142. dims = doc.GetDimensions()
  143. # Store the image-to-global transformation matrix
  144. i2g_mat = doc.GetImageToGlobalTransformationMatrix()
  145. # Define a temporary matrix defining the image-to-new-origin
  146. temp_mat = Matrix.FromTranslation(-dims.GetPhysicalSizeX() / 2, -dims.GetPhysicalSizeY() / 2, 0)
  147. # Temporarily set the image-to-global, generate and export the mesh
  148. doc.SetImageToGlobalTransformationMatrix(temp_mat)
  149. # 2021-09-10 12:57:40 - Import STL file
  151. path = config["stl_path"]
  152. with open(path + '/matmap.json') as f:
  153. matmap = json.load(f)
  155. matmap["i"] = "endoneurium"
  156. matmap["p"] = "perineurium"
  157. matmap["n"] = "epineurium"
  159. # set material priority
  160. mat_ind = len(matmap)
  161. for key in matpriority:
  162. for k, v in matmap.items():
  163. if v == matpriority[key]:
  164. matmap[k] = {"position": mat_ind, "material": v}
  165. mat_ind -= 1
  167. stl_files = [x for x in os.listdir(path) if x.endswith('.stl')]
  168. fromstl = [key for key in matmap.keys() if matmap[key]['material'] in ['fill', 'medium']]
  170. # get surfaces
  171. use_nastran = config["mesh"].get("use_nastran", False)
  172. if use_nastran:
  173. # 2023-05-11 12:12:16 - Import volume mesh file
  174. App.GetDocument().ImportVolumeMeshFromFile(path + "/alldomain.nas", 0.001, False)
  176. # 2023-05-11 12:12:24 - Generation of surface(s) from element set(s)
  177. App.GetDocument().CopyElementSetsToSurfaces(
  178. App.GetDocument().GetVolumeMeshByName("alldomain").GetElementSets(), True
  179. )
  181. # 2023-05-11 12:16:00 - Element set visibility modification
  182. App.GetDocument().ToggleElementSetVisibility(App.GetDocument().GetVolumeMeshByName("alldomain").GetElementSets())
  184. # 2023-05-11 12:17:00 - Volume mesh removal
  185. App.GetDocument().RemoveVolumeMesh(App.GetDocument().GetVolumeMeshByName("alldomain"))
  187. # get dommap
  188. with open(path + '/dommap.json') as f:
  189. dommap = json.load(f)
  191. # Rename elements to correct surface names
  192. for setname, setnums in dommap.items():
  193. for setnum in setnums:
  194. App.GetDocument().GetSurfaceByName(f"Element set {setnum} (from mesh)").SetName(setname)
  196. # 2023-05-15 10:48:28 - Surface object removal (removes medium)
  197. App.GetDocument().RemoveSurface(App.GetDocument().GetSurfaceByName("Element set 1 (from mesh)"))
  199. for name in fromstl:
  200. try:
  201. # 2023-05-15 10:48:28 - Surface object removal (removes fill)
  202. App.GetDocument().RemoveSurface(App.GetDocument().GetSurfaceByName(name))
  203. except:
  204. pass
  205. doc.ImportSurfaceFromStlFile(path + '/' + name + '.stl', True, 0.001, False)
  208. else:
  209. for file in stl_files:
  210. doc.ImportSurfaceFromStlFile(path + '/' + file, True, 0.001, False)
  213. # %% new fill code
  214. # 2021-12-11 13:30:06 - Pad
  215. doc.PadData(1000, 1000, 1000, 1000, 0, 0)
  217. m2s = [key for key in matmap.keys() if matmap[key]['material'] == 'fill']
  219. # 2021-12-11 13:30:20 - Generation of mask(s) from surface object(s)
  220. doc.CopySurfacesToMasks([doc.GetSurfaceByName(name) for name in m2s], doc.AccurateManifold, False)
  222. doc.ShrinkWrapData(Doc.TargetAllMasks, 10, 10, 10, 10, 10, 10)
  224. # 2021-12-11 13:30:20 - Generation of mask(s) from surface object(s)
  225. doc.RemoveSurfaces([doc.GetSurfaceByName(name) for name in m2s], False)
  227. for name in m2s:
  228. doc.GetGenericMaskByName(name + " (from surface)").SetName(name)
