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Effects of repetitive scanning ultrasound on the intraneuronal dendritic signalling of CA1 pyramidal neurons.

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Paper

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The authors' code

NEURON hoc · 420 lines · 7.8 KB · no license

  1. load_file ("K01.hoc") //MEGVÁLTOZTANI //sejt behívása, topológia.hoc
  2. load_file ("nrngui.hoc") //neuron graphical interface meghívása
  4. //fizikális paraméterek
  6. Rms=2590 //MEGVÁLTOZTANI //szóma membrán felületegységenkétni ellenállás
  7. Rmd=Rms //dendrit membrán felületegységenkétni ellenállás
  9. kapacitas=20
  10. axres=200.0 //MEGVÁLTOZTANI //axiális ellenállás vagyis az Ra
  11. restmemb=0 //membrán ellenállás vagyis az e_pas
  12. basalspinefelsz=0.79 //MEGVÁLTOZTANI
  13. apicalspinefelsz=1.38 //MEGVÁLTOZTANI
  14. basalspinesuruseg=1.057 //MEGVÁLTOZTANI /mikrometer
  15. apicalspinesuruseg=1.551 //MEGVÁLTOZTANI /mikrometer
  16. nrsecdend=21 //MEGVÁLTOZTANI, topológia.hoc-ból kinézni és kivonni 1-et //bazális dendritek száma
  17. nrsecapic=40 //MEGVÁLTOZTANI, topológia.hoc-ból kinézni és kivonni 1-et //apikális dendrtiek száma
  18. wopen("K01_DELAY.dat") //MEGVÁLTOZTANI
  19. darabolas=14 //kiszámolni
  20. Rinput=106.94204 //MEGVÁLTOZTANI
  24. //szóma fizikális paraméterei
  26. access soma
  28. insert pas
  29. soma g_pas = 1/Rms
  30. soma e_pas = restmemb
  32. soma nseg = 1 //MEGVÁLTOZTANI
  33. soma diam = 5.671 //MEGVÁLTOZTANI
  34. soma L = 5.671 //MEGVÁLTOZTANI
  35. soma Ra = axres
  36. soma cm = kapacitas
  40. // bazális és apikális dendritek fizikai paraméterei
  43. for i = 0, nrsecdend {
  44. access dend[i]
  45. insert pas
  46. dend[i] g_pas = 1/Rmd
  47. dend[i] e_pas = restmemb
  48. dend[i] Ra = axres
  49. dend[i] cm = kapacitas
  50. }
  52. for i = 0, nrsecapic {
  53. access apic[i]
  54. insert pas
  55. apic[i] g_pas = 1/Rmd
  56. apic[i] e_pas = restmemb
  57. apic[i] Ra = axres
  58. apic[i] cm = kapacitas
  59. }
  62. v_init = 0
  64. finitialize (v_init) //fizikai paraméterek ekkor állítódnak be
  67. access soma
  69. objectvar stim1
  71. access soma
  72. distance()
  74. exp1=0
  75. exp2=0
  77. dt=0.025
  79. exp1i_dend=0
  80. exp2i_dend=0
  81. exp1v_dend=0
  82. exp2v_dend=0
  84. exp1i_soma=0
  85. exp2i_soma=0
  86. exp1v_soma=0
  87. exp2v_soma=0
  89. TD=0
  90. LD=0
  91. PD=0
  93. tv_dend=0
  94. tv_soma=0
  95. ti_dend=0
  96. ti_soma=0
  98. somai=0
  99. dendi=0
  101. v_init = 0
  103. finitialize (v_init)
  105. fprint ("\n")
  107. i=0
  109. osszesspine=0
  111. plusz=-1 //ez a változó számolja az injektálásokat, de mivel elõször a spineok felrakása történik meg az értéke nem fog változni, de értéket kell neki adni mivel kiiratásra kerül
  115. // bazális dendritekre spine-ok felhelyezése
  119. for a = 0, nrsecdend {
  120. access dend[a]
  122. SNSZUMM=0
  124. SNSZUMM=dend[a].L*basalspinesuruseg //hány darab spine van az adott dendrit sectionön
