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Dissociable roles of prefrontal plasticity in decision-making strategy and execution of habitual behavior.

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The authors' code

Jupyter notebook · 176 lines · 4.2 KB · MIT

  1. # %%
  2. import pandas as pd
  3. import numpy as np
  4. import os
  5. import glob
  6. import openpyxl
  8. # %%
  9. path = r"data/sample1"
  10. outpath = os.path.join(path, "result")
  12. if os.path.exists(outpath):
  13. pass
  14. else:
  15. os.mkdir(outpath)
  17. fpath = os.path.join(path, "C.csv")
  18. wpath = os.path.join(path, "Events.csv")
  20. # %%
  21. df_in = pd.read_csv(fpath, index_col=0)
  23. # %%
  24. df_w = pd.read_csv(wpath)
  26. # %%
  27. # Unit ID
  28. df_f_id = df_in.iloc[:,0]
  29. df_f_id = df_f_id.drop_duplicates()
  30. l_id = df_f_id.T.tolist()
  32. # %%
  33. df_w_entry_all = df_w.iloc[:,0]
  34. df_w_entry_all = df_w_entry_all.dropna(how="all")
  35. list_w_entry_all = df_w_entry_all.tolist()
  36. list_w_entry_all = [i for i in list_w_entry_all if i != 0]
  38. # %%
  39. ### Rewarded Entry
  40. df_w_entry = df_w.iloc[:,1]
  41. df_w_entry = df_w_entry.dropna(how="all")
  42. l_entry = df_w_entry.T.tolist()
  43. l_entry = [i for i in l_entry if i != 0]
  45. # %%
  46. # Rewarded Entry End
  47. df_w_entryend = df_w.iloc[:,4]
  48. df_w_entryend = df_w_entryend.dropna(how="all")
  49. l_entryend = df_w_entryend.T.tolist()
  50. l_entryend = [i for i in l_entryend if i != 0]
  52. # %%
  53. df_w_press_all = df_w.iloc[:,2]
  54. df_w_press_all = df_w_press_all.dropna(how="all")
  55. list_w_press_all = df_w_press_all.tolist()
  57. # %%
  58. ### Press initiation
  59. df_w_press = df_w.iloc[:,3]
  60. df_w_press = df_w_press.dropna(how="all")
  61. l_press_init = df_w_press.tolist()
  62. l_press_init = [i for i in l_press_init if i != 0]
  64. for entry in list_w_entry_all:
  65. pre = entry - 17
  66. post = entry
  67. for press in l_press_init:
  68. if press > pre and press < post:
  69. l_press_init.remove(press)
  71. # %%
  72. ### Press end
  73. l_press_end =[]
  75. for press in l_press_init:
  76. for press2 in list_w_press_all:
  77. if press2-press > 17:
  78. press_end = list_w_press_all[list_w_press_all.index(press2)-1]
  79. l_press_end.append(press_end)
  80. break
  82. # %%
  83. ### SD normalized
  84. ### Rewarded Entry Whole
  86. df_e_add = pd.DataFrame()
  88. l_entry2 = []
  89. for i in range(len(l_entry)):
  90. l_entry2.append(int(l_entry[i]))
  92. l_entry2 = l_entry2[:-1]
  94. for entry in l_entry2:
  95. number = l_entry2.index(entry)
  96. e_end = l_entryend[number]
  97. f_pre1 = entry - 18 #Enter the desired number of frames.
  98. f_post1 = e_end + 1 #Enter the desired number of frames.
  100. l_e = []
  101. l_e_index = []
  103. for ID in l_id:
  104. # Variables
  105. df_ID_r = df_in.query("unit_id == @ID")
  106. df_ID = df_ID_r["frame"]
  107. df_ID_r = df_ID_r["C"]
  108. df_ID_z = ((df_ID_r)/ (df_ID_r.std()))
  109. df_ID = pd.concat([df_ID, df_ID_z], axis=1)
  111. df_add1 = df_ID.query("frame > @f_pre1 & frame < @f_post1")
  112. df_add1 = df_add1.reset_index()
  114. df_add1_n = df_add1["C"]
  116. l_e.append(df_add1_n[17:].mean())
  117. l_e_index.append("Mean_" + str(ID))
  119. else:
  120. sr_e = pd.Series(l_e, index = l_e_index)
  121. df_e_add = pd.concat([df_e_add, pd.DataFrame([sr_e])], axis=0)
  124. else:
  125. df_e_add = df_e_add.T
  126. df_e_add.columns = l_entry2
  127. spath_e = os.path.join(outpath, "Entry_result.xlsx")
  128. df_e_add.to_excel(spath_e)
  130. # %%
  131. ### SD normalized
  132. ### Press
  134. df_p_add = pd.DataFrame()
  136. l_press_i = []
  137. l_press_e = []
  138. for i in range(len(l_press_end)):
  139. l_press_e.append(int(l_press_end[i]))
  140. l_press_i.append(int(l_press_init[i]))
  143. for press in l_press_i:
  144. number = l_press_i.index(press)
  145. p_end = l_press_end[number]
  146. f_pre1 = press - 35 #Enter the desired number of frames.
  147. f_post1 = p_end + 18 #Enter the desired number of frames.
  149. l_p = []
  150. l_p_index = []
  152. for ID in l_id:
  153. df_ID_r = df_in.query("unit_id == @ID")
  154. df_ID = df_ID_r["frame"]
  155. df_ID_r = df_ID_r["C"]
  156. df_ID_z = ((df_ID_r)/ (df_ID_r.std()))
  157. df_ID = pd.concat([df_ID, df_ID_z], axis=1)
  159. df_add1 = df_ID.query("frame > @f_pre1 & frame < @f_post1")
  160. df_add1 = df_add1.reset_index()
  162. df_add1_n = df_add1["C"]
  164. l_p.append(df_add1_n[17:].mean())
  165. l_p_index.append("Mean_" + str(ID))
  167. else:
  168. sr_p = pd.Series(l_p, index = l_p_index)
  169. df_p_add = pd.concat([df_p_add, pd.DataFrame([sr_p])], axis=0)
  172. else:
  173. df_p_add = df_p_add.T
  174. df_p_add.columns = l_press_i
  175. spath_e2 = os.path.join(outpath, "Press_result.xlsx")
  176. df_p_add.to_excel(spath_e2)

