OSCR

A brain reward circuit inhibited by next-generation weight-loss drugs in mice.

Code ↔ Paper

9 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 9 matches
  1. [1] § Methods › Home-cage monitoring of Glp1rS33W mice › Grouped behaviour analysis ↔ network_plot.ipynb, lines 87–194 · score 0.73 · Behavioural transition networks, transition probabilities, NetworkX, undirected, global, Node
  2. [2] § Behavioural profiling in Glp1rS33W mice ↔ GLMM_beta_behavior.R, lines 1–55 · score 0.72 · random intercept, mouse ID, LiCl, beta, fitted, spent
  3. [3] § Methods › Home-cage monitoring of Glp1rS33W mice › Behaviour localization and categorization ↔ location_behavior.ipynb, lines 277–316 · score 0.69 · broader categories, food motivated, Pause, sniff, locations, hopper
  4. [4] § Behavioural profiling in Glp1rS33W mice ↔ network_plot.ipynb, lines 87–194 · score 0.67 · transition network, Transition probabilities, food motivated, Arrow, Node, raw
  5. [5] § Methods › Home-cage monitoring of Glp1rS33W mice › Grouped behaviour analysis ↔ build_dataset.ipynb, lines 160–197 · score 0.63 · outgoing transitions, starting behaviour, normalized transition, probabilities
  6. [6] § Behavioural profiling in Glp1rS33W mice ↔ PCA.R, lines 82–151 · score 0.63 · Principal component, LiCl, PC1, PC2, PCA, variance
  7. [7] § Behavioural profiling in Glp1rS33W mice ↔ bar_behavior_plots.R, lines 324–398 · score 0.59 · distance travelled, LiCl, motivated, fed, drinking, grooming
  8. [8] § Methods › Fibre photometry recordings › Calcium analysis (GCaMP) ↔ GCaMP Calcium Events.m, lines 312–341 · score 0.57 · scored trace, smoothed, events, calcium, max, min
  9. [9] § Methods › Homeostatic (SD) food intake ↔ GLMM_beta_behavior.R, lines 1–55 · score 0.52 · cage floor, dark cycle, SD, drug, vehicle, food

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 · 198 lines · 6.1 KB · MIT · 2 matches