  229. # %% end fill code
  230. # %% begin remesh code
  232. # 2022-05-26 10:01:04 - Remesh surface object
  233. doc.GetSurfaceByName("medium").Remesh(0.5)
  235. surfaces = [os.path.splitext(x)[0] for x in stl_files if os.path.splitext(x)[0] not in m2s]
  237. remsurfs = [s for s in surfaces if s != "medium"]
  239. print(remsurfs)
  241. # 2022-06-09 17:10:51 - Surface model creation
  242. remesh_model = App.GetDocument().CreateSurfaceModel("SurfModel")
  244. # 2022-06-09 17:10:58 - Objects mode activation
  245. App.GetDocument().EnableObjectsMode()
  247. # 2022-06-09 17:11:02 - Part creation
  248. remesh_model.AddSurfaces([App.GetDocument().GetSurfaceByName(x) for x in remsurfs])
  250. # 2022-06-09 17:11:03 - Models mode activation
  251. App.GetDocument().EnableModelsMode()
  253. # 2022-06-09 17:11:27 - Model activation
  254. App.GetDocument().SetActiveModel(remesh_model)
  256. # 2022-06-09 17:11:42 - Model configuration modification
  257. remesh_model.SetExportType(Model.StlFeCfdCad)
  259. # 2022-06-09 17:12:12 - Surface generation
  260. App.GetDocument().GenerateMesh()
  262. # 2022-06-09 17:15:06 - Conversion of meshed part(s) to surface object(s)
  263. remesh_model.CreateSurfacesFromParts([remesh_model.GetPartByName(x) for x in remsurfs])
  265. # 2022-06-09 17:15:09 - Objects mode activation
  266. App.GetDocument().EnableObjectsMode()
  267. for surfname in remsurfs:
  268. # 2022-06-09 17:15:21 - Rename surface object
  269. App.GetDocument().GetSurfaceByName(surfname).SetName(surfname + "_imported")
  271. # 2022-06-09 17:15:27 - Rename surface object
  272. App.GetDocument().GetSurfaceByName(surfname + " (from mesh)").SetName(surfname)
  274. # %% end remesh code
  275. # 2021-10-15 15:35:29 - FE model creation
  276. femod = doc.CreateFeModel("Model 1")
  278. # 2021-10-15 15:35:35 - Objects mode activation
  279. doc.EnableObjectsMode()
  281. # 2021-10-15 15:35:38 - Part creation
  282. doc.GetModelByName("Model 1").AddMasks(
  283. [doc.GetGenericMaskByName("n"), doc.GetGenericMaskByName("p"), doc.GetGenericMaskByName("i")]
  284. )
  287. # 2021-12-11 13:48:26 - Part creation
  288. doc.GetModelByName("Model 1").AddMasks([doc.GetGenericMaskByName(name) for name in m2s])
  290. # 2021-10-15 15:35:38 - Models mode activation
  291. doc.EnableModelsMode()
  293. # 2021-10-15 15:35:40 - Objects mode activation
  294. doc.EnableObjectsMode()
  296. # 2021-10-15 15:35:49 - Part creation
  297. femod.AddSurfaces([doc.GetSurfaceByName(surf) for surf in surfaces])
  299. # 2021-10-15 15:35:49 - Models mode activation
  300. doc.EnableModelsMode()
  302. # 2021-10-15 15:36:27 - Model configuration modification
  303. femod.SetUseSmartMaskSmoothing(True)
  304. # 2022-07-15 17:47:53 - Model configuration modification
  306. femod.SetSnapSurfacePartsToModelBounds(True)
  308. # 2022-07-15 17:47:58 - Model configuration modification\
  309. femod.SetNumSmartMaskSmoothingIterations(100)
  311. # 2021-10-15 15:36:30 - Model configuration modification
  312. femod.SetExportUnits(Model.MicronsUnits)
  314. # 2021-10-15 15:37:45 - Contact to boundary creation
  315. femod.AddSurfaceContact(femod.GetPartByName("i"), femod.GetPartByName("medium"))
  317. # 2021-10-15 15:38:14 - Model configuration modification
  318. femod.SetExportType(Model.ComsolNasVolume)
  320. # temp block
  321. # 2022-07-15 17:57:09 - Model configuration modification
  322. femod.SetUseSmallestElementImprovement(True)
  323. # 2022-07-15 18:50:44 - Model configuration modification
