A prefrontal cortex-lateral hypothalamus circuit controls stress-driven increased food intake.

Code ↔ Paper

1 match 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.

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[1] § Method › In vivo electrophysiology recordings › Recording single-unit activity of prefrontal cortical neurons ↔ preprocessing/kwik2dat.m, lines 1 to 24 · score 0.57 · Open Ephys, Analog, GUI, synchronized, TTL, filtered · Report a mismatch

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The authors' code

MATLAB · 327 lines · 12 KB · no license · 1 match

function kwik2dat(kwik_path,save_path,sync_channels,sync_input,local_copy,subtract_light)
% kwik2dat(kwik_path,save_path,sync_channel,sync_input)
% IN PROGRESS NOTES: started to set up to load in chunks (by channel for
% filtering/downsampling LFP, but then this couldn't do CAR...) didn't
% finish
%
% Converts kwik format recorded from open-ephys into flat binary
% Splits signal in two: one 500 Hz low-pass LFP(lfp.dat),
% one cross-channel median subtracted spike (spikes.dat)
% Also saves sync channel and parameters/header info
%
% kwk_path - path with kwik files
% save_path - path to save the converted data
%
% sync_channels - TTL channels with synchronization input to save
% (NOTE: input TTL channel as 1-indexed like in GUI, not 0-indexed like in
% saved data)
% sync_input - 'ttl' or 'adc', depending on digital/analog in of sync
% local_copy - true/false, copies data to temporary local folder for faster reading
% Kwik files from open ephys:
% .raw.kwd: continuous data from all channels
% .kwe: event data
%% Make save directory
if ~exist(save_path,'dir');
mkdir(save_path)
end
%% Get filenames for each experiment in path
kwd_dir = dir([kwik_path filesep '*.kwd']);
kwd_filename = [kwik_path filesep kwd_dir(1).name];
kwe_dir = dir([kwik_path filesep '*.kwe']);
kwe_filename = [kwik_path filesep kwe_dir(1).name];
settings_dir = dir([kwik_path filesep '*.xml']);
settings_filename = [kwik_path filesep settings_dir(1).name];
% Get index of electrophysiology channels in recordings
kwik_settings = xml2struct(settings_filename);
ephys_ch = find(cellfun(@(x) any(strfind(x.Attributes.name,'CH')), ...
kwik_settings.SETTINGS.SIGNALCHAIN.PROCESSOR{1}.CHANNEL_INFO.CHANNEL));
n_chan = length(ephys_ch);
% Get sample rate and gain
rec_info_locationInKWD = '/recordings/0';
rec_info = h5info(kwd_filename,rec_info_locationInKWD);
sample_rate_idx = cellfun(@(x) strcmp(x,'sample_rate'),{rec_info.Attributes.Name});
sample_rate = double(rec_info.Attributes(sample_rate_idx).Value);
ch_gain = cellfun(@(x) str2num(x.Attributes.gain), ...
kwik_settings.SETTINGS.SIGNALCHAIN.PROCESSOR{1}.CHANNEL_INFO.CHANNEL(ephys_ch));
if length(unique(ch_gain)) == 1
ch_gain = unique(ch_gain);
else
error('Gains set differently for different channel: no contingency for this in param file yet')
end
% Location in the H5 file where the recordings are
locationInKWD = '/recordings/0/data';
% Load header info
info = h5info(kwd_filename,locationInKWD);
%% If local_copy selected, move data to temporary local drive for loading
if local_copy
disp('Copying data to local drive...')
temp_path = 'C:\temp';
temp_filename = [temp_path filesep kwd_dir(1).name];
if ~exist(temp_path,'dir')
mkdir(temp_path)
end
copyfile(kwd_filename,temp_filename);
disp('Done');
% set data filename to the local filename
kwd_filename = temp_filename;
end
%% Save synchronization input as .mat
disp('Getting and saving sync...');
switch sync_input
case 'ttl'
% If sync was recorded as TTL through digital input
kwe_channels_loc = '/event_types/TTL/events/user_data/event_channels';
kwe_sample_stamp_loc = '/event_types/TTL/events/time_samples';
kwe_value_loc = '/event_types/TTL/events/user_data/eventID';
