Embracing Complexity in Defensive Networks

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Review Embracing Complexity in Defensive Networks Drew B. Headley,1 Vasiliki Kanta,1,2 Pinelopi Kyriazi,1,2 and Denis Pare´1,* 1Center

for Molecular & Behavioral Neuroscience, Rutgers University - Newark, 197 University Avenue, Newark, NJ 07102, USA and Neural Sciences Graduate Program, Rutgers University - Newark, 197 University Avenue, Newark, NJ 07102, USA *Correspondence: [email protected] https://doi.org/10.1016/j.neuron.2019.05.024 2Behavioral

The neural basis of defensive behaviors continues to attract much interest, not only because they are important for survival but also because their dysregulation may be at the origin of anxiety disorders. Recently, a dominant approach in the field has been the optogenetic manipulation of specific circuits or cell types within these circuits to dissect their role in different defensive behaviors. While the usefulness of optogenetics is unquestionable, we argue that this method, as currently applied, fosters an atomistic conceptualization of defensive behaviors, which hinders progress in understanding the integrated responses of nervous systems to threats. Instead, we advocate for a holistic approach to the problem, including observational study of natural behaviors and their neuronal correlates at multiple sites, coupled to the use of optogenetics, not to globally turn on or off neurons of interest, but to manipulate specific activity patterns hypothesized to regulate defensive behaviors.

Neuron invited us to contribute a review on the role of the amygdala in anxiety. However, many comprehensive reviews have been published on this topic recently (Calhoon and Tye, 2015; €thi and Lu €scher, 2014; McCullough et al., 2016; Tovote Lu et al., 2015). Thus, instead of writing a review whose sole merit over prior ones would be that it includes the latest findings, we elected to present a critical appraisal of the general approach and directions in this active field of research. We hope that colleagues and other readers will find our paper useful. Different Mammalian Species Possess a Similar Repertoire of Defensive Behaviors Defensive behaviors are a class of innate response dispositions retained by natural selection because they promote survival in the face of threats such as predators and aggressive con-specifics. Of course, organisms do not inherit the survival strategies themselves but the neuronal networks and physiological mechanisms that support them. As was noted early on (Darwin, 1872), many defensive behaviors and their precipitating conditions are similar in different mammalian species. As for physical attributes, this kinship likely resulted from the fact that they emerged from small variations to an overall design shared between species. Defensive behaviors have been classified in different ways including the nature of the threatening events (Gross and Canteras, 2012), the ecological conditions they evolved to accommodate (Mobbs, 2018), and the proximity of the threat (Fanselow, 2018; Perusini and Fanselow, 2015). The latter dimension is often used to distinguish fear and anxiety. According to this widespread view, fear occurs when organisms are faced with imminent proximal threats, causing short-lasting defensive responses such as fight or flight. By contrast, anxiety is usually defined as a long-lived state of apprehension that arises when unpredictable or uncertain perils are anticipated. However, the terms fear and anxiety imply the conscious experience of aversive states whose existence in other mammals than humans cannot be ascertained (LeDoux, 2017).

The repertoire of defensive behaviors is relatively limited. It includes vocalizations warning con-specifics of a threat, fast coordinated movements orienting the body toward unexpected stimuli, and behaviors minimizing the likelihood of detection by a predator (freezing) or, if detected, captured (escape) or killed (fight). These behaviors are associated with changes in autonomic tone (e.g., heart rate, blood pressure) and endocrine activity, which mobilize organisms for confrontations and allow them to intimidate potential aggressors (piloerection). Notably, defensive behaviors are also associated with attention systems that allow anticipation and rapid detection of threats (Ohman et al., 2001a, 2001b). Moreover, through experience, they can become associated with new stimuli. Defensive Behaviors as Flexible, Context-Dependent Responses to Threats Defensive behaviors are adapted to the nature and proximity of the threats. Generally, proximal and imminent threats require organisms to rapidly weigh different options and thus allow less flexibility in the selection of a defensive strategy. For instance, while behavioral freezing or retreat are efficient ways for animals to survive encounters with distant predators, fighting or fleeing are the only possible responses when the predator’s attack is imminent. In contrast, when threats are not immediate, defensive behaviors exhibit flexibility and context dependence. For instance, foraging rats seeking to minimize the risk of predation consider multiple factors such as current metabolic needs, familiarity with the environment, and the accessibility of escape routes (Mobbs et al., 2018). Choi and Kim’s (2010) foraging task vividly illustrates the adaptable and changing character of defensive strategies. In this task, hungry rats are confronted with a mechanical predator when they leave the safety of their nest to retrieve food pellets in an elongated arena. It was noted that when rats fail to retrieve food on a given trial, they are more ‘‘hesitant’’ on the next trial, that is, they wait longer before leaving their nest or do not forage Neuron 103, July 17, 2019 ª 2019 Elsevier Inc. 189

