Behavioral neuroscience, driven by technological advances such as computer vision and machine learning algorithms, is experiencing a revolution in behavioral data analysis. New technologies enable high-resolution, high-throughput capture and analysis of complex behaviors, generating large-scale data sets that require advanced computational approaches for their analysis. The development of robust and standardized tools for behavioral analysis is essential, and genomics, with its large-scale data generation technologies, offers a set of tools and computational approaches that can be of great value to behavioral neuroscience. (Bentzur; Alon; Shohat-Ophir, 2022)
Social interactions in animal groups create a dynamic and multidimensional environment, with each interaction influencing the following. The analysis of social behavior in groups, although complex, begins with the identification and tracking of individuals through computer vision algorithms. From there, a variety of behavioral characteristics, such as speed, angles, distance from the center and edge of the arena, can be extracted. The analysis of interactions between pairs of individuals exponentially increases the number of behavioral characteristics that can be extracted, including duration and frequency of behaviors, types of interactions, and formation of social networks. (Bentzur; Alon; Shohat-Ophir, 2022)
Analyzing behavioral and genomic datasets presents similar challenges. Both describe high-dimensional systems with multiple organizational levels, each with multiple dimensions. Computational approaches used to analyze complexity at an organizational level, such as single-cell RNA sequencing (scRNAseq) data in genomics, can be applied to analyzing other levels, such as social interactions in groups. (Bentzur; Alon; Shohat-Ophir, 2022)
Despite the differences between behavioral and genomic data, such as the continuous nature of behavioral measurements versus the “snapshots” at specific points in time in genomics, computational approaches from genomics can be adapted for behavioral analysis. Dimensionality reduction, widely used in genomics to simplify the analysis of complex datasets, can be applied to reveal underlying behavioral mechanisms. (Bentzur; Alon; Shohat-Ophir, 2022)
Cluster analysis, another powerful tool in genomics, can aid in extracting meaningful patterns from complex behavioral datasets. Rather than genes or cells, behavioral parameters and conditions or genetic manipulations are grouped together, allowing for the identification of groups that exhibit similar patterns and the elucidation of subsets of parameters that are co-regulated under certain conditions or genotypes. (Bentzur; Alon; Shohat-Ophir, 2022)
The study of behavioral variance, often overlooked in favor of analyzing averages, offers valuable insight into population differences and social dynamics. Variance between individuals and between groups, driven by unique social interactions and individual “trajectory” within a group, can be just as informative as behavioral averages. (Bentzur; Alon; Shohat-Ophir, 2022)
The concepts of individuality and group identity, well documented in behavioral ecology, can be explored in conjunction with analysis of variance tools to deepen our understanding of social dynamics. Individuality, expressed in behavioral characteristics that persist over time, and group identity, which emerges from interactions among group members, are important concepts that deserve further investigation. (Bentzur; Alon; Shohat-Ophir, 2022)
The application of the concept of variance in genomics, particularly in the context of single-cell gene expression and spatially resolved transcriptomics, provides a model for behavioral variance analysis. Variance in gene expression between individual cells, once considered noise, is now recognized as an important feature that can be influenced by cell type, physical location in tissue, and cell-cell interactions. (Bentzur; Alon; Shohat-Ophir, 2022)
Data accessibility and standardization of analyses, hallmarks of genomic research, are challenges to be overcome in behavioral neuroscience. The deposition of raw data from behavioral experiments in public archives, together with the standardization of sample preparation and analysis methods, would facilitate the investigation and comparison of data by different research groups. (Bentzur; Alon; Shohat-Ophir, 2022)
Behavioral neuroscience is entering the era of “big data,” with large datasets offering both opportunities and challenges for advancing our understanding of animal behavior. Adopting approaches from other fields, such as genomics, and establishing protocols for deposition of datasets and development of standardized analysis tools are essential for the advancement and maturation of behavioral neuroscience. (Bentzur; Alon; Shohat-Ophir, 2022)
Reference
BENTZUR, A.; ALON, S.; SHOHAT-OPHIR, G. Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets. International Journal of Molecular Sciences, vol. 23, no. 7, p. 3811, 2022.
