Imaging neuronal population activity in awake and anesthetized rodents.

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Imaging Neuronal Population Activity in Awake and Anesthetized Rodents David S. Greenberg, Damian J. Wallace and Jason N.D. Kerr Cold Spring Harb Protoc; doi: 10.1101/pdb.top083535 Email Alerting Service Subject Categories

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Topic Introduction

Imaging Neuronal Population Activity in Awake and Anesthetized Rodents David S. Greenberg, Damian J. Wallace, and Jason N.D. Kerr

Recent advances in in vivo two-photon imaging have extended the technique to permit the detection of action potentials (APs) in populations of spatially resolved neurons in awake animals. Although experimentally demanding, this technique’s potential applications include experiments to investigate perception, behavior, and other awake states. Here we outline experimental procedures for imaging neuronal populations in awake and anesthetized rodents. Details are provided on habituation to head fixation, surgery, head plate design, and dye injection. Determination of AP detection accuracy through simultaneous optical and electrophysiological recordings is also discussed. Basic problems of data analysis are considered, such as correction of signal background and baseline drift, AP detection, and motion correction. As an application of the method, the comparison of neuronal activity across arousal states is considered in detail, and some future directions are discussed.

INTRODUCTION

In addition to its excellent spatial resolution, two-photon imaging (Denk et al. 1990) works very well to record activity from cortical neurons because it is relatively noninvasive and neuronal populations can be sampled independently of their action potential firing rate. Furthermore, the same population of neurons can be revisited multiple times over the course of hours, days, weeks, and potentially months with absolute certainty about cell identity. As one of the main aims of population imaging is to accurately quantify the spiking activity simultaneously from many neurons (Kerr et al. 2005, 2007; Yaksi and Friedrich 2006; Sato et al. 2007; Greenberg et al. 2008), both single-cell and single-actionpotential (AP) resolution are required. If Ca2+ transients are used to infer electrical spiking activity, then the relationship between the AP and the corresponding Ca2+ transient needs to be established. Given that the majority of population imaging is performed on layer 2/3 cortical neurons, this is especially important as spontaneous firing rates can be well below 1 Hz, and sensory stimulation can be evoked well below one spike per stimulus, on average. Simultaneous electrophysiology (cell attached) and imaging of Ca2+ transients can be used to infer detection fidelity as this does not perturb the intracellular contents, unlike both whole-cell and sharp-electrode recordings. Inferring spikes from Ca2+ transients depends on multiple factors, such as the dye being used, the animal’s health, beam properties, the speed of sampling, and the detection system. Moreover, inferring spikes is fundamentally a signal-to-noise problem, because the Ca2+ transient peak evoked from a single action potential is 10% ΔF/F0 for organic indicators such as Oregon Green BAPTA-1 (OGB1). Consequently, to achieve single-AP resolution it is necessary to ensure a high sampling frequency so that each transient is sampled before it decays into the noise (Kerr and Denk 2008). In addition, because ester-based loading techniques lead to dense staining in all neuronal and astrocytic processes, Adapted from Imaging in Neuroscience (ed. Helmchen and Konnerth). CSHL Press, Cold Spring Harbor, NY, USA, 2011. © 2014 Cold Spring Harbor Laboratory Press Cite this introduction as Cold Spring Harb Protoc; doi:10.1101/pdb.top083535

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Imaging Neuronal Population Activity in Rodents

and this neuropil activity is highly correlated with ongoing up and down states (Kerr et al. 2005), it is critical to overfill the back aperture to avoid contamination by this neuropil signal in the somata signal (Kerr and Denk 2008). In this context, it is important to both understand the detection reliability for APs and also to be able to adjust imaging and collection parameters to improve fidelity when necessary. Two-photon microscopy is ideally suited for imaging activity in neuronal populations in the headfixed awake (Greenberg et al. 2008), headfixed moving (Dombeck et al. 2007, 2009), and freely moving animal (Sawinski et al. 2009). Not surprisingly, imaging in the awake animal is more problematic because sudden animal movements interfere with the imaging process. This is an inherent problem when using raster scanning to produce an image, because the relative position of the beam in the tissue has to be known to assign the detected photons to the correct pixel positions. There are several approaches to reduce movement of the animal and “reassign” the pixels post hoc (Dombeck et al. 2007; Greenberg and Kerr 2009). Here we describe procedures that enable calcium imaging in neuronal populations in both awake and anesthetized animals and the inference of spiking activity from the measured Ca2+ signals. This approach can be used to establish neuronal firing rates, spike firing correlations, and responses to sensory input in the awake behaving rodent.

