Quantifying Behavioral Structure and Persistence in Open-Field Assays Using Entropy and Spectral Metrics

  1. Sihak Lee
  2. Daniel J. Choi
  3. Soonwook Choi
  4. Zhanyan Fu

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Abstract

Traditional open-field assays quantify rodent behavior using low-dimensional scalar metrics that often overlook dynamical and temporally dependent aspects of spontaneous behavior. Here, we establish a high-throughput framework that integrates multi-camera 3D pose estimation from AVATAR-3D (1) with unsupervised autoregressive hidden Markov modeling using Keypoint-MoSeq (2–4) to transform recordings into time sequences of discrete, recurring behavioral units termed syllables. Drawing inspiration from information theory (5) and spectral analysis of Markov systems (6,7), we compute Shannon entropy and the second-largest eigenvalue (Eigen 2 ) of the syllable transition matrix. Entropy and Eigen 2 offer complementary lenses through which spontaneous behavior can be understood—one capturing dispersion of behavioral expression, the other reflecting temporal persistence and mixing dynamics. Together, these metrics quantify the organization of behavior as a stochastic dynamical system rather than a collection of isolated actions.

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  1. Arcadia Science

    While spectral analysis revealed minimal differences in Eigen2 between ketamine and saline treated animals across time binds, the first segment displayed significantly higher Eigen2 relative to saline. Given Eigen2 serves as a reflection of temporal persistence, this early stage increase suggests heightened short-term structural rigidity immediately following drug administration.

    It would be nice to see these data broken down on a mouse by mouse basis. Averaging twelve time series may be washing out real behavioral signal. Realistically, Eigen2 is extremely sensitive to rare transitions and 30 minutes X 12 mice may not be enough data to rule out the effects of transition drop-outs over time.

  5. Version published to 10.64898/2026.02.20.707059 on bioRxiv