Automatic identification of fluorescently labeled brain cells for rapid functional imaging

High-throughput deep tissue two-photon microscopy at kilohertz frame rates

High-speed laser scanning microscopes are essential for monitoring fast biological phenomena. However, existing strategies that achieve millisecond time resolution with two-photon microscopes (2PMs) are generally technically challenging and suffer from compromises among imaging field of view, excitation efficiency, and depth penetration in thick tissue. Here, we present a versatile solution that enables a conventional video-rate 2PM to perform 2D scanning at kilohertz frame rates over large fields of view. Our system is based on implementation of a scan multiplier unit that provides inertia-free multiplication of the scanning speed while preserving all the benefits of standard 2PM. We demonstrate kilohertz subcellular-resolution 2PM imaging with an order of magnitude higher imaging throughput than previously achievable and penetration depths exceeding 500 μm, which we apply to the study of neurovascular coupling dynamics in the mouse brain.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey

Calcium imaging has rapidly become a methodology of choice for real-time in vivo neuron analysis. Its application to large sets of data requires automated tools to annotate and segment cells, allowing scalable image segmentation under reproducible criteria. In this paper, we review and summarize the most recent methods for computational segmentation of calcium imaging. The contributions of the paper are three-fold: we provide an overview of the main algorithms taxonomized in three categories (signal processing, matrix factorization and machine learning-based approaches), we highlight the main advantages and disadvantages of each category and we provide a summary of the performance of the methods that have been tested on public benchmarks (with links to the public code when available).

High-speed volumetric two-photon fluorescence imaging of neurovascular dynamics

Understanding the structure and function of vasculature in the brain requires us to monitor distributed hemodynamics at high spatial and temporal resolution in three-dimensional (3D) volumes in vivo. Currently, a volumetric vasculature imaging method with sub-capillary spatial resolution and blood flow-resolving speed is lacking. Here, using two-photon laser scanning microscopy (TPLSM) with an axially extended Bessel focus, we capture volumetric hemodynamics in the awake mouse brain at a spatiotemporal resolution sufficient for measuring capillary size and blood flow. With Bessel TPLSM, the fluorescence signal of a vessel becomes proportional to its size, which enables convenient intensity-based analysis of vessel dilation and constriction dynamics in large volumes. We observe entrainment of vasodilation and vasoconstriction with pupil diameter and measure 3D blood flow at 99 volumes/second. Demonstrating high-throughput monitoring of hemodynamics in the awake brain, we expect Bessel TPLSM to make broad impacts on neurovasculature research.

Single Cortical Microinfarcts Lead to Widespread Microglia/Macrophage Migration Along the White Matter

Loss of cognitive function with aging is a complex and poorly understood process. Recently, clinical research has linked the occurrence of cortical microinfarcts to cognitive decline. Cortical microinfarcts form following the occlusion of penetrating vessels and are considered to be restricted to the proximity of the occluded vessel. Whether and how such local events propagate and affect remote brain regions remain unknown. To this end, we combined histological analysis and longitudinal diffusion tensor imaging (DTI), following the targeted-photothrombotic occlusion of single cortical penetrating vessels. Occlusions resulted in distant tissue reorganization across the mouse brain. This remodeling co-occurred with the formation of a microglia/macrophage migratory path along subcortical white matter tracts, reaching the contralateral hemisphere through the corpus callosum and leaving a microstructural signature detected by DTI-tractography. CX3CR1-deficient mice exhibited shorter trail lengths, differential remodeling, and only ipsilateral white matter tract changes. We concluded that microinfarcts lead to brain-wide remodeling in a microglial CX3CR1-dependent manner.

Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging

Motivation

Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters.

Results

We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users.

Availability and implementation

ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN.

Supplementary information

Supplementary data are available at Bioinformatics online.

