Photon shot noise limits on optical detection of neuronal spikes and estimation of spike timing

Large-scale deep tissue voltage imaging with targeted-illumination confocal microscopy

Voltage imaging with cellular specificity has been made possible by advances in genetically encoded voltage indicators. However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines the signal-to-noise ratio and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating while also maximizing signal detection efficiency. The resulting benefits in signal-to-noise ratio and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different genetically encoded voltage indicator classes.

Optical constraints on two-photon voltage imaging

Significance

Genetically encoded voltage indicators (GEVIs) are a valuable tool for studying neural circuits in vivo, but the relative merits and limitations of one-photon (1P) vs. two-photon (2P) voltage imaging are not well characterized.

Aim

We consider the optical and biophysical constraints particular to 1P and 2P voltage imaging and compare the imaging properties of commonly used GEVIs under 1P and 2P excitation.

Approach

We measure brightness and voltage sensitivity of voltage indicators from commonly used classes under 1P and 2P illumination. We also measure the decrease in fluorescence as a function of depth in mouse brain. We develop a simple model of the number of measurable cells as a function of reporter properties, imaging parameters, and desired signal-to-noise ratio (SNR). We then discuss how the performance of voltage imaging would be affected by sensor improvements and by recently introduced advanced imaging modalities.

Results

Compared to 1P excitation, 2P excitation requires ~104-fold more illumination power per cell to produce similar photon count rates. For voltage imaging with JEDI-2P in mouse cortex with a target SNR of 10 (spike height:baseline shot noise), a measurement bandwidth of 1 kHz, a thermally limited laser power of 200 mW, and an imaging depth of > 300 μm, 2P voltage imaging using an 80 MHz source can record from no more 12 cells simultaneously.

Conclusions

Due to the stringent photon-count requirements of voltage imaging and the modest voltage sensitivity of existing reporters, 2P voltage imaging in vivo faces a stringent tradeoff between shot noise and tissue photodamage. 2P imaging of hundreds of neurons with high SNR at depth > 300 μm will require either major improvements in 2P GEVIs or qualitatively new approaches to imaging.

A Large Field-of-view, Single-cell-resolution Two- and Three-Photon Microscope for Deep Imaging

In vivo imaging of large-scale neuron activity plays a pivotal role in unraveling the function of the brain's network. Multiphoton microscopy, a powerful tool for deep-tissue imaging, has received sustained interest in advancing its speed, field of view and imaging depth. However, to avoid thermal damage in scattering biological tissue, field of view decreases exponentially as imaging depth increases. We present a suite of innovations to overcome constraints on the field of view in three-photon microscopy and to perform deep imaging that is inaccessible to two-photon microscopy. These innovations enable us to image neuronal activities in a ~3.5-mm diameter field-of-view at 4 Hz with single-cell resolution and in the deepest cortical layer of mouse brains. We further demonstrate simultaneous large field-of-view two-photon and three-photon imaging, subcortical imaging in the mouse brain, and whole-brain imaging in adult zebrafish. The demonstrated techniques can be integrated into any multiphoton microscope for large-field-of-view and system-level neural circuit research.

Measurement of three-photon excitation cross-sections of fluorescein from 1154 nm to 1500 nm

Measurements of three-photon action cross-sections for fluorescein (dissolved in water, pH ∼11.5) are presented in the excitation wavelength range from 1154 to 1500 nm in ∼50 nm steps. The excitation source is a femtosecond wavelength tunable non-collinear optical parametric amplifier, which has been spectrally filtered with 50 nm full width at half maximum band pass filters. Cube-law power dependance is confirmed at the measurement wavelengths. The three-photon excitation spectrum is found to differ from both the one- and two-photon excitation spectra. The three-photon action cross-section at 1154 nm is more than an order of magnitude larger than those at 1450 and 1500 nm (approximately three times the wavelength of the one-photon excitation peak), which possibly indicates the presence of resonance enhancement.

Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy

Voltage imaging with cellular specificity has been made possible by the tremendous advances in genetically encoded voltage indicators (GEVIs). However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines signal-to-noise ratio (SNR) and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating, while also maximizing signal detection efficiency. The resulting benefits in SNR and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different GEVI classes.

A positively tuned voltage indicator for extended electrical recordings in the brain

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.

High-speed low-light in vivo two-photon voltage imaging of large neuronal populations

Monitoring spiking activity across large neuronal populations at behaviorally relevant timescales is critical for understanding neural circuit function. Unlike calcium imaging, voltage imaging requires kilohertz sampling rates that reduce fluorescence detection to near shot-noise levels. High-photon flux excitation can overcome photon-limited shot noise, but photobleaching and photodamage restrict the number and duration of simultaneously imaged neurons. We investigated an alternative approach aimed at low two-photon flux, which is voltage imaging below the shot-noise limit. This framework involved developing positive-going voltage indicators with improved spike detection (SpikeyGi and SpikeyGi2); a two-photon microscope ('SMURF') for kilohertz frame rate imaging across a 0.4 mm × 0.4 mm field of view; and a self-supervised denoising algorithm (DeepVID) for inferring fluorescence from shot-noise-limited signals. Through these combined advances, we achieved simultaneous high-speed deep-tissue imaging of more than 100 densely labeled neurons over 1 hour in awake behaving mice. This demonstrates a scalable approach for voltage imaging across increasing neuronal populations.

