All code is available in our github repository: https://github.com/ETHZ-INS/BehaviorFlow
The major ideas behind this work came from superstar Lukas von Ziegler, with lots of creative work from Fabienne Rössler and @osturmscience. Huge team effort as always 💪

Funded by the Swiss 3R Competence Center, helping to reduce/refine the use of animals in research 🙏💙

Although applicable across behavior tests, we focus on the widely-used open field test.

We share a unique resource: 443 open field videos containing 7 experimental designs, all fully analyzed with BFA and BFF and with the corresponding @deeplabcut tracking data!

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What's maybe even more useful: BFF enables predictions about future behavior! 🔥

We introduce a novel approach to segregate responders from non-responders. Rather than using one metric, we use the entire, high-dimensional behavioral flow data per🐭. It predicts who can extinguish fear after stress!

With stabilized clusters and a single, high-dimensional datapoint for each mouse, we now compare all🐭across all 6 experiments.

Very cool to see: Chronic stressors look very different than acute stressors. Acute stress aligns with noradrenaline release, as we would expect! 🥳

This is my favorite plot, it shows how lots of experiments can be compared in one graph 🤩

Next: To profile individual mice, we introduce Behavioral Flow Fingerprinting (BFF). BFF projects the entire transition matrix of every animal onto a single datapoint in 2D.

Check out how well we can separate mice that received different doses of yohimbine, a noradrenaline drug.

Across these datasets (>200 mice tested), we find that BFA increases statistical power and reveals phenotypes that would otherwise be missed.

The snapshot below shows an example where we were underpowered (n=8) to detect treatment effects (DREADD activation of the #LocusCoeruleus), but BFA unveils the latent phenotype! 🔵

Next, we train a machine learning classifier to faithfully reproduce our set of clusters, so we can start comparing clustering results across experiments. That was a very important improvement for us, to stabilize all clusters so we know e.g. cluster1=onset of a supported rear etc.

We apply this across 6 different experiments using #stress exposures, pharmacology, and noradrenaline circuit manipulation.

Applied to open field data, BFA repeatedly identifies treatment effects when the most advanced clustering approaches fail.

BFA is a simple add-on analysis (all code open source) that works with any clustering algorithm!
We show it with K-means (our first choice), B-SOiD & VAME. But try it with your favorite clustering method and tell us how it worked for you!