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
Population vector analysis for food type encoding
Objective: Population vector analysis to assess how LS GABAergic neuronal ensembles encode different food types (chow vs. high-fat diet) using dimensionality reduction and visualization of neural population activity patterns
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Protocol Steps
Viral injection and GRIN lens implantation
Inject AAV2/9-hEF1a-DIO-GCaMP6s into LS and implant GRIN lens 100 μm above injection site, secured with dental cement
View evidence from paper
“unilateral injections of AAV2/9-hEF1a-DIO-GCaMP6s were performed. A GRIN lens was inserted 100 μm above the injection site”
Recovery and baseplate attachment
Allow 4-6 weeks recovery, then fix baseplate matching miniscope to skull with dental cement
View evidence from paper
“After 4-6 weeks of GCaMP6s injection, a baseplate that matched the miniscope was fixed to each mouse's skull”
Adaptive training
Provide adaptive training sessions before imaging
View evidence from paper
“mice received 10-minute adaptive training for at least 3 days”
Calcium imaging during food consumption
Record calcium signals while randomly placing food pellets and recording mouse feeding behavior
View evidence from paper
“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”
Data acquisition
Acquire imaging data at 30-Hz frame rate using UCLA Miniscope-DAQ-DT-Software
View evidence from paper
“Imaging data were acquired at a 30-Hz frame rate and collected using UCLA Miniscope-DAQ-DT-Software”
Signal processing and extraction
Process calcium signals using CNMF-E software to extract motion-corrected fluorescence dynamics from individual neurons
View evidence from paper
“Calcium signal processing was performed using CNMF-E software to extract motion-corrected GCaMP6s fluorescence dynamics from individual neurons”
Baseline normalization
Quantify neuronal activity traces as Z-scores or ΔF/F values with baseline defined as mean fluorescence during first 2 seconds of each trial
View evidence from paper
“Neuronal activity traces were quantified as Z-scores or ΔF/F values, with baselines defined as the mean fluorescence during the first 2 seconds of each trial”
Population vector analysis
Construct n-dimensional activity vectors representing ensemble responses at each timepoint using Z-score normalized signals
View evidence from paper
“we employed population vector analysis. In brief, this approach constructs n-dimensional activity vectors (n = neuron count) representing ensemble responses at each timepoint through Z score normalized signals”
Dimensionality reduction
Apply PCA for dimensionality reduction, projecting high-dimensional vectors onto 2D visualization space
View evidence from paper
“PCA was subsequently applied for dimensionality reduction, projecting high-dimensional vectors onto a 2D visualization space”