Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning

Philipp D Lösel; Coline Monchanin; Renaud Lebrun; Alejandra Jayme; Jacob J Relle; Jean-Marc Devaud; Vincent Heuveline; Mathieu Lihoreau

doi:10.1371/journal.pcbi.1011529

Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning

PLoS Comput Biol. 2023 Oct 2;19(10):e1011529. doi: 10.1371/journal.pcbi.1011529. eCollection 2023 Oct.

Authors

Philipp D Lösel^{1

2

3}, Coline Monchanin^{4

5}, Renaud Lebrun^{6

7}, Alejandra Jayme^{1

2}, Jacob J Relle^{1

2}, Jean-Marc Devaud⁴, Vincent Heuveline^{1

2

8}, Mathieu Lihoreau⁴

Affiliations

¹ Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
² Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.
³ Department of Materials Physics, Research School of Physics, The Australian National University, Canberra, Australia.
⁴ Research Center on Animal Cognition (CRCA), Center for Integrative Biology (CBI); CNRS, University Paul Sabatier - Toulouse III, Toulouse, France.
⁵ Department of Biological Sciences, Macquarie University, Sydney, Australia.
⁶ Institut des Sciences de l'Evolution de Montpellier, CC64, Université de Montpellier, Montpellier, France.
⁷ BioCampus, Montpellier Ressources Imagerie, CNRS, INSERM, Université de Montpellier, Montpellier, France.
⁸ Heidelberg University Computing Centre (URZ), Heidelberg, Germany.

Abstract

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 micro-CT 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.

Copyright: © 2023 Lösel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

MeSH terms

Animals
Bees
Brain / anatomy & histology
Brain / diagnostic imaging
Cognition
Deep Learning*
Organ Size
X-Ray Microtomography

Grants and funding

Three-dimensional data acquisitions were performed using the micro-CT facilities of the MRI platform member of the national infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04, «Investments for the future»), and of the Labex CEMEB (ANR-10-LABX-0004), and NUMEV (ANR-10-LABX-0020), which supported RL. We further acknowledge the support by the projects ASTOR (05K2013) and NOVA (05K2016) funded by the German Federal Ministry of Education and Research (BMBF), Informatics for Life funded by the Klaus Tschira Foundation, the state of Baden-Württemberg through bwHPC, the Ministry of Science, Research and the Arts Baden-Württemberg (MWK) through the data storage service SDS@hd, and the German Research Foundation (DFG; INST 35/1314-1 FUGG and INST 35/1134-1 FUGG), which supported PDL, AJ, JJR, and VH. CM was funded by a PhD fellowship from the French Ministry of Higher Education, Research and Innovation. JMD and ML were funded by the Agence Nationale de la Recherche (3DNaviBee ANR-19-CE37-0024), the Agence de la Transition Ecologique (project LOTAPIS), and the European Commission (FEDER ECONECT MP0021763, ERC Cog BEE-MOVE GA101002644). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.