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Review altogether (Amir et al., 2015, 2018). Furthermore, after initiating foraging, they often show signs of ‘‘indecision,’’ alternating between moving toward or away from the food pellet, (Amir et al., 2015, 2018), suggesting that even rodents continuously evaluate circumstances when selecting a course of action. Evidence of such flexibility was also obtained in aversive conditioning paradigms. In this case, rats display different defensive strategies depending on the features of the environment. In a standard conditioning chamber, aversive conditioned stimuli (CS) appear to reflexively elicit behavioral freezing. However, if the chamber also allows rats to avoid the anticipated shock, either by moving to a different part of the chamber or by stepping onto a platform, trained rats eventually show marked trial-to-trial variations in defensive strategy. They may first freeze and then escape, or escape without first freezing, and they do so at variable latencies from CS onset (Bravo-Rivera et al., 2014; Kyriazi et al., 2018). In another striking example of context specificity, while rat dams conditioned to fear an odor froze to the presentation of the odor when tested alone, in the presence of their pups, the dams did not freeze but remained in contact with their pups or attacked the odor source (Rickenbacher et al., 2017). At the other end of the imminence spectrum are anticipatory defensive strategies (Mobbs, 2018) where in the absence of actual threats, but facing the possibility one might appear, organisms display increased alertness and vigilant scanning of their environment. This state, where processing time is the longest, calls upon prior experiences in the current or similar environments, including the availability of escape routes should one be needed, all the while taking the organism’s current metabolic needs into account. One task that engages these faculties is the Barnes maze, where animals are placed in an open arena and learn the location of an escape port. To understand the neural basis of defensive behaviors, it is important to use tasks that span their spectrum. This helps disambiguate the neural processes involved in the production of specific behaviors from those supporting the processing of threatening stimuli. For instance, since threat proximity determines which defensive behavior is selected, tasks with a continuum of threat imminence have proved particularly insightful. In humans, a Pac-Man-like game, requiring subjects to avoid an artificial predator in a maze, elicited activation of the ventromedial prefrontal cortex when the threat was far away and of the periaqueductal gray when it was near (Mobbs et al., 2007). In mice, serial cues that signaled the imminence of a shock evoked freezing followed by escape, and these behaviors were supported by different populations of central amygdala neurons (Fadok et al., 2017). It is also important to distinguish arousal and attention from threat processing. One way to do this is to use tasks that feature both aversive and appetitive elements, allowing one to assess the specificity of neural activity or manipulations with respect to valence, arousal, or behavior. As discussed in detail below, only by contrasting the activity of the same neurons with respect to multiple variables can we assess what they encode. Individual Differences in the Expression of Defensive Behaviors Not only do individuals show flexible expression of defensive behaviors, particularly with distal or anticipated threats, but they

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also vary in their disposition to express defensive behaviors. Numerous studies have reported on this phenomenon in multiple species. For instance, such differences were noted in the stimulus generalization (Duvarci et al., 2009) or extinction of conditioned fear responses (Milad and Quirk, 2002), in behavioral inhibition during social interaction tests (Rogers et al., 2008; Rosenbaum et al., 1993), in the long-term impact of acute stressors on defensive behaviors (Goswami et al., 2010; Krishnan et al., 2008; Siegmund et al., 2009), or in tests of anxiety-like behaviors in different strains of rats or mice (Cohen et al., 2006; O’Leary et al., 2013). In fact, there appears to be a continuum in the propensity of different subjects to emit defensive behaviors, and in humans, in how anxious they tend to feel. In the latter species, the term ‘‘trait anxiety’’ is used to refer to the long-term tendency of different individuals to experience consistently low or high levels of anxiety (Spielberger, 1983). As for defensive behaviors, these stable differences in disposition to anxiety are thought to be, in part, inherited. In support of this notion, comparing the incidence of anxiety disorders in homo- versus heterozygotic twins has revealed that the heritability of panic and phobic and generalized anxiety disorders is around 30%–40% (Hettema et al., 2001). Moreover, selective breeding of rodents for low or high anxiety-like behaviors results in markedly different behavioral phenotypes within a few generations (Landgraf and Wigger, 2002). Yet, the heritability of anxiety disorders (and by extension, of the disposition to defensive behaviors) does not depend on alterations in single genes, but in multiple genetic variants, each having a small contribution to individual phenotypes (Sharma et al., 2016). In addition, there is consensus that not only genetic but also epigenetic factors play an important role in determining these individual differences. Evidence for complex interactions between genes and environment, often dependent on developmental stage, have been reported (Felitti et al., 1998; Kendler et al., 1992; Tweed et al., 1989). Moreover, genetic and environmental factors are not independent as an individual’s genetics can lead him or her to seek particular environments (Kendler, 2001). While it is beyond the scope of the present paper to review the wide range of gene-environment interactions described so far, it is important to remind ourselves that they are ultimately expressed in neuronal networks. Given that multiple genes are involved in the genetic heritability of anxiety disorders (Sharma et al., 2016; and, likely, defensive behaviors) and considering that these genes regulate functions as disparate as monoamine or cholinergic signaling, neural development, stress or sex hormones, inflammatory disease pathways, and even the immune system (Daskalakis et al., 2018), it is likely that widespread alterations in multiple neuronal networks underlie individual differences in the disposition to express defensive behaviors. The Measure of ‘‘Anxiety’’ in Rodent Studies Contrasting with the heterogeneity of human anxiety disorders and the multifaceted nature of defensive behaviors, current research on the neural substrates of ‘‘anxiety’’ in rodents heavily relies on a handful of behavioral assays, most often the elevated plus maze (EPM) and open-field (OF) tests. These two tests rely on the assumption that, as occurs during foraging in the wild,

