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The vagus nerve promotes memory in rats via nutrient-induced septo-hippocampal acetylcholine signaling.

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  1. [1] § Methods › In vivo fiber photometry ↔ Neurophotometrics_LTL.m, lines 90–225 · score 0.63 · dependent signal, biexponential, artifacts, subtracted, excitation, Neurophotometrics

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  1. %% Import data:
  2. %Import the data as a numeric matrix where time in on the first column and
  3. %the signals are in all subsequent columns to the right.
  4. %The following code assumes recording sessions should produce 3 csv files (maybe more depending on
  5. %experiment: (1) rawdata; contains photometry data, (2) time; should be a
  6. %vector of time, here we have it as ms in 'time of day', (3) keydown file
  7. %with timestamps.
  9. %The time and keydown files get imported as a numeric matrix.
  10. % The photometry data get imported as a table. We import as:
  11. % table name: AnimalID, and the columns 0G or 1G (whichever region you're
  12. % recording from) and change its name to signal.
  15. D2708.time = D2708_time; %The variable to the right is the raw file that gets imported, it gets imported as numeric matrix,
  16. % and we're assigning it to a table.
  18. %theres usually a weird artifact within the first 1-5 data points so we cut the first ten to be safe.
  19. FP.rawdata(:,1) = D2708.time(20:end);
  20. FP.rawdata(:,2) = D2708.signal(20:end);
  22. %Assign the first time data point of the new table to startpoint.
  23. startpoint = FP.rawdata(1,1);
  25. %Here we normalize the keydown time values to start at zero and convert
  26. %them to seconds, they come in ms originally.
  27. D2708_sfs_keydown = (D2708_sfs_keydown - startpoint) ./ 1000;
  29. %Here we do the same thing but for the time column of our data.
  30. FP.rawdata(:,1) = (FP.rawdata(:,1) - startpoint)./1000;
  32. %quick check to make sure which is isosbestic
  33. plot(downsample(FP.rawdata(2:end,2),2),'b')
  34. hold on
  35. plot(downsample(FP.rawdata(:,2),2),'r')
  36. ylabel({'Raw fluorescence'});
  37. xlabel({'data point #'});
  38. title({'AnimalID Raw de-interleaved isosbestic and Ca dependent traces'});
  40. %% if running behavioral task where keydown corresponds to the start of Anymaze behavioral tracking then run the code below:
  42. FP.rawdata(:,1) = FP.rawdata(:,1) - D2708_sfs_keydown;
  44. % Define time window
  45. tMin = -10;
  46. tMax = 120;
  48. % Logical index based on time column
  49. idx = FP.rawdata(:,1) >= tMin & FP.rawdata(:,1) <= tMax;
  51. % Keep only the desired time range
  52. FP.rawdata = FP.rawdata(idx, :)
  56. %% De interleaving
  57. %Here we de-interleave the signal because the system alternates collection
  58. %between biomolecule-dependent signal and the isosbestic signal.
  60. %FP.calcium_dependent(:,1) = downsample(FP.rawdata(:,1),2);
  61. %FP.calcium_dependent(:,2) = downsample(FP.rawdata(:,2),2);
  62. FP.calcium_dependent(:,1) = downsample(FP.rawdata(2:end,1),2);
  63. FP.calcium_dependent(:,2) = downsample(FP.rawdata(2:end,2),2);
  65. %FP.isosbestic(:,1) = downsample(FP.rawdata(2:end,1),2);
  66. %FP.isosbestic(:,2) = downsample(FP.rawdata(2:end,2),2);
  67. FP.isosbestic(:,1) = downsample(FP.rawdata(:,1),2);
  68. FP.isosbestic(:,2) = downsample(FP.rawdata(:,2),2);
  70. %Then we plot them to take another look
  71. f3 = figure;
  74. subplot(2,1,1)
  75. plot(FP.calcium_dependent(:,1),FP.calcium_dependent(:,2),'b')
  77. subplot(2,1,2)
  78. plot(FP.isosbestic(:,1),FP.isosbestic(:,2),'r')
  81. f6 = figure;
  83. yyaxis left
  84. plot(FP.calcium_dependent(:,1),FP.calcium_dependent(:,2),'b')
  85. yyaxis right
  86. plot(FP.isosbestic(:,1),FP.isosbestic(:,2),'r');
  90. %% Methods of neural data processing:
  92. %Method 1: Substracting a running average over a long period of time from
  93. %your data. This works well -- for quick and dirty tests -- but is a bit of
  94. %a hack. It also tends to fail if you have shorter recordings and higher
  95. %SNR. ie Your recording in fiber 2. Let's use that as an example for the
  96. %different methods.
  99. f4 = figure;
