GESTURE: unsupervised genotype-specific behavioral phenotyping in rodents via graph-based representation learning

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Abstract The automated quantification of complex animal behavior is fundamental to neuroscience and pharmacology, yet converting high-dimensional pose data into reproducible and interpretable behavioral measures remains challenging. Here, we present GESTURE, an unsupervised, graph-based deep generative framework that discovers and quantifies behavioral structure from pose dynamics without genotype labels or manual behavioral annotations. Applied to mice with motor dysfunction and wild-type controls, GESTURE identified a shared vocabulary of behavioral motifs. Differential use of these motifs yielded distinct “behavioral fingerprints” that reliably separated genotypes. By modeling behavior as a sequence rather than a static partition, GESTURE also quantified its temporal organization. Affected animals maintained motifs for longer and transitioned between them more predictably, indicating a slowing and stereotyping of behavioral sequences rather than simply reduced activity. GESTURE’s graph-based representation enables training across multiple recordings and embeds behavior in a shared latent space, supporting consistent cross-animal comparisons and future cross-experiment alignment. Automatically derived measures of behavioral divergence tracked the temporal dynamics of expert-annotated disability scores, reaching agreement comparable to that of independent human raters. Node- and edge-level explainability analyses further indicated that the latent representations emphasize the animal’s core motor scaffold in a biologically plausible manner. These findings support GESTURE as an interpretable, scalable framework for automated behavioral phenotyping that links genetic perturbation to quantitative behavioral phenotypes. Author summary How can behavioral changes caused by a disease-associated mutation be measured automatically and consistently? Manual assessment is slow, difficult to scale, and can vary among observers. We studied mice carrying a mutation in a calcium-channel gene whose human counterpart is associated with episodic ataxia and related movement disorders. We developed GESTURE, which learns recurring movement patterns from the tracked coordinates of a freely moving mouse without genotype labels or manual behavioral annotations. By representing the body as a set of connected landmarks, GESTURE tracks how their configuration changes over time and discovers a shared vocabulary of movements. Affected and healthy mice used this vocabulary differently. Each animal’s pattern of use formed a distinctive “behavioral fingerprint”, and these fingerprints correctly identified every animal carrying the mutation. The analysis also quantified temporal features beyond overall activity: affected mice held each movement for longer and transitioned between movements more predictably. An automatically derived severity score agreed with expert judgment as closely as two trained raters agreed with each other and yielded the same result on every run.

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