EXPERIMENTAL PROCEDURES

For a general description of anesthesia and surgical procedures for preparing animals for in vivo twophoton microscopy, see In Vivo Two-Photon Calcium Imaging Using Multicell Bolus Loading (Garaschuk and Konnerth 2010) and In Vivo Two-Photon Calcium Imaging in the Visual System (Ohki and Reid 2014). What follows is a discussion on important points to consider when combining electrophysiology with population imaging with the goal of inferring spikes from Ca2+ transients. Habituation and Training for Awake Experiments

A period of habituation of the animal to both the experimenter and the experimental setup is critical for experiments with awake animals, and is best performed gradually over a period of about a week post head plate attachment. First, familiarize the animal with being regularly handled. We routinely use three to five 5–10 min sessions over 2 d, in which the animal is allowed to move around freely on the experimenters arms and over the experimental apparatus. Restraining the animal by holding the head plate for brief periods (30–60 sec) during the later sessions is a good way of gradually introducing the animal to periods of head fixation. The next stage of the training involves sessions of head fixation in which the animal is restrained for progressively increasing periods of time, typically starting with 5 min and then increasing to 10, 15, 20, 45, and 60 min. More than one head fixation session per day is ill advised. Head Plate and Head Plate Holders

A head plate design suitable for many different preparations in rats is shown in Figure 1Aa. Wide chamber designs with large side flanges for secure clamping are in general the most convenient option, particularly for imaging combined with electrophysiology. For experiments in awake animals, the size and weight of the head plate should be minimized. A recent design (Greenberg et al. 2008), including attachment points for chronically implanted ECoG electrode wires, is shown in Figure 1Ab. The design of the mechanism for secure clamping of the head plate is also more important for awake experiments. Specifically, the design should allow the animal to be restrained quickly by a single person. Designs with a single clamping point are an advantage, particularly during the habituation and training phases of the experiment, as are designs that prevent rotation of the head plate in the clamp (Fig. 1B). Cite this introduction as Cold Spring Harb Protoc; doi:10.1101/pdb.top083535

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FIGURE 1. Experimental procedures. (Aa) Large head plate design for simultaneous two-photon imaging and electrophysiology. Spring clamps fastened with small socket head screws (indicated by the black arrows) are used to hold a coverslip in place over the craniotomy. (Ab) Reduced head plate design for awake experiments. The arrow indicates attachment points for chronic EEG electrode wire plugs. Arrowhead shows where the screw from the head plate holder clamps the head plate for restricting rotational motion. (B) Clamp design for awake recordings (note clamping screw mentioned in A). (C ) Image showing indicator-loading pipette. (D) Craniotomy over somatosensory cortex. (E) Intrinsic optical signal after stimulation of E1 whisker showing the resulting “spot.” The outline of the darkest pixels is overlaid in both images indicating the position of E1 barrel column. (F ) Close-up view of the E1 area from a low-resolution twophoton image. Barrel field outlines obtained from cytochrome C staining are overlaid. Note the injection pipette tip at right. (G) L2/3 neurons (green) and astrocytes (yellow) stained with calcium indicator OGB-1 and sulforhodamine 101, respectively, in the E1 barrel column and surrounding tissue. A neuron within the E1 barrel border (red) was targeted with a patch electrode for cell-attached recording. (D–G, Reprinted from Kerr et al. 2007.)

