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Cholinergic-dependent dopamine signals in mouse dorsomedial striatum are regulated by frontal but not sensory cortices.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Slice physiology › Electrophysiology ↔ Fig4_population.ipynb, lines 115–141 · score 0.64 · Medium spiny neurons, Cholinergic interneurons, stimulations, cell, CINs
  2. [2] § Methods › Anatomical tracing ↔ SuppFig5_rabies.ipynb, lines 113–133 · score 0.52 · basal ganglia, rabies, thalamus, Brains, midbrain, DLS

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Jupyter notebook · 144 lines · 7.2 KB · CC-BY-4.0 · 1 match

  1. # %%
  2. import matplotlib.pyplot as plt
  3. import seaborn as sns
  4. import pandas as pd
  5. import numpy as np
  6. import scipy.stats as ss
  7. from scipy.signal import find_peaks
  8. import pyabf
  9. from statsmodels.formula.api import ols
  10. import statsmodels.api as sm
  11. import pingouin as pg
  14. # %%
  15. plt.rcParams["font.family"] = "arial"
  16. plt.rcParams["font.size"] = 6
  17. plt.rcParams['axes.linewidth'] = 0.5
  18. plt.rcParams['xtick.major.width'] = 0.25
  19. plt.rcParams['xtick.major.size'] = 2
  20. plt.rcParams['xtick.major.pad'] = 2
  22. plt.rcParams['ytick.major.width'] = 0.25
  23. plt.rcParams['ytick.major.size'] = 2
  24. plt.rcParams['ytick.major.pad'] = 2
  25. plt.rcParams['ytick.major.pad'] = 2
  27. plt.rcParams['axes.labelpad'] = 2
  29. # %%
  30. def bootstrap(df, cellType, sensArea):
  31. y1, y2 = sns.utils.ci(sns.algorithms.bootstrap(df[(df['cellType'] == cellType)&(df['sensArea'] == sensArea)]['absEPSC']))
  32. return y1, y2
  34. # %%
  35. allCells = pd.read_csv('../data/Fig4/Fig4_ephysPop.csv')
  37. # %%
  38. fig, ax = plt.subplots(1, 1, figsize = (1.7,1.5), dpi = 300, sharex = True, tight_layout = True)
  40. ax.fill_between(x = np.linspace(-0.2, 0.2), y1 = bootstrap(allCells, 'MSN', "ACC")[0], y2 = bootstrap(allCells,'MSN', "ACC")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  41. ax.fill_between(x = np.linspace(0.8, 1.2), y1 = bootstrap(allCells,'MSN', "PL")[0], y2 = bootstrap(allCells,'MSN', "PL")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  42. ax.fill_between(x = np.linspace(1.8, 2.2), y1 = bootstrap(allCells,'MSN', "V1")[0], y2 = bootstrap(allCells,'MSN', "V1")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  43. ax.fill_between(x = np.linspace(2.8, 3.2), y1 = bootstrap(allCells,'MSN', "A1")[0], y2 = bootstrap(allCells,'MSN', "A1")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  46. ax.plot([-0.3, 0.3], [allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'ACC')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'ACC')]['absEPSC'].mean()], lw = 1, color = 'k')
  47. ax.plot([0.7, 1.3], [allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'PL')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'PL')]['absEPSC'].mean()], lw = 1, color = 'k')
  48. ax.plot([1.7, 2.3], [allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'V1')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'V1')]['absEPSC'].mean()], lw = 1, color = 'k')
  49. ax.plot([2.7, 3.3], [allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'A1')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] == 'A1')]['absEPSC'].mean()], lw = 1, color = 'k')
  52. sns.stripplot(data = allCells[(allCells['cellType'] == 'MSN')&(allCells['sensArea'] != 'S1')], x = 'sensArea', y = 'absEPSC', order = ('ACC', 'PL', "V1", "A1"), color = 'orange',legend = False, size = 3, alpha = 0.3, dodge = True)
  53. ax.set_ylim(-50, 2000)
  55. ax.axhline(0, ls = ":", color = 'k', lw = 0.5)
  57. ax.set_ylabel('EPSC amplitude (pA)')
  59. ax.set_xlabel('Cortical inputs')
  60. ax.set_xticks([0,1,2,3])
  61. ax.set_xticklabels(['ACA', "PL", "VISp", "AUDp"])
  63. sns.despine()
  64. plt.savefig('../figOutputs/Fig4_MSN_pop.pdf', dpi = 300, transparent = True, bbox_inches = 'tight')
  67. # %%
  68. aov_table = pg.anova(dv='absEPSC',
  69. between=[ 'sensArea'],
  70. data=allCells[(allCells['cellType']== 'MSN')&(allCells['sensArea']!= 'S1')])
