Quantifying Sex Differences in Behavior in the Era of "Big" Data

Abstract

Sex differences are commonly observed in behaviors that are closely linked to adaptive function, but sex differences can also be observed in behavioral "building blocks" such as locomotor activity and reward processing. Modern neuroscientific inquiry, in pursuit of generalizable principles of functioning across sexes, has often ignored these more subtle sex differences in behavioral building blocks that may result from differences in these behavioral building blocks. A frequent assumption is that there is a default (often male) way to perform a behavior. This approach misses fundamental drivers of individual variability within and between sexes. Incomplete behavioral descriptions of both sexes can lead to an overreliance on reduced "single-variable" readouts of complex behaviors, the design of which may be based on male-biased samples. Here, we advocate that the incorporation of new machine-learning tools for collecting and analyzing multimodal "big behavior" data allows for a more holistic and richer approach to the quantification of behavior in both sexes. These new tools make behavioral description more robust and replicable across laboratories and species, and may open up new lines of neuroscientific inquiry by facilitating the discovery of novel behavioral states. Having more accurate measures of behavioral diversity in males and females could serve as a hypothesis generator for where and when we should look in the brain for meaningful neural differences.

Figure 1.

New tools to facilitate the discovery of sex differences in behavior. (A) Common behavioral paradigms used in neuroscience commonly rely on single-variable descriptions of complex behaviors for both sexes. Examples include fear conditioning (left), social avoidance (center), and the “resident intruder” paradigm for social behavior (right). (B) Recent examples of serendipitously discovered behaviors in these paradigms suggest that single variables are insufficient to describe sex differences in behavior. Examples include increased social vigilance in females (left), and increased “darting” behaviors in females during fear conditioning (right). (C) Adopting machine-learning strategies for pose tracking and video analysis will facilitate behavioral discovery by allowing the robust quantification of multiple behaviors, and may enable the discovery of new behaviors. One example is using video acquisition and pose tracking (in this case, DeepLabCut), to reveal behavioral “features” from supervised analyses (right top), but also applying unsupervised analyses (right bottom) to provide a more nuanced view of sex differences in complex behavior. Difference heat map may reveal behavior clusters that are enriched in one sex over the other.