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New Directions in Psychology
Anxiety disorders are the most common cause of mental ill health in the developed world, but our understanding of symptoms and treatments is not presently grounded in knowledge of the underlying neurobiological mechanisms. In this review, we discuss accumulating work that points to a role for prefrontal–subcortical brain circuitry in driving a core psychological symptom of anxiety disorders – negative affective bias. Specifically, we point to converging work across humans and animal models, suggesting a reciprocal relationship between dorsal and ventral prefrontal–amygdala circuits in promoting and inhibiting negative bias, respectively. We discuss how the developmental trajectory of these circuits may lead to the onset of anxiety during adolescence and, moreover, how effective pharmacological and psychological treatments may serve to shift the balance of activity within this circuitry to ameliorate negative bias symptoms. Together, these findings may bring us closer to a mechanistic, neurobiological understanding of anxiety disorders and their treatment. Keywords Anxiety, circuit, negative bias, prefrontal cortex Received: 19 December 2017; accepted: 9 April 2018 Introduction Anxiety disorders are the most common cause of mental illness in consist of shared symptoms which are similar manifestations of the developed world, with large social, economic and psychologi- relatively few underlying dimensions (Caspi et al., 2014; Clark cal impacts (DiLuca and Olesen, 2014; Shin and Liberzon, 2009; et al., 2017; Kaczkurkin et al., 2017; Kotov et al., 2017; Krueger Vos et al., 2016). A propensity towards the development of anxi- and Eaton, 2015; Lahey et al., 2012, 2017). ety disorders is heritable (Hettema et al., 2005), often begins in Recent efforts such as the Research Domain Criteria (RDoC; childhood or adolescence (Beesdo et al., 2009; Pine, 2007) and Insel et al., 2010), therefore, attempt to re-frame the investigation persists into adulthood (Copeland et al., 2014; Craske et al., of psychiatric disorders by advocating a trans-diagnostic approach 2017). It is estimated that close to one in four people will suffer focusing on the neurobiological mechanisms underpinning symp- from an anxiety disorder – including generalised anxiety (GAD), toms that cut across traditional categorical diagnoses. In particu- post-traumatic stress disorder (PTSD), social anxiety or phobias lar, one domain within the RDoC, Negative Valence Systems, – in their lifetime (Kessler et al., 2005, 2012), but currently avail- includes responses to aversive situations such as fear, anxiety, able psychological and pharmacological treatments are effective sustained threat and loss (/reward omission) that overlap with a for less than half of these individuals (Roy-Byrne, 2015; key concept from the clinical psychology literature – negative Community and Mental Health team, 2014) and progress in the affective bias. Negative biases in cognition are thought to promote discovery of anxiolytic drugs has been slow (Griebel and Holmes, and uphold key symptoms of many psychiatric conditions but are 2013). One reason for this treatment gap is that we have a limited understanding of the biological mechanisms by which anxiety Division of Psychology and Language Sciences, University College symptoms emerge or how these mechanisms are modulated by London, London, UK our current interventions. As such, we struggle to develop new Institute of Cognitive Neuroscience, University College London, treatments that can modulate known biological targets. Moreover, London, UK it is increasingly clear that our current diagnoses, based largely on self-reported symptoms, do not map clearly onto underlying biol- Corresponding author: ogy or indeed onto the latent structure of the self-reported symp- Oliver J. Robinson, Institute of Cognitive Neuroscience, University College toms themselves (Cuthbert and Insel, 2013; Kotov et al., 2017). London, Alexandra House, 17-19 Queen Square, London WC1N 3AZ, UK. Indeed, factor analyses suggest that many categorical disorders Email: o.robinson@ucl.ac.uk Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 Brain and Neuroscience Advances especially prominent in anxiety disorders, perpetuated by antici- development and treatment (Aue and Okon-Singer, 2015; Cisler pation of – and uncertainty about – future events (Grupe and and Koster, 2010; Dodd et al., 2017). Nitschke, 2011, 2013). Although none of our current treatments or diagnoses are based on a mechanistic neurobiological understand- Animal models of negative bias ing of negative bias, recent work has begun to delineate the role that interactions between the prefrontal cortex (PFC) and subcor- Research falling within the Negative Valence Systems domain of tical regions such as