TY - JOUR
T1 - Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning
AU - Lösel, Philipp D.
AU - Monchanin, Coline
AU - Lebrun, Renaud
AU - Jayme, Alejandra
AU - Relle, Jacob J.
AU - Devaud, Jean Marc
AU - Heuveline, Vincent
AU - Lihoreau, Mathieu
N1 - Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2023/10
Y1 - 2023/10
N2 - Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology 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 microCT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and 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 quantitative 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.
AB - Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology 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 microCT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and 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 quantitative 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.
UR - http://www.scopus.com/inward/record.url?scp=85174213817&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1011529
DO - 10.1371/journal.pcbi.1011529
M3 - Article
C2 - 37782674
AN - SCOPUS:85174213817
SN - 1553-734X
VL - 19
SP - 1
EP - 26
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 10
M1 - e1011529
ER -