  100. subplot(5,1,1)
  101. plot(FP.calcium_dependent(:,1),FP.calcium_dependent(:,2));
  102. title('Deinterleaved Raw Data')
  103. xlabel('Time of day in total ms')
  106. subplot(5,1,2)
  107. plot(FP.calcium_dependent(:,1),FP.calcium_dependent(:,2)-smooth(FP.calcium_dependent(:,2),5455));
  108. title('Linearize by Smoothing')
  109. xlabel('Time of day in total ms')
  111. ylabel('F')
  114. %Another method is to fit the data with a biexponential and subtract that
  115. %fit from the data. This is nicer, since it is based on biology (bleaching
  116. %is monoexponential + bleaching of fiber / heat mediated LED decay gives
  117. %you the second exponential.
  119. %The fiber bleaching + heat mediated decay can be minimized experimentally
  120. %by "pre bleaching" the fiber and pre-heating the LED (you can do this all
  121. %at once -- just set the light power to 100% for ~10 minutes before your
  122. %experient while you are getting everything else set up.)
  124. subplot(5,1,3)
  125. temp_fit = fit(FP.calcium_dependent(:,1),FP.calcium_dependent(:,2),'exp2');
  126. plot(FP.calcium_dependent(:,1),FP.calcium_dependent(:,2)-temp_fit(FP.calcium_dependent(:,1)));
  127. title('Linearize by Fitting with Biexponential')
  128. xlabel('Time of day in total ms')
  129. ylabel('F')
  132. % You can also fit, scale, and subtract the isosbestic signal from the
  133. % calcium dependent signal. This is probably the "best" of the three
  134. % methods desribed here -- as it is less affected by having good signal (ie
  135. % the former 2 methods get wonky when your SNR is super high and your
  136. % recording is short -- as it starts to fit the signal, rather than the
  137. % slow decay, which is bad news bears)
  139. subplot(5,1,4)
  140. %fit isosbestic
  141. temp_fit = fit(FP.calcium_dependent(:,1),FP.isosbestic(:,2),'exp2'); %note, in this case, I am ignoring the first 2000 points where there is this weird fast decay to get a better fit. experimentally, i normally set things up so this isn't an important time in the recording / animal is habituating.
  142. %scale fit to calcium dependent data
  143. fit2 = fitlm(temp_fit(FP.calcium_dependent(:,1)),FP.calcium_dependent(:,2));
  144. %calculate a crude dF/F by subtracting and then dividing by the fit
  145. figure(2)
  146. subplot(3,1,3)
  147. plot(FP.calcium_dependent(:,1),100*(FP.calcium_dependent(:,2)-(fit2.Fitted))./(fit2.Fitted))
  148. xlabel('Time (seconds)')
  149. ylabel('dF/F (%)')
  150. title('A5521 CA1 - processed and traces subtracted')
  153. FP.corrected_data = FP.calcium_dependent(:,1),100*(FP.calcium_dependent(:,2)-(fit2.Fitted))./(fit2.Fitted);
  154. %NOTE: To get a normalized measurement (ie dF/F) you will need to know what
  155. %the background is. For example, if you subtracted this fit from the raw
  156. %data and then divided by the fit -- it would look super wonky -- as you
  157. %are subtracting numbers above and below 0.
  159. %One way to try and get at this, is record for a second without any
  160. %excitation lights on. The signal will never be 0 -- but this will be a
  161. %low-end of what your background signal is. True "background" would be your
  162. %recording with the excitation lights on when no neurons are active (which
  163. %is hard to get). The former works great.
  165. %If your data are clean, the last method is probably the best (imho) -- but
  166. %there is an issue. We now have dF/F values that are less than 0 which
  167. %can't happen. Stupid fix is to add the absolute value of the lowest number
  168. %to the entire vector. Best method is to have some estimate of background
  169. %subtraction.
  171. %For argument's sake, let's say the background for the isosbestic signal
  172. %was 5000.
  173. subplot(5,1,5)
  174. FP.fakebackground =0;% find(min(D2708_bl.signal));
  175. %fit isosbestic
  176. temp_fit = fit(FP.calcium_dependent(:,1),FP.isosbestic(:,2),'exp2'); %note, in this case, I am ignoring the first 2000 points where there is this weird fast decay to get a better fit. experimentally, i normally set things up so this isn't an important time in the recording / animal is habituating.
  177. %scale fit to calcium dependent data