the amygdala play in the manifestation of RDoC in animal models often makes a distinction between fear negative bias in anxiety. It is this circuitry that is the focus of the and anxiety. In the psychological literature, anxiety is defined as present review. a prolonged state of heightened anticipatory arousal, often prompted by distal or unpredictable threats (Davis et al., 2010). Fear, on the contrary, is conceptualised as a ‘fight or flight’ reac- Negative bias in anxiety tion and typically involves active defence against immediate Anxiety disorders are characterised by a general ‘negative bias’ threat, usually dissipating upon removal of the threat. In ‘real- in both attention and memory towards affectively negative world’ terms, a person’s reaction to a spider on the table in front (/threatening/aversive) information that promotes and upholds of them might elicit a fear response (which may be exacerbated the anxious state while having knock-on effects in a wide range in cases of phobia), whereas the knowledge that a spider might be of other cognitive functions (e.g. learning, inhibitory control; in the room but uncertainty of its location might elicit anxiety. Craske et al., 2017; Robinson et al., 2013a). It has been widely Elevations in both responses could of course play a key role in shown, for instance, that people with anxiety tend to interpret driving negative affective bias (although see Fox and Shackman, neutral information in a more negative light, have maladaptive 2017; Shackman and Fox, 2016, for suggestions of why this attention biases towards threat even when threats are not imme- explicit distinction between fear and anxiety might be problem- diately present or relevant (this may be particularly prominent in atic with regard to the underlying neurobiology). some subtypes of anxiety such as social anxiety disorder; Abend Animal models are particularly useful in the investigation of et al., 2017) and have a bias towards learning about negative the neural basis of anxiety and fear because of their cross-spe- information (Abend et al., 2017; Hakamata et al., 2010; Monk cies overlap in the neural circuitry underlying these processes et al., 2006; Okon-Singer and Aue, 2017; Roy et al., 2008). It is (Davis, 2000; LeDoux, 2000); cross-species functional homo- of course possible to break negative biases into specific subtypes logues of brain circuitry can inform translational research across of bias (for a comprehensive attempt to do this, see Grupe and humans and rodents and can provide testable models, even if the Nitschke, 2013), but here we aim to build a broad preliminary circuits themselves are not directly conserved across species. model across disparate animal and human experimental data, For instance, startle response, in which whole-body jump is typi- along with clinical data, with the goal of drawing holistic con- cally used as an index in rodents, is paralleled by an eye-blink clusions. Similarly, the term negative bias of course encom- response in humans (Davis, 2001) and is a reliable experimental passes myriad constructs, including a distinction made by many measure of aversive responding on some cognitive tasks researchers between fear and anxiety (Davis et al., 2010), as well (Aylward and Robinson, 2017) but not necessarily on others as subcategories of learned versus prepotent fears (LeDoux, (Bradford et al., 2015). This startle reflex is heightened by both 2000; Phelps et al., 2004). However, in this review, we broadly fear and anxiety states across rats and humans (Grillon and focus across the Negative Valence Systems domain of the RDoC Davis, 2007; Grillon et al., 1991), but the subcortical circuitry (Insel et al., 2010) in an attempt to identify common patterns and responsible may differ (Robinson et al., 2012a). Specifically, build a simple model of how negative bias is generated at the fear responses are associated with the central amygdala, anxiety neurobiological level. If we were to be too fine-grained in our responses are associated with the bed nucleus of the stria termi- definitions of negative bias, we will find ourselves with multiple nalis, and both fear and anxiety responses are associated with the non-overlapping studies, which would limit interpretation. basolateral amygdala (BLA; Davis et al., 2010; Sengupta et al., To demonstrate our aim of bridging disparate experimental 2017; Tovote et al., 2015), although this distinction has been literature and clinical utility, consider Beck’s (1967) early challenged and warrants further investigation (Gungor and Paré, theory of elevated negative bias (or ‘negative schemata’). In 2016; Shackman and Fox, 2016). Nevertheless, taken together, this framework, Beck proposed a cognitive triad illustrating a rodent and human work points to the clear role of subcortical cycle among a negative view of the