Head Plate Implantation

For anesthetized preparations, dental cement provides a rigid and stable method for attaching the head plate to the skull. For good adhesion, keep fluid from the margins of the skin incision from getting between the bone and the dental cement. This is generally best achieved by carefully closing all skin incision margins with tissue glue. For awake experiments, a more secure method for head plate attachment is required. Use of bone screws may be suitable for larger animals, but take care so that the bone and/or screw do not impinge on the pia. For smaller animals, extremely secure attachment of the head plate can be achieved using a combination of dental cement and dental bonding products usually used for tooth restorations, such as OptiBond (Kerr Corporation). We apply a layer of OptiBond onto the exposed skull, followed by a layer of Charisma (Heraeus Kulzer), a light-curing composite used for dental fillings. This forms a foundation onto which we attach the head plate using dental cement (Greenberg et al. 2008; Houweling and Brecht 2008). Postoperative analgesia is essential following these surgical procedures. Soluble analgesics, such as aspirin, can be provided in the drinking water. Alternatively or in combination, a longer-lasting analgesic, such as flunixin-meglumine can be given at the end of the surgery. Targeting Sensory Areas

For many experiments, such as those targeting neurons in the cortical representation of a specific whisker in the somatosensory cortex (Kerr et al. 2007) or targeting the monocular or binocular regions 914

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in the primary visual cortex (Sawinski et al. 2009), accurate identification of a target area for bolus loading of the Ca2+ indicator can be a substantial advantage. Intrinsic optical imaging is ideally suited for this purpose (Fig. 1E). This can be performed either after performing a craniotomy or through the thinned skull before opening the bone. Intrinsic imaging through thinned bone is preferable for awake experiments, because it minimizes the bone opening over the area of interest. General Surgical Considerations

As for any in vivo preparation that is targeting recordings to the cortex, minimizing cortical insults during the surgical preparation is essential. For population imaging, particularly using sulforhodamine 101 as a counterstain (see In Vivo Labeling of Cortical Astrocytes with Sulforhodamine 101 (SR101) [Nimmerjahn and Helmchen 2012]), this is especially important, as proliferation and activation of glia during the surgery can dramatically affect the quality of the imaging. It is absolutely critical to avoid contact with the cortical surface, and to remove the dura in such a way that stray edges of the dura cannot get caught by the loading or recording pipettes during pipette placement and subsequent penetration of the cortical surface. It is also imperative to minimize movement of the cortex when performing population Ca2+ imaging experiments. Covering the exposed area with 1%–1.3% agar and fixing a coverslip in place over the agar layer is the gold standard. Important in this procedure is to avoid excessive downward pressure on the cortex when fastening the coverslip in place, and additionally to avoid unnecessary movement of pipettes through the agar. Preparing Dye for Injection

For calcium indicator labeling of neuronal populations, we use the multicell bolus loading technique (Stosiek et al. 2003). The main steps are provided below; for more details see In Vivo Two-Photon Calcium Imaging Using Multicell Bolus Loading (Garaschuk and Konnerth 2010). 1. Keep the dye desiccated at approximately –20˚C. 2. Prepare a 4:1 mixture of DMSO and F-127 Pluronic. This should be made fresh each day.

3. Add the 5 µL DMSO/Pluronic mix to the dye vial, mix it by pipetting it up and down, and sonicate it for 3 min. 4. Add 35 µL of normal rat Ringer solution and 5 µL of 40 µM Alexa Fluor 594 to the dye solution, mix it by pipetting it up and down, and mix it on a benchtop shaker for 2 min. 5. Remove bubbles by centrifugation. 6. Filter the solution through a microfilter (0.2-μm pore size). 7. Draw the solution into a glass pipette from the tip (1.5 µm internal diameter tip; Fig. 1C). 8. Backfill the pipette with additional dye solution. 9. Mount the pipette onto a headstage, and apply 30 kPa of pressure. 10. Guide the pipette into a target area in the tissue under visual control using an x − y − z manipulator. 11. Check whether the pipette tip is blocked. If it is, then withdraw the pipette and prepare another one. 12. Eject the dye at low pressure (20–30 kPa) for 2 min. Combining Imaging with Cell-Attached Electrophysiology

A number of sources provide equipment lists and solutions for performing electrophysiological cellattached targeted recordings (Margrie et al. 2002; Kerr et al. 2005, 2007; Yaksi and Friedrich 2006; Sato et al. 2007). There are several things to keep in mind when performing cell-attached electrophysiology.