  1. # %% [markdown]
  2. # ### This script makes example transition network plots
  3. #
  4. # #### Use the transition csv made in build_dataset.ipynb OR real data from normalized_transitions.csv
  6. # %%
  7. import matplotlib.pyplot as plt
  8. from matplotlib import cm
  9. from matplotlib.colors import to_rgba
  10. from matplotlib.patches import FancyArrowPatch, Circle
  11. from matplotlib.lines import Line2D
  12. import networkx as nx
  13. import numpy as np
  14. import os
  15. import pandas as pd
  17. # %%
  18. # Set the working directory
  19. path_to_csvs = r"C:\Users\irs3th\glp1r-reward-circuit"
  20. if not os.path.exists(path_to_csvs):
  21. raise FileNotFoundError(f"Cannot find the path: '{path_to_csvs}'")
  23. # Verify
  24. os.chdir(path_to_csvs)
  25. print("Current Working Directory:", os.getcwd())
  27. # %%
  28. # Read csv from build_dataset
  29. transition_df = pd.read_csv('normalized_transitions.csv')
  31. # %% [markdown]
  32. # ## Combining transitions by directionality
  33. #
  34. # #### This step merges reciprocal transitions so that, for example, "groom to move" and "move to groom" are combined into a single, bidirectional transition
  35. #
  36. # #### Only run this section if your dataset includes bidirectional transitions (the toy data does not so this code is for demonstration)
  37. #
  38. # #### If you're not combining directions, you can skip this step and move on to averaging
  40. # %%
  41. # collect all transition columns (skip id + group)
  42. transition_cols = [c for c in transition_df.columns if c not in ["id", "group"]]
  44. pairs = {}
  45. for col in transition_cols:
  46. src, dst = col.split("_", 1) # split only on first underscore
  47. reverse = f"{dst}_{src}"
  48. if reverse in transition_cols:
  49. # build a standardized name (alphabetical to avoid duplicates)
  50. merged_name = "_".join(sorted([src, dst]))
  51. pairs.setdefault(merged_name, set()).update([col, reverse])
  52. else:
  53. # transitions without a reverse pair stay as is
  54. pairs[col] = {col}
  56. # now combine
  57. for merged_name, cols in pairs.items():
  58. transition_df[merged_name] = transition_df[list(cols)].sum(axis=1)
  60. # drop the old directed columns
  61. transition_df = transition_df[["id", "group"] + list(pairs.keys())]
  63. # print(transition_df.head())
  66. # %%
  67. # Average normalized probability by group
  68. avg_transition_matrix = (
  69. transition_df
  70. .drop(columns=["id"]) # remove numeric ID
  71. .groupby("group")
  72. .mean()
  73. )
  74. filtered = avg_transition_matrix.reset_index()
  76. filtered = filtered[
  77. ~(
  78. filtered["group"].str.endswith("_light", na=False) |
  79. filtered["group"].str.startswith("veh", na=False) |
  80. filtered["group"].str.startswith("saline", na=False)|
  81. filtered["group"].str.startswith("no", na=False)
  83. )
  84. ]
  85. filtered
  87. # %% [markdown]
  88. # ### Plot for combined bidirectional transitions
  89. # ### Aesthetic updates
  90. # #### - scaled colors for edges
  91. # #### - double arrowheads
  92. # #### - more space between nodes and edges
  94. # %%
  95. # Node colors
  96. node_colors = {
  97. 'food motivated': 'blue',
  98. 'drink': 'green',
  99. 'move/explore': 'hotpink',
  100. 'groom': 'orange',
  101. 'shelter': 'grey'
  102. }
  104. behavior_numbering = {behavior: i + 1 for i, behavior in enumerate(node_colors)}
  105. number_to_behavior = {v: k for k, v in behavior_numbering.items()}
  107. # Calculate global max weight for scaling edges
  108. # filtered: rows = groups, columns = numeric transition probabilities
  109. global_max_weight = filtered.select_dtypes(float).max().max()
  111. # Grayscale colormap for edges
  112. cmap = cm.Greys
  114. for _, row in filtered.iterrows():
  115. group = row["group"] # fixed to get real group name
  116. G = nx.Graph() # undirected
  118. # Add edges with raw weights
  119. for col, value in row.items():
  120. try:
  121. val = float(value)
  122. if val > 0:
  123. source, target = col.rsplit('_', 1)
  124. if source in behavior_numbering and target in behavior_numbering:
  125. G.add_edge(
  126. behavior_numbering[source],
  127. behavior_numbering[target],
  128. weight=val
  129. )
  130. except Exception:
  131. continue
  133. # Layout (increase `k` to spread nodes apart more)
  134. pos = nx.spring_layout(G, seed=42, k=1, weight="weight")
  136. plt.figure(figsize=(5, 5))
  137. ax = plt.gca()
  139. # Draw nodes
  140. for node, (x, y) in pos.items():
  141. ax.scatter(x, y, s=400, color=node_colors[number_to_behavior[node]], zorder=3)
  142. # ax.text(x, y, str(node), color="white", fontsize=10,
  143. #ha="center", va="center", zorder=4)
  145. # Normalize edge colors
  146. desired_max = 0.6 # adjust as needed
  147. norm = plt.Normalize(vmin=0, vmax=desired_max)
  149. node_radius = 0.05 # adjust to push arrows further away from nodes
  151. # Draw edges with "constant linewidth" but grayscale color
  152. for u, v, d in G.edges(data=True):
  153. weight = d["weight"]
  154. lw = 3
  155. color = cmap(norm(weight))
  157. x1, y1 = pos[u]
  158. x2, y2 = pos[v]
  160. # Compute vector and shrink it at both ends
  161. dx, dy = x2 - x1, y2 - y1
  162. dist = np.sqrt(dx**2 + dy**2)
  163. if dist > 0:
  164. offset_x = dx / dist * node_radius
  165. offset_y = dy / dist * node_radius
  166. x1_off, y1_off = x1 + offset_x, y1 + offset_y
  167. x2_off, y2_off = x2 - offset_x, y2 - offset_y
  168. else:
  169. x1_off, y1_off, x2_off, y2_off = x1, y1, x2, y2
  171. # Draw double-headed arrow
  172. ax.annotate("",
  173. xy=(x2_off, y2_off), xycoords="data",
  174. xytext=(x1_off, y1_off), textcoords="data",
  175. arrowprops=dict(arrowstyle="<|-|>",
  176. color=color,
  177. lw=lw,
  178. alpha=0.9),
  179. zorder=2,
  180. )
  182. # Add grayscale colorbar
  183. sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
  184. sm.set_array([])
  185. cbar = plt.colorbar(sm, ax=ax, shrink=0.5, pad=0.02)
  186. cbar.set_label("Transition Weight", fontsize=10)
  188. plt.title(f"Behavior Transition Network: {group}")
  189. plt.axis("off")
  190. plt.tight_layout()
  191. filename = os.path.join(path_to_csvs, f"{str(group).replace('/', '_')}.png")
  192. #plt.savefig(filename, format="eps", dpi=600)
  193. plt.show()
  194. plt.close()
  197. # %%

