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The central amygdala gates exogenous glucagon-like peptide 1 signals.

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  1. [1] § Materials and methods › Fiber photometry recordings › Fiber photometry data analysis ↔ routine/processing.py, lines 14 to 100 · score 0.61 · Huber regression, nearest, photobleaching, model, fit, baseline · Report a mismatch

Paper

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The authors' code

Python · 161 lines · 5.8 KB · no license · 1 match

  1. import warnings
  3. import numpy as np
  4. import pandas as pd
  5. from scipy.ndimage import median_filter
  6. from scipy.optimize import curve_fit
  7. from scipy.signal import find_peaks
  8. from scipy.stats import zscore
  9. from sklearn.linear_model import HuberRegressor
  11. from .utilities import exp2, min_transform
  14. def photobleach_correction(
  15. data, baseline_sig, rois=None, min_trans=True, med_wnd=None, method="simple"
  16. ):
  17. if method == "simple":
  18. if med_wnd is not None:
  19. warnings.warn(
  20. "med_wnd is set but will be ignored when using 'simple' method"
  21. )
  22. elif method == "moment_to_moment":
  23. assert (
  24. med_wnd is not None
  25. ), "med_wnd must be set when using 'moment_to_moment' method"
  26. else:
  27. raise NotImplementedError(
  28. "method must be either 'simple' or 'moment_to_moment', got {} instead".format(
  29. method
  30. )
  31. )
  32. # auto set rois
  33. if rois is None:
  34. rois = list(
  35. set([r[0] for r in baseline_sig.keys()])
  36. | set([r[0] for r in baseline_sig.values()])
  37. )
  38. # making sure signal exists
  39. base_dict = dict()
  40. for (roi, sig), (base_roi, base_sig) in baseline_sig.items():
  41. if len(data.loc[data["signal"] == sig, roi]) == 0:
  42. continue
  43. if len(data.loc[data["signal"] == base_sig, base_roi]) == 0:
  44. warnings.warn(
  45. "Cannot find signal '{}' in roi '{}', skipping correction for signal '{}' roi '{}'".format(
  46. base_sig, base_roi, sig, roi
  47. )
  48. )
  49. continue
  50. base_dict[(roi, sig)] = (base_roi, base_sig)
  51. # fit baseline
  52. base_dfs = dict()
  53. for base_roi, base_sig in set(base_dict.values()):
  54. if base_sig in base_dfs:
  55. base_df = base_dfs[base_sig]
  56. else:
  57. base_df = data.loc[data["signal"] == base_sig].copy()
  58. base_df["signal"] = base_sig + "-fit"
  59. base_df[rois] = np.nan
  60. dat_fit = data.loc[data["signal"] == base_sig, base_roi]
  61. x = np.linspace(0, 1, len(dat_fit))
  62. base_fit = fit_exp2(dat_fit, x)
  63. if method == "moment_to_moment":
  64. base_df[base_roi] = median_filter(
  65. dat_fit - base_fit, med_wnd, mode="nearest"
  66. )
  67. else:
  68. base_df[base_roi] = base_fit
  69. base_dfs[base_sig] = base_df
  70. # correct signals
  71. sig_dfs = dict()
  72. for (roi, sig), (base_roi, base_sig) in base_dict.items():
  73. if sig in sig_dfs:
  74. sig_df = sig_dfs[sig]
  75. norm_df = sig_df.loc[sig_df["signal"] == sig + "-norm"].copy()
  76. zs_df = sig_df.loc[sig_df["signal"] == sig + "-norm-zs"].copy()
  77. else:
  78. norm_df = data.loc[data["signal"] == sig].copy()
  79. norm_df["signal"] = sig + "-norm"
  80. norm_df[rois] = np.nan
  81. zs_df = data.loc[data["signal"] == sig].copy()
  82. zs_df["signal"] = sig + "-norm-zs"
  83. zs_df[rois] = np.nan
  84. dat_sig = data.loc[data["signal"] == sig, roi]
  85. if method == "moment_to_moment":
  86. x = np.linspace(0, 1, len(dat_sig))
  87. sig_fit = fit_exp2(dat_sig, x)
  88. dat_sig = dat_sig - sig_fit
  89. baseline = np.array(base_dfs[base_sig][base_roi])
  90. model = HuberRegressor()
  91. model.fit(baseline.reshape((-1, 1)), dat_sig)
  92. norm_df[roi] = dat_sig - model.predict(baseline.reshape((-1, 1)))
  93. if min_trans:
  94. norm_df[roi] = min_transform(norm_df[roi])
  95. zs_df[roi] = zscore(norm_df[roi])
  96. sig_dfs[sig] = pd.concat([norm_df, zs_df])
  97. data_norm = pd.concat(
  98. [data] + list(base_dfs.values()) + list(sig_dfs.values()), ignore_index=True
  99. )
  100. return data_norm
  103. def fit_exp2(a, x):
  104. dmax, dmin = a[:50].median(), a[-50:].median()
  105. drg = dmax - dmin
  106. p0 = (drg, -10, drg, 0.1, dmin - drg)
  107. try:
  108. popt, pcov = curve_fit(exp2, x, a, p0=p0, method="trf", ftol=1e-6, maxfev=1e4)
  109. except:
  110. warnings.warn("Biexponential fit failed")
  111. popt = p0
  112. return exp2(x, *popt)
  115. def compute_dff(data, rois, sigs=["415nm", "470nm"]):
  116. if sigs is not None:
  117. data = data[data["signal"].isin(sigs)].copy()
  118. res_ls = []
  119. for sig, dat_sig in data.groupby("signal"):
  120. dat_fit = dat_sig.copy()
  121. dat_dff = dat_sig.copy()
  122. dat_fit["signal"] = sig + "-fit"
  123. dat_dff["signal"] = sig + "-dff"
  124. x = np.linspace(0, 1, len(dat_sig))
  125. for roi in rois:
  126. dat = dat_sig[roi]
  127. popt, pcov = curve_fit(
  128. exp2,
  129. x,
  130. dat,
  131. p0=(1.0, 0, 1.0, 0, dat.mean()),
  132. bounds=(
  133. np.array([-np.inf, -np.inf, -np.inf, -np.inf, dat.min()]),
  134. np.array([np.inf, np.inf, np.inf, np.inf, dat.max()]),
  135. ),
  136. )
  137. cur_fit = exp2(x, *popt)
  138. dat_fit[roi] = cur_fit
  139. dat_dff[roi] = 100 * (dat - cur_fit) / cur_fit
  140. res_ls.extend([dat_fit, dat_dff])
  141. return pd.concat([data] + res_ls, ignore_index=True)
  144. def find_pks(data, rois, prominence, freq_wd=None, sigs=None):
  145. for sig, dat_sig in data.groupby("signal"):
  146. if sigs is not None and sig in sigs:
  147. for roi in rois:
  148. dat = dat_sig[roi]
  149. pks, props = find_peaks(dat, prominence=prominence)
  150. pvec = np.zeros_like(dat, dtype=bool)
  151. pvec[pks] = 1
  152. data.loc[dat_sig.index, roi + "-pks"] = pvec
  153. if freq_wd is not None:
  154. dat_sig.loc[dat_sig.index, roi + "-freq"] = (
  155. dat_sig.loc[dat_sig.index, roi + "-pks"].rolling(freq_wd).sum()
  156. )
  157. return data
  160. def moving_average_filter(x, wnd, mode="same"):
  161. return np.convolve(x, np.ones(wnd) / wnd, mode=mode)

