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Comparing effective and functional connectivity.

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 › Simulation of a large-scale network of neurons ↔ PNASnexus2024/MATSimulation/C1_CoNNECT_cupy_202304261000.py, lines 36–83 · score 0.70 · standard deviation, inhibitory neurons, membrane, component, background, simulation
  2. [2] § Results › Excitatory-inhibitory dominance of ECs ↔ PNASnexus2024/ShinGLMCC/main_modelcomparison.py, lines 188–270 · score 0.61 · Putative excitatory, Excitatory inhibitory, excitatory connections, inhibitory connections, dominance, neurons
  3. [3] § Methods › Simulation of a large-scale network of neurons ↔ PNASnexus2024/PoissonSimulation/PoissonSimulation_forFig2_cython.py, lines 146–189 · score 0.60 · Ornstein Uhlenbeck process, noise, simulation, delta, spike
  4. [4] § Results › Excitatory-inhibitory dominance of ECs ↔ PNASnexus2024/ShinGLMCC/main_modelcomparison.py, lines 188–270 · score 0.58 · Putatively inhibitory, excitatory connections, inhibitory connections, dominance, neuron

Paper

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The authors' code

Python · 274 lines · 12 KB · GPL-3.0 · 2 matches

  1. """
  2. ##############################################################################
  3. main_modelcomparison.py
  4. by Yasuhiro Tsubo ([email hidden])
  5. modified ver. 2023.10.28
  6. ##############################################################################
  7. """
  9. ## ===================================================================
  10. ## External modules
  11. ## ===================================================================
  13. import os
  14. import sys
  15. import pickle
  17. import pandas as pd
  19. import ShinGLMCC.CJudge
  22. ## ===================================================================
  23. ## Parameters
  24. ## ===================================================================
  27. ## DATAID: Name of target dataset
  28. DATAID = sys.argv[1]
  30. ## CONNECTION_THRESHOLD: The significance level
  31. ## for determining the presence or absence of a connection
  32. CONNECTION_THRESHOLD = 0.0001
  34. ## ===================================================================
  35. ## Functions
  36. ## ===================================================================
  38. if __name__ == "__main__":
  40. #"""
  41. ## ---------------------------------------------------------
  42. ## Call CJudge.ClassicalCC() (3 sec)
  43. ## Check for significant differences in histogram bin values.
  44. ## Calculate the p-value for ClassicalCC method.
  45. ## [input]
  46. ## (DATAID)_cor_K.pkl: dfccK cross-correlation with resolution 1ms
  47. ## [output]
  48. ## (DATAID)_Classical_best.csv: dfbsc
  49. ## .........................................................
  50. ## +dfccK: (Pandas DataFrame)
  51. ## {
  52. ## * -WINHALF_MS to WINHALF_MS: (float)
  53. ## Correlation histogram values
  54. ## at Time lags -WINHALF_MS to WINHALF_MS.
  55. ## *"ref": (int) Reference Neuron ID.
  56. ## *"tar": (int) Target Neuron ID.
  57. ## }
  58. ## Rows: N(N-1), all neuron pairs, auto-indexed.
  59. ## (N= Total number of neurons.)
  60. ## .........................................................
  61. ## +dfbsc: (Pandas DataFrame)
  62. ## {
  63. ## *"ref": (int) Reference Neuron ID.
  64. ## *"tar": (int) Target Neuron ID.
  65. ## *"maxcc": (int) Maximum value of cross-correlation histogram.
  66. ## *"mincc": (int) Minimum value of cross-correlation histogram.
  67. ## *"avecc": (float) Average value of cross-correlation histogram.
  68. ## *"Ze": (float) (maxcc-avecc)/sqrt(avecc) Z-score.
  69. ## *"Zi": (float) (mincc-avecc)/sqrt(avecc) Z-score.
  70. ## *"alphae": (float) P-value for the presence of putative connection
  71. ## by Ze (Excitatory).
  72. ## *"alphai": (float) P-value for the presence of putative connection
  73. ## by Zi (Inhibitory).
  74. ## *"upcc": Upper cross-correlation value
  75. ## when "alphae"="CONNECTION_THRESHOLD"
  76. ## *"lowcc": Lower cross-correlation value
  77. ## when "alphai"="CONNECTION_THRESHOLD"
  78. ## *"ext": (int) Putative excitatory connection indicator.