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It is important to preserve the tissue and maintain conditions that are as close as possible to the conditions that will be used when collecting data in the absence of electrophysiology (i.e., when


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inferring spiking). For this purpose, we use a long tapered pipette with a tip resistance of 4–5 MΩ, and fill it with a combination of Na-based normal rat Ringer and Alexa Fluor 594.

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Minimize lateral movements of the pipette when it is in the tissue. The best way to do this is to determine where the neurons of interest are located and, given that the angle of the pipette relative to the brain surface is constant, calculate where on the surface the pipette should enter.

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Avoid having the tip become blocked, by applying pressure through the pipette. As a general guide, apply 30 kPa when moving through the agar and entering the tissue. Reduce the pressure to 1.2 kPa when stepping in 2-μm steps toward the target neurons. Further reduce the pressure to 0.2–0.5 kPa when approaching the neuron of choice, and finally, neutralize or apply small negative pressure when the pipette is close to the membrane and waiting for spiking.

There are two modes for electrically recording reliable spikes: One is cell-attached mode, in which the pipette is in direct contact with the cell membrane forming a loose seal with negative pressure, and the signal-to-noise ratio is high (10). The other is juxtasomal mode, in which the pipette tip is close to, but is not sealed against, the membrane, and the signal-to-noise ratio is 2–3. The cell-attached mode is susceptible to mechanical damage of the neuron, which renders the data suspect, but the method has the advantage that a whole-cell configuration can be obtained, thus allowing definite identification of the target neuron. Juxtasomal recordings can last for an hour or more and do not mechanically perturb the neuron, however, identifying the structure from where the spikes originate can sometimes be problematic. Targeting the electrode to the side of the neuron, so as to not create a physical obstacle between the beam and neuron, is advised (Fig. 2A). Blind juxtasomal recordings have been successfully made in the awake animal (de Kock and Sakmann 2008), but the risk of mechanical A

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FIGURE 2. Data analysis for AP detection and motion correction by combined optical and electrical recordings. (A) Image of neuronal 20% population during cell attached recording of a ΔF/F Model single neuron; arrowhead indicates pipette 5 sec (filled with Alexa 594). Scale bar, 40 µm. (B) Simultaneous calcium transients (upper) and Ephys cell-attached electrical recording (lower, number of APs indicated) obtained from the 1 1 22 2 1 1 11 1 same neurons and fitted with a model C 100 D 0.1 (middle; Greenberg et al. 2008) that detected AP–evoked calcium transients and assigned the correct number of APs to each transient. 0.5 (C ) Percentage of APs detected by the algorithm (blue) with a requirement of 1:1 correspon90 dence between electrically confirmed and inferred APs (n = 11 neurons). Electrically 0 0 0.5 1 0 0.5 1 confirmed APs were considered detected if Precision Δt (sec) Precision Δt (sec) the algorithm inferred an AP (not already asE signed to another electrically confirmed AP) with time difference ≤Δt. The green line shows the percentage of electrically confirmed APs within Δt of any inferred AP. (D) False pos50 μm itives (right, black line) occurred when an AP was inferred without a corresponding electriF cally confirmed AP (not already assigned to another inferred AP) with time difference ≤Δt. The red line shows the rate of inferred APs not within Δt of any electrically confirmed AP. (E) Two image frames (left, center) acquired immediately in sequence over periods of 100 msec each. When overlaid against each other (right) the image frames match at the bottom but not at the top, indicating that the brain moved 10 µm during the acquisition of one or both frames. (F ) Motion-corrected image frames (left, center), with each pixel assigned to the correct position. An overlay of the images (right) matches closely throughout the frame. (A–D, Adapted from Greenberg et al. 2008; E,F, adapted from Greenberg and Kerr 2009.)




distortions to the target neuron being imaged is increased with sudden animal movements, which will distort the calcium transients.