  72. print(aov_table.round(5))
  74. # %%
  75. fig, ax = plt.subplots(1, 1, figsize = (1.7,1.5), dpi = 300, sharex = True, tight_layout = True)
  77. ax.fill_between(x = np.linspace(-0.2, 0.2), y1 = bootstrap(allCells,'CIN', "ACC")[0], y2 = bootstrap(allCells,'CIN', "ACC")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  78. ax.fill_between(x = np.linspace(0.8, 1.2), y1 = bootstrap(allCells,'CIN', "PL")[0], y2 = bootstrap(allCells,'CIN', "PL")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  79. ax.fill_between(x = np.linspace(1.8, 2.2), y1 = bootstrap(allCells,'CIN', "V1")[0], y2 = bootstrap(allCells,'CIN', "V1")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  80. ax.fill_between(x = np.linspace(2.8, 3.2), y1 = bootstrap(allCells,'CIN', "A1")[0], y2 = bootstrap(allCells,'CIN', "A1")[1], color = '0.5', alpha = 0.4, edgecolor = None)
  83. ax.plot([-0.3, 0.3], [allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'ACC')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'ACC')]['absEPSC'].mean()], lw = 1, color = 'k')
  84. ax.plot([0.7, 1.3], [allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'PL')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'PL')]['absEPSC'].mean()], lw = 1, color = 'k')
  85. ax.plot([1.7, 2.3], [allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'V1')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'V1')]['absEPSC'].mean()], lw = 1, color = 'k')
  86. ax.plot([2.7, 3.3], [allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'A1')]['absEPSC'].mean(),allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] == 'A1')]['absEPSC'].mean()], lw = 1, color = 'k')
  89. sns.stripplot(data = allCells[(allCells['cellType'] == 'CIN')&(allCells['sensArea'] != 'S1')], x = 'sensArea', y = 'absEPSC', order = ('ACC', 'PL', "V1", "A1"), color = 'royalblue',legend = False, size = 3, alpha = 0.3, dodge = True)
  90. ax.set_ylim(-50, 1000)
  92. ax.axhline(0, ls = ":", color = 'k', lw = 0.5)
  94. ax.set_ylabel('EPSC amplitude (pA)')
  96. ax.set_xlabel('Cortical inputs')
  98. ax.set_xticks([0,1,2,3])
  99. ax.set_xticklabels(['ACA', "PL", "VISp", "AUDp"])
  101. sns.despine()
  102. plt.savefig('../figOutputs/Fig4_CIN_pop.pdf', dpi = 300, transparent = True, bbox_inches = 'tight')
  105. # %%
  106. aov_table = pg.anova(dv='absEPSC',
  107. between=[ 'sensArea'],
  108. data=allCells[(allCells['cellType']== 'CIN')&(allCells['sensArea']!= 'S1')])
  110. print(aov_table.round(10))
  112. # %%
  113. IO = pd.read_csv('../data/Fig4/Fig4_IOcurves.csv', index_col = [0])
  115. # %%
  116. palMSN = ['orange', 'chocolate', 'darkorange',]
  117. palCIN = ['royalblue', 'blue', 'navy',]
  119. fig, ax = plt.subplots(1, 2, figsize = (3,1.5), dpi = 300, sharex = True, sharey = True, tight_layout = True)
  120. sns.lineplot(ax = ax[0], data = IO[(IO['cellType'] == 'MSN')], x = 'stimDur', y = 'absEPSC', hue = 'sensArea', hue_order = ['ACC', "V1", "A1"], errorbar = 'se',
  121. palette = palMSN, err_style = 'bars',linewidth = 1, err_kws={'linewidth': 1, 'capsize':1}, legend = False)
  122. sns.lineplot(ax = ax[1], data = IO[(IO['cellType'] == 'CIN')], x = 'stimDur', y = 'absEPSC', hue = 'sensArea', hue_order = ['ACC', "V1", "A1"], errorbar = 'se',
  123. palette = palCIN, err_style = 'bars', linewidth = 1, err_kws={'linewidth': 1, 'capsize':1}, legend = False)
  124. sns.despine()
  126. ax[1].set_xscale('log')
  127. ax[0].set_ylim(-100,2000)
  128. ax[0].set_xticks([0.6, 1, 2, 5, 10])
  129. ax[0].set_xticklabels([0.6, 1, 2, 5, 10])
  131. ax[0].set_title("Medium spiny neurons", color = 'orange', fontsize = 6)
  132. ax[1].set_title("Cholinergic interneurons", color = 'royalblue', fontsize = 6)
  134. ax[0].set_xlabel('Stimulation (ms)')
  135. ax[1].set_xlabel('Stimulation (ms)')
  137. ax[0].set_ylabel("EPSC (pA)")
  138. ax[0].axhline(0, ls = ":", color = 'k', lw = 0.5)
  139. ax[1].axhline(0, ls = ":", color = 'k', lw = 0.5)
  141. plt.savefig('../figOutputs/Fig4_IO_pop.pdf', dpi = 300, transparent = True, bbox_inches = 'tight')
  143. # %%