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Review rodents are conflicted by the motivation to explore an open, naturally threatening area where needed resources might be located, or to stay in an enclosed, safe area where resources are scarce. Time in the open versus closed arms of the EPM and in the center versus near the walls of the OF are the main variables measured, with lower times in the EPM’s open arms and OF’s center indicating higher ‘‘anxiety.’’ However, these tests are notoriously unreliable. Beginning in the 1970s, many questioned the usefulness of these assays (see Ennaceur and Chazot, 2016 for a long list of critical reviews). Well-documented shortcomings include low test-retest reliability, inconsistent results depending on the duration of the tests (Fonio et al., 2012), lack of correlation between the two tests within a single strain or across strains of rats or mice (O’Leary et al., 2013; van der Staay et al., 2009), and inconsistent sensitivity of ‘‘anxiety’’ measures to different types of anxiolytic drugs (Cryan and Sweeney, 2011; Griebel and Holmes, 2013; Rodgers et al., 1997). Expectedly given the above, these assays of ‘‘anxiety’’ also yielded contradictory results in optogenetic studies. For instance, two recent reports stimulated a population of GABAergic neurons expressing protein kinase C-d (PKC-d+) in the central lateral amygdala (Botta et al., 2015; Cai et al., 2014). Previously, it had been reported that these PKC-d+ cells inhibit conditioned freezing (Ciocchi et al., 2010; Haubensak et al., 2010), leading to the expectation that optogenetically exciting PKC-d+ neurons would decrease freezing and result in more exploration of the open areas in the EPM and OF. Yet, one study reported the opposite (Botta et al., 2015) while the other found an increase in open arm exploration of the EPM but no change in the OF (Cai et al., 2014). To complicate matters further, optogenetic manipulations might appear to have ‘‘anxiolytic’’ effects in the OF or EPM, not because they reduce ‘‘anxiety,’’ but because they regulate a different function such as predation, which is incompatible with cautious exploratory behavior. Thus, reducing the many facets of anxiety to a few behavioral readouts of ambiguous significance is not a viable path forward. It seems more promising to consider multiple variables such as stretch-attend behaviors, defecation, and the stimulus generalization of fear responses, which are more reliably correlated across rodent strains and species (Botta et al., 2015; O’Leary et al., 2013). Studying the generalization of fear responses (in time, to other stimuli or contexts) is a particularly promising path forward given that generalization is a hallmark of anxiety disorders (Dunsmoor and Paz, 2015) and that rodents, like humans, exhibit marked individual differences in their propensity to generalization (e.g., Duvarci et al., 2009). Brain-wide Processing of Threats When a stimulus arrives at the periphery, a wave of activity cascades and reverberates throughout the nervous system. Threatening stimuli in particular quickly recruit neuromodulatory systems of the brainstem and basal forebrain, altering the transmission of sensory information and the state of the entire brain through multiple pre- and post-synaptic mechanisms (Brunton et al., 2017; McCormick et al., 1991; Steriade, 1995). Thus, the impact of threatening stimuli is not limited to the few

defensive networks current research focuses on. Case in point: when Fos expression was correlated across 84 brain regions during recall of contextual fear, 80% of them were found to participate (Vetere et al., 2017). Similarly, FosTRAP labeling revealed that cued fear conditioning (FC) recruits numerous brain areas, including cortical, thalamic, hypothalamic, and brainstem regions (DeNardo et al., 2019). Moreover, human functional imaging studies indicate that threats orchestrate a brain-wide rearrangement of activity patterns (de Voogd et al., 2018). The case of conditioned fear vividly illustrates how the processing of threatening stimuli depends on a broadly distributed network. While early studies suggested that the acquisition of conditioned fear to auditory cues entirely depended on plasticity in the lateral amygdala (LA; LeDoux, 2000), subsequent work revealed a much more complex situation. On the input side, it was found that structures first thought to passively relay auditory information about CSs to the amygdala are in fact critical sites of plasticity. For instance, neurons of the inferior colliculus (IC), auditory thalamus (medial geniculate; MG), and related cortical areas all display potentiated responses to CSs after conditioning (Weinberger, 2007, 2011). While some of these changes result from increased arousal, since they develop even when the conditioned and unconditioned stimuli (US) are unpaired (Ji and Suga, 2009), others reflect genuine associative plasticity (Kim et al., 2013; Letzkus et al., 2011; reviewed in Weinberger, 2011). Consistent with this idea, the IC and some MG subnuclei are the first sites in the auditory pathway where multiple information streams, including nociceptive inputs, are integrated (Casseday and Covey, 1996; Ledoux et al., 1987). Presumably, distributed plasticity in auditory afferent pathways allows organisms to respond adaptively to stimuli that vary in complexity. Consistent with this idea, while silencing the auditory cortex (AC) during FC to either tones or noise does not prevent learning (Zhang et al., 2018a), lesioning or inhibiting AC impairs conditioning to the presence of brief gaps in ongoing noise (Weible et al., 2014) or to frequency modulated sweeps (Ohl et al., 1999). Moreover, the perirhinal cortex (PRH), which receives inputs from the secondary auditory cortices and projects to LA (Romanski and LeDoux, 1993), is essential for the development of conditioned fear responses to the ultrasonic vocalizations of conspecifics, but not pure tones (Lindquist et al., 2004). On the output side, the central nucleus of the amygdala (CeA), which was initially conceptualized as a simple relay of potentiated LA inputs to downstream effectors, is now considered an essential site of plasticity (Ciocchi et al., 2010; Wilensky et al., 2006). Similarly, the ventrolateral periaqueductal gray, first thought to passively generate conditioned freezing in response to CeA inputs, was shown to affect the strength of aversive memories by regulating error prediction coding in LA (Ozawa et al., 2017). Moreover, several brain regions that were initially thought not to be involved in FC because pre-training lesions had no effect have since been shown to be critical. For instance, the bed nucleus of the stria terminalis (BNST) was found to regulate the stimulus specificity of conditioned fear (Duvarci et al., 2009). The basolateral nucleus (BL) of the amygdala (Anglada-Figueroa and Quirk, 2005) and prelimbic (PL) region of the medial

