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

Year: 2023

Type: article

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. Author Summary Bees, despite their small brains, possess a rich behavioural repertoire and show significant variations among individuals. In social bees this variability is key to the division of labour that maintains their complex social organizations, and has been linked to the maturation of specific brain areas as a result of development and foraging experience. This makes bees an ideal model for understanding insect cognitive functions and the neural mechanisms that underlie them. However, due to the scarcity of comparative data, the relationship between brain neuro-architecture and behavioural variance remains unclear. To address this problem, we developed an AI-based approach for automated analysis of brain images and analysed an unprecedentedly large dataset of honey bee and bumblebee brains. Through this process, we were able to identify previously undescribed anatomical features that correlate with known behaviours, supporting recent evidence of lateralized behaviour in foraging and pollination. Our method is open-source, easily accessible online, user-friendly, fast, accurate, and robust to different species, enabling large-scale comparative analyses across the animal kingdom. This includes investigating the impact of external stressors such as environmental pollution and climate change on cognitive development, helping us understand the mechanisms underlying the cognitive abilities of animals and the implications for their survival and adaptation.

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1011529

Authors: Philipp D. Lösel, Coline Monchanin, Renaud Lebrun, Alejandra Jayme, Jacob Relle, Jean-Marc Devaud, Vincent Heuveline, Mathieu Lihoreau

Institutions: Australian National University, Heidelberg University, Heidelberg Institute for Theoretical Studies, Centre National de la Recherche Scientifique, Université Toulouse III - Paul Sabatier, Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative de Toulouse, Macquarie University, Inserm, Université de Montpellier, Institut des Sciences de l'Evolution de Montpellier, BioCampus Montpellier

Language: English

Topic: Neurobiology and Insect Physiology Research

Subfield: Cellular and Molecular Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,655

Funders: Centre Méditerranéen de l’Environnement et de la Biodiversité, European Commission, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Bundesministerium für Bildung und Forschung, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Ministère de l'Enseignement Supérieur et de la Recherche, Klaus Tschira Stiftung

OpenAlex ID: W4387251781