High-Accuracy Detection of Neuronal Ensemble Activity in Two-Photon Functional Microscopy Using Smart Line Scanning

Two-photon functional imaging using genetically encoded calcium indicators (GECIs) is one prominent tool to map neural activity. Under optimized experimental conditions, GECIs detect single action potentials in individual cells with high accuracy. However, using current approaches, these optimized conditions are never met when imaging large ensembles of neurons. Here, we developed a method that substantially increases the signal-to-noise ratio (SNR) of population imaging of GECIs by using galvanometric mirrors and fast smart line scan (SLS) trajectories. We validated our approach in anesthetized and awake mice on deep and dense GCaMP6 staining in the mouse barrel cortex during spontaneous and sensory-evoked activity. Compared to raster population imaging, SLS led to increased SNR, higher probability of detecting calcium events, and more precise identification of functional neuronal ensembles. SLS provides a cheap and easily implementable tool for high-accuracy population imaging of neural GCaMP6 signals by using galvanometric-based two-photon microscopes.

CaImAn an open source tool for scalable calcium imaging data analysis

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.

Computer-aided neurophysiology and imaging with open-source PhysImage

Improved integration between imaging and electrophysiological data has become increasingly critical for rapid interpretation and intervention as approaches have advanced in recent years. Here, we present PhysImage, a fork of the popular public-domain ImageJ that provides a platform for working with these disparate sources of data, and we illustrate its utility using in vitro preparations from murine embryonic and neonatal tissue. PhysImage expands ImageJ's core features beyond an imaging program by facilitating integration, analyses, and display of 2D waveform data, among other new features. Together, with the Micro-Manager plugin for image acquisition, PhysImage substantially improves on closed-source or blended approaches to analyses and interpretation, and it furthermore aids post hoc automated analysis of physiological data when needed as we demonstrate here. Developing a high-throughput approach to neurophysiological analyses has been a major challenge for neurophysiology as a whole despite data analytics methods advancing rapidly in other areas of neuroscience, biology, and especially genomics. NEW & NOTEWORTHY High-throughput analyses of both concurrent electrophysiological and imaging recordings has been a major challenge in neurophysiology. We submit an open-source solution that may be able to alleviate, or at least reduce, many of these concerns by providing an institutionally proven mechanism (i.e., ImageJ) with the added benefits of open-source Python scripting of PhysImage data that eases the workmanship of 2D trace data, which includes electrophysiological data. Together, with the ability to autogenerate prototypical figures shows this technology is a noteworthy advance.

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data

Two-photon Ca²⁺ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In particular, manually drawing regions of interest is the most time-consuming aspect of data analysis. However, the development of automated image analysis pipelines, which will be essential for dealing with the likely future deluge of imaging data, remains a major challenge. To address this issue, we developed NeuroSeg, an open-source MATLAB program that can facilitate the accurate and efficient segmentation of neurons in two-photon Ca²⁺ imaging data. We proposed an approach using a generalized Laplacian of Gaussian filter to detect cells and weighting-based segmentation to separate individual cells from the background. We tested this approach on an in vivo two-photon Ca²⁺ imaging dataset obtained from mouse cortical neurons with differently sized view fields. We show that this approach exhibits superior performance for cell detection and segmentation compared with the existing published tools. In addition, we integrated the previously reported, activity-based segmentation into our approach and found that this combined method was even more promising. The NeuroSeg software, including source code and graphical user interface, is freely available and will be a useful tool for in vivo brain activity mapping.

ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and exterior, in which all pixels have maximally “similar” time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell’s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE (the proposed method) achieves a 67.5% success rate.

Benchmarking Spike Rate Inference in Population Calcium Imaging

A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.

Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data

Recent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca²⁺ indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function. Current practice is essentially manual, which not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. Here, we report a suite of computational tools, called Functional AStrocyte Phenotyping (FASP), for automatically quantifying the functional status of astrocytes. Considering the complex nature of Ca²⁺ signaling in astrocytes and low signal to noise ratio, FASP is designed with data-driven and probabilistic principles, to flexibly account for various patterns and to perform robustly with noisy data. In particular, FASP explicitly models signal propagation, which rules out the applicability of tools designed for other types of data. We demonstrate the effectiveness of FASP using extensive synthetic and real data sets. The findings by FASP were verified by manual inspection. FASP also detected signals that were missed by purely manual analysis but could be confirmed by more careful manual examination under the guidance of automatic analysis. All algorithms and the analysis pipeline are packaged into a plugin for Fiji (ImageJ), with the source code freely available online at https://github.com/VTcbil/FASP.