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Review prefrontal cortex (mPFC; Corcoran and Quirk, 2007) are required for the expression of conditioned fear. Intriguingly, the role of PL and the neighboring infralimbic (IL) area, a major regulator of fear extinction (Quirk and Mueller, 2008), is not limited to controlling conditioned freezing. In a task that requires active avoidance instead of freezing, inactivation of PL and IL alters avoidance without affecting freezing (Bravo-Rivera et al., 2014). These results suggest that PL and IL do not regulate fear behaviors per se, but a more general cognitive function such as the contextual modulation of mappings between cues and the responses they elicit (Moorman and Aston-Jones, 2015; Sharpe and Killcross, 2015). Moreover, structures that were not even considered in early models of conditioned fear, namely, the zona incerta (Zhou et al., 2018) as well as the cerebellum and its brainstem targets, are also critical for the formation and consolidation of conditioned fear (Strata et al., 2011; Supple and Kapp, 1993). Also unexpected, the recall of conditioned fear shows a changing dependence on different brain structures depending on time since conditioning. For instance, the paraventricular thalamic nucleus and secondary AC are required at late but not early time points (Do-Monte et al., 2015b; Sacco and Sacchetti, 2010). Optogenetics in the Study of Defensive Behaviors: Opportunities and Challenges While the dependency of threat processing on a plethora of brain regions is at odds with the notion that defensive behaviors depend on specific, dedicated circuits, it does not mean that there is no functional specialization across regions. Even simple behaviors involve numerous processes, each supported by specialized subsystems that interact in complex ways to produce the whole. In this context, can causality be ascribed to a specific structure or cell type? Many optogenetic studies have made such claims, some even concluding that particular networks are necessary and sufficient for anxiety or anxiolysis (Adhikari et al., 2015; Baek et al., 2019; Han and Friedman, 2012; McCall et al., 2015). Thanks to optogenetics, investigators can now manipulate, with exquisite temporal resolution, the activity of neurons with specific properties such as projection site, neurochemical identity, even responsiveness to particular stimuli, or recruitment during specific behaviors (Kim et al., 2017). The advantages of these methods over prior pharmacological tools are unquestionable. However, as discussed below, several factors complicate the interpretation of optogenetic results, particularly when excitatory opsins are used. First, optogenetically evoked neuronal activity generally differs from that seen during the behavior whose neuronal basis is being investigated. In fact, it is standard in the field to deliver light pulses at arbitrarily high frequencies (e.g., 20 Hz), even when studying structures such as the amygdala, where neurons have much lower firing rates (<1 Hz). Not only are optogenetically imposed firing rates often higher than normal, but these discharges are also aberrantly synchronized. In one study for instance, the amplitude of field potential responses evoked in LA by optogenetic activation of auditory inputs (Nabavi et al., 2014) was 30–50 times higher than that elicited by actual audi-

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tory stimuli (Collins and Pare´, 2000). This is consistent with unpublished reports of optogenetically induced convulsions. Other observations suggest that the interpretation of optogenetic findings should consider the relation between actual and optogenetically evoked activity patterns. For instance, the impact of optogenetic stimuli on behavior is frequency dependent, with low- or high-frequency stimuli sometimes producing different effects on the behavior under study (Do-Monte et al., 2015a). In other cases, optogenetic inhibition or excitation both disrupt the behavior (Jimenez et al., 2018), highlighting that the function of a network is not merely dependent on how excited it is but on how its activity is patterned. Closely related to this problem is the unknown relationship between the identity of the infected neurons and of the cells normally recruited during the behavior under study. This problem is alleviated by intersectional strategies where opsin expression is restricted to neurons that start expressing immediate early genes (IEGs) shortly after a behavior of interest (Gore et al., 2015; Reijmers et al., 2007). However, many factors complicate the interpretation of such studies. First, the time course of IEG expression varies dramatically across conditions (Bullitt et al., 1992; Dragunow and Faull, 1989; Herrera and Robertson, 1996). Second, some regions require very strong activation levels to exhibit IEG induction (Dragunow and Faull, 1989; French et al., 2001). Third, in the hippocampus and neocortex, only principal cells (PNs) show activity-related IEG expression; thus, interneurons are by default excluded in most studies (Kubik et al., 2007). Finally, IEG expression does not always correlate with neuronal activity or behavior; there are many examples where IEG levels do not change after plasticity induction or show similar changes when different behaviors are expressed (French et al., 2001; Harbuz and Jessop, 1999; Herrera and Robertson, 1996; Miyashita et al., 2009). More fundamentally, when interpreting optogenetic results, it can be tempting to conclude that the manipulated neurons encode a single dimension or behavior with all or most of them encoding the same one. However, as discussed below, the available evidence instead indicates that neurons concurrently encode multiple dimensions or behaviors and that the relevant codes exist at the population level. While the present review will not consider inhibitory opsins in detail, it should be noted that they are not without their interpretational problems. For instance, one can conceive of a situation where optogenetic inhibition of a cell group causes the loss of a behavior, not because the inhibited neurons fire differentially in relation to this behavior, but because this cell group contributes tonic excitatory inputs to another cluster of neurons, which plays a role in generating this behavior. Individual Neurons in Defensive Networks Show Complex, Multi-dimensional Responses The diversity of regions recruited by stimuli signaling threats and those driving behavioral responses imply that threat processing is not reflexive but involves a complex interplay between multiple distributed networks. The amygdala is such a nexus. Early studies indicated that a subset of neurons in the basolateral amygdala (BLA) developed potentiated responses to stimuli that had been associated with aversive events (e.g., Quirk

