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Genetically encoded voltage indicators (GEVIs) enable optical recording of electrical signals in the brain, providing subthreshold sensitivity and temporal resolution not possible with calcium indicators. However, one- and two-photon voltage imaging over prolonged periods with the same GEVI has not yet been demonstrated. Here, we report engineering of ASAP family GEVIs to enhance photostability by inversion of the fluorescence-voltage relationship. Two of the resulting GEVIs, ASAP4b and ASAP4e, respond to 100-mV depolarizations with ≥180% fluorescence increases, compared with the 50% fluorescence decrease of the parental ASAP3. With standard microscopy equipment, ASAP4e enables single-trial detection of spikes in mice over the course of minutes. Unlike GEVIs previously used for one-photon voltage recordings, ASAP4b and ASAP4e also perform well under two-photon illumination. By imaging voltage and calcium simultaneously, we show that ASAP4b and ASAP4e can identify place cells and detect voltage spikes with better temporal resolution than commonly used calcium indicators. Thus, ASAP4b and ASAP4e extend the capabilities of voltage imaging to standard one- and two-photon microscopes while improving the duration of voltage recordings.

Single-image super-resolution (SR) methods often encounter difficulties when applied to real-world images because of deviations between the degradation model and real-world degradation distribution. Moreover, SR methods suffer from insufficient generalization when faced with diverse degradation distributions. Recent studies have attempted to adopt more complex degradation models to simulate real-world degradation distributions, thereby improving the reconstruction quality and generalization ability of SR methods. However, these methods tend to produce over-smoothed results lacking fine-grained details. Furthermore, these methods neglect the disparity between synthetic and real-world images in the frequency domain. To improve the generalization ability and reconstruction quality of real-world SR methods, this study proposes a more practical degradation model. The proposed model employs aliasing estimation and an iterative frequency domain degradation algorithm to narrow the frequency domain gap between synthetic and real-world images, thus expanding the latent degradation distribution. The proposed model considers the impact of various factors such as camera shake, defocus, and image compression, effectively simulating the complex and diverse degradation processes in the real world. Experiments demonstrate that combining the proposed degradation model with existing SR methods can improve the image perceptual quality on multiple test benchmarks with different degradation distributions. [ABSTRACT FROM AUTHOR]

Imaging methods that span both functional measures in living tissue and anatomical measures in fixed tissue have played critical roles in advancing our understanding of the brain. However, making direct comparisons between different imaging modalities, particularly spanning living and fixed tissue, has remained challenging. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, reaching this goal is difficult because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas. We then develop a registration pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments. [ABSTRACT FROM AUTHOR]

Optical imaging of neuronal voltage dynamics is invaluable to studying brain functions. However, high-speed imaging at significant depth is challenging due to the limitations of the short pixel dwell time and the maximum permissible excitation power in tissues. We report high-speed, deep voltage imaging by applying adaptive excitation, which illuminates the regions of interest only. We imaged neuronal voltage activities across two planes down to 500-630 μm depth in the awake mouse brain. Our techniques can be straightforwardly applied to typical two-photon microscopes., Competing Interests: Additional Declarations: There is NO Competing Interest.

The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, this is challenging because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA). We then develop a two-step pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments., Significance: Large-scale functional imaging experiments in Drosophila have given us new insights into neural activity in various sensory and behavioral contexts. However, precisely registering volumetric images from different studies has proven challenging, limiting quantitative comparisons of data across experiments. Here, we address this limitation by developing BIFROST, a registration pipeline robust to differences across experimental setups and datasets. We benchmark this pipeline by genetically labeling cell types in the fly brain and demonstrate sub-10 micron registration precision, both across specimens and across laboratories. We further demonstrate accurate registration between in-vivo brain volumes and ultrastructural connectomes, enabling direct structure-function comparisons in future experiments.