Wide-field fluorescence lifetime imaging of neuron spiking and subthreshold activity in vivo

The development of voltage-sensitive fluorescent probes suggests fluorescence lifetime as a promising readout for electrical activity in biological systems. Existing approaches fail to achieve the speed and sensitivity required for voltage imaging in neuroscience applications. We demonstrated that wide-field electro-optic fluorescence lifetime imaging microscopy (EO-FLIM) allows lifetime imaging at kilohertz frame-acquisition rates, spatially resolving action potential propagation and subthreshold neural activity in live adult Drosophila. Lifetime resolutions of <5 picoseconds at 1 kilohertz were achieved for single-cell voltage recordings. Lifetime readout is limited by photon shot noise, and the method provides strong rejection of motion artifacts and technical noise sources. Recordings revealed local transmembrane depolarizations, two types of spikes with distinct fluorescence lifetimes, and phase locking of spikes to an external mechanical stimulus.

Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator

The ability to optically image cellular transmembrane voltages at millisecond-timescale resolutions can offer unprecedented insight into the function of living brains in behaving animals. Here, we present a point mutation that increases the sensitivity of Ace2 opsin-based voltage indicators. We use the mutation to develop Voltron2, an improved chemigeneic voltage indicator that has a 65% higher sensitivity to single APs and 3-fold higher sensitivity to subthreshold potentials than Voltron. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, although with lower baseline fluorescence. In multiple in vitro and in vivo comparisons with its predecessor across multiple species, we found Voltron2 to be more sensitive to APs and subthreshold fluctuations. Finally, we used Voltron2 to study and evaluate the possible mechanisms of interneuron synchronization in the mouse hippocampus. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

Evaluation of Neurotoxicity With Human Pluripotent Stem Cell-Derived Cerebral Organoids

The recent development of human cerebral organoids provides an invaluable in vitro model of human brain development to assess the toxicity of natural or man-made toxic substances. By recapitulating key aspects of early human neurodevelopment, investigators can evaluate with this three-dimensional (3D) model the effect of certain compounds on the formation of neuronal networks and their electrophysiological properties with more physiological relevance than neurons grown in monolayers and in cultures composed of a unique cell type. This promising potential has contributed to the development of a large number of diverse protocols to generate human cerebral organoids, making interlaboratory comparisons of results difficult. Based on a previously published protocol to generate human cortical organoids (herein called cerebral organoids), we detail several approaches to evaluate the effect of chemicals on neurogenesis, apoptosis, and neuronal function when exogenously applied to cultured specimens. Here, we take as an example 4-aminopyridine, a potassium channel blocker that modulates the activity of neurons and neurogenesis, and describe a simple and cost-effective way to test the impact of this agent on cerebral organoids derived from human induced pluripotent stem cells. We also provide tested protocols to evaluate neurogenesis in cerebral organoids with ethynyl deoxyuridine labeling and neuronal activity with live calcium imaging and microelectrode arrays. Together, these protocols should facilitate the implementation of cerebral organoid technologies in laboratories wishing to evaluate the effects of specific compounds or conditions on the development and function of human neurons with only basic cell culture equipment. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Generation of human cerebral organoids from pluripotent stem cells Support Protocol 1: Human pluripotent stem cell culture Basic Protocol 2: Evaluation of neurogenesis in cerebral organoids with ethynyl deoxyuridine labeling Basic Protocol 3: Calcium imaging in cerebral organoids Basic Protocol 4: Electrophysiological evaluation of cerebral organoids with microelectrode arrays Support Protocol 2: Immunostaining of cerebral organoids.

Dual-polarity voltage imaging of the concurrent dynamics of multiple neuron types

Genetically encoded fluorescent voltage indicators are ideally suited to reveal the millisecond-scale interactions among and between targeted cell populations. However, current indicators lack the requisite sensitivity for in vivo multipopulation imaging. We describe next-generation green and red voltage sensors, Ace-mNeon2 and VARNAM2, and their reverse response-polarity variants pAce and pAceR. Our indicators enable 0.4- to 1-kilohertz voltage recordings from >50 spiking neurons per field of view in awake mice and ~30-minute continuous imaging in flies. Using dual-polarity multiplexed imaging, we uncovered brain state–dependent antagonism between neocortical somatostatin-expressing (SST + ) and vasoactive intestinal peptide–expressing (VIP + ) interneurons and contributions to hippocampal field potentials from cell ensembles with distinct axonal projections. By combining three mutually compatible indicators, we performed simultaneous triple-population imaging. These approaches will empower investigations of the dynamic interplay between neuronal subclasses at single-spike resolution.

Two-photon calcium imaging of neuronal activity

In vivo two-photon calcium imaging (2PCI) is a technique used for recording neuronal activity in the intact brain. It is based on the principle that, when neurons fire action potentials, intracellular calcium levels rise, which can be detected using fluorescent molecules that bind to calcium. This Primer is designed for scientists who are considering embarking on experiments with 2PCI. We provide the reader with a background on the basic concepts behind calcium imaging and on the reasons why 2PCI is an increasingly powerful and versatile technique in neuroscience. The Primer explains the different steps involved in experiments with 2PCI, provides examples of what ideal preparations should look like and explains how data are analysed. We also discuss some of the current limitations of the technique, and the types of solutions to circumvent them. Finally, we conclude by anticipating what the future of 2PCI might look like, emphasizing some of the analysis pipelines that are being developed and international efforts for data sharing.

Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy

Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons.

Geometric framework to predict structure from function in neural networks

Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.

Decontaminate Traces From Fluorescence Calcium Imaging Videos Using Targeted Non-negative Matrix Factorization

Fluorescence microscopy and genetically encoded calcium indicators help understand brain function by recording large-scale in vivo videos in assorted animal models. Extracting the fluorescent transients that represent active periods of individual neurons is a key step when analyzing imaging videos. Non-specific calcium sources and background adjacent to segmented neurons contaminate the neurons’ temporal traces with false transients. We developed and characterized a novel method, temporal unmixing of calcium traces (TUnCaT), to quickly and accurately unmix the calcium signals of neighboring neurons and background. Our algorithm used background subtraction to remove the false transients caused by background fluctuations, and then applied targeted non-negative matrix factorization to remove the false transients caused by neighboring calcium sources. TUnCaT was more accurate than existing algorithms when processing multiple experimental and simulated datasets. TUnCaT’s speed was faster than or comparable to existing algorithms.

Fluorescence imaging of large-scale neural ensemble dynamics

Recent progress in fluorescence imaging allows neuroscientists to observe the dynamics of thousands of individual neurons, identified genetically or by their connectivity, across multiple brain areas and for extended durations in awake behaving mammals. We discuss advances in fluorescent indicators of neural activity, viral and genetic methods to express these indicators, chronic animal preparations for long-term imaging studies, and microscopes to monitor and manipulate the activity of large neural ensembles. Ca²⁺ imaging studies of neural activity can track brain area interactions and distributed information processing at cellular resolution. Across smaller spatial scales, high-speed voltage imaging reveals the distinctive spiking patterns and coding properties of targeted neuron types. Collectively, these innovations will propel studies of brain function and dovetail with ongoing neuroscience initiatives to identify new neuron types and develop widely applicable, non-human primate models. The optical toolkit's growing sophistication also suggests that "brain observatory" facilities would be useful open resources for future brain-imaging studies.

Shot noise limits on binary detection in multiphoton imaging

Much of fluorescence-based microscopy involves detection of if an object is present or absent (i.e., binary detection). The imaging depth of three-dimensionally resolved imaging, such as multiphoton imaging, is fundamentally limited by out-of-focus background fluorescence, which when compared to the in-focus fluorescence makes detecting objects in the presence of noise difficult. Here, we use detection theory to present a statistical framework and metric to quantify the quality of an image when binary detection is of interest. Our treatment does not require acquired or reference images, and thus allows for a theoretical comparison of different imaging modalities and systems.

Segmentation of Neurons from Fluorescence Calcium Recordings Beyond Real-time

Fluorescent genetically encoded calcium indicators and two-photon microscopy help understand brain function by generating large-scale in vivo recordings in multiple animal models. Automatic, fast, and accurate active neuron segmentation is critical when processing these videos. In this work, we developed and characterized a novel method, Shallow U-Net Neuron Segmentation (SUNS), to quickly and accurately segment active neurons from two-photon fluorescence imaging videos. We used temporal filtering and whitening schemes to extract temporal features associated with active neurons, and used a compact shallow U-Net to extract spatial features of neurons. Our method was both more accurate and an order of magnitude faster than state-of-the-art techniques when processing multiple datasets acquired by independent experimental groups; the difference in accuracy was enlarged when processing datasets containing few manually marked ground truths. We also developed an online version, potentially enabling real-time feedback neuroscience experiments.

Rational engineering of ratiometric calcium sensors with bright green and red fluorescent proteins

Ratiometric genetically encoded calcium indicators (GECIs) record neural activity with high brightness while mitigating motion-induced artifacts. Recently developed ratiometric GECIs primarily employ cyan and yellow-fluorescent fluorescence resonance energy transfer pairs, and thus fall short in some applications that require deep tissue penetration and resistance to photobleaching. We engineered a set of green-red ratiometric calcium sensors that fused two fluorescent proteins and calcium sensing domain within an alternate configuration. The best performing elements of this palette of sensors, Twitch-GR and Twitch-NR, inherited the superior photophysical properties of their constituent fluorescent proteins. These properties enabled our sensors to outperform existing ratiometric calcium sensors in brightness and photobleaching metrics. In turn, the shot-noise limited signal fidelity of our sensors when reporting action potentials in cultured neurons and in the awake behaving mice was higher than the fidelity of existing sensors. Our sensor enabled a regime of imaging that simultaneously captured neural structure and function down to the deep layers of the mouse cortex.

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson's correlation coefficient, Spearman's rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.

