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

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.

SUBMITTER: Losel PD

PROVIDER: S-EPMC10569549 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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

Lösel Philipp D PD Monchanin Coline C Lebrun Renaud R Jayme Alejandra A Relle Jacob J JJ Devaud Jean-Marc JM Heuveline Vincent V Lihoreau Mathieu M

PLoS computational biology 20231002 10

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 in ...[more]

PMID: 37782674

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Project description:BackgroundChitin and cuticle coatings are important to the environmental and immune defense of honey bees and insect pollinators. Pesticides or environmental effects may target the biochemistry of insect chitin and cuticle coating. Denaturing of chitin involves a combination of deacetylation, intercalation, oxidation, Schweiger-peeling, and the formation of amine hydrochloride salt. The term "denatured chitin" calls attention to structural and property changes to the internal membranes and external carapace of organisms so that some properties affecting biological activities are diminished.Methodology/principal findingsA case study was performed on honey bees using silver staining and microscopic computer-tomographic x-ray radiography (micro-CT). Silver nitrate formed counter-ion complexes with labile ammonium cations and reacted with amine hydrochloride. Silver was concentrated in the peritrophic membrane, on the abdomen, in the glossa, at intersegmental joints (tarsi), at wing attachments, and in tracheal air sacs. Imaged mono-esters and fatty acids from cuticle coating on external surfaces were apparently reduced by an alcohol pretreatment.Conclusions/significanceThe technique provides 3-dimensional and sectional images of individual honey bees consistent with the chemistries of silver reaction and complex formation with denatured chitin. Environmental exposures and influences such as gaseous nitric oxide intercalant, trace oxidants such as ozone gas, oligosachharide salt conversion, exposure to acid rain, and chemical or biochemical denaturing by pesticides may be studied using this technique. Peritrophic membranes, which protect against food abrasion, microorganisms, and permit efficient digestion, were imaged. Apparent surface damage to the corneal lenses of compound eyes by dilute acid exposure consistent with chitin amine hydrochloride formation was imaged. The technique can contribute to existing insect pathology research, and may provide an additional tool for research on CCD.

Project description:Micro-CT is widely used for small animal imaging in preclinical studies of cardiopulmonary disease, but further development is needed to improve spatial resolution, temporal resolution, and material contrast. We present a technique for visualizing the changing distribution of iodine in the cardiac cycle with dual source micro-CT.The approach entails a retrospectively gated dual energy scan with optimized filters and voltages, and a series of computational operations to reconstruct the data. Projection interpolation and five-dimensional bilateral filtration (three spatial dimensions + time + energy) are used to reduce noise and artifacts associated with retrospective gating. We reconstruct separate volumes corresponding to different cardiac phases and apply a linear transformation to decompose these volumes into components representing concentrations of water and iodine. Since the resulting material images are still compromised by noise, we improve their quality in an iterative process that minimizes the discrepancy between the original acquired projections and the projections predicted by the reconstructed volumes. The values in the voxels of each of the reconstructed volumes represent the coefficients of linear combinations of basis functions over time and energy. We have implemented the reconstruction algorithm on a graphics processing unit (GPU) with CUDA. We tested the utility of the technique in simulations and applied the technique in an in vivo scan of a C57BL∕6 mouse injected with blood pool contrast agent at a dose of 0.01 ml∕g body weight. Postreconstruction, at each cardiac phase in the iodine images, we segmented the left ventricle and computed its volume. Using the maximum and minimum volumes in the left ventricle, we calculated the stroke volume, the ejection fraction, and the cardiac output.Our proposed method produces five-dimensional volumetric images that distinguish different materials at different points in time, and can be used to segment regions containing iodinated blood and compute measures of cardiac function.We believe this combined spectral and temporal imaging technique will be useful for future studies of cardiopulmonary disease in small animals.

Project description:For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contouring leads to a faster workflow, which limits exposure to anaesthesia and thereby, reducing its impact on experimental results and on animal wellbeing. Here, we trained the 2D and 3D U-Net architectures of no-new-Net (nnU-Net) for autocontouring of the thorax in mouse micro-CT images. We trained the models only on native CTs and evaluated their performance using an independent testing dataset (i.e., native CTs not included in the training and validation). Unlike previous studies, we also tested the model performance on an external dataset (i.e., contrast-enhanced CTs) to see how well they predict on CTs completely different from what they were trained on. We also assessed the interobserver variability using the generalized conformity index ([Formula: see text]) among three observers, providing a stronger human baseline for evaluating automated contours than previous studies. Lastly, we showed the benefit on the contouring time compared to manual contouring. The results show that 3D models of nnU-Net achieve superior segmentation accuracy and are more robust to unseen data than 2D models. For all target organs, the mean surface distance (MSD) and the Hausdorff distance (95p HD) of the best performing model for this task (nnU-Net 3d_fullres) are within 0.16 mm and 0.60 mm, respectively. These values are below the minimum required contouring accuracy of 1 mm for small animal irradiations, and improve significantly upon state-of-the-art 2D U-Net-based AIMOS method. Moreover, the conformity indices of the 3d_fullres model also compare favourably to the interobserver variability for all target organs, whereas the 2D models perform poorly in this regard. Importantly, the 3d_fullres model offers 98% reduction in contouring time.

Dataset Information

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

Publications

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

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