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Review et al., 1995). These neurons received enhanced synaptic drive and in turn tended to project to specific downstream structures that mediated conditioned fear behaviors, such as CeA (Namburi et al., 2015). Similar specificity on the afferent and efferent ends was found for cells that are recruited during fear extinction (Senn et al., 2014). These results suggest a straightforward way to think about the processing of threatening stimuli by the BLA: particular subsets of neurons are dedicated to linking stimuli with specific behavioral responses. Complicating this picture is the fact that individual amygdala neurons often exhibit responses to multiple stimuli that evoke disparate behaviors. When subjects are trained on tasks that feature aversive and appetitive elements, many BLA neurons encode both. Shabel and Janak (2009) found that 26% of neurons responded similarly to cues predicting either a shock or a sucrose reward. Similar results were obtained on a naturalistic foraging task featuring a robotic predator, with 20% of responsive neurons activated by the food and predator (Kim et al., 2018). When mice were trained to discriminate between two sounds, one predicting sucrose delivery and the other quinine, 30% of responsive neurons coded for both CSs (Beyeler et al., 2016). Moreover, a third of those neurons that responded to one of the CSs also fired when the liquid was delivered. Additionally, BLA neurons not only respond to discrete stimuli but also contexts. In one study, the population of neurons that encoded an aversive CS overlapped with that encoding an anxiety-like state, and the direction of their change in firing rate was the same in both cases (Lee et al., 2017). The overlap between neurons engaged by cued and contextual freezing is incompatible with a projection-specific account of how the BLA controls defensive behaviors since the expression of cued fear is thought to depend on CeA (Goosens and Maren, 2001) while that of contextual fear relies upon BNST (Duvarci et al., 2009; Sullivan et al., 2004; but see Goode and Maren, 2017; Gungor and Pare´, 2016). Emotional Stimuli and Behaviors Are Represented at the Population Level This mixing of responses within single BLA neurons does not necessarily mean that coding for valence or behavior is non-specific. Ultimately what matters is whether downstream structures can decode the information present in BLA spike trains, not whether it is simple for neuroscientists to do so. Since multiple neurons participate in driving downstream targets, it does not matter whether individual neurons are mixed. Instead, the crucial factor is whether the identity of a stimulus, context, or behavior activates distinct ensembles. Since individual neurons, through the elaborate information processing capabilities of their dendrites, can act as complex feature detectors, they could be particularly sensitive to specific patterns of afferent population activity (Mel, 1992; Poirazi et al., 2003). Recent advances in our understanding of how populations of BLA neurons represent emotional stimuli and behaviors underscored the importance of population-level representations. In a task that requires rats to obtain water rewards and avoid shocks, very few neurons responded similarly to stimuli and behaviors of the same valence (Kyriazi et al., 2018). More often,

neurons fired in relation to a heterogeneous mixture of stimuli and behaviors, irrespective of valence. For instance, a neuron could respond to the cues signaling impending rewards and footshocks, along with behaviors of opposing valence like freezing and reward anticipation. By itself, such a neuron would provide ambiguous emotional information to downstream targets such as the CeA, striatum, or nucleus accumbens (NAc). But, so long as each neuron encodes a different complement of stimuli and behaviors, a downstream region receiving inputs from many such neurons could be driven with specificity for valence or behavior. By switching the unit of representation from single neurons to populations, with the important factor being that downstream neurons can discriminate one ensemble from another, the similarity between population responses to different task events becomes highly relevant (Figure 1A). To get an intuition for this, imagine an active ensemble as represented by a population vector (a list of all neurons with a 1 signifying active and a 0 inactive), and each vector is associated with an event. For each vector, we can measure how well it matches particular patterns of ensemble activity and then map the strengths of these matches in a lower-dimensional space. In this space, two ensembles can be related to each other in three different ways (Figure 1B). Overlapping ensembles occur if the same cells tend to respond similarly to two different events. Independent or orthogonal ensembles arise when each neuron’s allocation to one ensemble does not affect its recruitment by a different ensemble. In that case, any overlap will be at chance level. Last, ensembles can be opposing when belonging to one ensemble lowers the chance that a neuron will be recruited by the other. The job of a downstream neuron that must discriminate between two conditions is to find a surface in this space that separates the points corresponding to the two types of events (Figure 1C). The ability for a downstream region to perform this task is enhanced by reducing the variability in the population response across repeated occurrences of the same event, and by minimizing the overlap in the population response between different types of events. Emerging from this is the logic that if two different events recruit similar neuronal ensembles, then they are more likely to drive similar downstream targets. Conversely, if two different types of events drive distinct ensembles, they will engage dissimilar downstream targets. Several studies have measured these relationships by calculating the similarity between BLA ensembles recruited by distinct events (Figure 1D). With respect to innate anxiety, during exploration of an OF or EPM, approximately 30% of BLA neurons €ndemann et al., 2019). showed selective firing for location (Gru The ensemble that responded to corner entry in the OF also responded to entering the open and non-preferred arm of the EPM, while those that responded upon approaching the middle of the OF also responded during entry into the closed arm of the EPM. These populations were largely opposing to one another, and the authors interpreted them as representing exploratory and defensive states, respectively. Presentation of a neutral sound activated an ensemble of neurons orthogonal to either of the two previous populations. Neutral cue driven ensembles were also independent of those activated by either shocks or water

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Figure 1. Population Coding Provides a Comprehensive View of Neuronal Representations (A) The same population code can be seen from several vantage points. Active neurons are colored, while inactive ones are gray. The Ensemble view is the specific population of neurons that are activated by an event. A Population Vector view lists the activities of all sampled neurons in a particular region, irrespective of whether they are activated or not. This can be thought of as a point in a space with a dimensionality equal to the number of sampled neurons. Such spaces are difficult to visualize, so investigators often use dimensionality reduction techniques (e.g., principal component analysis) to project the high-dimensional Population Vector view into a Dimensional Reduction view. (B) Different task events often activate different ensembles, and their relationships can be evident from each vantage point. However, the Dimensional Reduction view (bottom) is often most helpful for visualizing their relationships. (C) Two different types of events (type 1 and type 2) can activate overlapping ensembles, but downstream neurons are still capable of distinguishing between the events so long as the overlap is not complete. (D) The relationships between multiple types of events can be visualized using a correlation matrix, by taking the mean response for each event type and measuring its correlation with the mean of another event type. Such a plot will summarize the relationships between event-evoked ensembles and sometimes reveal how events are ‘‘categorized’’ within a region.

rewards (Grewe et al., 2017). These overt reinforcers of opposing valence activated non-overlapping ensembles (Figure 2A; Zhang and Li, 2018), while those of the same valence overlapped (Grewe et al., 2017). Emotional Learning Shifts the Population Code for Cues toward that of Reinforcers The independence between cue and reinforcer populations is reduced with conditioning. When a previously neutral cue be-