Voltage Imaging with a NIR-Absorbing Phosphine Oxide Rhodamine Voltage Reporter

The development of fluorescent dyes that emit and absorb light at wavelengths greater than 700 nm and that respond to biochemical and biophysical events in living systems remains an outstanding challenge for noninvasive optical imaging. Here, we report the design, synthesis, and application of near-infrared (NIR)-absorbing and -emitting optical voltmeter based on a sulfonated, phosphine-oxide (po) rhodamine for voltage imaging in intact retinas. We find that po-rhodamine based voltage reporters, or poRhoVRs, display NIR excitation and emission profiles at greater than 700 nm, show a range of voltage sensitivities (13 to 43% ΔF/F per 100 mV in HEK cells), and can be combined with existing optical sensors, like Ca2+-sensitive fluorescent proteins (GCaMP), and actuators, like light-activated opsins ChannelRhodopsin-2 (ChR2). Simultaneous voltage and Ca2+ imaging reveals differences in activity dynamics in rat hippocampal neurons, and pairing poRhoVR with blue-light based ChR2 affords all-optical electrophysiology. In ex vivo retinas isolated from a mouse model of retinal degeneration, poRhoVR, together with GCaMP-based Ca2+ imaging and traditional multielectrode array (MEA) recording, can provide a comprehensive physiological activity profile of neuronal activity, revealing differences in voltage and Ca2+ dynamics within hyperactive networks of the mouse retina. Taken together, these experiments establish that poRhoVR will open new horizons in optical interrogation of cellular and neuronal physiology in intact systems.

Improved hyperacuity estimation of spike timing from calcium imaging

Two-photon imaging is a major recording technique used in neuroscience. However, it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low SNR, all of which severely limit the potential of two-photon imaging to elucidate neuronal dynamics with high temporal resolution. We developed a hyperacuity algorithm (HA_time) based on an approach that combines a generative model and machine learning to improve spike detection and the precision of spike time inference. Bayesian inference was performed to estimate the calcium spike model, assuming constant spike shape and size. A support vector machine using this information and a jittering method maximizing the likelihood of estimated spike times enhanced spike time estimation precision approximately fourfold (range, 2–7; mean, 3.5–4.0; 2SEM, 0.1–0.25) compared to the sampling interval. Benchmark scores of HA_time for biological data from three different brain regions were among the best of the benchmark algorithms. Simulation of broader data conditions indicated that our algorithm performed better than others with high firing rate conditions. Furthermore, HA_time exhibited comparable performance for conditions with and without ground truths. Thus HA_time is a useful tool for spike reconstruction from two-photon imaging.

Super-resolution optical classifier with high photon efficiency

We propose and demonstrate a photon-efficient optical classifier to overcome the Rayleigh limit in spatial resolution. It utilizes mode-selective sum-frequency generation and single-pixel photon detection to resolve closely spaced incoherent sources based on photon counting statistics. Super-resolving and photon efficient, this technique can find applications in microscopy, light detection and ranging, and astrophysics.

Blind deconvolution for spike inference from fluorescence recordings

The parallel developments of genetically-encoded calcium indicators and fast fluorescence imaging techniques allows one to simultaneously record neural activity of extended neuronal populations in vivo. To fully harness the potential of functional imaging, one needs to infer the sequence of action potentials from fluorescence traces. Here we build on recently proposed computational approaches to develop a blind sparse deconvolution (BSD) algorithm based on a generative model for inferring spike trains from fluorescence traces. BSD features, (1) automatic (fully unsupervised) estimation of the hyperparameters, such as spike amplitude, noise level and rise and decay time constants, (2) a novel analytical estimate of the sparsity prior, which yields enhanced robustness and computational speed with respect to existing methods, (3) automatic thresholding for binarizing spikes that maximizes the precision-recall performance, (4) super-resolution capabilities increasing the temporal resolution beyond the fluorescence signal acquisition rate. BSD also uniquely provides theoretically-grounded estimates of the expected performance of the spike reconstruction in terms of precision-recall and temporal accuracy for each recording. The performance of the algorithm is established using synthetic data and through the SpikeFinder challenge, a community-based initiative for spike-rate inference benchmarking based on a collection of joint electrophysiological and fluorescence recordings. Our method outperforms classical sparse deconvolution algorithms in terms of robustness, speed and/or accuracy and performs competitively in the SpikeFinder challenge. This algorithm is modular, easy-to-use and made freely available. Its novel features can thus be incorporated in a straightforward way into existing calcium imaging packages.

Biophysical Parameters of GEVIs: Considerations for Imaging Voltage

Genetically encoded voltage indicators (GEVIs) continue to evolve, resulting in many different probes with varying strengths and weaknesses. Developers of new GEVIs tend to highlight their positive features. A recent article from an independent laboratory has compared the signal/noise ratios of a number of GEVIs. Such a comparison can be helpful to investigators eager to try to image the voltage of excitable cells. In this perspective, we will present examples of how the biophysical features of GEVIs affect the imaging of excitable cells in an effort to assist researchers when considering probes for their specific needs.

Ultrafast Two-Photon Imaging of a High-Gain Voltage Indicator in Awake Behaving Mice

Optical interrogation of voltage in deep brain locations with cellular resolution would be immensely useful for understanding how neuronal circuits process information. Here, we report ASAP3, a genetically encoded voltage indicator with 51% fluorescence modulation by physiological voltages, submillisecond activation kinetics, and full responsivity under two-photon excitation. We also introduce an ultrafast local volume excitation (ULoVE) method for kilohertz-rate two-photon sampling in vivo with increased stability and sensitivity. Combining a soma-targeted ASAP3 variant and ULoVE, we show single-trial tracking of spikes and subthreshold events for minutes in deep locations, with subcellular resolution and with repeated sampling over days. In the visual cortex, we use soma-targeted ASAP3 to illustrate cell-type-dependent subthreshold modulation by locomotion. Thus, ASAP3 and ULoVE enable high-speed optical recording of electrical activity in genetically defined neurons at deep locations during awake behavior.

