PT  - JOURNAL ARTICLE
AU  - Philipp D. Lösel
AU  - Coline Monchanin
AU  - Renaud Lebrun
AU  - Alejandra Jayme
AU  - Jacob Relle
AU  - Jean-Marc Devaud
AU  - Vincent Heuveline
AU  - Mathieu Lihoreau
TI  - Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning
AID  - 10.1101/2022.10.12.511944
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.10.12.511944
4099  - http://biorxiv.org/content/early/2022/11/30/2022.10.12.511944.short
4100  - http://biorxiv.org/content/early/2022/11/30/2022.10.12.511944.full
AB  - Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations that provide critical insights into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition.Competing Interest StatementThe authors have declared no competing interest.