Exhaustive Multi-Parametric Assessment of the Behavioral Array of Daily Activities of Mice Using Cluster and Factor Analysis.

Using automated supervised behavioral assessment software, we recorded and analyzed 24 h non-interrupted recordings of mice for a duration of 11 days. With the assistance of free R programming, we used correlation matrix-based hierarchical clustering and factor analysis to separate the 33 activities into meaningful clusters and groups without losing the exhaustive nature of the findings. These groups represent novel meaningful behavioral patterns exhibited by mice in home cage. Thirty-three activities were separated into 5 clusters based on dissimilarity between activities and 6 factors based on statistical modeling. Using these two methods, we describe and compare behavioral arrays of two groups of animals: 1. Continuously recorded for 11 days in social isolation and 2. Intermittently socially isolated for recording on days 1, 3, 5, 8, and 10, while socializing on the other days. This is the first work to our knowledge that interprets mouse home cage activities throughout a 24 h period and proposes a base line of a daily routine of a healthy C57Bl/6J mouse that can be used for various experimental paradigms, including disease, neuroinflammation, or drug testing to trace behavioral changes that follow intervention. In this work, we defined the necessary acclimatization period for the 24 h recording paradigm of home cage behavior. We demonstrated the behavioral changes that are associated with the effect of social isolation, intermittent socialization, and re-introduction to a familiar home cage. We provide the full description of the codes used in R.

Supervised and Unsupervised Learning Technology in the Study of Rodent Behavior.

Quantifying behavior is a challenge for scientists studying neuroscience, ethology, psychology, pathology, etc. Until now, behavior was mostly considered as qualitative descriptions of postures or labor intensive counting of bouts of individual movements. Many prominent behavioral scientists conducted studies describing postures of mice and rats, depicting step by step eating, grooming, courting, and other behaviors. Automated video assessment technologies permit scientists to quantify daily behavioral patterns/routines, social interactions, and postural changes in an unbiased manner. Here, we extensively reviewed published research on the topic of the structural blocks of behavior and proposed a structure of behavior based on the latest publications. We discuss the importance of defining a clear structure of behavior to allow professionals to write viable algorithms. We presented a discussion of technologies that are used in automated video assessment of behavior in mice and rats. We considered advantages and limitations of supervised and unsupervised learning. We presented the latest scientific discoveries that were made using automated video assessment. In conclusion, we proposed that the automated quantitative approach to evaluating animal behavior is the future of understanding the effect of brain signaling, pathologies, genetic content, and environment on behavior.

NLR-Dependent Regulation of Inflammation in Multiple Sclerosis.

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) associated with inappropriate activation of lymphocytes, hyperinflammatory responses, demyelination, and neuronal damage. In the past decade, a number of biological immunomodulators have been developed that suppress the peripheral immune responses and slow down the progression of the disease. However, once the inflammation of the CNS has commenced, it can cause serious permanent neuronal damage. Therefore, there is a need for developing novel therapeutic approaches that control and regulate inflammatory responses within the CNS. Nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) are intracellular regulators of inflammation expressed by many cell types within the CNS. They redirect multiple signaling pathways initiated by pathogens and molecules released by injured tissues. NLR family members include positive regulators of inflammation, such as NLRP3 and NLRC4 and anti-inflammatory NLRs, such as NLRX1 and NLRP12. They exert immunomodulatory effect at the level of peripheral immune responses, including antigen recognition and lymphocyte activation and differentiation. Also, NLRs regulate tissue inflammatory responses. Understanding the molecular mechanisms that are placed at the crossroad of innate and adaptive immune responses, such as NLR-dependent pathways, could lead to the discovery of new therapeutic targets. In this review, we provide a summary of the role of NLRs in the pathogenesis of MS. We also summarize how anti-inflammatory NLRs regulate the immune response within the CNS. Finally, we speculate the therapeutic potential of targeting NLRs in MS.