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comes predictive of reinforcement, the ensemble representing that cue begins to overlap with that encoding the reinforcer (Figure 2B). In mice trained to associate a tone CS with a shock US, the CS coding ensemble became more similar to the US ensemble (Grewe et al., 2017). This effect was driven mainly by changes in the ensemble coding for the CS, with individual neurons either increasing or decreasing their CS responses, while the US ensemble remained relatively stable. Similar realignments of cue ensembles to their respective reinforcers occurred

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Figure 2. Multiple Population Codes Coexist within the BLA All arrows indicate the location of the schematized relationship between ensembles in the Dimensional Reduction view. (A) Presently, valence is the most studied code in the BLA. Populations encoding negatively or positively valenced events occupy different parts of space. Events of the same valence can be broken out in the same space and will often occupy similar regions. (B) Learning can shift population codes, such as making an ensemble that encodes a cue more similar to the ensemble encoding the reinforcer. Further training, such as extinction, can further alter the code. (C) Besides valence, ensembles in the BLA simultaneously encode whether behaviors are active or passive and the animal’s movement speed. Since these occupy different locations in the population space, they can be independently decoded. (D) LA and BL appear to differ in how they encode events of the same valence. In LA, events of the same valence tend to activate similar ensembles, while this is less so in BL.

on a task using both appetitive and aversive reinforcers (Zhang and Li, 2018) and between stimuli signaling the approach of a potentially painful stimulus and the painful stimulus itself (Corder et al., 2019). Once these changes occur, they may preclude the BLA from returning to its pretraining state. Extinction of a CS-shock association did not cause the CS ensemble to return to its initial pretraining state but instead to move away from the US ensemble along a new direction (Grewe et al., 2017). This coincided with the appearance of new neurons coding for extinguished cues, which in turn project to regions implicated in the suppression of emotional behaviors, like IL (Senn et al., 2014). Of note, the relationships in coding between innate and acquired defensive behaviors were similar across BLA neurons projecting to the mPFC, hippocampus, and €ndemann et al., 2019). This similarity suggests that NAc (Gru many regions downstream of the BLA have access to the same set of representations. Thus, the function of an amygdala projection arises not only from the region it targets but also from how that region decodes the information it receives. The CS ensembles that develop as a result of FC are weakly related to those that code for innate exploratory and defensive €ndemann et al., 2019). However, neubehaviors in the OF (Gru

rons activated at the onset of CS-evoked freezing are more likely to overlap with the defensive ensemble, while those activated at freezing offset overlapped with the exploratory ensemble. These results highlight the convoluted relationship between ensembles coding for threatening cues and those representing defensive behaviors. Complicating this picture is the fact that defensive behaviors occur during cue presentation, making it unclear which is driving a neuron’s response. To disentangle this, Kyriazi et al. (2018) used a model fitting approach that decomposed the spiking of single neurons into separate components for stimuli and behaviors. They found that ensembles of principal BLA neurons responded similarly to stimuli and behaviors that shared the same valence, complementing what we already established, namely, that overlapping ensembles encode the stimulus and reinforcer (Grewe et al., € ndemann et al., 2019). 2017; Zhang and Li, 2018; but see Gru Moreover, neurons responded similarly to both active (avoidance) and passive (freezing) defensive behaviors (Figure 2C). These results are inconsistent with the notion that passive defensive behaviors are largely driven by CeA-projecting neurons (Jimenez and Maren, 2009), while active behaviors are mediated by NAc projecting neurons (Ramirez et al., 2015).

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Review A solution to this problem is offered by the fact that BLA contains a population code not only for aversive behaviors but also an orthogonal one for active versus passive behaviors (Kyriazi et al., 2018). Downstream neurons that receive information about both codes might be able to tune their synaptic weights to decode the appropriate behavior. For instance, a neuron could be sensitive to the presence of the negative valence ensemble and the active behavior ensemble, which in combination would allow it to decode that active avoidance should be produced. Indeed, while CeA has traditionally been associated with freezing, it also contains neurons that are sensitive to escape behavior (Fadok et al., 2017). Alternatively, it could be that projection-specific subpopulations of BLA neurons only need to show a tendency to respond to appropriate behaviors (Beyeler et al., 2016, 2018), with this slight bias being sufficient to stimulate projection specific populations that drive particular behaviors. However, to establish the specificity of a particular projection in mediating a behavior, one must also consider whether the downstream structure tends to elicit the behavior irrespective of the source of afferent drive. For instance, glutamatergic inputs from BLA to NAc promote appetitive behaviors while those arising from the mPFC do not (Stuber et al., 2011). Besides a segregation between coding for different defensive behaviors, it is also important to understand the relationship between negative and positive valence. Several studies have found that BLA ensembles encoding negative valence tend to oppose or are orthogonal to those coding for positive valence (Figure 2A; € ndemann et al., 2019; Kyriazi et al., 2018; Corder et al., 2019; Gru Zhang and Li, 2018). However, this tendency is not uniform across the BLA (Figure 2D). LA neurons tend to respond similarly to CSs, reinforcers, and behaviors of the same valence (Grewe et al., 2017; Kyriazi et al., 2018), while the ensembles in the BL €ndemann et al., 2019; Kyriazi et al., are less likely to do so (Gru 2018). These dissimilarities could arise from differences in the regions that project to LA and BL, with LA receiving mainly sensory information from the thalamus, neocortex, and PRH, while more refined sensory or mnemonic information is conveyed to BL via secondary sensory areas, entorhinal cortex, and hippocampus (McDonald, 1998). In addition, intra-amygdaloid projections €nen form a feedforward circuit, with LA projecting to BL (Pitka et al., 1997). These factors may promote the refinement and separation of population codes along the dorsoventral axis of the BLA. Finally, these codes may also be expanded to cover a wider variety of species-specific needs. For instance, primates maintain extensive social networks, with individuals valued according to their relative standing in a dominance hierarchy. Unit recordings in monkeys have revealed that the same population of BLA neurons that encode the value of cues predicting water reward also represent the social rank of other monkeys in the same colony (Munuera et al., 2018). Complexity in the Temporal Domain Not only are the neuronal ensembles activated important, but the timing of their activation can also be crucial. Many optogenetic investigations use 20-Hz stimulation because it favorably activates channelrhodopsin while minimizing desensitization (Jackman et al., 2014). However, several studies have found that vary-

