Analysis of behavioral flow resolves latent phenotypes
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Abstract
The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple testing, exhibit poor transferability across experiments and fail to exploit the rich behavioral profiles of individual animals. Here we introduce a pipeline to capture each animal’s behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a large dataset of 771 behavior recordings of freely moving mice—including stress exposures, pharmacological and brain circuit interventions—to identify hidden treatment effects, reveal subtle variations on the level of individual animals and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, substantially enhances statistical power and enables predictions of an animal’s future behavior.
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To determine the best number of clusters, we chose an approach used previously for segmenting behavior by VAME21 and MoSeq17. To this end, we first partitioned the recorded behavior into 100 clusters, and then chose the number of clusters which represented 95% of the imaging frames. This mark indicated about 70 clusters (Suppl. Figure 1A), so we subsequently re-ran the clustering approach using 70 clusters. Although nominal p-values revealed that CSI and control animals behaved differently on many of these clusters, only 4 out of 70 clusters survived the appropriate multiple testing correction (Benjamini-Yekutieli, Figure 1D). Visual inspection of these significant clusters reveals - in agreement with the classical analysis - that they capture the time the animals spent in the center of the open field. Specifically, these four clusters …
To determine the best number of clusters, we chose an approach used previously for segmenting behavior by VAME21 and MoSeq17. To this end, we first partitioned the recorded behavior into 100 clusters, and then chose the number of clusters which represented 95% of the imaging frames. This mark indicated about 70 clusters (Suppl. Figure 1A), so we subsequently re-ran the clustering approach using 70 clusters. Although nominal p-values revealed that CSI and control animals behaved differently on many of these clusters, only 4 out of 70 clusters survived the appropriate multiple testing correction (Benjamini-Yekutieli, Figure 1D). Visual inspection of these significant clusters reveals - in agreement with the classical analysis - that they capture the time the animals spent in the center of the open field. Specifically, these four clusters represent movement of the mouse from the periphery into the center (cluster 41, Suppl. Video 1), movement in or through the center (cluster 23, Suppl. Video 2), orienting and turning in the center (cluster 69, Suppl. Video 3) and movement from the center back to the periphery (cluster 45, Suppl. Video 4).
I genuinely appreciate your work on this new algorithm for behavioral analysis! I really liked how you compared some classical behavior analysis versus the unsupervised clustering approach. Also, I appreciate how you tested the BFA with different clustering approaches. I'm curious to find out whether it was possible to assign distinct behavior to each of the 70 clusters in Figure 1.
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