  324. femod.SetAdditionalMeshQualityImprovementMaximumOffSurfaceDistance(0.01)
  325. # 2022-07-15 17:57:29 - Model configuration modification
  326. femod.SetSmallestElementImprovementCharacteristicLengthTarget(config['mesh']["max_edge_length"])
  327. # 2022-07-15 17:57:31 - Model configuration modification
  328. femod.SetUseLimitMaximumDisplacement(True)
  329. # 2022-07-15 17:57:37 - Model configuration modification
  330. femod.SetMaximumDisplacementRatio(0.2)
  331. # end temp block
  333. for ob, ob_info in matmap.items():
  334. # 2021-10-15 15:38:45 - Model configuration modification
  335. femod.SetEditAdvancedParametersManuallyOnPart(femod.GetPartByName(ob), True)
  337. if ob_info["material"] == "conductor":
  338. # 2021-10-15 15:38:52 - Model configuration modification
  339. femod.SetTargetMinimumEdgeLengthOnPart(femod.GetPartByName(ob), 0.0005)
  340. else:
  341. # 2021-10-15 15:38:52 - Model configuration modification
  342. femod.SetTargetMinimumEdgeLengthOnPart(femod.GetPartByName(ob), config['mesh']['min_edge_length'])
  344. # 2021-10-15 15:38:55 - Model configuration modification`
  345. femod.SetTargetMaximumErrorOnPart(femod.GetPartByName(ob), config['mesh']['max_error'])
  347. # 2021-10-15 15:39:00 - Model configuration modification
  348. if ob_info["material"] == "conductor":
  349. femod.SetMaximumEdgeLengthOnPart(femod.GetPartByName(ob), 0.01)
  350. else:
  351. femod.SetMaximumEdgeLengthOnPart(femod.GetPartByName(ob), config['mesh']["max_edge_length"])
  353. # 2021-10-15 15:39:13 - Model configuration modification
  354. femod.SetInternalChangeRateOnPart(femod.GetPartByName(ob), config['mesh']["internal_change_rate"])
  356. femod.SetSurfaceChangeRateOnPart(doc.GetActiveModel().GetPartByName(ob), config['mesh']["surface_change_rate"])
  358. femod.SetTargetNumberElementsAcrossLayerOnPart(
  359. doc.GetActiveModel().GetPartByName(ob), config['mesh']["n_layer_elements"]
  360. )
  362. femod.SetSmoothAgainstBackground(config['mesh']["smooth_against_background"])
  364. try:
  365. femod.GetPartsContainer().GetPartByName(ob).MoveTo(ob_info["position"])
  366. except Exception:
  367. pass
  369. femod.GetPartByName(ob).SetMaterial(PlaceholderMaterial(ob_info["material"]))
  371. # 2022-04-26 16:26:12 - Model configuration modification
  372. if config['mesh'].get('second_order') is True:
  373. femod.SetHigherOrder(True)
  374. femod.SetCurvedEdges(True)
  375. else:
  376. print('Warning: Using first order elements')
  378. # required twice
  379. for ob, ob_info in matmap.items():
  380. try:
  381. femod.GetPartsContainer().GetPartByName(ob).MoveTo(ob_info["position"])
  382. except Exception:
  383. pass
  385. doc.GetGenericMaskByName("p").Activate()
  387. # 2022-06-13 12:32:23 - Morphological filter
  388. App.GetDocument().ApplyOpenFilter(Doc.TargetMask, 2, 2, 0, 0.0)
  390. # 2021-11-02 12:40:33 - Isolated cavity and island removal
  391. doc.RemoveIsolatedCavitiesAndIslands([doc.GetMaskByName("n"), doc.GetMaskByName("p"), doc.GetMaskByName("i")], 101, 101)
  393. doc.ShrinkWrapData(Doc.TargetAllMasks, 10, 10, 10, 10, 10, 10)
  395. doc.SaveAs(config['outpath'] + '/mesh_debug.sip')
  396. if config["run_type"] == "cluster":
  397. # 2021-10-26 14:00:20 - Project save
  398. doc.GenerateMesh()
  399. # 2021-12-17 08:44:39 - COMSOL export
  400. doc.ExportComsolNasVolume(config["outpath"] + '/mesh.nas', False)
  401. # 2021-10-26 14:00:20 - Project save
  402. doc.SaveAs(config['outpath'] + '/mesh.sip')