kwe_sample_rate_loc = '/recordings/0';
ttl_channels = h5read(raw_kwd_filename,kwe_channels_loc);
ttl_samplestamp = h5read(raw_kwd_filename,kwe_sample_stamp_loc);
ttl_values = h5read(raw_kwd_filename,kwe_value_loc);
ttl_sample_rate = double(h5readatt(raw_kwd_filename,kwe_sample_rate_loc,'sample_rate'));
% Save seperately all TTL events that belong to the specified sync channel
sync = struct('timestamps',cell(size(sync_channels)), ...
'values',cell(size(sync_channels)));
for curr_sync = 1:length(sync_channels)
sync_events = ttl_channels == (sync_channels(curr_sync)-1);
sync(curr_sync).timestamps = double(ttl_samplestamp(sync_events))/ttl_sample_rate;
sync(curr_sync).values = logical(ttl_values(sync_events));
end
sync_save_filename = [save_path filesep 'sync.mat'];
save(sync_save_filename,'sync');
case 'adc'
% If sync was recorded through analog input
sync = struct('timestamps',cell(size(sync_channels)), ...
'values',cell(size(sync_channels)));
for curr_sync = 1:length(sync_channels)
% Find the sync ADC channel index
recorded_sync_ch = find(cellfun(@(x) strcmp(['ADC' num2str(sync_channels(curr_sync))],x.Attributes.name), ...
kwik_settings.SETTINGS.SIGNALCHAIN.PROCESSOR{1}.CHANNEL_INFO.CHANNEL));
% Load in the corresponding trace
sync_trace = h5read(kwd_filename, locationInKWD,[recorded_sync_ch,1],[1,Inf]);
% Binarize the sync trace based on half-max
sync_trace = sync_trace >= max(sync_trace/2);
sync_samplestamp = find((~sync_trace(1:end-1) & sync_trace(2:end)) | ...
(sync_trace(1:end-1) & ~sync_trace(2:end)))+1;
sync(curr_sync).timestamps = sync_samplestamp/sample_rate;
sync(curr_sync).values = sync_trace(sync_samplestamp);
end
sync_save_filename = [save_path filesep 'sync.mat'];
save(sync_save_filename,'sync');
end
%% Filter and save spikes and LFP
% Load in each channel separately (because probably too large to load in at
% once, can be faster if multiple but easier to just to all channels
% individually for now)
% NOTE: this involves a lot of tricks in writing the file, because it
% writes in column order but should be written in row-order. This means
% that the first sample for each channel is written separately, then all
% other samples are written after n delayed bytes (and int16 = 2 bytes)
%%%% Could alternatively make downsampled LFP signal first and then load
%%%% back in and filter, but stuff from higher frequencies would be aliased
%%%% in then
disp('Loading channels, preprocessing, saving...');
if local_copy
lfp_save_filename = [temp_path filesep 'lfp.dat'];
spikes_save_filename = [temp_path filesep 'spikes.dat'];
else
lfp_save_filename = [save_path filesep 'lfp.dat'];
spikes_save_filename = [save_path filesep 'spikes.dat'];
end
lfp_fid = fopen(lfp_save_filename, 'w');
spikes_fid = fopen(spikes_save_filename, 'w');
% Figure out memory situation
dmem = memory;
memToLeaveFree = 4 * 2^30; % num of GB to keep free
memToAllocate = dmem.MemAvailableAllArrays - memToLeaveFree;
memToAllocate = max(0, memToAllocate);
nint16s = memToAllocate/2;
n_samples = info.Dataspace.Size(2);
load_chans_n = floor(nint16s/n_samples);
%%%%% AT THE MOMENT THE FILE WRITING ISNT SET UP FOR CHUNKS: SO JUST DO ALL
load_chans_n = n_chan;
%%%%%
split_chans_n = [0,ceil(linspace(floor(n_chan/load_chans_n)-1,n_chan,floor(n_chan/load_chans_n)))];
load_chans = mat2cell(ephys_ch,1,diff(split_chans_n));
for curr_chan_chunk = 1:length(load_chans)
curr_chans = load_chans{curr_chan_chunk};
disp(['Channels ' num2str(curr_chans(1)) '-' num2str(curr_chans(end))]);
curr_dat = h5read(kwd_filename,locationInKWD,[curr_chans(1),1],[length(curr_chans),Inf]);
%%%%%%%%%%% TESTING
if subtract_light
disp('Subtracting mean-estimated light artifacts...');
% get response to light onset/offset for each light