  178. fit2 = fitlm(temp_fit(FP.calcium_dependent(:,1)),FP.calcium_dependent(:,2));
  179. %calculate a crude dF/F by subtracting and then dividing by the fit
  181. plot(FP.calcium_dependent(:,1),100*(FP.calcium_dependent(:,2)-((fit2.Fitted)-FP.fakebackground))./(fit2.Fitted-FP.fakebackground))
  182. xlabel('Time of day in total ms')
  183. ylabel('dF/F (%)')
  184. title('Linearizing + Normalizing Using Isosbestic + Background Subtracting')
  187. %Now, if the ultimate goal is to show short sections of data chopped up
  188. %around an event, then it doesn't make a whole heap of difference which
  189. %method you use. In most cases, even uncorrected data, over 30 second
  190. %segments, looks fine at a shorter time scale (since bleaching occurs over
  191. %many minutes). However, it is good practice + necessary if you want to say
  192. %something like "signal later on in the trial was of a different magnitude
  193. %than signal earlier in the trial."
  195. FP.corrected_data(:,1) = FP.calcium_dependent(:,1); %we'll keep the time vector in the first column to make things easier
  199. temp_fit = fit(FP.calcium_dependent(:,1),FP.isosbestic(:,2),'exp2'); %note, in this case, I am ignoring the first 2000 points where there is this weird fast decay to get a better fit. experimentally, i normally set things up so this isn't an important time in the recording / animal is habituating.
  200. %scale fit to calcium dependent data
  201. fit2 = fitlm(temp_fit(FP.calcium_dependent(:,1)),FP.calcium_dependent(:,2));
  202. %calculate a crude dF/F by subtracting and then dividing by the fit
  204. FP.corrected_data(:,2) = 100*(FP.calcium_dependent(:,2)-((fit2.Fitted)-FP.fakebackground))./(fit2.Fitted-FP.fakebackground);
  205. clear temp_fit fit2
  208. f5 = figure;
  209. subplot(2,1,1)
  211. plot(FP.corrected_data(:,1),FP.corrected_data(:,2))
  212. title('Corrected Data')
  213. xlabel('time (seconds)')
  214. ylabel('dF/F')
  216. FP.corrected_data(:,1) = FP.corrected_data(:,1);
  217. FP.corrected_data(:,2) = FP.corrected_data(:,2);
  220. %note, there is still some wonky stuff going on (ie not super duper linear.
  221. %this is partially due to using a fake background (not in subplot 4 of
  222. %previous graph, you don't really see this) -- and partially some artifact
  223. %of the fit function. it tends to work better if you fit with corrected
  224. %time data (ie not time of day in total ms -- but relative time in seconds)
  225. %-- not sure why that is, but didn't want to muddy the waters here
  230. %%
  231. %Referencing behaviors
  232. %1) Indexing: Find datapoint in FP data that is closest to behavioral
  233. %timestamp
  234. %2) Chop up data about these points
  235. %3) Make dope graphs
  236. %4) Profit
  238. %For this, since the FP and keystroke data are in the same "time space" (ie
  239. %timestamped by teh same computer in the same format, we are going to loop
  240. %through each keystroke timestamp and go from there
  242. FP.bl = 12000; %number of datapoints to look at before keydown
  243. FP.seg_dur = 36000; %number of datapoints to look at after keydown
  244. t = [];
  245. temp = [];
  246. FP.saline.D2708 = []; %necessary if you re-run this part of the code changing baseline or seg_dur
  247. for i = 1
  248. t = find(FP.corrected_data(:,1) >= D2708_keydown(i)); %find all FP data indices that are greater than or equal to keystroke timestampe
  249. temp(i,:) = t(1); %take the first one
  251. %FP.seg_dur = length(FP.corrected_data)-temp;
  255. %note, these data were already corrected -- so, theoretically, the
  256. %differences in baseline activity are real. if you'd like to baseline
  257. %correct these data, you can subtract the mean of the bs period
  259. %FP.ERF.data(:,i) = FP.ERF.data(:,i) - mean(FP.ERF.data(1:FP.bl,i));
  261. end
  262. new_start = [];
  263. for ii = 1 % 8 is the number of csp and csn trials in a session.
  264. FP.saline.D2708(1,:) = FP.corrected_data(temp(ii)-FP.bl:temp(ii)+FP.seg_dur,2)
  265. %new_start(ii) = FP.saline_Day3.data_inj_4014_CA3(ii,1);
  266. %FP.saline_Day3.a4014_CA3_salinex3_final(ii,:) = FP.saline_Day3.data_inj_4014_CA3(ii,:) - new_start(ii);
  267. end
  269. FP.saline.D2708 = FP.saline.D2708';