Kilohertz two-photon fluorescence microscopy imaging of neural activity in vivo

Understanding information processing in the brain requires us to monitor neural activity at high spatiotemporal resolution. Using an ultrafast two-photon fluorescence microscope (2PFM) empowered by all-optical laser scanning, we imaged neural activity in vivo at up to 3,000 frames per second and submicron spatial resolution. This ultrafast imaging method enabled monitoring of both supra- and sub-threshold electrical activity down to 345 μm below the brain surface in head-fixed awake mice.

Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain

1300 nm three-photon calcium imaging has emerged as a useful technique to allow calcium imaging in deep brain regions. Application to large-scale neural activity imaging entails a careful balance between recording fidelity and perturbation to the sample. We calculated and experimentally verified the excitation pulse energy to achieve the minimum photon count required for the detection of calcium transients in GCaMP6s-expressing neurons for 920 nm two-photon and 1320 nm three-photon excitation. By considering the combined effects of in-focus signal attenuation and out-of-focus background generation, we quantified the cross-over depth beyond which three-photon microscopy outpeforms two-photon microscopy in recording fidelity. Brain tissue heating by continuous three-photon imaging was simulated with Monte Carlo method and experimentally validated with immunohistochemistry. Increased immunoreactivity was observed with 150 mW excitation power at 1 and 1.2 mm imaging depths. Our analysis presents a translatable model for the optimization of three-photon calcium imaging based on experimentally tractable parameters.

CELLULAR RESOLUTION IMAGING OF NEURONAL ACTIVITY ACROSS SPACE AND TIME IN THE MAMMALIAN BRAIN

While the action potential has long been understood to be the fundamental bit of information in brain, how these spikes encode representations of stimuli and drive behavior remains unclear. Large-scale neuronal recordings with cellular and spike-time resolution spanning multiple brain regions are needed to capture relevant network dynamics that can be sparse and distributed across the population. This review focuses on recent advancements in optical methods that have pushed the boundaries for simultaneous population recordings at increasing volumes, distances, depths, and speeds. The integration of these technologies will be critical for overcoming fundamental limits in the pursuit of whole brain imaging in mammalian species.

Wide. Fast. Deep: Recent Advances in Multiphoton Microscopy of In Vivo Neuronal Activity

Multiphoton microscopy (MPM) has emerged as one of the most powerful and widespread technologies to monitor the activity of neuronal networks in awake, behaving animals over long periods of time. MPM development spanned across decades and crucially depended on the concurrent improvement of calcium indicators that report neuronal activity as well as surgical protocols, head fixation approaches, and innovations in optics and microscopy technology. Here we review the last decade of MPM development and highlight how in vivo imaging has matured and diversified, making it now possible to concurrently monitor thousands of neurons across connected brain areas or, alternatively, small local networks with sampling rates in the kilohertz range. This review includes different laser scanning approaches, such as multibeam technologies as well as recent developments to image deeper into neuronal tissues using new, long-wavelength laser sources. As future development will critically depend on our ability to resolve and discriminate individual neuronal spikes, we will also describe a simple framework that allows performing quantitative comparisons between the reviewed MPM instruments. Finally, we provide our own opinion on how the most recent MPM developments can be leveraged at scale to enable the next generation of discoveries in brain function.

Enhanced genetically encoded voltage indicators advance their applications in neuroscience

Genetically encoded voltage indicators report membrane voltage with high spatiotemporal resolution. Extensive recent efforts to improve the GEVIs' brightness, sensitivity, and kinetics have greatly increased the GEVIs' signal-to-noise performance over ten-fold and lowered their response time to the sub-millisecond regime. Such capabilities have broadened the GEVIs' ability to measure membrane voltage of neural populations at cellular resolution in vitro and in vivo, all at high speeds. The GEVIs' high voltage fidelity and fast response have revealed novel physiological phenomena in multiple neuroscientific applications. Such applications portend future targeted studies of voltage activity that take advantage of the GEVIs' ability to report rapid dynamics from genetically-targeted neural populations.

A high-speed, bright, red fluorescent voltage sensor to detect neural activity

Genetically encoded voltage indicators (GEVIs) have emerged as a technology to optically record neural activity with genetic specificity and millisecond-scale temporal resolution using fluorescence microscopy. GEVIs have demonstrated ultra-fast kinetics and high spike detection fidelity in vivo, but existing red-fluorescent voltage indicators fall short of the response and brightness achieved by green fluorescent protein-based sensors. Furthermore, red-fluorescent GEVIs suffer from incomplete spectral separation from green sensors and blue-light-activated optogenetic actuators. We have developed Ace-mScarlet, a red fluorescent GEVI that fuses Ace2N, a voltage-sensitive inhibitory rhodopsin, with mScarlet, a bright red fluorescent protein (FP). Through fluorescence resonance energy transfer (FRET), our sensor detects changes in membrane voltage with high sensitivity and brightness and has kinetics comparable to the fastest green fluorescent sensors. Ace-mScarlet's red-shifted absorption and emission spectra facilitate virtually complete spectral separation when used in combination with green-fluorescent sensors or with blue-light-sensitive sensors and rhodopsins. This spectral separation enables both simultaneous imaging in two separate wavelength channels and high-fidelity voltage recordings during simultaneous optogenetic perturbation.