exposure_time = mean([median(diff(sync(3).timestamps)),median(diff(sync(4).timestamps))]);
surround_samples = -1:(floor(exposure_time*sample_rate)-1);
% Define light sync channels
light_sync = [3,4];
% Go through illum 1/2 onset/offset, subtract
for curr_sync = light_sync
for curr_state = 0:1
for curr_chan = 1:size(curr_dat,1)
curr_chan_dat = curr_dat(curr_chan,:);
% Define and pull samples
pull_samples = bsxfun(@plus,round(sample_rate* ....
sync(curr_sync).timestamps(sync(curr_sync).values == curr_state))',surround_samples);
curr_samples = curr_chan_dat(pull_samples);
% Get the mean light response, normalized to pre-light
curr_artifact = int16(nanmean(bsxfun(@minus,curr_samples,curr_samples(:,surround_samples == -1)),1));
curr_subtract = bsxfun(@minus,curr_samples,curr_artifact);
% Replace values
curr_chan_dat(pull_samples) = curr_subtract;
curr_dat(curr_chan,:) = curr_chan_dat;
end
end
end
end
%%%%%%%%%%% TESTING
% Split the signal in two:
% 1) LFP (low-pass filtered and downsampled)
disp('Filtering, downsampling, saving LFP...');
fprintf('%2d',0);
lfp_cutoff = 500;
[b, a] = butter(3, lfp_cutoff/sample_rate, 'low');
lfp_downsamp = (sample_rate/lfp_cutoff)/2;
% filter by channel individually to avoid increasing data size
dat_lfp = zeros(length(curr_chans),ceil(n_samples/lfp_downsamp),'int16');
for filt_chan = 1:length(curr_chans)
curr_lfp = filter(b,a,single(curr_dat(filt_chan,:)'))';
curr_lfp = curr_lfp(1:lfp_downsamp:end);
dat_lfp(filt_chan,:) = int16(curr_lfp);
clear curr_lfp
fprintf('%c%c%2d',8,8,floor(100*filt_chan/length(curr_chans)));
end
fwrite(lfp_fid,dat_lfp,'int16');
%%% Use this stuff later for chunking
%fseek(lfp_fid,(curr_chan(1)-1)*2,'bof');
%fwrite(lfp_fid,dat_lfp(:,1),'int16');
%fwrite(lfp_fid,dat_lfp(:,2:end),'int16',(n_chan-1)*2);
clear dat_lfp
% 2) Spikes with median across channels subtracted
disp('Subtracting common median and saving spikes...')
fprintf('%2d',0);
% (do this in chunks: median can destroy memory)
max_t = 1e6;
n_chunks = ceil(size(curr_dat,2)/max_t);
t_chunks = round(linspace(1,size(curr_dat,2),n_chunks+1));
for curr_chunk = 1:n_chunks
curr_t = t_chunks(curr_chunk):t_chunks(curr_chunk+1);
% Get channel offset by median across time
curr_chunk_offset = median(curr_dat(:,curr_t),2);
% Remove channel offset for chunk
curr_dat(:,curr_t) = bsxfun(@minus,curr_dat(:,curr_t),curr_chunk_offset);
% Get and remove median across channels
curr_chunk_median = median(curr_dat(:,curr_t),1);
curr_dat(:,curr_t) = bsxfun(@minus,curr_dat(:,curr_t),curr_chunk_median);
% Add back offset so there aren't jumps if median drifts
curr_dat(:,curr_t) = bsxfun(@plus,curr_dat(:,curr_t),curr_chunk_offset);
fprintf('%c%c%2d',8,8,floor(100*curr_chunk/n_chunks));
end
fwrite(spikes_fid,curr_dat,'int16');
%%% Use this stuff later for chunking
%fseek(spikes_fid,(curr_chan(1)-1)*2,'bof');
%fwrite(spikes_fid,curr_dat(:,1),'int16');
%fwrite(spikes_fid,curr_dat(:,2:end),'int16',(n_chan-1)*2);
end
fclose(lfp_fid);
fclose(spikes_fid);
disp('Done');
%% Save parameters/header information in separate file
params = {'raw_path',['''' kwik_path '''']; ...
'n_channels',num2str(length(ephys_ch)); ...
'sample_rate',num2str(sample_rate); ...
'gain',num2str(ch_gain); ...
'lfp_cutoff',num2str(lfp_cutoff)};
param_filename = [save_path filesep 'dat_params.txt'];
formatSpec = '%s = %s\r\n';
fid = fopen(param_filename,'w');
for curr_param = 1:size(params,1)
fprintf(fid,formatSpec,params{curr_param,:});
end
fclose(fid);
%% If local copy, move local processed to server, delete local data copy
if local_copy
disp('Moving local copy to server...')
movefile(lfp_save_filename,[save_path filesep 'lfp.dat']);
movefile(spikes_save_filename,[save_path filesep 'spikes.dat']);
delete(temp_filename)
disp('Done')
end

