Seasonal variations of serotonin in the visual system of an ant revealed by immunofluorescence and a machine learning approach

Hibernation, as an adaptation to seasonal environmental
changes in temperate or boreal regions, has profound effects
on mammalian brains. Social insects of temperate regions
hibernate as well, but despite abundant knowledge on
structural and functional plasticity in insect brains, the
question of how seasonal activity variations affect insect
central nervous systems has not yet been thoroughly
addressed. Here, we studied potential variations of serotoninimmunoreactivity
in visual information processing centres in
the brain of the long-lived ant species Lasius niger. Quantitative
immunofluorescence analysis revealed stronger serotonergic
signals in the lamina and medulla of the optic lobes of wild or
active laboratory workers than in hibernating animals. Instead
of statistical inference by testing, differentiability of seasonal
serotonin-immunoreactivity was confirmed by a machine
learning analysis using convolutional artificial neuronal
networks (ANNs) with the digital immunofluorescence images
as input information. Machine learning models revealed
additional differences in the third visual processing centre, the
lobula. We further investigated these results by gradientweighted
class activation mapping. We conclude that seasonal
activity variations are represented in the ant brain, and that
machine learning by ANNs can contribute to the discovery of
such variations.