Schmid, G. | bioRxiv Science

bioRxiv2022

Label-free imaging of large samples: 3D rendering and morphological analysis within histological workflows using serial block face imaging

Serial block face imaging (SFBI) is a method used to generate 3-dimensional (3D) reconstruction of a sample via serial image acquisition. Several SBFI approaches have been proposed for large samples, differing in the ability to generate contrast as well as in the nature of the detected signal. We propose a new system that detects the endogenous autofluorescence signal of paraffin-embedded samples. The sample preparation is simplified compared to other approaches, and adapted to be integrated into a routine histological preparation. More specifically, it was designed to limit reagent toxicity and to be compatible with downstream histological processing. We show the usefulness of the technique with a wide range of tissues based on the intrinsic autofluorescence signal. Optimization of quality section recovery offers the possibility to develop correlative approaches and multimodal analysis between the 3D dataset with the 2-dimensional (2D) sections. In addition, contrast and resolution of block-face images allow us to successfully perform post processing analysis and morphology quantifications. Overall, our methodology offers a simple, cost effective and rapid approach to obtain quantitative data on a large sample with no specific staining.

bioengineering↗

bioRxiv2021

Nash equilibria in human sensorimotor interactions explained by Q-Learning

The Nash equilibrium concept has previously been shown to be an important tool to understand human sensorimotor interactions, where different actors vie for minimizing their respective effort while engaging in a multi-agent motor task. However, it is not clear how such equilibria are reached. Here, we compare different reinforcement learning models based on haptic feedback to human behavior in sensorimotor versions of three classic games, including the Prisoners Dilemma, and the symmetric and asymmetric matching pennies games. We find that a discrete analysis that reduces the continuous sensorimotor interaction to binary choices as in classical matrix games does not allow to distinguish between the different learning algorithms, but that a more detailed continuous analysis with continuous formulations of the learning algorithms and the game-theoretic solutions affords different predictions. In particular, we find that Q-learning with intrinsic costs that disfavor deviations from average behavior explains the observed data best, even though all learning algorithms equally converge to admissible Nash equilibrium solutions. We therefore conclude that it is important to study different learning algorithms for understanding sensorimotor interactions, as such behavior cannot be inferred from a game-theoretic analysis alone, that simply focuses on the Nash equilibrium concept, as different learning algorithms impose preferences on the set of possible equilibrium solutions due to the inherent learning dynamics.

neuroscience↗