kwik2dat.m at commit fcea2b2, no license · at the source

Overview

Authors: L F Supiot^1,2, K L Kooij¹, W Du¹, A A C Benschop¹, A S J Nicolson¹, R Haak¹, I G Wolterink-Donselaar¹, M C M Luijendijk¹, D Riga^1,2, R A H Adan¹, R B Poorthuis¹, F J Meye¹

ORCID iDs: L F Supiot, A S J Nicolson, R Haak, M C M Luijendijk, R A H Adan, F J Meye

Institutions: Utrecht University (Netherlands); University Medical Center Utrecht (Netherlands); Amsterdam University Medical Centers (Netherlands)

Journal: Nature communications, volume 17, issue 1, article 4620

Dates: received 30 July 2024; accepted 9 March 2026; published online 31 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41467-026-71073-z · PMID 41912543 · PMCID PMC13199376 · OpenAlex W7143407544

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: mouse (organism)

Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials, Connectivity, Single-unit activity, calcium imaging

Keywords: Cellular neuroscience, Synaptic plasticity, Stress and resilience, Reward

MeSH: Eating*, Hypothalamic Area, Lateral*, Prefrontal Cortex*, Stress, Psychological*, Animals, Male, Mice, Mice, Inbred C57BL, Neural Pathways, Neuronal Plasticity, Neurons, Synapses (* major topic)

Topic: Regulation of Appetite and Obesity (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex

Funding: Dutch Research Council (NWO) (VIDI grant 203.102, Gravitation 024.004.012); EC | Horizon 2020 Framework Programme (ERC 804089, ReCoDE)

Citations: cited by 4 papers (Europe PMC); 57 references in the paper

Abstract

Stress can drive overconsumption of high-fat foods. The medial prefrontal cortex (mPFC) is implicated in such stress-eating, but the underlying circuit mechanisms remain unclear. Here, we show that mPFC projections to the lateral hypothalamus (LHA) are required for stress-induced fat intake in male mice. We find that mPFC-LHA stimulation in sated states increases fat intake. Social stress acutely engages mPFC-LHA neurons, and inhibiting this pathway selectively prevents stress-driven excess fat intake. Circuit mapping shows that mPFC neurons innervate GABAergic and glutamatergic LHA (LHAVGLUT2) neurons, but that social stress preferentially engages mPFC-LHAVGLUT2 neurons and causes plasticity at mPFC-LHAVGLUT2 synapses. Specifically, stress weakens mPFC synapses onto LHAVGLUT2 neurons that curtail food intake, while strengthening mPFC synapses onto midbrain-projecting LHAVGLUT2 neurons linked to stress-eating. We show that LHAVGLUT2 neurons are required downstream mPFC targets for transforming stress into heightened fat intake. Overall, we identify the mPFC-LHA as a multi-branched network, indispensable for stress-eating.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

cortex-lab/spikes

License: none: the authors keep all their rights

State: the link answers, verified on 3 November 2026

Evidence: files inventoried

Commit: fcea2b20e736b533e5baf612752b66121a691128, 4 February 2024

Languages: MATLAB (101), C (3)