network_plot.ipynb at commit 1174a09, under MIT · at the source

Overview

Authors: Elizabeth N. Godschall1, Taha Bugra Gungul1, Isabelle R. Sajonia1, Aleyna K. Buyukaksakal1, Orien Li1, Sophia Ogilvie1, Austin B. Keeler1, Guilian Tian2, Yu Shi1, Omar Koita3,4, Chloe Xinzhu Guo1, Tyler C. J. Deutsch5,6, Eric J. Steacy5,7, Maisie Crook1, YuChen Zhang7, Nicholas J. Conley1,7, Gulsun Memi1,8, Addison N. Webster1,7, O. Yipkin Calhan1, Weile Liu1
and 13 other authorsAmani Akkoub1, Karan Malik1, Kaleigh I. West1, Sara Michel-Le1, Arun Karthikeyan1, Grace van Gerven1, Olivia A. Dell’Aglio1, Kevin T. Beier2, Larry S. Zweifel3,4, Manoj K. Patel5,7, John N. Campbell1,7,9, Christopher D. Deppmann1,7,9,10,11, Ali D. Güler1,7,9
ORCID iDs: Elizabeth N. Godschall, Austin B. Keeler, Chloe Xinzhu Guo, Tyler C. J. Deutsch, Eric J. Steacy, Gulsun Memi, Kevin T. Beier, Larry S. Zweifel, John N. Campbell, Christopher D. Deppmann, Ali D. Güler
  1. Department of Biology, University of Virginia,Charlottesville, VA USA
  2. Department of Physiology and Biophysics, University of California, Irvine,Irvine, CA USA
  3. Department of Pharmacology, University of Washington,Seattle, WA USA
  4. Department of Psychiatry, University of Washington,Seattle, WA USA
  5. Department of Anesthesiology, University of Virginia,Charlottesville, VA USA
  6. Edward Via College of Osteopathic Medicine,Blacksburg, VA USA
  7. Neuroscience Graduate Program, University of Virginia,Charlottesville, VA USA
  8. Department of Physiology, School of Medicine, Adiyaman University,Adiyaman, Turkey
  9. Program in Fundamental Neuroscience, University of Virginia,Charlottesville, VA USA
  10. Department of Cell Biology, University of Virginia,Charlottesville, VA USA
  11. Department of Biomedical Engineering, University of Virginia,Charlottesville, VA USA
Institutions: University of Virginia (United States); University of California, Irvine (United States); University of Washington (United States); Edward Via College of Osteopathic Medicine (United States); Adıyaman University (Türkiye)
Journal: Nature, volume 654, issue 8120, pages 1055-1064
Dates: received 12 December 2024; accepted 24 March 2026; published online 6 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41586-026-10444-4 · PMID 42092139 · PMCID PMC13293854 · OpenAlex W7160447125
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, Single-unit activity, calcium imaging
Keywords: Neural circuits, Feeding behaviour
MeSH: Anti-Obesity Agents*, Brain*, Feeding Behavior*, Glucagon-Like Peptide-1 Receptor Agonists*, Neural Pathways*, Reward*, Weight Loss*, Amygdala, Animals, Dopamine, Eating, Female, Glucagon-Like Peptide-1 Receptor, Homeostasis, Humans, Male, Mice, Neurons, Nucleus Accumbens (* major topic)
Topic: Regulation of Appetite and Obesity (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS122834, R01 NS120702); NIMH NIH HHS (R01 MH135538)
Citations: cited by 6 papers (Europe PMC); 57 references in the paper
Research resources: anti-DsRed RRID:AB_10013483, anti-c-FOS RRID:AB_2231974, Cy3-conjugated donkey anti-goat RRID:AB_2307351, Cy3-conjugated donkey anti-rabbit RRID:AB_2307443, RRID:AB_2337258, Alexa-Fluor 488 donkey anti-goat RRID:AB_2340428, Cy5-conjugated donkey anti-rabbit RRID:AB_2340607, RRID:AB_2340612, anti-human GLP1R RRID:AB_2812404, A total of 200–400 nl virus RRID:Addgene_104491, A total of 200–400 nl virus RRID:Addgene_105448-AAV9, A total of 200–400 nl virus RRID:Addgene_105540-AAV8, A total of 200–400 nl virus RRID:Addgene_135762, A total of 200–400 nl virus RRID:Addgene_20298, A total of 200–400 nl virus RRID:Addgene_27056, RRID:Addgene_50457, A total of 200–400 nl virus RRID:Addgene_50459, A total of 200–400 nl virus RRID:Addgene_50465-AAV8, RRID:Addgene_66295, A total of 200–400 nl virus RRID:Addgene_71760, Dat-Cre mice RRID:IMSR_JAX:006660, RRID:IMSR_JAX:007914, Glp1r-IRES-Cre mice RRID:IMSR_JAX:029283, Glp1rflox/flox mice RRID:IMSR_JAX:035238, Gcg-Cre mice RRID:MMRRC_051056-MU

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 9 matches between paragraphs and lines of code.