processing.py at commit c860df8, no license · at the source

Overview

Authors: Miguel Duran1, Ningxiang Zeng1, Elam J. Cutts1, Anusha Polamarasetty1, Melissa Rodriguez1, Kirk M. Habegger2, J. Andrew Hardaway1
ORCID iDs: J. Andrew Hardaway
  1. Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
  2. Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
Institutions: University of Alabama at Birmingham (United States)
Journal: Molecular metabolism, volume 110, article 102403
Dates: received 10 April 2026; accepted 13 June 2026; published online 18 June 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.molmet.2026.102403 · PMID 42314903 · PMCID PMC13332456 · OpenAlex W7165172499
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: GLP-1 receptor, Central amygdala, Chemogenetics, Fiber photometry, Exendin-4
MeSH: Central Amygdaloid Nucleus*, Glucagon-Like Peptide 1*, Animals, Exenatide, Glucagon-Like Peptide-1 Receptor, Glucagon-Like Peptide-1 Receptor Agonists, Male, Mice, Mice, Inbred C57BL, Neurons, Signal Transduction (* major topic)
Topic: Diabetes Treatment and Management (Endocrinology, Diabetes and Metabolism, Medicine), according to OpenAlex
Funding: F31DK138767; K01DK115902; R03DK129561; R01DK140308
Citations: not cited yet (Europe PMC); 93 references in the paper
Research resources: Chicken anti-GFP RRID:AB_2307313, Normal Donkey Serum RRID:AB_2337258, RRID:AB_2340607, Rabbit anti-mCherry RRID:AB_2552323, RRID:Addgene_104492, RRID:Addgene_44362, pAAV-hSyn-DIO-mCherry (6.25∗1012 GC/ml) RRID:Addgene_50459, Mouse: STOCK Ssttm2.1(cre)Zjh/J RRID:IMSR_JAX:013044, RRID:IMSR_JAX:028862, Mouse: STOCK Glp1rtm1.1(cre)Lbrl/RcngJ RRID:IMSR_JAX:029283, RRID:MMRRC_011559-UCD, ImageJ/FIJI RRID:SCR_002285, Graphpad Prism v10 RRID:SCR_002798, Adobe Illustrator RRID:SCR_010279, pCLAMP v10 RRID:SCR_011323, Microsoft Excel RRID:SCR_016137, Bonsai RRID:SCR_017218, Keyence BZ-X810 Fluorescent Microscope RRID:SCR_023617