Size: 118 files, 104 scripts

Software Heritage: archived

Found in: the text, “Spike sorting”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (10 files), Signal Processing Toolbox (3 files), Optimization Toolbox (1 file), Open Ephys analysis tools (1 file)

Availability: 2 checks, the latest on 3 November 2026: the link answers

3 November 2026: the link answers
30 September 2026: the link answers

105 files

analysis/WithinRanges.m, MATLAB, 105 lines
analysis/clusterAverage.m, MATLAB, 27 lines
analysis/computeRawRMS.m, MATLAB, 37 lines
analysis/computeSpikeWidths.m, MATLAB, 30 lines
analysis/eventLockedAvg.m, MATLAB, 61 lines
analysis/findTempForEachClu.m, MATLAB, 26 lines
analysis/getWaveForms.m, MATLAB, 68 lines
analysis/helpers/CCG.m, MATLAB, 205 lines
analysis/helpers/CCGBinContents.m, MATLAB, 29 lines
analysis/helpers/CCGHeart.c, C, 207 lines
analysis/helpers/CCG_mine.m, MATLAB, 205 lines
analysis/helpers/CanonCor2.m, MATLAB, 53 lines
analysis/helpers/best_fit_line.m, MATLAB, 23 lines
analysis/helpers/clip.m, MATLAB, 11 lines
analysis/helpers/countUnique.m, MATLAB, 9 lines
analysis/helpers/eventTrigTrace.m, MATLAB, 31 lines
analysis/helpers/histdiff.c, C, 292 lines
analysis/helpers/histdiff.m, MATLAB, 24 lines
analysis/helpers/histdiffMulti.c, C, 303 lines
analysis/helpers/myGaussWin.m, MATLAB, 9 lines
analysis/helpers/nanconv.m, MATLAB, 114 lines
analysis/helpers/schmitt.m, MATLAB, 83 lines
analysis/helpers/schmittTimes.m, MATLAB, 16 lines
analysis/kernel/KernelEventSeries.m, MATLAB, 51 lines
analysis/kernel/KernelModel.m, MATLAB, 109 lines
analysis/kernel/KernelModelToeplitz.m, MATLAB, 30 lines
analysis/kilosortPredictData.m, MATLAB, 102 lines
analysis/ksDriftmap.m, MATLAB, 82 lines
analysis/lfpBandPower.m, MATLAB, 61 lines
analysis/makeAllBA.m, MATLAB, 70 lines
analysis/myACG.m, MATLAB, 70 lines
analysis/psthAndBA.m, MATLAB, 36 lines
analysis/psthByDepth.m, MATLAB, 51 lines
analysis/psthRasterAndCounts.m, MATLAB, 44 lines
analysis/readDat.m, MATLAB, 25 lines
analysis/replaceMissingSitesP3.m, MATLAB, 38 lines
analysis/rocArea.m, MATLAB, 44 lines
analysis/slidingChoiceProbability.m, MATLAB, 44 lines
analysis/sparseNoiseRF.m, MATLAB, 218 lines
analysis/spikeTrigLFP.m, MATLAB, 39 lines
analysis/templatePositionsAmplitudes.m, MATLAB, 82 lines
analysis/timestampsToBinned.m, MATLAB, 34 lines
analysis/trueSpikeSNR.m, MATLAB, 109 lines
exampleScript.m, MATLAB, 148 lines
preprocessing/applyCARtoDat.m, MATLAB, 76 lines
preprocessing/applyCorrection.m, MATLAB, 9 lines
preprocessing/extractMedianWFs.m, MATLAB, 55 lines
preprocessing/extractSyncChannel.m, MATLAB, 16 lines
preprocessing/extractSyncChannelFromFile.m, MATLAB, 45 lines
preprocessing/helpers/findMinDiffs.m, MATLAB, 17 lines
preprocessing/helpers/totalMinDiffs.m, MATLAB, 12 lines
preprocessing/imecPhase3/extractSyncChan.m, MATLAB, 20 lines