sip_mesher_ds.py at commit 3417be7, under GPL-2.0 · at the source

Overview

Authors: Daniel P. Marshall1, Aniruddha R. Upadhye2, Ozge N. Buyukcelik2, Andrew J. Shoffstall2, Warren M. Grill1,3,4,5, Nicole A. Pelot1
ORCID iDs: Daniel P. Marshall, Aniruddha R. Upadhye, Ozge N. Buyukcelik, Andrew J. Shoffstall, Warren M. Grill, Nicole A. Pelot
  1. Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
  2. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
  3. Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27708, USA
  4. Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina 27708, USA
  5. Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina 27708, USA
Institutions: Duke University (United States); Case Western Reserve University (United States); Duke Medical Center (United States)
Journal: APL bioengineering, volume 10, issue 1, article 016112
Dates: received 22 October 2025; accepted 22 January 2026; published online 2 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1063/5.0308450 · PMID 41782809 · PMCID PMC12956375 · OpenAlex W7133183935
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), computational (subfield)
Methods: Connectivity, Statistics, fMRI & imaging
Topic: Vagus Nerve Stimulation Research (Neurology, Neuroscience), according to OpenAlex
Funding: NIH Office of the Director 10.13039/100000052 (OT2 OD025340, 75N98022C00018)
Citations: cited by 6 papers (Europe PMC); 72 references in the paper

Abstract

Implanted vagus nerve stimulation is FDA-approved to treat epilepsy, depression, and stroke sequelae and is under development for other disorders such as heart failure and rheumatoid arthritis. Anatomically realistic computational models enable the design of electrodes and stimulation parameters that activate nerve fibers that mediate therapeutic responses, and avoid activating fibers that cause side effects. Conventional modeling techniques assume constant longitudinal morphology, extruding a single cross section to define the three-dimensional nerve geometry. However, recent imaging data showed that human vagus nerves have extensive fascicle splitting and merging along their length. Therefore, we developed a pipeline to simulate true three-dimensional (true-3D) models of peripheral nerve stimulation from segmentations of micro-computed tomography imaging. We implemented models of n = 4 human vagus nerves and systematically evaluated extrusion vs true-3D model responses to electrical stimulation across population dose-response relationships, fiber-specific thresholds, recruitment order, and spatial selectivity. Despite the complex morphology of the human vagus nerve, extrusion models replicated the true-3D neural responses if: (1) the nerve morphology was deformed to a circular cross section, as occurs with chronic cuff implants, and (2) the extruded cross section was centered under the depolarizing electrode contact. Our pipeline provides a foundation for advanced modeling of peripheral nerve stimulation and the design of more selective stimulation therapies.