Technologies for imaging neural activity in large volumes

Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.

Generalizing cell segmentation and quantification

Background

In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.

Results

To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods.

Conclusions

The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users.

Improving data quality in neuronal population recordings

Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.

Facing the challenge of mammalian neural microcircuits: taking a few breaths may help

Breathing in mammals is a seemingly straightforward behaviour controlled by the brain. A brainstem nucleus called the preBötzinger Complex sits at the core of the neural circuit generating respiratory rhythm. Despite the discovery of this microcircuit almost 25 years ago, the mechanisms controlling breathing remain elusive. Given the apparent simplicity and well-defined nature of regulatory breathing behaviour, the identification of much of the circuitry, and the ability to study breathing in vitro as well as in vivo, many neuroscientists and physiologists are surprised that respiratory rhythm generation is still not well understood. Our view is that conventional rhythmogenic mechanisms involving pacemakers, inhibition or bursting are problematic and that simplifying assumptions commonly made for many vertebrate neural circuits ignore consequential detail. We propose that novel emergent mechanisms govern the generation of respiratory rhythm. That a mammalian function as basic as rhythm generation arises from complex and dynamic molecular, synaptic and neuronal interactions within a diverse neural microcircuit highlights the challenges in understanding neural control of mammalian behaviours, many (considerably) more elaborate than breathing. We suggest that the neural circuit controlling breathing is inimitably tractable and may inspire general strategies for elucidating other neural microcircuits.

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data

Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of [Formula: see text] neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements.

Ultra-large field-of-view two-photon microscopy

We present a two-photon microscope that images the full extent of murine cortex with an objective-limited spatial resolution across an 8 mm by 10 mm field. The lateral resolution is approximately 1 µm and the maximum scan speed is 5 mm/ms. The scan pathway employs large diameter compound lenses to minimize aberrations and performs near theoretical limits. We demonstrate the special utility of the microscope by recording resting-state vasomotion across both hemispheres of the murine brain through a transcranial window and by imaging histological sections without the need to stitch.

Monitoring activity in neural circuits with genetically encoded indicators

Recent developments in genetically encoded indicators of neural activity (GINAs) have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning. Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators (GCaMPs), sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function.

Detecting cells using non-negative matrix factorization on calcium imaging data

We propose a cell detection algorithm using non-negative matrix factorization (NMF) on Ca2+ imaging data. To apply NMF to Ca2+ imaging data, we use the bleaching line of the background fluorescence intensity as an a priori background constraint to make the NMF uniquely dissociate the background component from the image data. This constraint helps us to incorporate the effect of dye-bleaching and reduce the non-uniqueness of the solution. We demonstrate that in the case of noisy data, the NMF algorithm can detect cells more accurately than Mukamel's independent component analysis algorithm, a state-of-art method. We then apply the NMF algorithm to Ca2+ imaging data recorded on the local activities of subcellular structures of multiple cells in a wide area. We show that our method can decompose rapid transient components corresponding to somas and dendrites of many neurons, and furthermore, that it can decompose slow transient components probably corresponding to glial cells.

Microscopy image segmentation tool: robust image data analysis

We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

Two-Photon Processor and SeNeCA: a freely available software package to process data from two-photon calcium imaging at speeds down to several milliseconds per frame