  126. osszesspine=osszesspine+SNSZUMM
  128. F=(3.14*dend[a].L*dend[a].diam+SNSZUMM*basalspinefelsz)/(3.14*dend[a].L*dend[a].diam) //F paraméter meghatározása
  130. dend[a].cm=dend[a].cm*F //kapacitás változásai
  132. dend[a].g_pas=dend[a].g_pas*F //Rmd változásai
  134. }
  137. for a = 0, nrsecapic {
  138. access apic[a]
  140. SNSZUMM=0
  142. SNSZUMM=apic[a].L*apicalspinesuruseg //hány darab spine van az adott dendrit sectionön
  144. osszesspine=osszesspine+SNSZUMM
  146. F=(3.14*apic[a].L*apic[a].diam+SNSZUMM*apicalspinefelsz)/(3.14*apic[a].L*apic[a].diam) //F paraméter meghatározása
  148. apic[a].cm=apic[a].cm*F //kapacitás változásai
  150. apic[a].g_pas=apic[a].g_pas*F //Rmd változásai
  152. }
  154. plusz=-1
  156. for a = 0, nrsecdend {
  157. access dend[a]
  158. parts=1
  159. abc=0
  160. darabok=0
  162. if (dend[a].L>darabolas) {
  164. parts=int (dend[a].L/darabolas)+1
  165. print (parts)
  166. darabok = (parts % 2)
  167. if (darabok==0) {
  168. dend[a] nseg=parts+1
  169. }
  170. if (darabok!=0) {
  171. dend[a] nseg=parts+2
  172. }
  173. }
  175. access soma
  176. distance()
  178. v_init = 0
  179. finitialize (v_init)
  181. for q = 1,parts {
  182. access dend[a]
  183. v_init = 0
  184. finitialize (v_init)
  185. plusz=plusz+1
  187. darabolashelye=0 // darabolás helyét jelölõ változó, ami a parts aktuális értéke szerint kap értéket
  189. if (parts==1) {
  190. darabolashelye=0.5
  191. }
  192. if (parts!=1) {
  193. darabolashelye=q*1/parts
  194. }
  196. objectvar stim1
  197. dend[a] stim1 = new IClamp(darabolashelye)
  198. stim1.del = 0
  199. stim1.dur = 0.3
  200. stim1.amp = 1
  202. exp1i_dend=0
  203. exp2i_dend=0
  204. exp1v_dend=0
  205. exp2v_dend=0
  207. exp1i_soma=0
  208. exp2i_soma=0
  209. exp1v_soma=0
  210. exp2v_soma=0
  212. TD=0
  213. LD=0
  214. PD=0
  216. tv_dend=0
  217. tv_soma=0
  218. ti_dend=0
  219. ti_soma=0
  221. somai=0
  222. dendi=0
  224. i=0
  226. while (i<400) {
  227. access dend[a]
  228. if (i<=0.3) {
  229. somai=1
  230. dendi=1
  231. } else {
  232. somai=0
  233. dendi=0
  234. }
  235. exp1i_dend=exp1i_dend+i*(dendi)
  236. exp2i_dend=exp2i_dend+dendi
  238. exp1v_dend=exp1v_dend+i*(dend[a].v(darabolashelye))
  239. exp2v_dend=exp2v_dend+dend[a].v(darabolashelye)
  242. exp1i_soma=exp1i_soma+i*(somai)
  243. exp2i_soma=exp2i_soma+somai
  245. exp1v_soma=exp1v_soma+i*(soma.v)
  246. exp2v_soma=exp2v_soma+soma.v
  248. fadvance()
  250. i=i+dt
  252. }
  254. tv_dend=exp1v_dend/exp2v_dend
  256. ti_dend=exp1i_dend/exp2i_dend
  258. tv_soma=exp1v_soma/exp2v_soma
  260. LD=tv_dend-ti_dend
  261. PD=tv_soma-tv_dend
  262. TD=LD+PD
  264. abc = 0
  265. if (q == parts) { abc=(dend[a].L-(q-1)*20) }
  266. if (q != parts) { abc=(20) }
  268. //ezen sejteknél az átmérõ (diam) nem változik egy section-ön belül, de ezt más esetben ellenõrizni kell
