Frontiers in Behavioral Neuroscience (May 2022)

Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies

  • Benjamin de Bivort,
  • Sean Buchanan,
  • Kyobi Skutt-Kakaria,
  • Erika Gajda,
  • Julien Ayroles,
  • Chelsea O’Leary,
  • Pablo Reimers,
  • Jamilla Akhund-Zade,
  • Rebecca Senft,
  • Ryan Maloney,
  • Sandra Ho,
  • Zach Werkhoven,
  • Matthew A.-Y. Smith

DOI
https://doi.org/10.3389/fnbeh.2022.836626
Journal volume & issue
Vol. 16

Abstract

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Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a “computational ethology natural history” study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.

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