Fig4_population.ipynb, under CC-BY-4.0 · at the source

Overview

Authors: Hannah C Goldbach1,2,3,4, Rachele Rimondini1, Evan S Swanson1,2, Jung Hoon Shin1,2, Michael E Authement1,2,4, Lucy G Anderson1, Han Bin Kwon1,2, Ron Paletzki5, Charles R Gerfen5, Linda M Amarante1,4, Richard J Krauzlis3, Veronica A Alvarez1,2,4
ORCID iDs: Hannah C Goldbach, Evan S Swanson, Jung Hoon Shin, Han Bin Kwon, Charles R Gerfen, Richard J Krauzlis, Veronica A Alvarez
  1. Section on Neurobiology of Compulsive Behaviors, National Institute of Mental Health, Bethesda, MD USA
  2. Laboratory on Neurobiology of Compulsive Behaviors, National Institute on Alcohol Abuse and Alcoholism, Rockville, MD USA
  3. Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, Maryland USA
  4. Center on Compulsive Behaviors, National Institutes of Health, Bethesda, Maryland USA
  5. Section on Neuroanatomy, National Institute of Mental Health, Bethesda, Maryland USA
Institutions: National Institutes of Health (United States); National Institute on Alcohol Abuse and Alcoholism (United States); National Eye Institute (United States); National Institute of Mental Health (United States)
Journal: Nature communications, volume 17, issue 1, article 9601
Dates: received 29 September 2025; accepted 12 August 2026; published online 2 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-77168-x · PMID 42711343 · PMCID PMC13554099 · OpenAlex W7205010258
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Neural circuits, Sensorimotor processing
MeSH: Auditory Cortex*, Cholinergic Neurons*, Corpus Striatum*, Dopamine*, Frontal Lobe*, Visual Cortex*, Acetylcholine, Animals, Interneurons, Male, Mice, Mice, Inbred C57BL, Photic Stimulation (* major topic)
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA) (ZIAA000421); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (ZIAMH002987); Intramural NIH HHS (ZIA MH002987)
Citations: cited by 2 papers (Europe PMC); 74 references in the paper
Research resources: blocked with 10% normal goat serum RRID:AB_2336990, 1:800 RRID:AB_2338046, 1:500 RRID:AB_2536526, RRID:Addgene_111068, AAV9-hSyn-dLight1.3b65 RRID:Addgene_135762, AAV9-Syn-FLEX-jGCaMP8s-WPRE66 RRID:Addgene_162377, AAV9-Syn-FLEX-jGCaMP8f-WPRE66 RRID:Addgene_162379, RRID:Addgene_172360, RRID:Addgene_26973, RRID:Addgene_59171, DAT-IRES-cre63 RRID:IMSR_JAX:006660, RRID:IMSR_JAX:007914, a Pvalb-IRES-Cre line RRID:IMSR_JAX:017320, pDyn-IRES-cre64 RRID:IMSR_JAX:027958, a SST-IRES-CRE line61 RRID:IMSR_JAX:028864, RRID:IMSR_JAX:031661, Drd1a-Cre RRID:MMRRC_030989-UCD, an open-source Python package70 RRID:SCR_02235345