UVACircMetNeuLab/glp1r-reward-circuit

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 1174a09f338ffd61ec686e58d76e302bbad3be4b, 26 March 2026
Languages: Jupyter (5), R (3), MATLAB (2)
Size: 32 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (environment.yml), 5 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), tidyverse (3 files), ggplot2 (2 files), Matplotlib (2 files), SciPy (2 files), emmeans (1 file), glmmTMB (1 file), h5py (1 file), NetworkX (1 file), OpenCV (1 file), patchwork (1 file), Plotly (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
12 files
At the source: github.com/UVACircMetNeuLab/glp1r-reward-circuit

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/s41586-026-10444-4.

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;
  • 10 scripts, each with its path and the digest of its content;
  • 9 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

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41586-026-10444-4.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 33 authors, 2 keywords, 19 MeSH terms, 2 funders, 55 references, 25 RRIDs.

Cite

This paper

Godschall, E. N., Gungul, T. B., Sajonia, I. R., Buyukaksakal, A. K., Li, O., Ogilvie, S., Keeler, A. B., Tian, G., Shi, Y., Koita, O., Guo, C. X., Deutsch, T. C. J., Steacy, E. J., Crook, M., Zhang, Y., Conley, N. J., Memi, G., Webster, A. N., Yipkin Calhan, O., . . . Güler, A. D. (2026). A brain reward circuit inhibited by next-generation weight-loss drugs in mice. Nature, 654(8120), 1055-1064. https://doi.org/10.1038/s41586-026-10444-4

BibTeX

@article{godschall2026brain,
author = {Godschall, Elizabeth N. and Gungul, Taha Bugra and Sajonia, Isabelle R. and Buyukaksakal, Aleyna K. and Li, Orien and Ogilvie, Sophia and Keeler, Austin B. and Tian, Guilian and Shi, Yu and Koita, Omar and Guo, Chloe Xinzhu and Deutsch, Tyler C. J. and Steacy, Eric J. and Crook, Maisie and Zhang, YuChen and Conley, Nicholas J. and Memi, Gulsun and Webster, Addison N. and Yipkin Calhan, O. and Liu, Weile and Akkoub, Amani and Malik, Karan and West, Kaleigh I. and Michel-Le, Sara and Karthikeyan, Arun and van Gerven, Grace and Dell’Aglio, Olivia A. and Beier, Kevin T. and Zweifel, Larry S. and Patel, Manoj K. and Campbell, John N. and Deppmann, Christopher D. and Güler, Ali D.},
title = {{A brain reward circuit inhibited by next-generation weight-loss drugs in mice}},
journal = {Nature},
year = {2026},
month = may,
volume = {654},
number = {8120},
pages = {1055--1064},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10444-4},
url = {https://doi.org/10.1038/s41586-026-10444-4},
pmid = {42092139},
pmcid = {PMC13293854}
}

RIS

TY - JOUR
AU - Godschall, Elizabeth N.
AU - Gungul, Taha Bugra
AU - Sajonia, Isabelle R.
AU - Buyukaksakal, Aleyna K.
AU - Li, Orien
AU - Ogilvie, Sophia
AU - Keeler, Austin B.
AU - Tian, Guilian
AU - Shi, Yu
AU - Koita, Omar
AU - Guo, Chloe Xinzhu
AU - Deutsch, Tyler C. J.
AU - Steacy, Eric J.
AU - Crook, Maisie
AU - Zhang, YuChen
AU - Conley, Nicholas J.
AU - Memi, Gulsun
AU - Webster, Addison N.
AU - Yipkin Calhan, O.
AU - Liu, Weile
AU - Akkoub, Amani
AU - Malik, Karan
AU - West, Kaleigh I.
AU - Michel-Le, Sara
AU - Karthikeyan, Arun
AU - van Gerven, Grace
AU - Dell’Aglio, Olivia A.
AU - Beier, Kevin T.
AU - Zweifel, Larry S.
AU - Patel, Manoj K.
AU - Campbell, John N.
AU - Deppmann, Christopher D.
AU - Güler, Ali D.
TI - A brain reward circuit inhibited by next-generation weight-loss drugs in mice
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/05/06
VL - 654
IS - 8120
SP - 1055
EP - 1064
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10444-4
UR - https://doi.org/10.1038/s41586-026-10444-4
LA - en
ER -

CSL-JSON

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