  270. fprint ("%d %d %f %f %f %f %f %f %f %f %f %f %f %f\n", a,plusz,abc, q,LD,PD,TD,parts,distance(darabolashelye), dend[a].L, nseg, dend[a].diam, (3.14*abc*dend[a].diam+abc*basalspinesuruseg*basalspinefelsz), dend[a].L*3.14*dend[a].diam)
  271. }
  272. }
  274. exp1=0
  275. exp2=0
  277. exp1i_dend=0
  278. exp2i_dend=0
  279. exp1v_dend=0
  280. exp2v_dend=0
  282. exp1i_soma=0
  283. exp2i_soma=0
  284. exp1v_soma=0
  285. exp2v_soma=0
  287. TD=0
  288. LD=0
  289. PD=0
  291. tv_dend=0
  292. tv_soma=0
  293. ti_dend=0
  294. ti_soma=0
  296. somai=0
  297. dendi=0
  299. v_init = 0
  303. for a = 0, nrsecapic {
  304. access apic[a]
  305. parts=1
  306. abc=0
  307. darabok=0
  309. if (apic[a].L>darabolas) {
  311. parts=int (apic[a].L/darabolas)+1
  312. print (parts)
  313. darabok = (parts % 2)
  314. if (darabok==0) {
  315. apic[a] nseg=parts+1
  316. }
  317. if (darabok!=0) {
  318. apic[a] nseg=parts+2
  319. }
  320. }
  321. access soma
  322. distance()
  324. v_init = 0
  325. finitialize (v_init)
  327. for q = 1,parts {
  328. access apic[a]
  329. v_init = 0
  330. finitialize (v_init)
  332. plusz=plusz+1
  334. darabolashelye=0 // darabolás helyét jelölõ változó, ami a parts aktuális értéke szerint kap értéket
  336. if (parts==1) {
  337. darabolashelye=0.5
  338. }
  339. if (parts!=1) {
  340. darabolashelye=q*1/parts
  341. }
  343. objectvar stim1
  344. apic[a] stim1 = new IClamp(darabolashelye)
  345. stim1.del = 0
  346. stim1.dur = 0.3
  347. stim1.amp = 1
  349. exp1i_dend=0
  350. exp2i_dend=0
  351. exp1v_dend=0
  352. exp2v_dend=0
  354. exp1i_soma=0
  355. exp2i_soma=0
  356. exp1v_soma=0
  357. exp2v_soma=0
  359. TD=0
  360. LD=0
  361. PD=0
  363. tv_dend=0
  364. tv_soma=0
  365. ti_dend=0
  366. ti_soma=0
  368. somai=0
  369. dendi=0
  371. i=0
  373. while (i<400) {
  374. access apic[a]
  375. if (i<=0.3) {
  376. somai=1
  377. dendi=1
  378. } else {
  379. somai=0
  380. dendi=0
  381. }
  383. exp1i_dend=exp1i_dend+i*(dendi)
  384. exp2i_dend=exp2i_dend+dendi
  386. exp1v_dend=exp1v_dend+i*(apic[a].v(darabolashelye))
  387. exp2v_dend=exp2v_dend+apic[a].v(darabolashelye)
  389. exp1i_soma=exp1i_soma+i*(somai)
  390. exp2i_soma=exp2i_soma+somai
  392. exp1v_soma=exp1v_soma+i*(soma.v)
  393. exp2v_soma=exp2v_soma+soma.v
  395. fadvance()
  397. i=i+dt
  398. }
  400. tv_dend=exp1v_dend/exp2v_dend
  402. ti_dend=exp1i_dend/exp2i_dend
  404. tv_soma=exp1v_soma/exp2v_soma
  406. LD=tv_dend-ti_dend
  407. PD=tv_soma-tv_dend
  408. TD=LD+PD
  410. abc = 0
  411. if (q == parts) { abc=(apic[a].L-(q-1)*20) }
  412. if (q != parts) { abc=(20) } //ez az általános hossz változó
  413. //ezen sejteknél az átmérõ (diam) nem változik egy section-ön belül, de ezt más esetben ellenõrizni kell
  415. fprint ("%d %d %f %f %f %f %f %f %f %f %f %f %f %f\n", a,plusz,abc,q,LD,PD,TD,parts,distance(darabolashelye), apic[a].L, nseg, apic[a].diam, (3.14*abc*apic[a].diam+abc*apicalspinesuruseg*apicalspinefelsz), apic[a].L*3.14*apic[a].diam)
  416. }
  417. }
  419. wopen()

