Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice

Abstract

Synaptic plasticity encodes learning as changes in the strength of synapses, sub-micron structures that mediate communication between brain cells. Due to their small size and high density, synapses are extremely difficult to image in vivo, limiting our ability to directly relate synaptic plasticity with behavior. Here, we developed a combination of computational and biological methods to overcome these challenges. First, we trained a deep learning image restoration algorithm that combines the advantages of ex vivo super-resolution and in vivo imaging modalities to overcome limitations specific to each optical system. Applied to in vivo images from transgenic mice expressing fluorescently labeled synaptic proteins, this restoration algorithm super-resolved diffraction-limited synapses, enabling identification and logitudinal tracking of synaptic plasticity underlying behavior with unprecedented spatial resolution. More generally, our method demonstrates the capabilities of image enhancement to learn from ex vivo data and imaging techniques to improve in vivo imaging resolution.