Abstract

Everyday decisions depend on associations between sensory stimuli, actions, and outcomes. The striatum supports these sensorimotor associations through dopamine-dependent plasticity. Recent work has characterized a local striatal microcircuit in which cholinergic interneurons (CINs) modulate dopamine release via activation of nicotinic receptors on dopamine axons. Here, we show that visual stimuli evoke dopamine in the dorsomedial striatum partly through this cholinergic mechanism. Using anatomical and functional methods to identify the pathways involved, we found that visual and auditory cortices lack connectivity with CINs and were unable to drive cholinergic-dependent dopamine release. Frontal regions, which were activated by visual stimuli, strongly recruited CINs, producing robust dopamine release both ex vivo and in vivo. These findings reveal a fundamental distinction between sensory and frontal corticostriatal inputs, demonstrating that only the latter can evoke cholinergic-dependent dopamine signals. This work establishes a framework for understanding how cortical circuits shape striatal dopamine to support reinforcement learning.

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 2 matches between paragraphs and lines of code.

Zenodo 21651635

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: Jupyter (27)
Size: 28 files, 27 scripts
Software Heritage: not checked
Found in: the references
Holds: README, 27 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (27 files), NumPy (27 files), pandas (27 files), seaborn (27 files), SciPy (22 files), Pingouin (14 files), h5py (5 files), pyABF (3 files), statsmodels (3 files)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
27 files
At the source:

Code availability

All code used to produce the figures and run statistical testing can be found on Zenodo74

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;
  • 27 scripts, each with its path and the digest of its content;
  • 2 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

All processed data used in this study are available in the source file. Raw data will be made available upon request, to prevent duplication and saturating storage demand due to the large size. Source data are provided in this paper.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 13 MeSH terms, 3 funders, 73 references, 18 RRIDs.

Cite

This paper

Goldbach, H. C., Rimondini, R., Swanson, E. S., Shin, J. H., Authement, M. E., Anderson, L. G., Kwon, H. B., Paletzki, R., Gerfen, C. R., Amarante, L. M., Krauzlis, R. J., & Alvarez, V. A. (2026). Cholinergic-dependent dopamine signals in mouse dorsomedial striatum are regulated by frontal but not sensory cortices. Nature communications, 17(1), 9601. https://doi.org/10.1038/s41467-026-77168-x

BibTeX

@article{goldbach2026cholinergic,
author = {Goldbach, Hannah C and Rimondini, Rachele and Swanson, Evan S and Shin, Jung Hoon and Authement, Michael E and Anderson, Lucy G and Kwon, Han Bin and Paletzki, Ron and Gerfen, Charles R and Amarante, Linda M and Krauzlis, Richard J and Alvarez, Veronica A},
title = {{Cholinergic-dependent dopamine signals in mouse dorsomedial striatum are regulated by frontal but not sensory cortices}},
journal = {Nature communications},
year = {2026},
month = sep,
volume = {17},
number = {1},
pages = {9601},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-77168-x},
url = {https://doi.org/10.1038/s41467-026-77168-x},
pmid = {42711343},
pmcid = {PMC13554099}
}

RIS

TY - JOUR
AU - Goldbach, Hannah C
AU - Rimondini, Rachele
AU - Swanson, Evan S
AU - Shin, Jung Hoon
AU - Authement, Michael E
AU - Anderson, Lucy G
AU - Kwon, Han Bin
AU - Paletzki, Ron
AU - Gerfen, Charles R
AU - Amarante, Linda M
AU - Krauzlis, Richard J
AU - Alvarez, Veronica A
TI - Cholinergic-dependent dopamine signals in mouse dorsomedial striatum are regulated by frontal but not sensory cortices
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/09/02
VL - 17
IS - 1
SP - 9601
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-77168-x
UR - https://doi.org/10.1038/s41467-026-77168-x
LA - en
ER -

CSL-JSON

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