BEHAVE: Behavioral Ethology for Human Assessment via Variational Encoding

Zhanqi Zhang, Chi K. Chou, Holden Rosberg, William Perry, Jared W Young, Arpi Minassian, Gal Mishne, Mikio Christian Aoi

Reviewer: ~Zhanqi_Zhang1

Presenter: ~Zhanqi_Zhang1

TL;DR: BEHAVE introduces an ML framework for large-scale naturalistic behavioral data, providing novel metrics that reveal bipolar disorder signatures and advance data-driven, translational neuroscience.

Abstract: Quantifying spontaneous human behavior remains a major challenge in psychiatry and neuroscience. We present BEHAVE (Behavioral Ethology for Human Assessment via Variational Encoding), a framework that combines computer vision and unsupervised latent-variable models to capture fine-scale, naturalistic behaviors. BEHAVE segments continuous motion into interpretable motifs and introduces novel metrics of temporal structure, repertoire diversity, and stereotypy. In a naturalistic open-field assay of individuals with euthymic bipolar disorder (BD) and healthy controls, these metrics revealed subtle yet robust BD-associated differences, including reduced exploratory transitions and repertoire narrowing. Compared to clinical scales and standard action-recognition models, BEHAVE achieved superior classification of BD. This approach offers a scalable, bias-resistant path to decoding neuropsychiatric states from natural behavior and lays the foundation for translational biomarkers.

Length: short paper (up to 4 pages)

Domain: methods

Format Check: Yes, the presenting author will definitely attend in person because they attending NeurIPS for other complementary reasons.

Author List Check: The author list is correctly ordered and I understand that additions and removals will not be allowed after the abstract submission deadline.

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Submission Number: 54