DATA ANALYSIS Normalization of Fluorescence Values

Optical AP detection in vivo exploits the increased fluorescence over baseline associated with discharge of a single AP (Kerr et al. 2005, 2007; Yaksi and Friedrich 2006; Sato et al. 2007), which for OGB-1 is 10%. To infer AP times, first obtain the normalized time-fluorescence function DF/F0 =

F − F0 . (100%), F0

where F is the measured fluorescence from a neuronal somata over time, and F0 is the baseline fluorescence value. F is generally an average over pixel gray scale values inside a region of interest (ROI). ΔF/F0 values do not depend on the units or scaling of F, but it is critical that F does not contain an additive offset arising from hardware, software, other image channels, or autofluorescence. One common way around this is to subtract background fluorescence from within the frame, which is difficult when using bulk-labeled tissue, as all structures are labeled. Most studies, therefore, use a blood vessel for estimating background, which is valid when gray values in the blood vessel are not 0, a condition that can occur if an “offset” value is subtracted from the whole frame at the digitization stage. In addition, this approach assumes that background fluorescence is homogeneous across the frame, including the blood vessel, which is probably not the case. Roughly speaking, F0 is the value of F in the absence of AP activity. Because baseline fluorescence values may drift over time, especially over longer imaging times of continuous sampling, the set of F values in a moving window may be used to estimate F0 (Greenberg et al. 2008) or to correct drift in ΔF/F0 values computed from a single choice of F0 (Dombeck et al. 2007). Frequent discharge of APs and burst firing can bias the results of moving window techniques, especially in awake animals. This problem has been addressed by performing multiple rounds of AP detection and re-estimation of F0 by excluding F values following a detected transient (Greenberg et al. 2008). Estimating Noise Levels and Neuropil Contamination

To quantify noise levels without introducing a strong bias based on the rate of AP discharge, we use only the negative ΔF/F0 values, whose root-mean-square produces a result 5 µm in a stationary animal (Dombeck et al. 2007; Greenberg et al. 2008). This movement can occur within an imaging frame, causing different parts of the image frame to be displaced by differing amounts and in opposite directions. So far, two approaches to motion correction for two-photon imaging of awake animals have been described. One method (Dombeck et al. 2007) is based on a hidden Markov model and the other (Greenberg and Kerr 2009) is based on the Lucas–Kanade algorithm (Lucas and Kanade 1981). In both methods, motion correction requires estimating the time-displacement function D describing brain motion in each image frame, and correcting each pixel’s position (Fig. 2E, F). D is Cite this introduction as Cold Spring Harb Protoc; doi:10.1101/pdb.top083535

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obtained without direct measurement of brain motion by matching image frames to a template acquired under anesthesia, or matching frames to each other. A cost function is defined indicating how well the corrected image matches the template for a given choice of D, and optionally containing additional image-independent terms that impose a prior distribution on D. D is represented using a finite set of parameters, and the parameter values are chosen to minimize the cost function. The full mathematical details for implementation of these methods can be found in the original publications (Dombeck et al. 2007; Greenberg and Kerr 2009). After motion correction, gaps in the image may appear because of fast motions, and some parts of a neuron may not be scanned at all. To avoid values of F being dependent on whether a neuron’s bright or dark pixels were scanned, some normalization scheme must be used: Either F must be normalized depending on which pixels are present in each frame (Greenberg and Kerr 2009) or ΔF/F0 values must be calculated for each pixel and averaged over each frame’s available pixels (Dombeck et al. 2007). Initial results show motion to be greater in rats than mice, greater in older animals, and greater along the rostrocaudal axis than the mediolateral axis. Motion along scan lines is easier to correct and less likely to create gaps than motion across scan lines, so scanning on the rostrocaudal axis is advantageous. AP Detection