Using slow frame rate imaging to extract fast receptive fields

In functional imaging, large numbers of neurons are measured during sensory stimulation or behavior. This data can be used to map receptive fields that describe neural associations with stimuli or with behavior. The temporal resolution of these receptive fields has traditionally been limited by image acquisition rates. However, even when acquisitions scan slowly across a population of neurons, individual neurons may be measured at precisely known times. Here, we apply a method that leverages the timing of neural measurements to find receptive fields with temporal resolutions higher than the image acquisition rate. We use this temporal super-resolution method to resolve fast voltage and glutamate responses in visual neurons in Drosophila and to extract calcium receptive fields from cortical neurons in mammals. We provide code to easily apply this method to existing datasets. This method requires no specialized hardware and can be used with any optical indicator of neural activity.

Kilohertz two-photon brain imaging in awake mice

Two-photon microscopy is a mainstay technique for imaging in scattering media and normally provides frame-acquisition rates of ~10–30 Hz. To track high-speed phenomena, we created a two-photon microscope with 400 illumination beams that collectively sample 95,000–211,000 μm² areas at rates up to 1 kHz. Using this microscope, we visualized microcirculatory flow, fast venous constrictions, and neuronal Ca²⁺ spiking with millisecond-scale timing resolution in the brains of awake mice.

Multiphoton minimal inertia scanning for fast acquisition of neural activity signals

OBJECTIVE

Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as travelling salesman scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms.

APPROACH

We describe here the adaptive spiral scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR).

MAIN RESULTS

Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to 'park' the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Cramér-Rao Bound on evoked calcium traces recorded simultaneously with electrophysiology traces to calculate the lower bound estimate of the spike timing occurrence.

SIGNIFICANCE

The results show that TSS and SSA achieve comparable accuracy in spike time estimates compared to raster scanning, despite lower SNR. SSA is an easily implementable way for standard multi-photon laser scanning systems to gain temporal precision in the detection of action potentials while scanning hundreds of active cells.

Bright and photostable chemigenetic indicators for extended in vivo voltage imaging

Genetically encoded voltage indicators (GEVIs) enable monitoring of neuronal activity at high spatial and temporal resolution. However, the utility of existing GEVIs has been limited by the brightness and photostability of fluorescent proteins and rhodopsins. We engineered a GEVI, called Voltron, that uses bright and photostable synthetic dyes instead of protein-based fluorophores, thereby extending the number of neurons imaged simultaneously in vivo by a factor of 10 and enabling imaging for significantly longer durations relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In the mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously over a 15-minute period of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

Bayesian hypothesis testing and experimental design for two-photon imaging data

Variability, stochastic or otherwise, is a central feature of neural activity. Yet the means by which estimates of variation and uncertainty are derived from noisy observations of neural activity is often heuristic, with more weight given to numerical convenience than statistical rigour. For two-photon imaging data, composed of fundamentally probabilistic streams of photon detections, the problem is particularly acute. Here, we present a statistical pipeline for the inference and analysis of neural activity using Gaussian Process regression, applied to two-photon recordings of light-driven activity in ex vivo mouse retina. We demonstrate the flexibility and extensibility of these models, considering cases with non-stationary statistics, driven by complex parametric stimuli, in signal discrimination, hierarchical clustering and other inference tasks. Sparse approximation methods allow these models to be fitted rapidly, permitting them to actively guide the design of light stimulation in the midst of ongoing two-photon experiments.

GCaMP6 ΔF/F dependence on the excitation wavelength in 3-photon and 2-photon microscopy of mouse brain activity

A fundamental challenge in calcium imaging is to minimize the excitation laser power while still maintaining a sufficient signal-to-noise ratio to distinguish individual transients in the fluorescence traces. It is important to characterize relative fluorescence (i.e., ΔF/F) dependence on the excitation wavelength in vivo where the environment cannot be controlled effectively during imaging. Leveraging time division multiplexing of two excitation beams to achieve nearly simultaneous 2-photon and 3-photon imaging of the calcium transients, we measured systematically the ΔF/F dependence on the excitation wavelength in 2-photon and 3-photon in vivo imaging of neuronal activity in mouse brain labeled with GCaMP6s. The technique can be applied to in vivo measurements of other fluorescence sensors.

Spike Estimation from Fluorescence Signals Using High-Resolution Property of Group Delay

Spike estimation from calcium (Ca²⁺) fluorescence signals is a fundamental and challenging problem in neuroscience. Several models and algorithms have been proposed for this task over the past decade. Nevertheless, it is still hard to achieve accurate spike positions from the Ca²⁺ fluorescence signals. While existing methods rely on data-driven methods and the physiology of neurons for modelling the spiking process, this work exploits the nature of the fluorescence responses to spikes using signal processing. We first motivate the problem by a novel analysis of the high-resolution property of minimum-phase group delay (GD) functions for multi-pole resonators. The resonators could be connected either in series or in parallel. The Ca²⁺ indicator responds to a spike with a sudden rise, that is followed by an exponential decay. We interpret the Ca²⁺ signal as the response of an impulse train to the change in Ca²⁺ concentration, where the Ca²⁺ response corresponds to a resonator. We perform minimum-phase group delay-based filtering of the Ca²⁺ signal for resolving spike locations. The performance of the proposed algorithm is evaluated on nine datasets spanning various indicators, sampling rates and, mouse brain regions. The proposed approach: GDspike, is compared with other spike estimation methods including MLspike, Vogelstein de-convolution algorithm, and data-driven Spike Triggered Mixture (STM) model. The performance of GDSpike is superior to that of the Vogelstein algorithm and is comparable to that of MLSpike. It can also be used to post-process the output of MLSpike, which further enhances the performance.