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.

wmglab-duke/3D_nerve_pipeline

License: GPL-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 3417be7b0869886e4939e8635230944c6d3db1d7, 19 March 2026
Languages: Python (80), Java (15), NEURON (2), Shell (1)
Size: 346 files, 98 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY”
Holds: README, license file, environment (pyproject.toml, requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (32 files), Matplotlib (30 files), seaborn (12 files), pandas (10 files), SciPy (10 files), OpenCV (7 files), NEURON (2 files), Pillow (2 files), scikit-image (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
100 files
At the source: github.com/wmglab-duke/3D_nerve_pipeline

Zenodo 8298703

License: GPL-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (84 files), NumPy (23 files), Matplotlib (22 files), pandas (6 files), SciPy (6 files), seaborn (6 files), OpenCV (5 files), scikit-image (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
150 files
At the source:

Zenodo 18475038

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source:

wmglab-duke/ascent

License: GPL-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2e513cd06e9f986ea3b2d56288ac6b9e362c8706, 18 August 2026
Languages: Python (68), Java (6), NEURON (2), Shell (1)
Size: 418 files, 77 scripts
Software Heritage: archived
Found in: the Zenodo archive record
Holds: README, license file, environment (pyproject.toml, requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (27 files), Matplotlib (26 files), seaborn (9 files), pandas (8 files), SciPy (7 files), OpenCV (5 files), NEURON (3 files), scikit-image (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
79 files
At the source: github.com/wmglab-duke/ascent/tree/v1.2.2

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 323 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

No dataset and no data link were found in the paper.

Data availability

The computational pipeline code that underlies the findings of this study is openly available from GitHub at https://github.com/wmglab-duke/3D_nerve_pipeline; and Zenodo at Ref. 71. The data and plotting code that support the findings of this study are openly available from SPARC.science (https://doi.org/10.26275/u5yy-gi6w) at Ref. 72.

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

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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, 6 authors, 1 funder, 69 references.

Cite

This paper

Marshall, D. P., Upadhye, A. R., Buyukcelik, O. N., Shoffstall, A. J., Grill, W. M., & Pelot, N. A. (2026). Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology. APL bioengineering, 10(1), 016112. https://doi.org/10.1063/5.0308450

BibTeX

@article{marshall2026computational,
author = {Marshall, Daniel P. and Upadhye, Aniruddha R. and Buyukcelik, Ozge N. and Shoffstall, Andrew J. and Grill, Warren M. and Pelot, Nicole A.},
title = {{Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology}},
journal = {APL bioengineering},
year = {2026},
month = mar,
volume = {10},
number = {1},
pages = {016112},
publisher = {American Institute of Physics},
issn = {2473-2877},
doi = {10.1063/5.0308450},
url = {https://doi.org/10.1063/5.0308450},
pmid = {41782809},
pmcid = {PMC12956375}
}

RIS

TY - JOUR
AU - Marshall, Daniel P.
AU - Upadhye, Aniruddha R.
AU - Buyukcelik, Ozge N.
AU - Shoffstall, Andrew J.
AU - Grill, Warren M.
AU - Pelot, Nicole A.
TI - Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology
T2 - APL bioengineering
J2 - APL Bioeng
PY - 2026
DA - 2026/03/02
VL - 10
IS - 1
SP - 016112
SN - 2473-2877
PB - American Institute of Physics
DO - 10.1063/5.0308450
UR - https://doi.org/10.1063/5.0308450
LA - en
ER -

CSL-JSON

{
"id": "10.1063/5.0308450",
"type": "article-journal",
"title": "Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology",
"container-title": "APL bioengineering",
"author": [
{
"family": "Marshall",
"given": "Daniel P."
},
{
"family": "Upadhye",
"given": "Aniruddha R."
},
{
"family": "Buyukcelik",
"given": "Ozge N."
},
{
"family": "Shoffstall",
"given": "Andrew J."
},
{
"family": "Grill",
"given": "Warren M."
},
{
"family": "Pelot",
"given": "Nicole A."
}
],
"container-title-short": "APL Bioeng",
"volume": "10",
"issue": "1",
"page": "016112",
"DOI": "10.1063/5.0308450",
"PMID": "41782809",
"PMCID": "PMC12956375",
"ISSN": "2473-2877",
"publisher": "American Institute of Physics",
"URL": "https://doi.org/10.1063/5.0308450",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
2
]
]
}
}

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