Source Paper
High-fat diet disrupts a septal control on feeding to promote obesity in male mice
Jiang S, Lai S, Jing H, Wu X, Li F et al.
Nat Commun • 2025
Source Paper
Jiang S, Lai S, Jing H, Wu X, Li F et al.
Nat Commun • 2025
Objective: To assess whether trial types (chow vs. HFD consumption) could be predicted from trial-by-trial population activities of LS GABA neurons during consumption epochs using machine learning classification
This is a Population decoding analysis of food consumption protocol using Mus musculus as the model organism. The procedure involves 12 procedural steps, 2 equipment items, 3 materials. Extracted from a 2025 paper published in Nat Commun.
Model and subjects
Mus musculus • Gad2-Cre mice • male • adult • not specified for decoding analysis
Study window
~6 week study window
Core workflow
Viral injection and imaging preparation • Adaptive training • Calcium imaging during food consumption
Primary readouts
Key equipment and reagents
Verified items
0
Direct vendor links
0
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Inject AAV2/9-hEF1a-DIO-GCaMP6s into LS and implant GRIN lens with baseplate for miniscope
“After 4-6 weeks of GCaMP6s injection, a baseplate that matched the miniscope (UCLA Miniscope V4, Open Ephys) was fixed”
Train mice for imaging sessions
“Before imaging sessions, mice received 10-minute adaptive training for at least 3 days”
Record calcium signals while mice consume food pellets
“we randomly placed a food pellet for each freely moving mouse in turn, and simultaneously recorded video of the process whereby the mouse ate the food. There were at least 10 food intake periods”
Acquire imaging data at specified frame rate
“Imaging data were acquired at a 30-Hz frame rate and collected using UCLA Miniscope-DAQ-DT-Software”
Extract motion-corrected fluorescence dynamics from individual neurons
“Calcium signal processing was performed using CNMF-E software to extract motion-corrected GCaMP6s fluorescence dynamics from individual neurons”
Z-score normalize neuronal activities across trials
“First, neuronal activities were z-scored across trials to normalize the data”
Apply PCA to reduce data dimensions
“Principal component analysis (PCA) was then applied to the matrix of z-scored trial-by-trial activities, and the first two principal components (PCs) were retained”
Split dataset into training and test sets
“the dataset was split such that a randomly selected subset comprising 75% of trials from each food type (chow and HFD) served as the training set, while the remaining 25% constituted the test set”
Train linear SVM classifier for two-class decoding
“Using the low-dimensional PC data from the training set, a linear-kernel SVM classifier ('linear') was trained for two-class decoding (chow vs. HFD trials)”
Validate classifier on test set
“The trained classifier was then validated using the 'predict' function to classify the trial-by-trial activities in the test set”
Generate shuffled datasets with randomly reassigned labels
“For control purposes, shuffled datasets were generated by randomly reassigning trial-type labels (chow or HFD) to the neuronal activities”
Repeat classification process multiple times for robustness
“To ensure robustness, the entire classification process—including random train/test splitting, classifier training, testing, and shuffling—was repeated 1000 times”
This section explains what the experiment is doing, which readouts matter, what the data artifacts usually look like, and how the analysis should flow from raw capture to reported result.
To assess whether trial types (chow vs.
Objective
To assess whether trial types (chow vs. HFD consumption) could be predicted from trial-by-trial population activities of LS GABA neurons during consumption epochs using machine learning classification
Subjects
From paperMus musculus • Gad2-Cre mice • male • adult
Sample count
From papernot specified for decoding analysis
Viral injection and imaging preparation (4-6 weeks recovery period)
Adaptive training (10-minute sessions for at least 3 days)
Calcium imaging during food consumption (at least 10 food intake periods per session)
Data acquisition (continuous during sessions)
Classification accuracy for distinguishing chow vs HFD consumption trials
From paperThis readout is central to the experiment's endpoint interpretation and should be reviewed before running the analysis.
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Statistical significance compared to shuffled control data
From paperThis readout is central to the experiment's endpoint interpretation and should be reviewed before running the analysis.
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Average classification rate across 1000 iterations
From paperThis readout is central to the experiment's endpoint interpretation and should be reviewed before running the analysis.
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Classification accuracy for distinguishing chow vs HFD consumption trials
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Statistical significance compared to shuffled control data
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Average classification rate across 1000 iterations
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
Preprocessing / cleaning
Review raw outputs for quality, remove unusable captures, and organize the data into a comparison-ready table or image set.
Scoring or quantification
Quantify the primary readouts for this experiment: Classification accuracy for distinguishing chow vs HFD consumption trials; Statistical significance compared to shuffled control data; Average classification rate across 1000 iterations.
Statistical comparison
Statistical method not yet structured for this page.
Reporting output
Report representative outputs alongside summary comparisons for Classification accuracy for distinguishing chow vs HFD consumption trials, Statistical significance compared to shuffled control data, Average classification rate across 1000 iterations.
Source links and direct wording from the methods section for validation and deeper review.
Citation
Jiang S et al. (2025). High-fat diet disrupts a septal control on feeding to promote obesity in male mice. Nat Commun
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Direct vendor pages are linked from the protocol above. This section stays focused on the full comparison view and the prep checklist.
Gather these items before starting the experiment. Check off items as you prepare.
Open Ephys • V4
MathWorks • not specified
Shenzhen Ready Biological Medicine Co., Ltd • D12492
Beijing Keao Xieli Feed Co., Ltd • 2252
Taitool Bioscience • S0351-9
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Current status surfaces were computed from experiment data updated Feb 28, 2026.
Source access
Jump back into the original paper or the methods evidence section when you need exact wording, exclusions, or method-specific caveats.
This protocol has structured steps plus evidence quotes, and is ready for canonical sync.
Steps
12
Evidence Quotes
12
Protocol Items
5
Linked Products
0
Canonical Sync
Pending
What this means
The completeness score reflects how much structured protocol data is present: steps, methods evidence, listed materials, linked products, and paper provenance.
Computed from the current experiment record updated Feb 28, 2026.
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Steps
12
Evidence
12
Specific Products
0/0
Canonical Sync
Pending
What this score means
The verification score reflects evidence coverage, subject detail, paper provenance, step depth, and whether linked products resolve to specific item pages instead of generic searches.
Computed from the current experiment record updated Feb 28, 2026.
A page can have structured steps and still need review when evidence is thin, product links are generic, or canonical protocol coverage is still pending.
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