Biology subjects

Reynaud, J.

Publications and source records attributed to Reynaud, J..

4 recordsLinked to original sources

bioRxiv2026

Swimming speed of schooling fish controls social interaction strength in open-loop immersive virtual reality

Collective motion in fish schools emerges from local attraction, alignment, and repulsion rules whose strengths vary with behavioural and biomechanical context. Although swimming speed is known to shape turning capacity, spatial positioning, and information flow, its causal influence on social interaction strength remains poorly quantified. Using an open-loop immersive virtual-reality system, we expose freely swimming Hemigrammus rhodostomus to a virtual conspecific moving along circular trajectories at six controlled speeds. This approach isolates the effect of neighbour speed on attraction, alignment, vertical positioning, and wall avoidance. Across all speeds, real fish reliably track the virtual fish and adjust their own speed toward that of the partner. Behavioural distributions reveal systematic changes in spatial positioning and perception angles, with fish shifting from leader-like to follower-like configurations as virtual-fish speed increases. A data-driven 3D self-propelled particle model, in which only the amplitudes of interaction functions are modulated, quantitatively reproduces these patterns. Model-based reconstructions shows that higher partner speed weakens both horizontal and vertical attraction, strengthens alignment, and increases wall repulsion. These results demonstrate that fish tune their social-interaction rules in a speed-dependent manner, revealing swimming speed as a key control parameter shaping coordination in minimal groups and, potentially, in larger schools.

animal behavior and cognition↗

bioRxiv2025

Closed-loop real-virtual interactions validate 3D model of social coordination in fish

Collective motion in animal groups arises from social interactions rules, yet uncovering these rules requires quantitative models grounded in real behavior. We developed a fully three-dimensional, data-driven model of pairwise interactions in the schooling fish Hemigrammus rhodostomus, reconstructing attraction, alignment from experiments with two real fish swimming freely in a hemispherical bowl. Simulations of this model quantitatively reproduced empirical distributions of speed, distance, and orientation. We then embedded the model into a closed-loop virtual reality system, allowing a real fish to interact in real time with a virtual conspecific whose movements were governed by the same interaction rules. This biohybrid setup revealed that the model captures key social interactions underlying coordinated swimming. Our results establish a robust validation framework linking data, models, and behavior, paving the way for hybrid biological-digital collectives.

animal behavior and cognition↗

bioRxiv2024

Proposing a methodology for axon-centric analysis of IOP-induced mechanical insult

PurposeIOP-induced mechanical insult on retinal ganglion cell axons within the optic nerve head (ONH) is believed to be a key factor in axonal damage and glaucoma. However, most studies focus on tissue-level mechanical deformations, overlooking that axons are long and thin, and that their susceptibility to damage likely depends on the insults type (e.g. stretch/compression) and orientation (longitudinal/transverse). We propose an axon-centric approach to quantify IOP-induced mechanical insult from an axon perspective. MethodsWe used optical coherence tomography (OCT) scans from a healthy monkey eye along with histological images of cryosections to reconstruct the axon-occupied volume including detailed lamina cribrosa (LC) pores. Tissue-level strains were determined experimentally using digital volume correlation from OCT scans at baseline and elevated IOPs, then transformed into axonal strains using axon paths estimated by a fluid mechanics simulation. ResultsAxons in the LC and post-LC regions predominantly experienced longitudinal compression and transverse stretch, whereas those in the pre-LC and ONH rim mainly suffered longitudinal stretch and transverse compression. No clear patterns were observed for tissue-level strains. ConclusionsOur approach allowed discerning axonal longitudinal and transverse mechanical insults, which are likely associated with different mechanisms of axonal damage. The technique also enabled quantifying insult along individual axon paths, providing a novel link relating the retinal nerve fiber layer and the optic nerve through the LC via individual axons. This is a promising approach to establish a clearer connection between IOP-induced insult and glaucoma. Further studies should evaluate a larger cohort.

bioengineering↗

bioRxiv2021

A high-accuracy and high-efficiency digital volume correlation method to characterize in-vivo optic nerve head biomechanics from optical coherence tomography

In-vivo optic nerve head (ONH) biomechanics characterization is emerging as a promising way to study eye physiology and pathology. We propose a high-accuracy and high-efficiency digital volume correlation (DVC) method for the purpose of characterizing the in-vivo ONH deformation from volumes acquired by optical coherence tomography (OCT). Using a combination of synthetic tests and analysis of OCTs from monkey ONHs subjected to acute and chronically elevated intraocular pressure, we demonstrate that our proposed methodology overcomes several challenges for conventional DVC methods. First, it accounts for large ONH rigid body motion in the OCT volumes which could otherwise lead to analysis failure; second, sub-voxel-accuracy displacement can be guaranteed despite high noise and low image contrast of some OCT volumes; third, computational efficiency is greatly improved, such that the memory consumption of our method is substantially lower than with conventional methods; fourth, we introduce a parameter measuring displacements confidence. Test of image noise effects showed that the proposed DVC method had displacement errors smaller than 0.028 voxels with speckle noise and smaller than 0.037 voxels with Gaussian noise; The absolute (relative) strain errors in the three directions were lower than 0.0018 (4%) with speckle noise and than 0.0045 (8%) with Gaussian noise. Compared with conventional DVC methods, the proposed DVC method had substantially improved overall displacement and strain errors under large body motions (lower by up to 70%), with 75% lower computation times, while saving about 30% memory. The study thus demonstrates the potential of the proposed technique to investigate ONH biomechanics.

bioengineering↗