Neurophotometrics_LTL.m at commit 3922016, no license · at the source

Overview

Authors: Logan Tierno Lauer1, Anna M. R. Hayes1, Andrea N. Suarez1,2, Alexander Bashaw1, Molly E. Klug1, Alicia E. Kao1, Robert Cheng1, Jessica J. Rea1,2, Keshav S. Subramanian1,2, Anna Nourbash1, Kristen N. Donohue1, Lindsey A. Schier1,2, Kevin Myers3, Léa Décarie-Spain1,4,5, Scott E. Kanoski1,2
ORCID iDs: Logan Tierno Lauer, Anna M. R. Hayes, Alicia E. Kao, Jessica J. Rea, Kevin Myers, Léa Décarie-Spain, Scott E. Kanoski
  1. Human and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California,Los Angeles, CA USA
  2. Neuroscience Graduate Program, University of Southern California,Los Angeles, CA USA
  3. Department of Psychology, O’Leary Center, Bucknell University,Lewisburg, PA USA
  4. Centre de recherche du CHUM, Université de Montréal,Montreal, QC Canada
  5. Department of Nutrition, Université de Montréal,Montreal, QC Canada
Institutions: University of Southern California (United States); Bucknell University (United States); Université de Montréal (Canada)
Journal: Nature communications, volume 17, issue 1, article 7154
Dates: received 14 July 2025; accepted 22 May 2026; published online 4 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73896-2 · PMID 42236727 · PMCID PMC13396592 · OpenAlex W7163332424
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: rat (organism), cognitive (subfield)
Methods: Statistics, Preprocessing, Single-unit activity, calcium imaging
Keywords: Feeding behaviour, Hippocampus
MeSH: Acetylcholine*, Hippocampus*, Memory*, Nutrients*, Vagus Nerve*, Animals, Cholinergic Neurons, Diet, Western, Male, Rats, Signal Transduction, Vagotomy (* major topic)
Topic: Vagus Nerve Stimulation Research (Neurology, Neuroscience), according to OpenAlex
Funding: NIDDK (DK123423)
Citations: cited by 2 papers (Europe PMC); 85 references in the paper
Research resources: RRID:AB_2340619

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.

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Cite

This paper

Tierno Lauer, L., Hayes, A. M. R., Suarez, A. N., Bashaw, A., Klug, M. E., Kao, A. E., Cheng, R., Rea, J. J., Subramanian, K. S., Nourbash, A., Donohue, K. N., Schier, L. A., Myers, K., Décarie-Spain, L., & Kanoski, S. E. (2026). The vagus nerve promotes memory in rats via nutrient-induced septo-hippocampal acetylcholine signaling. Nature communications, 17(1), 7154. https://doi.org/10.1038/s41467-026-73896-2

BibTeX

@article{tiernolauer2026vagus,
author = {Tierno Lauer, Logan and Hayes, Anna M. R. and Suarez, Andrea N. and Bashaw, Alexander and Klug, Molly E. and Kao, Alicia E. and Cheng, Robert and Rea, Jessica J. and Subramanian, Keshav S. and Nourbash, Anna and Donohue, Kristen N. and Schier, Lindsey A. and Myers, Kevin and Décarie-Spain, Léa and Kanoski, Scott E.},
title = {{The vagus nerve promotes memory in rats via nutrient-induced septo-hippocampal acetylcholine signaling}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7154},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73896-2},
url = {https://doi.org/10.1038/s41467-026-73896-2},
pmid = {42236727},
pmcid = {PMC13396592}
}

RIS

TY - JOUR
AU - Tierno Lauer, Logan
AU - Hayes, Anna M. R.
AU - Suarez, Andrea N.
AU - Bashaw, Alexander
AU - Klug, Molly E.
AU - Kao, Alicia E.
AU - Cheng, Robert
AU - Rea, Jessica J.
AU - Subramanian, Keshav S.
AU - Nourbash, Anna
AU - Donohue, Kristen N.
AU - Schier, Lindsey A.
AU - Myers, Kevin
AU - Décarie-Spain, Léa
AU - Kanoski, Scott E.
TI - The vagus nerve promotes memory in rats via nutrient-induced septo-hippocampal acetylcholine signaling
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/04
VL - 17
IS - 1
SP - 7154
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73896-2
UR - https://doi.org/10.1038/s41467-026-73896-2
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-73896-2",
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"given": "Alicia E."
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{
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{
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In common: rat, 2 references
[10] doi:10.7554/elife.110795 [code]
REM sleep prefrontal high-frequency oscillation chains mediate distinct cortical - hippocampal reactivation patterns compared to NREM sleep.
Journal: eLife
In common: Statistics and Machine Learning Toolbox, rat, 1 reference

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