  79. ## *"inh": (int) Putative inhibitory connection indicator.
  80. ## (1 if significant, 0 otherwise)
  81. ## }
  82. ## Rows: N(N-1), auto-indexed. (N: Total number of neurons.)
  84. ## rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
  85. with open(DATAID+"_cor_K.pkl","rb") as f:
  86. dfccK = pickle.load(f)
  87. ## rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
  89. print("call CJudge.ClassicalCC ...")
  90. dfbsc = ShinGLMCC.CJudge.classicalCC(dfccK,CONNECTION_THRESHOLD)
  91. print("done")
  93. ## wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww
  94. dfbsc.to_csv(DATAID+"_Classical_best.csv",index=None)
  95. ## wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww
  97. ## ---------------------------------------------------------
  98. #"""
  101. #"""
  102. ## ---------------------------------------------------------
  103. ## Call CJudge.Grouping() (2 sec)
  104. ## Group by the presence or absence of connections
  105. ## estimated by the three estimation methods.
  106. ## [input]
  107. ## (DATAID)_Classical_best.csv: dfbsc
  108. ## (DATAID)_GLM_best.csv: dfbsg
  109. ## (DATAID)_Shin_best.csv: dfbss
  110. ## [output]
  111. ## (DATAID)_group.csv: dfgrp
  112. ## .........................................................
  113. ## +dfbsc: (Pandas DataFrame)
  114. ## {
  115. ## *"ref": (int) Reference Neuron ID.
  116. ## *"tar": (int) Target Neuron ID.
  117. ## *"maxcc": (int) Maximum value of cross-correlation histogram.
  118. ## *"mincc": (int) Minimum value of cross-correlation histogram.
  119. ## *"avecc": (float) Average value of cross-correlation histogram.
  120. ## *"Ze": (float) (maxcc-avecc)/sqrt(avecc) Z-score.
  121. ## *"Zi": (float) (mincc-avecc)/sqrt(avecc) Z-score.
  122. ## *"alphae": (float) P-value for the presence of putative connection
  123. ## by Ze (Excitatory).
  124. ## *"alphai": (float) P-value for the presence of putative connection
  125. ## by Zi (Inhibitory).
  126. ## *"upcc": Upper cross-correlation value
  127. ## when "alphae"="CONNECTION_THRESHOLD"
  128. ## *"lowcc": Lower cross-correlation value
  129. ## when "alphai"="CONNECTION_THRESHOLD"
  130. ## *"ext": (int) Putative excitatory connection indicator.
  131. ## *"inh": (int) Putative inhibitory connection indicator.
  132. ## (1 if significant, 0 otherwise)
  133. ## }
  134. ## Rows: N(N-1), auto-indexed. (N: Total number of neurons.)
  135. ## .........................................................
  136. ## +dfbs[g/s]: (Pandas DataFrame)
  137. ## {
  138. ## * 0 to WINHALF_MS: (float) Optimised values of parameter a(t)
  139. ## at Time lags 0 to WINHALF_MS.
  140. ## !! Note that only the right-half data was extracted.
  141. ## *"ref": (int) Reference Neuron ID.
  142. ## *"tar": (int) Target Neuron ID.
  143. ## *"J": (float) Optimised values of parameter JR
  144. ## (Synapric weight for the right side.)
  145. ## *"logpost": (float) Log-posterior across all ranges
  146. ## with the estimated parameters.
  147. ## *"loglike": (float) Log-likelihood for right-half side data.
  148. ## *"delay": (int) Delay parameter of synaptic function. [ms]
  149. ## *"tau": (int) Decay time constant of synaptic function. [ms]
  150. ## *"alpha": (float) P-value for the presence of putative connection.
  151. ## *"ext": (int) Putative excitatory connection indicator.
  152. ## *"inh": (int) Putative inhibitory connection indicator.
  153. ## (1 if significant, 0 otherwise)
  154. ## }
  155. ## Rows: N(N-1), all neuron pairs, auto-indexed.
  156. ## (N= Total number of neurons.)
  157. ## .........................................................
  158. ## +dfgrp: (Pandas DataFrame)
  159. ## {
  160. ## *"ref": (int) Reference Neuron ID.
  161. ## *"tar": (int) Target Neuron ID.
  162. ## *"group": (str) Connection codes
  163. ## ("N"= none, "C"= Classical, "G"= GLMCC, "S"= ShinGLMCC,
  164. ## e.g., "CG" indicates connections estimated
  165. ## by Clasical & GLM only.)
  166. ## *"Classical", "GLM", "Shin": (int)
  167. ## Connectivity status by respective methods
  168. ## (1 for detected, 0 for not detected)
  169. ## *"ext-[c/g/s]", "inh-[c/g/s]": (int)
  170. ## Putative excitatory ("ext") or inhibitory ("inh")
  171. ## connection indicator per method.
