Restoration of Two-Photon Ca2+ Imaging Data Through Model Blind Spatiotemporal Filtering

A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds

A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to images with large fields of view from a mouse model and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pretrained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.

Calcium image analysis in the moving gut

BACKGROUND

The neural control of gastrointestinal muscle relies on circuit activity whose underlying motifs remain limited by small-sample calcium imaging recordings confounded by motion artifact, paralytics, and muscle dissections. We present a sequence of resources to register images from moving preparations and identify out-of-focus events in widefield fluorescent microscopy.

METHODS

Our algorithm uses piecewise rigid registration with pathfinding to correct movements associated with smooth muscle contractions. We developed methods to identify loss-of-focus events and to simulate calcium activity to evaluate registration.

KEY RESULTS

By combining our methods with principal component analysis, we found populations of neurons exhibit distinct activity patterns in response to distinct stimuli consistent with hypothesized roles. The image analysis pipeline makes deeper insights possible by capturing concurrently calcium dynamics from more neurons in larger fields of view. We provide access to the source code for our algorithms and make experimental and technical recommendations to increase data quality in calcium imaging experiments.

CONCLUSIONS

These methods make feasible large population, robust calcium imaging recordings and permit more sophisticated network analyses and insights into neural activity patterns in the gut.

Fast and Accurate Motion Correction for Two-Photon Ca2+ Imaging in Behaving Mice

Two-photon Ca2+ imaging is a widely used technique for investigating brain functions across multiple spatial scales. However, the recording of neuronal activities is affected by movement of the brain during tasks in which the animal is behaving normally. Although post-hoc image registration is the commonly used approach, the recent developments of online neuroscience experiments require real-time image processing with efficient motion correction performance, posing new challenges in neuroinformatics. We propose a fast and accurate image density feature-based motion correction method to address the problem of imaging animal during behaviors. This method is implemented by first robustly estimating and clustering the density features from two-photon images. Then, it takes advantage of the temporal correlation in imaging data to update features of consecutive imaging frames with efficient calculations. Thus, motion artifacts can be quickly and accurately corrected by matching the features and obtaining the transformation parameters for the raw images. Based on this efficient motion correction strategy, our algorithm yields promising computational efficiency on imaging datasets with scales ranging from dendritic spines to neuronal populations. Furthermore, we show that the proposed motion correction method outperforms other methods by evaluating not only computational speed but also the quality of the correction performance. Specifically, we provide a powerful tool to perform motion correction for two-photon Ca2+ imaging data, which may facilitate online imaging experiments in the future.