Two-Photon Processor (TPP) is a versatile, ready-to-use, and freely available software package in MATLAB to process data from in vivo two-photon calcium imaging. TPP includes routines to search for cell bodies in full-frame (Search for Neural Cells Accelerated; SeNeCA) and line-scan acquisition, routines for calcium signal calculations, filtering, spike-mining, and routines to construct parametric fields. Searching for somata in artificial in vivo data, our algorithm achieved better performance than human annotators. SeNeCA copes well with uneven background brightness and in-plane motion artifacts, the major problems in simple segmentation methods. In the fast mode, artificial in vivo images with a resolution of 256 × 256 pixels containing ∼100 neurons can be processed at a rate up to 175 frames per second (tested on Intel i7, 8 threads, magnetic hard disk drive). This speed of a segmentation algorithm could bring new possibilities into the field of in vivo optophysiology. With such a short latency (down to 5–6 ms on an ordinary personal computer) and using some contemporary optogenetic tools, it will allow experiments in which a control program can continuously evaluate the occurrence of a particular spatial pattern of activity (a possible correlate of memory or cognition) and subsequently inhibit/stimulate the entire area of the circuit or inhibit/stimulate a different part of the neuronal system. TPP will be freely available on our public web site. Similar all-in-one and freely available software has not yet been published.

Two-way communication with neural networks in vivo using focused light

Neuronal networks process information in a distributed, spatially heterogeneous manner that transcends the layout of electrodes. In contrast, directed and steerable light offers the potential to engage specific cells on demand. We present a unified framework for adapting microscopes to use light for simultaneous in vivo stimulation and recording of cells at fine spatiotemporal resolutions. We use straightforward optics to lock onto networks in vivo, to steer light to activate circuit elements and to simultaneously record from other cells. We then actualize this 'free' augmentation on both an 'open' two-photon microscope and a leading commercial one. By following this protocol, setup of the system takes a few days, and the result is a noninvasive interface to brain dynamics based on directed light, at a network resolution that was not previously possible and which will further improve with the rapid advance in development of optical reporters and effectors. This protocol is for physiologists who are competent with computers and wish to extend hardware and software to interface more fluidly with neuronal networks.

Emergence of population bursts from simultaneous activation of small subsets of preBötzinger complex inspiratory neurons

During rhythmic movements, central pattern generators (CPGs) trigger bursts of motor activity with precise timing. However, the number of neurons that must be activated within CPGs to generate motor output is unknown. In the mammalian breathing rhythm, a fundamentally important motor behavior, the preBötzinger Complex (preBötC) produces synchronous population-wide bursts of activity to control inspiratory movements. We probed mechanisms underlying inspiratory burst generation in the preBötC using holographic photolysis of caged glutamate in medullary slices from neonatal mice. With stimulation parameters determined to confine photoactivation to targeted neurons, simultaneous excitation of 4-9 targeted neurons could initiate ectopic, endogenous-like bursts with delays averaging 255 ms, placing a critical and novel boundary condition on the microcircuit underlying respiratory rhythmogenesis.

The smallest stroke: occlusion of one penetrating vessel leads to infarction and a cognitive deficit

Microinfarctions are present in the aged and injured human brain. Their clinical relevance is controversial, with postulated sequelae ranging from cognitive sparing to vascular dementia. To address the consequences of microinfarcts, we used controlled optical methods to create occlusions of individual penetrating arterioles or venules in rat cortex. Single microinfarcts, targeted to encompass all or part of a cortical column, impaired performance in a macrovibrissa-based behavioral task. Furthermore, the targeting of multiple vessels resulted in tissue damage that coalesced across cortex, even though the intervening penetrating vessels were acutely patent. Post-occlusion administration of memantine, a glutamate receptor antagonist that reduces cognitive decline in Alzheimer's disease, ameliorated tissue damage and perceptual deficits. Collectively, these data imply that microinfarcts likely contribute to cognitive decline. Strategies that have received limited success in the treatment of ischemic injury, which include therapeutics against excitotoxicity, may be successful against the progressive nature of vascular dementia.

Two-photon microscopy as a tool to study blood flow and neurovascular coupling in the rodent brain

The cerebral vascular system services the constant demand for energy during neuronal activity in the brain. Attempts to delineate the logic of neurovascular coupling have been greatly aided by the advent of two-photon laser scanning microscopy to image both blood flow and the activity of individual cells below the surface of the brain. Here we provide a technical guide to imaging cerebral blood flow in rodents. We describe in detail the surgical procedures required to generate cranial windows for optical access to the cortex of both rats and mice and the use of two-photon microscopy to accurately measure blood flow in individual cortical vessels concurrent with local cellular activity. We further provide examples on how these techniques can be applied to the study of local blood flow regulation and vascular pathologies such as small-scale stroke.