  172. ## ([c] Classical, [c] GLMCC, [s] ShinGLMCC)
  173. ## (1 if significant, 0 otherwise)
  174. ## }
  175. ## Rows: N(N-1), all neuron pairs, auto-indexed.
  176. ## (N= Total number of neurons.)
  178. ## rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
  179. dfbsc = pd.read_csv(DATAID+"_Classical_best.csv")
  180. dfbsg = pd.read_csv(DATAID+"_GLM_best.csv")
  181. dfbss = pd.read_csv(DATAID+"_Shin_best.csv")
  182. ## rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
  184. print("call CJudge.Grouping ...")
  185. dfgrp = ShinGLMCC.CJudge.Grouping(dfbsc,dfbsg,dfbss)
  186. print("done")
  188. ## wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww
  189. dfgrp.to_csv(DATAID+"_group.csv",index=None)
  190. ## wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww
  192. ## ---------------------------------------------------------
  193. #"""
  196. #"""
  197. ## ---------------------------------------------------------
  198. ## Call CJudge.EIdominance()
  199. ## Compute the excitatory Inhibitory dominance.
  200. ## [input]
  201. ## (DATAID)_group.csv: dfgrp
  202. ## [output]
  203. ## (DATAID)_EId_Classical.csv: dfeic
  204. ## (DATAID)_EId_GLM.csv: dfeig
  205. ## (DATAID)_EId_Shin.csv: dfeis
  206. ## (DATAID)_EId.csv: dfeid
  207. ## (DATAID)_grpnum.csv: dfgpn
  208. ## .........................................................
  209. ## +dfgrp: (Pandas DataFrame)
  210. ## {
  211. ## *"ref": (int) Reference Neuron ID.
  212. ## *"tar": (int) Target Neuron ID.
  213. ## *"group": (str) Connection codes
  214. ## ("N"= none, "C"= Classical, "G"= GLMCC, "S"= ShinGLMCC,
  215. ## e.g., "CG" indicates connections estimated
  216. ## by Clasical & GLM only.)
  217. ## *"Classical", "GLM", "Shin": (int)
  218. ## Connectivity status by respective methods
  219. ## (1 for detected, 0 for not detected)
  220. ## *"ext-[c/g/s]", "inh-[c/g/s]": (int)
  221. ## Putative excitatory ("ext") or inhibitory ("inh")
  222. ## connection indicator per method.
  223. ## ([c] Classical, [c] GLMCC, [s] ShinGLMCC)
  224. ## (1 if significant, 0 otherwise)
  225. ## }
  226. ## Rows: N(N-1), all neuron pairs, auto-indexed.
  227. ## (N= Total number of neurons.)
  228. ## .........................................................
  229. ## +dfei[c/g/s]: (Pandas DataFrame)
  230. ## {
  231. ## *"ref": (int) Reference Neuron ID.
  232. ## *"c": Number of putative connection.
  233. ## *"e": Number of putative excitatory connection.
  234. ## *"i": Number of putative inhibitory connection.
  235. ## *"dom": Excitatory Inhibitory dominance.
  236. ## (dom= (e+i)/(e+i) )
  237. ## }
  238. ## Rows: Number of neurons with non-zero putative connections.
  239. ## .........................................................
  240. ## +dfeid: (Pandas DataFrame)
  241. ## {
  242. ## *"average absolute values": (float)
  243. ## Average absolute value of "Excitatory Inhibitory dominance".
  244. ## *"expressing perfect consistency": (float)
  245. ## Proportion of "Excitatory Inhibitory dominance"
  246. ## equaling exactly -1 or +1.
  247. ## }
  248. ## Rows: "Classical","GLM","Shin"
  249. ## .........................................................
  250. ## +dfgpn: (Pandas DataFrame)
  251. ## {
  252. ## *"num": (int)
  253. ## Number of neurons assigned to each "Connection code".
  254. ## }
  255. ## Rows: Connection codes ("C","CG","CGS","CS","G","GS","N","S")
  257. ## rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
  258. dfgrp = pd.read_csv(DATAID+"_group.csv")
  259. ## rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr
  261. print("call CJudge.EIdominance ...")
  262. dfeid,dfgpn,dfeic,dfeig,dfeis = ShinGLMCC.CJudge.EIdominance(DATAID,dfgrp)
  263. print("done")
  265. ## wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww
  266. dfeic.to_csv(DATAID+"_EId_Classical.csv",index=None)
  267. dfeig.to_csv(DATAID+"_EId_GLM.csv",index=None)
  268. dfeis.to_csv(DATAID+"_EId_Shin.csv",index=None)
  269. dfeid.to_csv(DATAID+"_EId.csv")
  270. dfgpn.to_csv(DATAID+"_grpnum.csv")
  271. ## wwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww
  273. ## ---------------------------------------------------------
  274. #"""