Having calculated ΔF/F0 values for each neuron and each image frame, the next step is to transform these optical signals into AP counts. This is a relatively simple problem for single spikes well separated in time, such as when neurons fire infrequently with a majority of single spikes (Kerr et al. 2005), or during sensory stimulation in conditions where the majority of responses are single spikes (Kerr et al. 2007; Sato et al. 2007). The problem becomes more difficult when spiking is more complex, as is found in the awake animal (Dombeck et al. 2007, 2009; Greenberg et al. 2008; Sawinski et al. 2009) and during recordings that are continuous over many minutes. An AP detection algorithm must deal with both single isolated APs and trains of APs whose calcium transients summate and should not produce too many false-positive detections in the absence of APs (Fig. 2B). Several algorithms have been proposed, and a few have been tested by combining imaging with simultaneous targeted single cell electrophysiology, which, at this stage, is the only sure way to evaluate the accuracy of AP detection. When evaluating AP detection accuracy, it is important to evaluate both the percentage of APs that are detected (Fig. 2C) and the rate of false-positive detections (Fig. 2D), because optimizing one measure, such as detection, often comes at the expense of the other. With the approach discussed here, it was possible to detect 95% of all spikes while keeping the false-positive rate to 1 in every 40 sec of continuous imaging. Once APs have been detected, computing other quantities of interest, such as firing rates, stimulus response rates, and correlations (pair-wise and higher-order), is straightforward. In general, data for which APs have been detected are amenable to the same analyses as extracellular recordings, with the exceptions that neuronal identities and spatial locations are unambiguously determined. For populations of cortical neurons loaded with calcium indicator in vivo, the observed ΔF/F0 signals can be modeled as a linear convolution of spike counts, s, and a model transient, r. The model transient, obtained from cell-attached recordings (for a review, see Kerr and Denk 2008), indicates the height and shape of a Ca2+ transient evoked by a single AP. The fact that a linear convolution is assumed, means that the fluorescence signal from arbitrary spike patterns can be predicted by adding one model Ca2+ transient at the position of each AP. This means that we can consider many possible spiking patterns and compare the predicted fluorescence signal from each one to the observed fluorescence data. Overall, the AP detection problem can be posed: Find s such that DF/F0 ≈ s ◦ r or alternatively Find s to minimize the error function ||DF/F0 − s ◦ r||22 . 918




The latter formulation, which is a least-squares minimization, is also likelihood maximization when Gaussian white noise is assumed. To be useful for AP detection in anesthetized and awake animals, an algorithm should produce an accurate estimate of the sequence of integer spike counts s that is not sensitive to small noise fluctuations in the optical signal ΔF/F0. Cases in which AP firing is infrequent and contains mainly single spikes, successfully validated approaches include template matching based on cross-covariance (Kerr et al. 2005), linear regression with a moving window (Kerr et al. 2007), or regression over episodic stimulus-triggered data (Sato et al. 2007). Although effective and robust with respect to noise, these methods are limited to data sets with sparse AP firing and are not applicable to recordings in awake animals that show higher firing rates and a greater occurrence of bursts of APs (Dombeck et al. 2007, 2009; Greenberg et al. 2008; Sawinski et al. 2009). To deal with multiple nearby AP transients, which can summate to produce complex shapes in the ΔF/F0 signals, other methods are necessary. Simply using a linear filter to directly deconvolve optical signals into spike counts is problematic, because small noise variations can introduce large oscillations into the final results. Two main approaches exist to deal with this problem: Using a nonlinear filtering step to remove noise fluctuations before a linear deconvolution step, and using a nonlinear process to fit the spike counts s to the fluorescence data ΔF/F0. The nonlinear prefiltering approach for nonsparse data has so far proven quite successful for AP detection in the zebrafish olfactory bulb, using explants of the intact brain and nose (Yaksi and Friedrich 2006). In this study, several rounds of an iterative nonlinear smoothing procedure were used to remove small fluorescence peaks arising from noise fluctuations. Afterward, a linear deconvolution was performed using decay constants of 3 sec–6 sec, producing an excellent match to simultaneous electrophysiological recordings. Although this approach has yet to be tested on the shorter decay constants and higher noise levels found in mammalian cortical neurons in vivo, it may be the method of choice for neurons firing at high rates (>10 Hz) for prolonged periods. The second approach for nonsparse data, applying a nonlinear fitting procedure directly to the observed data, has been used to detect APs in cortical neurons in awake rats (Greenberg et al. 2008). In this study, the AP detection problem was posed as: Find s to minimize ||DF/F0 − s ◦ r||22 , such that for each image frame i, si = 0 or si ≥ 1. The advantage of this formulation is that it imposes realistic constraints on AP counts; for example, they cannot be negative, and half of an AP cannot occur in isolation. Because that constraint obviously applies to the true spiking activity, imposing it on the algorithm’s output will in general improve the match between the two. More specifically, positive noise fluctuations in the absence of APs will not change the algorithm’s output unless they are large enough to be better fit by increasing s from 0 to 1 at some point. The disadvantage of this formulation is the difficulty of finding the global minimum of the least squares error. If n image frames are identified as possibly containing APs, the constraint on s gives rise to a space of possible solutions containing 2n separate components, with each component corresponding to a different subset of image frames for which s > 0. Performing a separate least squares minimization on each component is not computationally feasible, but this problem can be dealt with in two ways. First, a preliminary step can reduce n by identifying image frames that cannot contain an AP transient (e.g., frames that show a fluorescence decrease compared with the previous frame). Second, the fact that an idealized AP-evoked transient is nonnegative at all time points allows, using algebraic manipulations of the observed data, some of the 2n components to be removed from consideration. For example, it was reported that for n = 33 points, which gives 8.6 billion components, only 700,000 (0.008%) of these had to be tested to find the global least squares minimum (Greenberg et al. 2008). Overall, this algorithm is more computationally intensive than other methods, but is so far the only one whose ability to detect AP activity in mammalian cortical neurons in vivo has been validated using electrophysiology, both during sparse activity and when nearby APs summated to create complex transient shapes in the ΔF/F0 signals (Fig. 2B). Several other methods that apply nonlinear fitting steps directly to the data appear quite promising, but are still awaiting validation in vivo. These include a method based on support vector Cite this introduction as Cold Spring Harb Protoc; doi:10.1101/pdb.top083535