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.

Repositories

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OpenBehavior/Modular-Stereotaxic-Holders

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 88639b72a483eb3d5334a1c93836b1d82eb3b9c1, 6 September 2022
Size: 9 files, 0 scripts
Software Heritage: not archived
Found in: the text, “Stereotaxic surgery”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/OpenBehavior/Modular-Stereotaxic-Holders

Hardaway-Lab/FED3-Photometry_Workflows

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c860df8c711c840a4b016fe6c0f53a9efa5d09b0, 31 August 2024
Languages: Python (10), Jupyter (3)
Size: 37 files, 13 scripts
Software Heritage: not archived
Found in: the text, “Fiber photometry data analysis”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (7 files), NumPy (6 files), Plotly (2 files), scikit-learn (2 files), SciPy (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: github.com/Hardaway-Lab/FED3-Photometry_Workflows

Tracing map

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  • 13 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Read it in the paper: doi.org/10.1016/j.molmet.2026.102403.

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Version 2, 28 September 2026

  • Authors: added J. Andrew Hardaway (0000-0003-3893-2658); removed J. Andrew Hardaway

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 11 MeSH terms, 4 funders, 92 references, 18 RRIDs.

Cite

This paper

Duran, M., Zeng, N., Cutts, E. J., Polamarasetty, A., Rodriguez, M., Habegger, K. M., & Hardaway, J. A. (2026). The central amygdala gates exogenous glucagon-like peptide 1 signals. Molecular metabolism, 110, 102403. https://doi.org/10.1016/j.molmet.2026.102403

BibTeX

@article{duran2026central,
author = {Duran, Miguel and Zeng, Ningxiang and Cutts, Elam J. and Polamarasetty, Anusha and Rodriguez, Melissa and Habegger, Kirk M. and Hardaway, J. Andrew},
title = {{The central amygdala gates exogenous glucagon-like peptide 1 signals}},
journal = {Molecular metabolism},
year = {2026},
month = jun,
volume = {110},
pages = {102403},
publisher = {Elsevier},
issn = {2212-8778},
doi = {10.1016/j.molmet.2026.102403},
url = {https://doi.org/10.1016/j.molmet.2026.102403},
pmid = {42314903},
pmcid = {PMC13332456}
}

RIS

TY - JOUR
AU - Duran, Miguel
AU - Zeng, Ningxiang
AU - Cutts, Elam J.
AU - Polamarasetty, Anusha
AU - Rodriguez, Melissa
AU - Habegger, Kirk M.
AU - Hardaway, J. Andrew
TI - The central amygdala gates exogenous glucagon-like peptide 1 signals
T2 - Molecular metabolism
J2 - Mol Metab
PY - 2026
DA - 2026/06/18
VL - 110
SP - 102403
SN - 2212-8778
PB - Elsevier
DO - 10.1016/j.molmet.2026.102403
UR - https://doi.org/10.1016/j.molmet.2026.102403
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.molmet.2026.102403",
"type": "article-journal",
"title": "The central amygdala gates exogenous glucagon-like peptide 1 signals",
"container-title": "Molecular metabolism",
"author": [
{
"family": "Duran",
"given": "Miguel"
},
{
"family": "Zeng",
"given": "Ningxiang"
},
{
"family": "Cutts",
"given": "Elam J."
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{
"family": "Polamarasetty",
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{
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{
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"given": "J. Andrew"
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],
"container-title-short": "Mol Metab",
"volume": "110",
"page": "102403",
"DOI": "10.1016/j.molmet.2026.102403",
"PMID": "42314903",
"PMCID": "PMC13332456",
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"language": "en",
"issued": {
"date-parts": [
[
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18
]
]
}
}

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