main_modelcomparison.py at commit 411e4da, under GPL-3.0 · at the source

Overview

Authors: Shigeru Shinomoto1,2, Yasuhiro Tsubo3
  1. Graduate School of Biostudies, Kyoto University, 606-8501 Kyoto, Japan
  2. Research Organization of Open Innovation and Collaboration, Ritsumeikan University, 567-8570 Osaka, Japan
  3. College of Information Science and Engineering, Ritsumeikan University, 567-8570 Osaka, Japan
Institutions: Ritsumeikan University (Japan); Kyoto University (Japan)
Journal: Scientific reports, volume 16, issue 1, article 12161
Dates: received 2 October 2025; accepted 26 February 2026; published online 5 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-42580-2 · PMID 41787015 · PMCID PMC13076884 · OpenAlex W7133835668
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), systems (subfield)
Methods: Machine learning, Single-unit activity, calcium imaging
Keywords: Computational biology and bioinformatics, Neuroscience
MeSH: Brain*, Nerve Net*, Neurons*, Action Potentials, Animals, Calcium, Models, Neurological (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (22H05163)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

yasuhirotsubo/neuroscience

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 411e4da0262ba6d2604724ef76e15dd9aff08fb5, 28 August 2024
Languages: Python (23), Shell (1)
Size: 26 files, 24 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: license file, environment (PNASnexus2024/PoissonSimulation/setup.py)
Not found: README, CITATION.cff, tests, continuous integration, documentation
Tools: pandas (18 files), NumPy (15 files), Matplotlib (6 files), SciPy (6 files), CuPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
25 files
At the source: github.com/yasuhirotsubo/neuroscience

Code availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-42580-2.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 2 keywords, 7 MeSH terms, 1 funder, 50 references.

Cite

This paper

Shinomoto, S., & Tsubo, Y. (2026). Comparing effective and functional connectivity. Scientific reports, 16(1), 12161. https://doi.org/10.1038/s41598-026-42580-2

BibTeX

@article{shinomoto2026comparing,
author = {Shinomoto, Shigeru and Tsubo, Yasuhiro},
title = {{Comparing effective and functional connectivity}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {12161},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-42580-2},
url = {https://doi.org/10.1038/s41598-026-42580-2},
pmid = {41787015},
pmcid = {PMC13076884}
}

RIS

TY - JOUR
AU - Shinomoto, Shigeru
AU - Tsubo, Yasuhiro
TI - Comparing effective and functional connectivity
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/05
VL - 16
IS - 1
SP - 12161
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-42580-2
UR - https://doi.org/10.1038/s41598-026-42580-2
LA - en
ER -

CSL-JSON

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"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "12161",
"DOI": "10.1038/s41598-026-42580-2",
"PMID": "41787015",
"PMCID": "PMC13076884",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-42580-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
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5
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]
}
}

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