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machines (Sasaki et al. 2008) and a detailed parametric model solved using particle filters (Vogelstein et al. 2009). It should be noted that AP detection produces spike counts si for each imaging frame i. For imaging at 10–20 Hz, detected transients are temporally uncertain within a 50–100 msec window. In comparing detected APs to stimulation or behavior, the position of an ROI in the frame determines its timing; fluorescence measurements for neurons at the top and bottom of a single image frame are separated in time by almost a full frame duration.

EXAMPLE OF APPLICATION: SPONTANEOUS POPULATION ACTIVITY BEFORE AND AFTER ANESTHESIA

Once accurate AP detection has been confirmed and brain motions can be successfully compensated, the firing patterns obtained in awake animals can begin to yield insights into questions of biological interest. As an example, we describe a comparison of firing patterns in the same neuronal population before and after anesthesia (Greenberg et al. 2008) (Fig. 3). In this study, infrared videography and ECoG recordings were used to separate recordings into different arousal states: Awake, anesthetized, and sessile (when the animal is unanesthetized but displays no movement for extended periods, and might be asleep). The detected APs were used to calculate properties for single neurons such as firing rates (Fig. 3C) and the tendency toward burst firing, as well as population properties such as correlations (Fig. 3D). The effect of arousal state on these properties was examined, both statistically across all recorded neurons (Fig. 3D, H) and by tracking individual neurons, pairs, or subpopulations across arousal states (Fig. 3E, I). For example, although the neurons firing at the highest rates in awake animals also fired at the highest rates under anesthesia (Fig. 3D), the neuronal pairs that correlated most closely during wakefulness were not more likely to be correlated under anesthesia. Thus, under anesthesia, firing rates are mostly conserved, whereas correlations are reshaped or reset. The spatial information provided by two-photon imaging also allowed the determination of the spatial distribution of arousal-mediated changes, firing rates, and correlations activity with single neuron precision (Fig. 3C, G). Finally, it had previously been reported that in acute slices, neuronal pairs with higher firing rates tended to have higher correlations (de la Rocha et al. 2007). Using two-photon imaging, it was found that this phenomenon occurs in vivo during anesthesia but not during wakefulness (Fig. 3J). Overall, the ability to track individual neurons and pairs across arousal states yielded insights that could not have been gained by recording different states in separate animals. Future Prospects for Awake Population Imaging