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ing the frequency of stimulation can result in different behavioral effects. Using post-training pharmacological manipulations, it was shown that the BLA facilitates the consolidation of aversive emotional experiences (McGaugh, 2004). With the advent of optogenetics came the possibility to examine the contributions of specific cell types or projections to memory consolidation. It was initially shown that 40-Hz stimulation of principal BLA neurons enhanced the consolidation of contextual fear, while 20 Hz did not (Huff et al., 2013). Arguing against the possibility that merely increasing the amount of stimulation drove such an effect, 80-Hz activation of BLA projections to the ventral hippocampus (vHip) did not boost consolidation of contextual fear, while 40 Hz did (Huff et al., 2016). A dependence on stimulation frequency was also seen in the Barnes maze, wherein 8 Hz but neither 4- nor 40-Hz stimulation of BLA axons in the medial entorhinal cortex improved consolidation (Wahlstrom et al., 2018). Frequency specificity was also found for the control of freezing behavior by the mPFC. Rhythmic inhibition of the mPFC at 4 Hz, but not at adjacent frequencies or randomly, promoted freezing (Karalis et al., 2016), while excitation at frequencies above 5 Hz in IL suppressed freezing (Do-Monte et al., 2015a). These behaviorally efficacious frequencies likely reflect normally occurring rhythms, and in particular suggest the importance of oscillations at 40 (gamma), 8 (theta), and 4 Hz. These oscillations are discussed in turn below. Oscillations in the 40- to 100-Hz range, known as gamma, are ubiquitous in the brain. In cortex, gamma oscillations emerge from reciprocal interactions between PNs and fast-spiking interneurons (FSIs). Specifically, the firing of PNs excites FSIs, which in turn deliver feedback inhibition. When this inhibition wanes, the next gamma cycle begins (Sohal et al., 2009; Buzsa´ki and Wang, 2012). The BLA also contains reciprocally connected PNs and FSIs, in similar proportions as in cortex (McDonald and Mascagni, 2001; Woodruff and Sah, 2007), and it too exhibits gamma (Bauer et al., 2007; Amir et al., 2018; Stujenske et al., 2014). Indeed, PNs and FSIs of the BLA preferentially spike during gamma oscillations in a manner similar to their cortical counterparts (Amir et al., 2018), and a detailed biophysical model of the BL nucleus generated gamma autonomously, reproducing the properties of gamma recorded in vivo (Feng et al., 2019). While gamma oscillations barely alter the firing rate of BLA cells, they strongly synchronize their activity (Amir et al., 2018), amplifying the influence of BLA neurons on their targets. Since gamma power increases in the BLA during threatening stimuli, this rhythm is likely involved in the genesis of defensive behaviors. Indeed, gamma power increases in the human amygdala during the presentation of aversive stimuli (Oya et al., 2002) and, in rodents, during anxiogenic situations (Amir et al., 2018). For instance, when rats forage for food under the threat of predation, they occasionally pause and engage in a behavior consistent with vigilance, during which gamma oscillations in BL are enhanced (Amir et al., 2018). In cortex, gamma oscillations are thought to facilitate information transfer between connected regions (Fries, 2015). Much evidence indicates that the same principle applies to interactions between the BLA and its numerous targets (McDonald, 1998). For instance, the BLA and rhinal cortices exhibit correlated

Neuron

Review variations in gamma power and BLA gamma entrains unit activity in the rhinal cortices (Bauer et al., 2007). Moreover, during learning, the coordination of BLA gamma with the rhinal cortices (Bauer et al., 2007) and striatum (Popescu et al., 2009) is enhanced. Similarly, during the presentation of stimuli signaling safety, gamma oscillations are coordinated between the BLA and mPFC (Stujenske et al., 2014). Another shared feature of gamma in cortical and amygdala networks is their modulation by the phase of theta oscillations. Stimuli that predict aversive events elicit robust theta in the BLA (Seidenbecher et al., 2003), and the entrainment of BLA gamma by theta is increased during such stimuli (Stujenske et al., 2014). BLA theta is also enhanced during the innate anxiety evoked by exploring an OF and it abates with familiarity (Jacinto et al., 2013). Theta oscillations also coordinate interactions between the BLA and other regions involved in defensive behavior. During REM sleep, which is associated with strong theta oscillations in limbic structures (Bland, 1986), both PNs and FSIs in BL entrain to theta oscillations in the entorhinal cortex (Pare´ and Gaudreau, 1996). REM sleep has also been associated with the consolidation emotional memories (reviewed in Headley and Pare´, 2017), and theta frequency stimulation of amygdala axons in the entorhinal cortex boosts memory consolidation (Wahlstrom et al., 2018). In addition, theta-band interactions between the BLA and vHip during post-training REM sleep support the consolidation of cued FC (Popa et al., 2010). Much evidence indicates that theta orchestrates interactions between the BLA and associated cortical regions in a behaviorally relevant manner. For instance, presentation of fearful faces in humans enhances the modulation of hippocampal gamma by theta in the amygdala (Zheng et al., 2017). Moreover, during the expression of cued and contextual fear, hippocampal-BLA theta coherence is increased in rodents (Seidenbecher et al., 2003; Narayanan et al., 2007). Similar results were obtained for the interactions between the BLA and other high-order cortical regions including the anterior cingulate cortex (Taub et al., 2018), secondary AC (Cambiaghi et al., 2016), and mPFC (Likhtik et al., 2014). Moving to lower frequencies, a 4-Hz respiration-related rhythm (Karalis and Sirota, 2018), coherently expressed in the BLA and mPFC, was recently found to be crucial for the production of conditioned freezing (Dejean et al., 2016). An ensemble of mPFC neurons is synchronously driven during the ascending phase of this oscillation, and their activation is correlated with conditioned freezing (Dejean et al., 2016). Crucially, artificial evocation of this rhythm in the mPFC with optogenetic stimulation elicits freezing (Karalis et al., 2016). Returning to the point of this section, that the timing of endogenous activities in the circuits underlying defensive behavior is relevant to their function, optogenetic disruption of this mPFC assembly disrupted freezing only when delivered during the ascending phase of the 4-Hz oscillation. Thus, the importance of timing extends to the moment-tomoment fluctuations in the state of the brain itself. Conclusions: Where to from Here? When an environmental stimulus elicits a defensive behavior, there must be a chain of causality extending from sensory recep-