Improved spike inference accuracy by estimating the peak amplitude of unitary [Ca2+ ] transients in weakly GCaMP6f-expressing hippocampal pyramidal cells

KEY POINTS

The amplitude of unitary, single action potential-evoked [Ca²⁺ ] transients negatively correlates with GCaMP6f expression, but displays large variability among hippocampal pyramidal cells with similarly low expression levels. The summation of fluorescence signals is frequency-dependent, supralinear and also shows remarkable cell-to-cell variability. The main source of spike inference error is variability in the peak amplitude, and not in the decay or supralinearity. We developed two procedures to estimate the peak amplitudes of unitary [Ca²⁺ ] transients and show that spike inference performed with MLspike using these unitary amplitude estimates in weakly GCaMP6f-expressing cells results in error rates of ∼5%.

ABSTRACT

Investigating neuronal activity using genetically encoded Ca²⁺ indicators in behaving animals is hampered by inaccuracies in spike inference from fluorescent tracers. Here we combine two-photon [Ca²⁺ ] imaging with cell-attached recordings, followed by post hoc determination of the expression level of GCaMP6f, to explore how it affects the amplitude, kinetics and temporal summation of somatic [Ca²⁺ ] transients in mouse hippocampal pyramidal cells (PCs). The amplitude of unitary [Ca²⁺ ] transients (evoked by a single action potential) negatively correlates with GCaMP6f expression, but displays large variability even among PCs with similarly low expression levels. The summation of fluorescence signals is frequency-dependent, supralinear and also shows remarkable cell-to-cell variability. We performed experimental data-based simulations and found that spike inference error rates using MLspike depend strongly on unitary peak amplitudes and GCaMP6f expression levels. We provide simple methods for estimating the unitary [Ca²⁺ ] transients in individual weakly GCaMP6f-expressing PCs, with which we achieve spike inference error rates of ∼5%.

Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning

Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies for discovery of neuronal coding properties. Here, to exploit the full spatiotemporal information in two-photon calcium imaging movies, we propose a 3D convolutional neural network to identify and segment active neurons. By utilizing a variety of two-photon microscopy datasets, we show that our method outperforms state-of-the-art techniques and is on a par with manual segmentation. Furthermore, we demonstrate that the network trained on data recorded at a specific cortical layer can be used to accurately segment active neurons from another layer with different neuron density. Finally, our work documents significant tabulation flaws in one of the most cited and active online scientific challenges in neuron segmentation. As our computationally fast method is an invaluable tool for a large spectrum of real-time optogenetic experiments, we have made our open-source software and carefully annotated dataset freely available online.

Optimizing Strategies for Developing Genetically Encoded Voltage Indicators

Genetically encoded optical indicators of neuronal activity enable unambiguous recordings of input-output activity patterns from identified cells in intact circuits. Among them, genetically encoded voltage indicators (GEVIs) offer additional advantages over calcium indicators as they are direct sensors of membrane potential and can adeptly report subthreshold events and hyperpolarization. Here, we outline the major GEVI designs and give an account of properties that need to be carefully optimized during indicator engineering. While designing the ideal GEVI, one should keep in mind aspects such as membrane localization, signal size, signal-to-noise ratio, kinetics and voltage dependence of optical responses. Using ArcLight and derivatives as prototypes, we delineate how a probe should be optimized for the former properties and developed along other areas in a need-based manner. Finally, we present an overview of the GEVI engineering process and lend an insight into their discovery, delivery and diagnosis.

Fast, in vivo voltage imaging using a red fluorescent indicator

Genetically encoded voltage indicators (GEVIs) are emerging optical tools for acquiring brain-wide cell-type-specific functional data at unparalleled temporal resolution. To broaden the application of GEVIs in high-speed multispectral imaging, we used a high-throughput strategy to develop voltage-activated red neuronal activity monitor (VARNAM), a fusion of the fast Acetabularia opsin and the bright red fluorophore mRuby3. Imageable under the modest illumination intensities required by bright green probes (<50 mW mm-2), VARNAM is readily usable in vivo. VARNAM can be combined with blue-shifted optical tools to enable cell-type-specific all-optical electrophysiology and dual-color spike imaging in acute brain slices and live Drosophila. With enhanced sensitivity to subthreshold voltages, VARNAM resolves postsynaptic potentials in slices and cortical and hippocampal rhythms in freely behaving mice. Together, VARNAM lends a new hue to the optical toolbox, opening the door to high-speed in vivo multispectral functional imaging.

Information-Theoretic Approach and Fundamental Limits of Resolving Two Closely Timed Neuronal Spikes in Mouse Brain Calcium Imaging

OBJECTIVE

Although optical imaging of neurons using fluorescent genetically encoded calcium sensors has enabled large-scale in vivo experiments, the sensors' slow dynamics often blur closely timed action potentials into indistinguishable transients. While several previous approaches have been proposed to estimate the timing of individual spikes, they have overlooked the important and practical problem of estimating interspike interval (ISI) for overlapping transients.

