Behaviour Analysis Toolkit

A comprehensive analysis toolkit for calcium imaging and behavioral data from Pavlovian texture discrimination experiments. This repository contains both Python and MATLAB implementations for analyzing two-photon calcium imaging and licking behavior during associative learning.

Code for the behavioural task can be found at https://github.com/clee162/whisker_tasks.

Overview

End-to-end pipelines for:

• Calcium imaging: Neural decoding, selectivity analysis, population statistics (Python)
• Behavior: Lick rate, anticipatory licking, discrimination metrics (Python & MATLAB)
• Machine learning: SVM decoding, linear encoding models (Python)
• Visualization: Heatmaps, traces, raster plots, histograms

Designed for two-photon imaging during Pavlovian conditioning, but adaptable to any calcium imaging dataset with dF/F in CSV format.

🐍 Python Analysis → python_analysis/

Use for: Calcium imaging analysis, automated data processing, machine learning, batch workflows

Key features:

• Automated data discovery and trigger extraction from TIFFs
• Behavioural analysis
• Longitudinal ROI tracking across sessions
• Statistical metrics: selectivity indices, AUC, significance testing
• SVM population/single-neuron decoding
• Linear encoding models
• Comprehensive visualization suite

Quick start:

pip install -r requirements.txt
cd python_analysis/data_processing
python load_dff_aux_batch_auto.py
# Then run scripts in calcium_imaging/

📖 Full Python Documentation →

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🧮 MATLAB Analysis → matlab_analysis/

Use for: Lick behavior analysis of text file output from whisker_tasks

Key features:

• Lick rate plots across days and cohorts
• Anticipatory lick quantification
• Raster plots with shaded error bars
• Histogram analysis
• Whisker trim session detection

Quick start:

% Edit matlab_analysis/lick_analysis/textfile_lickanalysis.m
% Set paths and animal IDs, then run

📖 Full MATLAB Documentation →

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Repository Structure

BehaviourAnalysis/
├── python_analysis/          # Python pipeline (see dedicated README)
│   ├── calcium_imaging/      # Analysis & visualization scripts
│   ├── data_processing/      # Data loading, trigger extraction
│   └── behavioral_analysis/  # Lick analysis (Python version)
├── matlab_analysis/          # MATLAB pipeline (see dedicated README)
│   └── lick_analysis/        # Lick behavior scripts
├── data/                     # Data directory (not tracked)
│   ├── raw/                  # Raw experimental data
│   └── processed/            # Processed outputs
├── requirements.txt          # Python dependencies
└── LICENSE                   # MIT License

Installation

Python

pip install -r requirements.txt

Requires Python 3.7+ with numpy, pandas, scipy, matplotlib, scikit-learn, tifffile.

MATLAB

• Base MATLAB installation
• Image Processing Toolbox (optional, for TIFFs)
• Third-party: shadedErrorBar.m (included)

Data Formats

• CaImAn outputs: dff_components.csv (ROIs × frames)
• Imaging TIFFs: ScanImage/NI resonant with auxiliary triggers
• Behavioral text: Arduino output from PavlovianTexture.ino
• Tracking: Longitudinal ROI matches (optional)

Citations

If you use this code, please cite:

@software{lee2025whiskertasks,
  author = {Lee, Candice},
  title = {Whisker Tasks Analysis: Behavioural and Calcium Imaging Analysis},
  year = {2025},
  url = {https://github.com/cleeneuro/whisker_tasks_analysis},
  version = {1.0.0}
}

CaImAn:
Giovannucci A., Friedrich J., et al. (2019). CaImAn: An open source tool for scalable Calcium Imaging data Analysis. eLife 8:e38173.

shadedErrorBar:
Rob Campbell (2025). raacampbell/shadedErrorBar. GitHub. https://github.com/raacampbell/shadedErrorBar

License

MIT License - see LICENSE file.

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📖 For detailed documentation, see:

• Python Analysis README - Complete guide to calcium imaging pipeline
• MATLAB Analysis README - Complete guide to lick behavior analysis