Modelling collective behavior in groups of mice housed under semi-naturalistic conditions

Public Review:

Summary:

In this manuscript, Chen et al. investigate the statistical structure of social interactions among mice living together in the ECO-Hab. They use maximum entropy models (MEM) from statistical physics that include individual preferences and pair-wise interactions among mice to describe their collective behavior. They also use this model to track the evolution of these preferences and interactions across time and in one group of mice injected with TIMP-1, an enzyme regulating synaptic plasticity. The main result is that they can explain group behavior (the probability of being together in one compartment) by a MEM that only includes pair-wise interactions. Moreover, the impact of TIMP-1 is to increase the variance of the couplings J_ij, the preference for the compartment containing food, as well as the dissatisfaction triplet index (DTI).

Strengths:

The ECO-Hab is a really nice system to ask questions about the sociability of mice and to tease apart sociability from individual preference. Moreover, combining the ECO-Hab with the use of MEM is a powerful and elegant approach that can help statistically characterize complex interactions between groups of mice -- an important question that requires fine quantitative analysis.

Weaknesses:

However, there is a risk in interpreting these models. In my view, several of the comparisons established in the current study would require finer and more in-depth analysis to be able to establish firmer conclusions (see below). Also, the current study, which closely resembles previous work by Shemesh et al., finds a different result but does not provide the same quantitative model comparison included there, nor a conclusive explanation of why their results are different. In total, I felt that some of the results required more solid statistical testing and that some of the conclusions of the paper were not entirely justified. In particular, the results from TIMP-1 require proper interaction tests (group x drug) which I couldn't find. This is particularly important when the control group has a smaller N than the drug groups.

Author Response:

We thank the reviewers for careful reading, acknowledging the strength of our manuscript, and pointing out its weakness, which we will address in the revised version as described below.

(1) We will supplement our analysis with finer statistical testing and analysis, such as cross-validation and a more detailed analysis of the relation between the inferred model and the intrinsic timescales of the system. For the effect of the drug TIMP-1 on the animal, we will first explore the possibility of assessing the results using a multifactor ANOVA test, with the caveat that the distribution of interactions is not Gaussian. We will further test the effect of different group size on the significance of our results by considering subgroups of animals in the drug group, and compare the statistics between the (subsampled) drug group and the controlled group.

(2) Our manuscript is similar with that of Shemesh et al. in that we both analyze socially interacting mice by constructing maximum entropy models (MEM) of the co-localization patterns of mice. The difference is in the setup and the number of mice (4 mice in Shemesh et al, 10-15 in our work), as we outlined in the manuscript. To further supplement our current argument of the difference of our results in the Discussion section, we will learn a MEM model up to triplet interactions for our Eco-HAB mice data, and compare to our current MEM model up to pairwise interactions using test-set validation or the Bayesian information criterion (BIC).