preprocessing/imecPhase3/makeForPRBimecP3.m, MATLAB, 23 lines
preprocessing/imecPhase3/splitGLXBinFile.m, MATLAB, 69 lines
preprocessing/kwik2dat.m, MATLAB, 327 lines, 1 match
preprocessing/loadSync.m, MATLAB, 60 lines
preprocessing/makeChanMapFromSGLMeta.m, MATLAB, 52 lines
preprocessing/makeCorrection.m, MATLAB, 47 lines
preprocessing/oe2dat.m, MATLAB, 157 lines
preprocessing/phyHelpers/addGainToParamsPy.m, MATLAB, 29 lines
preprocessing/phyHelpers/getMetaFname.m, MATLAB, 24 lines
preprocessing/phyHelpers/loadAllKsDir.m, MATLAB, 62 lines
preprocessing/phyHelpers/loadKSdir.m, MATLAB, 96 lines
preprocessing/phyHelpers/loadParamsPy.m, MATLAB, 48 lines
preprocessing/phyHelpers/makeAmplitudesTSV.m, MATLAB, 20 lines
preprocessing/phyHelpers/readClusterGroupsCSV.m, MATLAB, 27 lines
preprocessing/phyHelpers/sparsePCs.m, MATLAB, 55 lines
preprocessing/phyHelpers/sparsePCsTopPC.m, MATLAB, 23 lines
preprocessing/phyHelpers/writeBordersCSV.m, MATLAB, 15 lines
preprocessing/phyHelpers/writePhyTSV.m, MATLAB, 29 lines
preprocessing/phyHelpers/writeToParamsPy.m, MATLAB, 21 lines
preprocessing/readSpikeGLXmeta.m, MATLAB, 88 lines
preprocessing/spikeGLXdigitalParse.m, MATLAB, 43 lines
preprocessing/text2struct.m, MATLAB, 24 lines
preprocessing/xml2struct.m, MATLAB, 183 lines
visualization/+ks/computeAndPlotWFampHist.m, MATLAB, 69 lines
visualization/computeWFampsOverDepth.m, MATLAB, 15 lines
visualization/evRastersGUI.m, MATLAB, 763 lines
visualization/eventsAlignedRasters.m, MATLAB, 58 lines
visualization/eventsAlignedRasters2.m, MATLAB, 45 lines
visualization/eventsAlignedRastersWithGrouping.m, MATLAB, 92 lines
visualization/helpers/colorcet.m, MATLAB, 5,902 lines
visualization/helpers/colormap_BlueWhiteRed.m, MATLAB, 12 lines
visualization/helpers/colormap_RedWhiteBlue.m, MATLAB, 9 lines
visualization/helpers/colormap_greyZero_blackred.m, MATLAB, 6 lines
visualization/helpers/getOr.m, MATLAB, 26 lines
visualization/helpers/makepretty.m, MATLAB, 24 lines
visualization/helpers/rasterize.m, MATLAB, 19 lines
visualization/movieWithTracesSpikes.m, MATLAB, 469 lines
visualization/plotAsPhase3.m, MATLAB, 68 lines
visualization/plotAsProbe.m, MATLAB, 39 lines
visualization/plotDriftmap.m, MATLAB, 120 lines
visualization/plotLFPpower.m, MATLAB, 49 lines
visualization/plotPSTHbyDepth.m, MATLAB, 38 lines
visualization/plotUnitScatter.m, MATLAB, 132 lines
visualization/plotWFampCDFs.m, MATLAB, 30 lines
visualization/plotWaveform.m, MATLAB, 32 lines
visualization/popRasterViewer.m, MATLAB, 509 lines
visualization/psthViewer.m, MATLAB, 403 lines
visualization/quickMovieSpikes.m, MATLAB, 29 lines
visualization/quickMovieSpikesALF.m, MATLAB, 98 lines
visualization/rasterAndPSTHbyCond.m, MATLAB, 74 lines
visualization/simpleWFplot.m, MATLAB, 25 lines
visualization/waveformSpaceTimeSlice.m, MATLAB, 55 lines
README.md, Text, 8 lines

