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Linking brain and behavior states in Zebrafish Larvae locomotion using hidden Markov models

Fig 2

3-state Markov Chain and hidden Markov models - Stronger persistence emerges from better labeling:

(a) Diagram of the 3-state Hidden Markov Model (HMM) with normal emissions for Forward bouts, and gamma emissions for Turning bouts. Example emission distributions were taken at 26°C. (b) Example trajectory at 22°C. Each point represents a swim bout, with the left color for the threshold labeling (Markov Chain model), and the right color for the HMM labeling using the Viterbi algorithm. Top: 2D trajectory. Bottom: reorientation angle for this trajectory, with the threshold as a dashed line. (c) Probability of observing a streak of consecutive forward bouts (black) or same-direction turning bouts (pink), for MC (circles) and HMM (triangles), at 22°C. Inset: Exponential decay characteristic length (, solid lines), and theoretical persistence length computed from the transition matrix (, dashed lines). (d) Ratio of persistence length (observed vs. no-memory null model) vs. temperature, for Forward (s = F, black) and turning (, pink) bouts. (e) stubbornness factor at q = 0 intermediary Forward bouts, . (f) stubbornness factor at q = 1 intermediary Forward bouts, . (e-f) Shaded bands represent the estimated errors from aggregated fish data (see Materials and methods 4.5).

doi: https://doi.org/10.1371/journal.pcbi.1013762.g002