DELAY.HOC at commit 3267848, no license · at the source

Overview

Authors: Bence Pelyvas1,2, Ervin Wolf2,3, Zita Kepes4, Ervin Berenyi1,2, Tamas Papp1,2,3, Attila Somogyi1,2,3
  1. Department of Radiology and Imaging Science, Institute of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
  2. Doctoral School of Medical Sciences, University of Debrecen, Debrecen, Hungary
  3. Department of Anatomy, Histology and Embryology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
  4. Department of Nuclear Medicine and Translational Imaging, Institute of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
Institutions: University of Debrecen (Hungary)
Journal: Frontiers in behavioral neuroscience, volume 20, article 1871826
Dates: received 3 May 2026; accepted 22 July 2026; published online 7 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnbeh.2026.1871826 · PMID 42630358 · PMCID PMC13493503 · OpenAlex W7196948087
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), none (in silico) (organism)
Methods: Statistics
Keywords: CA1 pyramidal cell, computer simulation, hippocampus, neurodevelopment, NEURON simulator, signal attenuation, ultrasound
Topic: Ultrasound and Hyperthermia Applications (Biomedical Engineering, Engineering), according to OpenAlex
Funding: Debreceni Egyetem
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Introduction: Neural development is regulated by several spatiotemporally changing factors, which are essential for enabling neurons to develop functional networks, during early developmental stages. Additionally, external physical stimuli, like scanning prenatal ultrasound (US) examination, may influence neural development. Aim of our study is to examine potential consequences of diagnostic level ultrasound exposure during the early phase of hippocampal CA1 neural network development by using computational models.

Methods: Using in silico modelling, this study explores and compares multiple features of intraneuronal dendritic signal propagation in US-treated and control CA1 pyramidal neurons to look for possible alterations caused by US. We used morphological data of CA1 neurons based on previous morphometric datasets and built high-fidelity subthreshold passive and active segmental cable models of these neurons in the NEURON simulator. To simulate dendritic signalling either a current was injected or a synapse was activated at hundreds of dendritic points of model neurons, eliciting local postsynaptic potentials (PSPs), and multiple descriptors of dendritic impulse propagation between dendritic points and soma were computed.

Results: Our computer models predict that diagnostic level US treatment does not induce changes in specific membrane resistance and capacitance of neuronal membrane. Correlative analyses of simulated dendritic impulse propagation in US treated (UT) and non-treated control neurons (NT) revealed minor differences in attenuations and delays of somatopetally propagating PSPs in the passive model, and these alterations became even less pronounced when hyperpolarization-activated cyclic nucleotide-gated (HCN) cation channels and A-type potassium (KA) channels were inserted into the model. Synaptic input pattern recognition between UT and NT neurons showed no significant alterations.