tors to motor effectors. Often this has been conceived as an arc through the CNS that allows sensory signals to drive motor actions. This motivated the search for brain regions that respond to aversive cues and whose stimulation or suppression controls the defensive behavior. Upon identifying such a site, it is tempting to claim that it is essential for the behavior. But what is the value of such a claim when numerous sites in the brain meet these criteria for the same behavior? Moreover, a region could participate in the production of a behavior but in a redundant manner or could be essential but only because it indirectly supports the behavior. Fixating on circuits that are necessary and sufficient fosters an atomistic understanding of the brain and constrains us to a rigid and incomplete picture of how neuronal networks normally produce defensive behaviors. Such restricted circuits are of limited value in a world where a variety of stimuli must drive the same defensive behavior, and where different behaviors can be evoked by the same stimulus depending on the context or individual’s state. This flexibility is incompatible with the notion of rigid, dedicated circuits. Instead, we should focus on understanding how brain regions implicated in defensive behaviors interact under a variety of circumstances. To this end, we suggest that the new research directions reviewed above be extended and integrated together. First, we must use behavioral tasks that probe the nervous system along multiple functional axes, allowing us to distinguish neuronal activity driven by stimuli versus generating behaviors, or related to affective valence versus arousal. Second, we have to ask how populations, not single neurons, encode these dimensions, since it is the aggregate activity of many neurons that drives downstream regions and contributes to behavior. Third, we must understand the temporal dynamics of these responses and how they coordinate between brain regions. Finally, our manipulations must be as close as possible to the spatiotemporal properties of the interactions under investigation. With respect to the first point, the interpretation of particular neural processes is limited by the circumstances under which they are observed. If only aversive stimuli are presented, then it will be unclear whether a neural event is related to their aversiveness, the sensory aspects of the stimuli, or arousal. Tasks that feature both appetitive and aversive elements help mitigate this problem, such as naturalistic foraging tasks (Choi and Kim, 2010), discriminative Pavlovian paradigms (Beyeler et al., 2016), or ones that fuse elements from both (Kyriazi et al., 2018). With respect to the second point, the activity of a population of neurons will be more relevant to its effect on downstream targets than the activity of any one cell. This will depend on the subset of neurons that are, and are not, activated. Understanding how these populations map onto environmental stimuli and behaviors tells us what events they treat as similar, and this representational space provides fundamental insights into the computations a region carries out (Kriegeskorte and Kievit, 2013). As to the third point, the codes identified in one region are only relevant insofar as they affect downstream regions. Often, only a narrow slice of these activities is impactful (Semedo et al., 2019; Zandvakili and Kohn, 2015). Since behaviors emerge when brain regions act in concert, not in isolation, understanding these interactions will be crucial to uncovering how defensive behaviors are

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Review produced. Besides the association between spiking activities in interconnected regions (e.g., Kim et al., 2018), we must also consider that these interactions often occur on specific timescales or frequency bands evident in the local field potential. Finally, given the spatiotemporal specificity of neuronal activities during defensive behavior, it is crucial that our manipulations adhere to those constraints if we want to drive, or not drive, downstream regions in a physiologically realistic way (Jazayeri and Afraz, 2017). This is essential since our goal is to understand how neural circuits normally produce defensive behaviors. Thus, we should strive for our stimulation to be aligned with normally occurring activities (e.g., Diehl et al., 2018). This can entail targeting optogenetic stimulation to ensembles defined by their responsiveness to behaviors and cues—not necessarily their projection targets—or manipulating the activities of regions in a similar manner to their endogenous rhythms (e.g., Dejean et al., 2016). New, emerging methods for optogenetically manipulating the activity of particular subsets of neurons (Packer et al., 2015; Carrillo-Reid et al., 2017) in a close-loop configuration (Zhang et al., 2018b) suggest that tools to perform such experiments will soon become widely available. ACKNOWLEDGMENTS This work was supported by R01 grants MH098738, MH107239, and MH112505 from NIMH (to D.P.) and funding from the Behavioral and Neural Sciences Graduate Program of Rutgers University – Newark to V.K. and P.K. REFERENCES Adhikari, A., Lerner, T.N., Finkelstein, J., Pak, S., Jennings, J.H., Davidson, T.J., Ferenczi, E., Gunaydin, L.A., Mirzabekov, J.J., Ye, L., et al. (2015). Basomedial amygdala mediates top-down control of anxiety and fear. Nature 527, 179–185. Amir, A., Lee, S.C., Headley, D.B., Herzallah, M.M., and Pare´, D. (2015). Amygdala signaling during foraging in a hazardous environment. J. Neurosci. 35, 12994–13005. Amir, A., Headley, D.B., Lee, S.C., Haufler, D., and Pare´, D. (2018). Vigilanceassociated gamma oscillations coordinate the ensemble activity of basolateral amygdala neurons. Neuron 97, 656–669.e7. Anglada-Figueroa, D., and Quirk, G.J. (2005). Lesions of the basal amygdala block expression of conditioned fear but not extinction. J. Neurosci. 25, 9680–9685.

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