Behavior of C57BL/6 mothers was dominated by interchanges between the arched-back nursing (ABN) and licking and grooming (LG) states, with the latter serving to anchor the former and act as a punctuation between nest and non-nest behavior (see also Figure 1). Non-nest behavior involved rapid interchanges between activity (ACT) and eating (EAT). Interestingly, drinking was primarily assigned to the ACT state, suggesting that this behavior preferentially interrupted exploratory cage activity rather than consumptive behavior. Finally the grooming (GRO) state was unique in its heterogeneity and appeared to serve as a brief transition state between nest and non-nest behavior. It is also the state which is less robust to a variation of initial model parameter settings (Figure S5). Several features distinguished the structure of maternal behavior in BALB/c and C57BL/6 mothers. Most strikingly, in BALB/c mothers LG did not serve as a central transition state between nursing and non-nursing behavior (Figure 5). Instead, direct transitions between the GRO state and nursing states were frequent and when coupled with an increase in the frequency and duration of the BLN state at the expense of the ABN state, contributed to a looser and less stereotyped nursing behavior in this strain. Finally, while BALB/c mothers spent considerable time sleeping outside of the nest (SLP), C57BL/6 mothers never showed this behavior, instead resting in the nest (generally as part of BLN). Overall the structure of maternal behavior as evidenced by the HMM suggested different maternal styles in the two strains, with C57BL/6 mothers being more pup- oriented and BALB/c mothers being more self and environment- oriented. It remains possible that at least some of the behavioral differences observed between C57BL/6 and BALB/c mothers derive from differences in behavior of their offspring. However, although we did not directly measure pup ultrasound vocalization, for example, that fact that offspring of our C57BL/6 and BALB/c mothers were genetically similar reciprocal hybrids (CxB6 and B6xC, respectively) is likely to have minimized such differences. Finally, we speculate that the differences in maternal behavior between C57BL/6 and BALB/c mothers reflect components of adaptive strategies found in wild mice that have become fixed in laboratory inbred strains as part of their mosaic genetic origin. It may be, for example, that changes in foraging demand selected for a more environment or more pup-oriented behavioral strategy. Our HMM analysis showed that reciprocal hybrid strains, on the other hand, behaved similarly to each other as suggested by our initial analysis of observed variables but differed from both of their parental strains (Figure S6, S7). In general, reciprocal hybrids showed more stable nursing behavior, with longer and more frequent ABN punctuated (as in C57BL/6) by LG. Interestingly, like BALB/c mothers, reciprocal hybrids spend a significant amount of time sleeping out of the nest (SLP), underscoring the unusual nature of the nest-sleeping seen in C57BL/6 mothers and suggesting that it might be part of their pup-oriented maternal strategy. An assessment of the frequency, duration, composition, and transitions between HMM states exposed several features of maternal behavior not revealed by simple frequency analysis of observed behaviour. For example, comparing the p-values obtained by MANOVA of the raw variables (see Table 1) with the same values obtained by standard t-test of HMM state frequencies (see Table S9, 12) we can conclude that this second analysis produces more statistically significant differences than the first one. Additionally a specific feature of the HMM analysis is the possibility to gather, from single observed behaviors, the entire behavioral sequence of a mo- ther during one hour of observation (for example see Figure 3–5). This kind of analysis is impossible using classical statistical HMM of Maternal Behavior methods (e.g ANOVA, MANOVA, Principal Component Anal- ysis, etc.) which require a pooling of temporal sequences into frequencies. HMM analysis instead not only allows to describe behavioral sequences/states (clustering frequently associated behaviors), but also to perform more sophisticated analyses on the frequencies of behaviors within HMM states and frequencies of transitions between HMM states (see Table S8, S10, S11, and S13), to also detect even subtle differences between individuals. For instance, a MANOVA analysis on raw behav- ioural variables did not allow to distinguish between reciprocal hybrid mothers (see Table 1), whereas our HMM analysis did (see Table S11, S12). We made several assumptions during our HMM analysis that may have biased our results. First, for simplicity we opted to train a single HMM, MatHMM, on behavioral sequences from all mothers, assuming that the gross structure of maternal behavior was similar between strains. Second, our choice of seven HMM states, although based on extensive earlier experience in scoring maternal behavior in these strains [10,16] clearly forced the clustering of behavior into a limited number of states. We tested the validity of these assumptions and found that MatHMM was a better model than more simple models where ABN [resp. LG] and BLN [resp.GRO] states were merged (see Table S4), indicating that the presence of all these states are necessary to capture the specificities of BALB/c and C57BL/6 mouse behavioral strategies. Tests done on more complex models where a random state was added to MatHMM suggested that our seven-state model is sufficiently complex to adequately model the heterogeneity in our set of sequences (see Table S4). Furthermore our behavioural profile analysis on randomized models (Figures S2, S3, S4, and S5) strongly indicates that most states from our model MatHMM are not sensitive to variations in the initial parameters that were initially imposed. This suggests that LG, ABN, BLN, SLP, ACT, EAT states are likely to be good general descriptors of behavioral sequences in mouse laboratory strains, possibly reflecting distinct neurophysiological states of the animal, although as yet no data exists to support this hypothesis. In conclusion, although we cannot rule out that more complex models could better explain the behavioral data, we find that our results should not be biased by our choice of initial parameters. In summary, we have applied the HMM formalism to extract behavioral states from sequences of observed behaviors. A comparison of HMM analysis with traditional frequency analysis underscored the power of this technique to reveal critical information about clustering among behaviors and about transitions between these behavioral states. The analysis of behavioural sequences is an important tool to reveal behavioral strategies adopted by different individuals. Up to now mouse behavioral studies have been limited to the analysis of frequencies and duration of single behavioral variables and have rarely focused their attention on the analysis of the global behavioral strategy adopted. We expect that the implementation of classical methods used to analyse mouse behavior in combination with HMM formalism will facilitate the gathering of important information on brain mechanisms underlying the construction of behavioural strategies. This may further our understanding of human pathologies where the execution of simple behaviors is intact but the use of complex behavioral sequences/strategies is compromised. Finally, our findings provide a comprehensive description of maternal behavior in the laboratory mouse, reveal distinct maternal strategies in the C56BL/6 and BALB/c inbred strains, and show that the transmission of maternal behaviour from PLoS ONE | www.plosone.org 8 March 2011 | Volume 6 | Issue 3 | e14753