Discussion: We conclude that subthreshold somatopetal signalling properties of US-treated CA1 neuronal membranes remain predominantly at the level of non-treated cells. This conservation is due primarily to HCN channels, contributing to net membrane conductance at rest in an inhomogeneous manner over the somato-dendritic surface. The HCN channels balance the effects of US-induced morphological alterations on dendritic signalling. Conservation of signalling properties align with our independent prediction on the conservation of synaptic integration and input pattern recognition in UT neurons.

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

Repository

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

somogyia/Intraneuronal-dendritic-signalling

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 326784849df7d9200d9bda04a6e71d48b18c3c1a, 25 March 2026
Languages: NEURON (4)
Size: 4 files, 4 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/somogyia/Intraneuronal-dendritic-signalling

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:

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

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The [.hoc]hoc code for the simulation of dendritic signalling is shared on at: https://github.com/somogyia/Intraneuronal-dendritic-signalling. The underlying data sets of reconstructed neurons and the large amount of raw data of simulations are available from the corresponding author on request.

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 3, 28 September 2026

  • Funding: added Debreceni Egyetem

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 7 keywords, 50 references.

Cite

This paper

Pelyvas, B., Wolf, E., Kepes, Z., Berenyi, E., Papp, T., & Somogyi, A. (2026). Effects of repetitive scanning ultrasound on the intraneuronal dendritic signalling of CA1 pyramidal neurons. Frontiers in behavioral neuroscience, 20, 1871826. https://doi.org/10.3389/fnbeh.2026.1871826

BibTeX

@article{pelyvas2026effects,
author = {Pelyvas, Bence and Wolf, Ervin and Kepes, Zita and Berenyi, Ervin and Papp, Tamas and Somogyi, Attila},
title = {{Effects of repetitive scanning ultrasound on the intraneuronal dendritic signalling of CA1 pyramidal neurons}},
journal = {Frontiers in behavioral neuroscience},
year = {2026},
month = aug,
volume = {20},
pages = {1871826},
publisher = {Frontiers Media SA},
issn = {1662-5153},
doi = {10.3389/fnbeh.2026.1871826},
url = {https://doi.org/10.3389/fnbeh.2026.1871826},
pmid = {42630358},
pmcid = {PMC13493503}
}

RIS

TY - JOUR
AU - Pelyvas, Bence
AU - Wolf, Ervin
AU - Kepes, Zita
AU - Berenyi, Ervin
AU - Papp, Tamas
AU - Somogyi, Attila
TI - Effects of repetitive scanning ultrasound on the intraneuronal dendritic signalling of CA1 pyramidal neurons
T2 - Frontiers in behavioral neuroscience
J2 - Front Behav Neurosci
PY - 2026
DA - 2026/08/07
VL - 20
SP - 1871826
SN - 1662-5153
PB - Frontiers Media SA
DO - 10.3389/fnbeh.2026.1871826
UR - https://doi.org/10.3389/fnbeh.2026.1871826
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnbeh.2026.1871826",
"type": "article-journal",
"title": "Effects of repetitive scanning ultrasound on the intraneuronal dendritic signalling of CA1 pyramidal neurons",
"container-title": "Frontiers in behavioral neuroscience",
"author": [
{
"family": "Pelyvas",
"given": "Bence"
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{
"family": "Wolf",
"given": "Ervin"
},
{
"family": "Kepes",
"given": "Zita"
},
{
"family": "Berenyi",
"given": "Ervin"
},
{
"family": "Papp",
"given": "Tamas"
},
{
"family": "Somogyi",
"given": "Attila"
}
],
"container-title-short": "Front Behav Neurosci",
"volume": "20",
"page": "1871826",
"DOI": "10.3389/fnbeh.2026.1871826",
"PMID": "42630358",
"PMCID": "PMC13493503",
"ISSN": "1662-5153",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnbeh.2026.1871826",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
7
]
]
}
}

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