METHODS

We use statistical detection theory to find the minimum detectable ISI under different levels of signal-to-noise ratio (SNR), model complexity, and recording speed. We also derive the Cramer-Rao lower bounds (CRBs) for the problem of ISI estimation. We use Monte-Carlo simulations with biologically derived parameters to numerically obtain the minimum detectable ISI and evaluate the performance of our estimators. Furthermore, we apply our detector to distinguish overlapping transients from experimentally obtained calcium imaging data.

RESULTS

Experiments based on simulated and real data across different SNR levels and recording speeds show that our algorithms can accurately distinguish two fluorescence signals with ISI on the order of tens of milliseconds, shorter than the waveform's rise time. Our study shows that the statistically optimal ISI estimators closely approached the CRBs.

CONCLUSION

Our work suggests that full analysis using recording speed, sensor kinetics, SNR, and the sensor's stochastically distributed response to action potentials can accurately resolve ISIs much smaller than the fluorescence waveform's rise time in modern calcium imaging experiments.

SIGNIFICANCE

Such analysis aids not only in future spike detection methods, but also in future experimental design when choosing sensors of neuronal activity.

Automatic Cell Segmentation by Adaptive Thresholding (ACSAT) for Large-Scale Calcium Imaging Datasets

Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.

Optimal neural inference of stimulus intensities

In natural data, the class and intensity of stimuli are correlated. Current machine learning algorithms ignore this ubiquitous statistical property of stimuli, usually by requiring normalized inputs. From a biological perspective, it remains unclear how neural circuits may account for these dependencies in inference and learning. Here, we use a probabilistic framework to model class-specific intensity variations, and we derive approximate inference and online learning rules which reflect common hallmarks of neural computation. Concretely, we show that a neural circuit equipped with specific forms of synaptic and intrinsic plasticity (IP) can learn the class-specific features and intensities of stimuli simultaneously. Our model provides a normative interpretation of IP as a critical part of sensory learning and predicts that neurons can represent nontrivial input statistics in their excitabilities. Computationally, our approach yields improved statistical representations for realistic datasets in the visual and auditory domains. In particular, we demonstrate the utility of the model in estimating the contrastive stress of speech.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Long-term optical brain imaging in live adult fruit flies

Time-lapse in vivo microscopy studies of cellular morphology and physiology are crucial toward understanding brain function but have been infeasible in the fruit fly, a key model species. Here we use laser microsurgery to create a chronic fly preparation for repeated imaging of neural architecture and dynamics for up to 50 days. In fly mushroom body neurons, we track axonal boutons for 10 days and record odor-evoked calcium transients over 7 weeks. Further, by using voltage imaging to resolve individual action potentials, we monitor spiking plasticity in dopamine neurons of flies undergoing mechanical stress. After 24 h of stress, PPL1-α’3 but not PPL1-α’2α2 dopamine neurons have elevated spike rates. Overall, our chronic preparation is compatible with a broad range of optical techniques and enables longitudinal studies of many biological questions that could not be addressed before in live flies.

Time-lapse imaging studies of more than a day in the fly brain have been infeasible until now. Here the authors present a laser microsurgery approach to create a permanent window in the fly cuticle to enable time-lapse imaging of neural architecture and dynamics for up to 10–50 days.

Full-field interferometric imaging of propagating action potentials

Currently, cellular action potentials are detected using either electrical recordings or exogenous fluorescent probes that sense the calcium concentration or transmembrane voltage. Ca imaging has a low temporal resolution, while voltage indicators are vulnerable to phototoxicity, photobleaching, and heating. Here, we report full-field interferometric imaging of individual action potentials by detecting movement across the entire cell membrane. Using spike-triggered averaging of movies synchronized with electrical recordings, we demonstrate deformations up to 3 nm (0.9 mrad) during the action potential in spiking HEK-293 cells, with a rise time of 4 ms. The time course of the optically recorded spikes matches the electrical waveforms. Since the shot noise limit of the camera (~2 mrad/pix) precludes detection of the action potential in a single frame, for all-optical spike detection, images are acquired at 50 kHz, and 50 frames are binned into 1 ms steps to achieve a sensitivity of 0.3 mrad in a single pixel. Using a self-reinforcing sensitivity enhancement algorithm based on iteratively expanding the region of interest for spatial averaging, individual spikes can be detected by matching the previously extracted template of the action potential with the optical recording. This allows all-optical full-field imaging of the propagating action potentials without exogeneous labels or electrodes.

Long-Term Optical Access to an Estimated One Million Neurons in the Live Mouse Cortex

A major technological goal in neuroscience is to enable the interrogation of individual cells across the live brain. By creating a curved glass replacement to the dorsal cranium and surgical methods for its installation, we developed a chronic mouse preparation providing optical access to an estimated 800,000-1,100,000 individual neurons across the dorsal surface of neocortex. Post-surgical histological studies revealed comparable glial activation as in control mice. In behaving mice expressing a Ca²⁺ indicator in cortical pyramidal neurons, we performed Ca²⁺ imaging across neocortex using an epi-fluorescence macroscope and estimated that 25,000-50,000 individual neurons were accessible per mouse across multiple focal planes. Two-photon microscopy revealed dendritic morphologies throughout neocortex, allowed time-lapse imaging of individual cells, and yielded estimates of >1 million accessible neurons per mouse by serial tiling. This approach supports a variety of optical techniques and enables studies of cells across >30 neocortical areas in behaving mice.