State-induced adaptation of self-guided behavioral sequences

Behavior analysis typically relies on trial-based repetitions of constrained tasks. We aimed to uncover how age, pain, and itch influence the patterns of behavioral sequences and their transitions in freely behaving animals using MoSeq, a machine learning network analysis tool developed by the Datta lab. Mice of different ages were treated with chloroquine (itch) or formalin (pain) and placed in an open field arena and recorded for 30 minutes via a high-speed color camera and infrared depth camera. A trained autoregressive hidden Markov model classified the mouse movements into sub-second syllables. Next, we compared the frequencies and patterns of each syllable, as well as the transition probabilities between them. The classification network showed that one-year-old mice move more slowly and cautiously, displaying a higher usage of freezing, pausing and slow walk syllables compared to younger mice. Chloroquine treatment increased the occurrence of scratching and the transitions towards this state. Finally, formalin injection markedly altered mouse behavior, with the appearance of de novo syllables, e.g. hobbling and hunching, and significant alterations in the transition matrix. In summary, we identified age-, itch- and pain-induced alterations of self-guided behavioral sequences. Future work will focus on understanding how modulating the activity of selected spinal neuron types contributes to changes in behavioral syllables and transitions in physiological (aging) and pathological (chronic pain and itch) conditions.