At the source: github.com/cortex-lab/spikes/blob/master/preprocessing/applyCARtoDat.m

Zenodo 18670225

License: CC-BY-4.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Size: 2 files

Software Heritage: not checked

Found in: the text, “In vivo fiber photometry recordings”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (5 files), pandas (5 files), Matplotlib (4 files), h5py (2 files), SciPy (2 files), seaborn (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

5 files

240807_fat_eating_graphs.py, Python, 625 lines
241028_fat_eating.py, Python, 329 lines
general functions/Functions_plots_fateating.py, Python, 800 lines
general functions/Functions_prepocessing_fateating.py, Python, 145 lines
general functions/analyzeanimal_fat_eating_250204_2side_sliding_average.py, Python, 834 lines

At the source:

Code availability

The location for the codes used for the analyses in the current study have been referenced in the Methods section of the manuscript.

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
109 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);
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 detailed outcomes of the statistical test are provided in Supplementary Data 1. Source Data are provided with this paper. Other data files are available from the corresponding author on request. Source data are provided with 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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 4 keywords, 12 MeSH terms, 2 funders, 57 references.

Cite

This paper

Supiot, L. F., Kooij, K. L., Du, W., Benschop, A. A. C., Nicolson, A. S. J., Haak, R., Wolterink-Donselaar, I. G., Luijendijk, M. C. M., Riga, D., Adan, R. A. H., Poorthuis, R. B., & Meye, F. J. (2026). A prefrontal cortex-lateral hypothalamus circuit controls stress-driven increased food intake. Nature communications, 17(1), 4620. https://doi.org/10.1038/s41467-026-71073-z

BibTeX

@article{supiot2026prefrontal, author = {Supiot, L F and Kooij, K L and Du, W and Benschop, A A C and Nicolson, A S J and Haak, R and Wolterink-Donselaar, I G and Luijendijk, M C M and Riga, D and Adan, R A H and Poorthuis, R B and Meye, F J}, title = {{A prefrontal cortex-lateral hypothalamus circuit controls stress-driven increased food intake}}, journal = {Nature communications}, year = {2026}, month = mar, volume = {17}, number = {1}, pages = {4620}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-71073-z}, url = {https://doi.org/10.1038/s41467-026-71073-z}, pmid = {41912543}, pmcid = {PMC13199376} }

RIS

TY - JOUR AU - Supiot, L F AU - Kooij, K L AU - Du, W AU - Benschop, A A C AU - Nicolson, A S J AU - Haak, R AU - Wolterink-Donselaar, I G AU - Luijendijk, M C M AU - Riga, D AU - Adan, R A H AU - Poorthuis, R B AU - Meye, F J TI - A prefrontal cortex-lateral hypothalamus circuit controls stress-driven increased food intake T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/03/31 VL - 17 IS - 1 SP - 4620 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-71073-z UR - https://doi.org/10.1038/s41467-026-71073-z LA - en ER -

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{ "id": "10.1038/s41467-026-71073-z", "type": "article-journal", "title": "A prefrontal cortex-lateral hypothalamus circuit controls stress-driven increased food intake", "container-title": "Nature communications", "author": [ { "family": "Supiot", "given": "L F" }, { "family": "Kooij", "given": "K L" }, { "family": "Du", "given": "W" }, { "family": "Benschop", "given": "A A C" }, { "family": "Nicolson", "given": "A S J" }, { "family": "Haak", "given": "R" }, { "family": "Wolterink-Donselaar", "given": "I G" }, { "family": "Luijendijk", "given": "M C M" }, { "family": "Riga", "given": "D" }, { "family": "Adan", "given": "R A H" }, { "family": "Poorthuis", "given": "R B" }, { "family": "Meye", "given": "F J" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "4620", "DOI": "10.1038/s41467-026-71073-z", "PMID": "41912543", "PMCID": "PMC13199376", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-71073-z", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 31 ] ] } }

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