arXiv:2605.13455v2 Announce Type: replace 
Abstract: Synapses are densely packed submicron structures that dynamically reorganize during learning and memory formation. Longitudinal \textit{in vivo} imaging of fluorescently tagged synaptic receptors offers a promising opportunity to study large-scale synaptic dynamics and how these processes are disrupted in neurological disease. However, in vivo imaging with 2-photon microscopy uses low laser power and therefore suffers from low signal-to-noise ratio (SNR) and high shot noise, nonlinear tissue motion between days, nonstationary fluctuations in synaptic fluorescence, and significant blur induced by the microscope point spread function (PSF). Together, these factors make it challenging to detect and track synapses, especially in regions with high synaptic density. This paper presents a novel template-based framework for modeling synapses as varying luminance point sources that move under a nonlinear tissue deformation. Taking a unified Bayesian approach, we apply this model to microscopy data by deriving a posterior that incorporates a diffeomorphic mapping for domain warping, a Gaussian point spread function for the imaging process, and a Poisson observation model for raw photon counts. The Bayesian solution simultaneously: (1) Constructs a probabilistic template of synapse locations, (2) denoises and deconvolves the image data, (3) infers fluorescence intensities, (4) performs diffeomorphic image registration to correct for tissue motion, and (5) provides confidence regions for these parameter estimates. We demonstrate the framework on both a 2D+t simulated dataset and a 3D+t longitudinal \textit{in vivo} microscopy dataset of fluorescent synapses imaged in a mouse over two weeks.

تقدم دراسة حديثة إطارًا جديدًا لتتبع المشابك في الجسم الحي باستخدام أساليب بايزي، حيث تتناول التحديات مثل انخفاض نسبة الإشارة إلى الضوضاء وحركة الأنسجة غير الخطية في المجهر الضوئي ثنائي الفوتون. هذا النهج يقوم بنمذجة المشابك كمصادر ضوء متغيرة، مما يعزز القدرة على مراقبة الديناميات المشبكية الضرورية لفهم التعلم وتكوين الذاكرة.

Un estudio reciente presenta un nuevo marco para rastrear sinapsis in vivo utilizando métodos bayesianos, abordando desafíos como las bajas relaciones señal-ruido y el movimiento no lineal del tejido en la microscopía de dos fotones. Este enfoque modela las sinapsis como fuentes de luminiscencia variable, mejorando la capacidad para observar la dinámica sináptica crucial para comprender el aprendizaje y la formación de la memoria.

Une étude récente présente un nouveau cadre pour le suivi des synapses in vivo utilisant des méthodes bayésiennes, abordant des défis tels que les faibles rapports signal-bruit et le mouvement tissulaire non linéaire en microscopie à deux photons. Cette approche modélise les synapses comme des sources de lumière variable, améliorant la capacité à observer la dynamique synaptique cruciale pour comprendre l'apprentissage et la formation de la mémoire.

A recent study introduces a novel framework for tracking synapses in vivo using Bayesian methods, addressing challenges such as low signal-to-noise ratios and nonlinear tissue motion in 2-photon microscopy. This approach models synapses as varying luminance point sources, enhancing the ability to observe synaptic dynamics crucial for understanding learning and memory formation.