The experimental and computational procedures outlined above can be used to record APs in populations of spatially resolved neurons in awake animals. So far, highly accurate detection of single APs has been confirmed only for frame scans of bulk-loaded organic calcium indicators. Consequently, the state of the art is a recording at 10 Hz over a few hours from 20 to 30 neurons simultaneously, with AP detection rates in the range of 90%–95% and false-positive rates of 0.025 Hz. Although useful, this scenario is not close to exploiting the true potential of the technique, and we close this discussion by outlining two promising future developments that are likely to yield improvements in accuracy, temporal resolution, duration, and number of simultaneously recorded neurons. The first important advance will be the development of new calcium indicators. Genetically encoded calcium indicators are already capable of detecting single APs in vivo (Wallace et al. 2008; Tian et al. 2009), but with lower accuracy than organic dyes, and they can suffer from uneven expression or physiological side effects. However, they promise to extend the time window in which stained neurons can be recorded from hours to months (Mank et al. 2008; Tian et al. 2009). Chronic imaging is exceptionally promising for the study of learning-related changes in population firing patterns in behaving animals. Efforts to develop new organic indicators (Lavis and Raines 2008) may improve both recording duration and the quality of fluorescence signals, thereby improving the 920




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FIGURE 3. Imaging of the same neuronal population in awake and anesthetized states. (A) Stained L2/3 neuronal (green) and astrocyte (yellow) populations before (left) and after (right) anesthesia. Scale bar, 50 µm. (B) Calcium transients recorded from the same population before and after anesthesia. (C ) Neurons pseudocolored according to percentage increase (red) or decrease (green) in firing rate on anesthesia. (D) Box-and-whisker plot comparing firing rates in awake, sessile, and anesthetized states. (E) Individual neuronal firing rates during awake and anesthetized periods. (F ) Pair-wise correlations of spiking activity shown for awake (left) and anesthetized periods (right) as colored linkages between neurons and correlation matrices (insets). (G) Correlation difference between awake and anesthetized states. (H ) Box-and-whisker plot comparing pair-wise correlations in awake, sessile, and anesthetized states. (I) Correlations in the spiking of individual neuronal pairs during awake and anesthetized periods. (J ) Correlation between neuronal pairs during awake (black) and anesthetized periods (red), compared with pair-wise geometric mean of firing rates. Dashed lines show average correlation for AW and AN states. (Adapted from Greenberg et al. 2008.)

accuracy of AP detection or allowing a reduced beam dwell time and thus more simultaneously recorded neurons. Both types of dyes may allow targeting of specific cell types.

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Dynamic resting state functional connectivity in awake and anesthetized rodents.

Functional imaging of neuronal activity of auditory cortex by using Cal-520 in anesthetized and awake mice.

Two-photon imaging of cerebral hemodynamics and neural activity in awake and anesthetized marmosets.

Critical Changes in Cortical Neuronal Interactions in Anesthetized and Awake Rats.

Erratum: Genetically expressed voltage sensor ArcLight for imaging large scale cortical activity in the anesthetized and awake mouse (erratum).

Chronic multiscale imaging of neuronal activity in the awake common marmoset.

Comparison of Cardiopulmonary Parameters in awake and anesthetized chickens.

Coding odor identity and odor value in awake rodents.

Tracheal extubation in children: halothane versus isoflurane, anesthetized versus awake.

Two-photon imaging of neural activity in awake mobile mice.

Reconstruction of burst activity from calcium imaging of neuronal population via Lq minimization and interval screening.

Extracellular brain glucose levels reflect local neuronal activity: a microdialysis study in awake, freely moving rats.

High-speed two-photon calcium imaging of neuronal population activity using acousto-optic deflectors.

Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo.

Stimulus-evoked changes in neostriatal dopamine levels in awake and anesthetized rats as measured by microdialysis.

Pharmacological MRI response to a selective dopamine transporter inhibitor, GBR12909, in awake and anesthetized rats.

Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat.

Ventilation-perfusion relationship in young healthy awake and anesthetized-paralyzed man.

High-throughput imaging of neuronal activity in Caenorhabditis elegans.

Neuronal serotonin in the regulation of maternal behavior in rodents.

Neurohypophysial electrical activity in the anesthetized cat.

Simple platform for chronic imaging of hippocampal activity during spontaneous behaviour in an awake mouse.

Imaging neuronal populations in behaving rodents: paradigms for studying neural circuits underlying behavior in the mammalian cortex.

Oestrogenic activity of enoxolone in rodents.