FIg5&7_Event_extraction.ipynb at commit 2c6a061, under MIT · at the source

Overview

Authors: Nozomi Asaoka1, Diane Pagano1, Yasunori Hayashi1
ORCID iDs: Nozomi Asaoka, Yasunori Hayashi
  1. Department of Pharmacology, Kyoto University Graduate School of Medicine,Kyoto, Japan
Institutions: Kyoto University (Japan)
Journal: Nature communications, volume 17, issue 1, article 6822
Dates: received 26 August 2025; accepted 6 July 2026; published online 27 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75706-1 · PMID 42509234 · PMCID PMC13408161 · OpenAlex W7171429318
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Cognitive neuroscience, Synaptic plasticity
MeSH: Decision Making*, Habits*, Neuronal Plasticity*, Prefrontal Cortex*, Animals, Behavior, Animal, Male, Mice, Mice, Inbred C57BL, Motivation (* major topic)
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (JP22K15291, JP25K02556, JP18H05434, JP22H04981); Japan Agency for Medical Research and Development (JP24wm0625507); Uehara Memorial Foundation; Takeda Science Foundation; Astellas Foundation for Research on Metabolic Disorders; Naito Foundation; NOVARTIS Foundation (Japan) for the Promotion of Science; Human Frontier Science Program (HFSP) (RGP0022/2013); MEXT | JST | Core Research for Evolutional Science and Technology (CREST) (JPMJCR20E4)
Citations: not cited yet (Europe PMC); 77 references in the paper
Research resources: Male C57BL/6J mice RRID:IMSR_JAX:000664

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.

Nozomi-Asaoka/Custom-code-for-Asaoka-et-al-2026

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2c6a0619548ce23514943ee7c35ab83d8bbc3e20, 3 July 2026
Languages: Jupyter (3)
Size: 9 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 3 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), pandas (3 files), Matplotlib (2 files), scikit-learn (2 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/Nozomi-Asaoka/Custom-code-for-Asaoka-et-al-2026.git

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-75706-1.

Tracing map

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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-75706-1.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 2 keywords, 10 MeSH terms, 9 funders, 77 references, 1 RRID.

Cite

This paper

Asaoka, N., Pagano, D., & Hayashi, Y. (2026). Dissociable roles of prefrontal plasticity in decision-making strategy and execution of habitual behavior. Nature communications, 17(1), 6822. https://doi.org/10.1038/s41467-026-75706-1

BibTeX

@article{asaoka2026dissociable,
author = {Asaoka, Nozomi and Pagano, Diane and Hayashi, Yasunori},
title = {{Dissociable roles of prefrontal plasticity in decision-making strategy and execution of habitual behavior}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {6822},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75706-1},
url = {https://doi.org/10.1038/s41467-026-75706-1},
pmid = {42509234},
pmcid = {PMC13408161}
}

RIS

TY - JOUR
AU - Asaoka, Nozomi
AU - Pagano, Diane
AU - Hayashi, Yasunori
TI - Dissociable roles of prefrontal plasticity in decision-making strategy and execution of habitual behavior
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/27
VL - 17
IS - 1
SP - 6822
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75706-1
UR - https://doi.org/10.1038/s41467-026-75706-1
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Asaoka",
"given": "Nozomi"
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{
"family": "Hayashi",
"given": "Yasunori"
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"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6822",
"DOI": "10.1038/s41467-026-75706-1",
"PMID": "42509234",
"PMCID": "PMC13408161",
"ISSN": "2041-1723",
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"language": "en",
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"date-parts": [
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