Local diversity and fine-scale organization of receptive fields in mouse visual cortex

Many thousands of cortical neurons are activated by any single sensory stimulus, but the organization of these populations is poorly understood. For example, are neurons in mouse visual cortex--whose preferred orientations are arranged randomly--organized with respect to other response properties? Using high-speed in vivo two-photon calcium imaging, we characterized the receptive fields of up to 100 excitatory and inhibitory neurons in a 200 μm imaged plane. Inhibitory neurons had nonlinearly summating, complex-like receptive fields and were weakly tuned for orientation. Excitatory neurons had linear, simple receptive fields that can be studied with noise stimuli and system identification methods. We developed a wavelet stimulus that evoked rich population responses and yielded the detailed spatial receptive fields of most excitatory neurons in a plane. Receptive fields and visual responses were locally highly diverse, with nearby neurons having largely dissimilar receptive fields and response time courses. Receptive-field diversity was consistent with a nearly random sampling of orientation, spatial phase, and retinotopic position. Retinotopic positions varied locally on average by approximately half the receptive-field size. Nonetheless, the retinotopic progression across the cortex could be demonstrated at the scale of 100 μm, with a magnification of ≈ 10 μm/°. Receptive-field and response similarity were in register, decreasing by 50% over a distance of 200 μm. Together, the results indicate considerable randomness in local populations of mouse visual cortical neurons, with retinotopy as the principal source of organization at the scale of hundreds of micrometers.

Neural activity imaging with genetically encoded calcium indicators

Genetically encoded calcium indicators (GECIs), together with modern microscopy, allow repeated activity measurement, in real time and with cellular resolution, of defined cellular populations. Recent efforts in protein engineering have yielded several high-quality GECIs that facilitate new applications in neuroscience. Here, we summarize recent progress in GECI design, optimization, and characterization, and provide guidelines for selecting the appropriate GECI for a given biological application. We focus on the unique challenges associated with imaging in behaving animals.

Random access multiphoton (RAMP) microscopy for investigation of cerebral blood flow regulation mechanisms

The processes by which blood flow is regulated at the capillary network level in the brain has been a source of continual debate. It is generally accepted that cerebral blood flow regulation occurs primarily at the arteriolar level. It has been recently suggested, however, that the capillary network is likewise under dynamic regulation. The exact mechanisms of capillary regulation remain unknown. Previously, the limiting factor in determining how the cerebrovascular network is regulated has been the speed at which multiphoton images of large (~200μm²) capillary and arteriole systems can be acquired. Conventional laser scanning microscopy systems are temporally limited in two dimensions. We have developed a Random Access Multiphoton (RAMP) microscope, which operates on the principles of Acousto-optic beam scanning and therefore has no moving parts, specifically for the purpose of imaging blood flow virtually simultaneously throughout the capillary network. We demonstrate the ability to survey blood flow simultaneously in 100 capillaries.

Nonlinearity of two-photon Ca2+ imaging yields distorted measurements of tuning for V1 neuronal populations

We studied the relative accuracy of drifting gratings and noise stimuli for functionally characterizing neural populations using two-photon calcium imaging. Calcium imaging has the potential to distort measurements due to nonlinearity in the conversion from spikes to observed fluorescence. We demonstrate a dramatic impact of fluorescence saturation on functional measurements in ferret V1 by showing that responses to drifting gratings strongly violate contrast invariance of orientation tuning, a fundamental property of the spike rates. The observed relationship is consistent with saturation that clips the high-contrast tuning curve peaks by ∼40%. The nonlinearity was also apparent in mouse V1 responses to drifting gratings, but not as strong as in the ferret. Contrast invariance holds, however, for tuning curves measured with a randomized grating stimulus. This finding is consistent with prior work showing that the linear portion of a linear-nonlinear system can be recovered with reverse correlation. Furthermore, we demonstrate that a noise stimulus is more effective at keeping spike rates in the linear operating regime of a saturating nonlinearity, which both maximizes signal-to-noise ratios and simplifies the recovery of fast spike dynamics from slow calcium transients. Finally, we uncover spatiotemporal receptive fields by removing the nonlinearity and slow calcium transient from a model of fluorescence generation, which allowed us to observe dynamic sharpening of orientation tuning. We conclude that for two-photon recordings it is imperative that one considers the nonlinear distortion when designing stimuli and interpreting results, especially in sensory areas, species, or cell types with high firing rates.

