Computational imaging seeks to achieve novel capabilities and overcome conventional limitations by combining optics and algorithms. In this seminar, I will discuss two computational imaging technologies developed in Boston University Computational Imaging Systems lab, including Intensity Diffraction Tomography and Computational Miniature Mesoscope. In our intensity diffraction tomography system, we demonstrate 3D quantitative phase imaging on a simple LED array microscope. We develop both single-scattering and multiple-scattering models to image complex biological samples. In our Computational Miniature Mesoscope, we demonstrate single-shot 3D high-resolution fluorescence imaging across a wide field-of-view in a miniaturized platform. We develop methods to characterize 3D spatially varying aberrations and physical simulator-based deep learning strategies to achieve fast and accurate reconstructions. Broadly, I will discuss how synergies between novel optical instrumentation, physical modeling, and model- and learning-based computational algorithms can push the limits in biomedical microscopy and neural imaging.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

Neural computations occurring simultaneously in multiple cerebral cortical regions are critical for mediating behaviors. Progress has been made in understanding how neural activity in specific cortical regions contributes to behavior. However, there is a lack of tools that allow simultaneous monitoring and perturbing neural activity from multiple cortical regions. We have engineered a suite of technologies to enable easy, robust access to much of the dorsal cortex of mice for optical and electrophysiological recordings. First, I will describe microsurgery robots that can programmed to perform delicate microsurgical procedures such as large bilateral craniotomies across the cortex and skull thinning in a semi-automated fashion. Next, I will describe digitally designed, morphologically realistic, transparent polymer skulls that allow long-term (+300 days) optical access. These polymer skulls allow mesoscopic imaging, as well as cellular and subcellular resolution two-photon imaging of neural structures up to 600 µm deep. We next engineered a widefield, miniaturized, head-mounted fluorescence microscope that is compatible with transparent polymer skull preparations. With a field of view of 8 × 10 mm2 and weighing less than 4 g, the ‘mini-mScope’ can image most of the mouse dorsal cortex with resolutions ranging from 39 to 56 µm. We used the mini-mScope to record mesoscale calcium activity across the dorsal cortex during sensory-evoked stimuli, open field behaviors, social interactions and transitions from wakefulness to sleep.

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their fluorescence are often inefficient and the results hard to interpret. Here we introduce Suite2p: a fast, accurate, parameter-free and complete pipeline that registers raw movies, detects active and/or inactive cells (using Cellpose), extracts their calcium traces and infers their spike times. Suite2p runs faster than real time on standard workstations and outperforms state-of-the-art methods on newly developed ground-truth benchmarks for motion correction and cell detection.