Heuristically optimal path scanning for high-speed multiphoton circuit imaging

Population dynamics of patterned neuronal firing are fundamental to information processing in the brain. Multiphoton microscopy in combination with calcium indicator dyes allows circuit dynamics to be imaged with single-neuron resolution. However, the temporal resolution of fluorescent measures is constrained by the imaging frequency imposed by standard raster scanning techniques. As a result, traditional raster scans limit the ability to detect the relative timing of action potentials in the imaged neuronal population. To maximize the speed of fluorescence measures from large populations of neurons using a standard multiphoton laser scanning microscope (MPLSM) setup, we have developed heuristically optimal path scanning (HOPS). HOPS optimizes the laser travel path length, and thus the temporal resolution of neuronal fluorescent measures, using standard galvanometer scan mirrors. Minimizing the scan path alone is insufficient for prolonged high-speed imaging of neuronal populations. Path stability and the signal-to-noise ratio become increasingly important factors as scan rates increase. HOPS addresses this by characterizing the scan mirror galvanometers to achieve prolonged path stability. In addition, the neuronal dwell time is optimized to sharpen the detection of action potentials while maximizing scan rate. The combination of shortest path calculation and minimization of mirror positioning time allows us to optically monitor a population of neurons in a field of view at high rates with single-spike resolution, ∼ 125 Hz for 50 neurons and ∼ 8.5 Hz for 1,000 neurons. Our approach introduces an accessible method for rapid imaging of large neuronal populations using traditional MPLSMs, facilitating new insights into neuronal circuit dynamics.

Fluctuating and sensory-induced vasodynamics in rodent cortex extend arteriole capacity

Neural activity in the brain is followed by localized changes in blood flow and volume. We address the relative change in volume for arteriole vs. venous blood within primary vibrissa cortex of awake, head-fixed mice. Two-photon laser-scanning microscopy was used to measure spontaneous and sensory evoked changes in flow and volume at the level of single vessels. We find that arterioles exhibit slow (<1 Hz) spontaneous increases in their diameter, as well as pronounced dilation in response to both punctate and prolonged stimulation of the contralateral vibrissae. In contrast, venules dilate only in response to prolonged stimulation. We conclude that stimulation that occurs on the time scale of natural stimuli leads to a net increase in the reservoir of arteriole blood. Thus, a "bagpipe" model that highlights arteriole dilation should augment the current "balloon" model of venous distension in the interpretation of fMRI images.

Regression-based identification of behavior-encoding neurons during large-scale optical imaging of neural activity at cellular resolution

The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals.

Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing

In vivo 2-photon calcium imaging would benefit from the use of multiple excitation beams to increase scanning speed, signal-to-noise ratio, field of view, or to image different axial planes simultaneously. We adapted a spatiotemporal multiplexing approach to circumvent the problem of light scattering ambiguity in deep tissue inherent to multifocal 2-photon microscopy. We demonstrate 2-photon calcium imaging at multiple axial planes in the in vivo mouse brain to monitor network activity of large ensembles of cortical neurons in three spatial dimensions.

Chronic optical access through a polished and reinforced thinned skull

We present a method to form an optical window in the mouse skull that spans millimeters and is stable for months without inflammation of the brain. This enabled us to repeatedly image blood flow in cortical capillaries of awake animals and determine long-range correlations in speed. We further demonstrate repeated cortical imaging of dendritic spines, microglia, and angioarchitecture, as well as illumination to drive motor output via optogenetics and induce microstrokes via photosensitizers.