world, the self and the regions, and the extended amygdala in particular (along with its future. This model encompasses wide-ranging cognitive func- inter-connections and external projections), in driving aversive/ tions from memory to attention but nonetheless forms the fear responding and hence negative bias (Boeke et al., 2017; basis of successful psychological treatments such as cogni- Campese et al., 2015, 2017; Sengupta et al., 2017; Terburg et al., tive-behavioural therapy (CBT), highlighting potential advan- 2012; Tovote et al., 2015). tages in taking a broad approach to linking cognitive research However, regions of the brain rarely, if ever, work in isolation. with clinical practice. Within the hierarchy of neural processing, these subcortical We therefore review converging evidence across humans and regions also interact with ‘higher’ cortical areas. This is perhaps animal models suggesting that negative bias may arise, at least in best illustrated by work exploring the weakening of learned aver- part, from activity within prefrontal regions and their interactions sive responses during extinction. During fear extinction, a cue with subcortical regions (Etkin and Wager, 2007; Shin and which previously indicated the onset of an aversive event no Liberzon, 2009). These circuits may underpin the ability to longer predicts a negative outcome, so the individual must ‘extin- engage or disengage attention from threats and may be critical to guish’ their original aversive response. Animal models of condi- understanding the mechanistic basis of negative bias as well as its tioned fear extinction indeed implicate subcortical regions such Carlisi and Robinson 3 as the BLA, but they extend the circuitry to encompass medial information flow between these regions in a circuit that drives the prefrontal cortical regions as well. In particular, within the rodent overall output. PFC, subdivisions of the infralimbic (IL) and the prelimbic (PL) Collectively, animal work therefore suggests a putative neural have been posited to play distinct roles in the expression and mechanism of negative bias; one (bi-directional PL–amygdala) extinction of conditioned fear, with the IL supporting fear extinc- circuit may serve to facilitate negative bias, while another (bi- tion as expressed by the amygdala and the PL conversely promot- directional IL–amygdala) circuit may serve to suppress negative ing fear expression as expressed by the amygdala (Klavir et al., bias (Calhoon and Tye, 2015). This simplified heuristic provides 2017; Morgan and LeDoux, 1995; Sierra-Mercado et al., 2011; a framework with which to consider neurobiological research in Vidal-Gonzalez et al., 2006). In monkeys, activity in the dorsal anxious humans. anterior cingulate (dACC) is correlated with the BLA during fear learning and memory acquisition (Klavir et al., 2013; Livneh and Paz, 2012), and in rodents, PL response to cues predicting an Neurobiological basis of negative bias aversive event increases post-fear learning (Burgos-Robles et al., in humans 2009). Using pharmacological or electrical stimulation and optogenetic approaches, it has been shown that, on the other Perhaps unusually for a symptom related to psychiatric disorder, hand, increased activity in the rodent IL predicts fear extinction negative bias in anxiety can be adaptive. For example, when one in the amygdala (Do-Monte et al., 2015; Klavir et al., 2017; is walking home late at night and hears an unexpected noise Milad and Quirk, 2002, 2012). Having said that, recent rodent down a dark alley, an appraisal of this situation as potentially work has suggested that this dissociation may not be as clear-cut threatening raises awareness, preparing the body’s fight or flight as previously thought. For example, the role of the IL in fear response in the event of immediate danger. In other words, nega- extinction has been challenged in optogenetics work showing tive bias in anxiety can promote harm avoidance. However, if this that extinction recall was intact after the silencing of IL neurons heightened anxiety and negative bias does not subside when one and that stimulation of ventromedial prefrontal cortex (vmPFC) is subsequently safe at home, this response becomes maladaptive inputs to the amygdala facilitated extinction memory formation and can impair daily functioning (i.e. it transitions into a patho- but not retrieval (Bukalo et al., 2015; Do-Monte et al., 2015). logical state). Thus, it was suggested by the pioneers of CBT that Moreover, the distinction between PL/dorsal and IL/ventral pre- a biased appraisal of threat (i.e. negative bias) leading to catastro- frontal regions being responsible for fear expression and suppres- phising or excessive worry is a central characteristic of anxiety sion, respectively, has been challenged (Giustino and Maren, disorders (Beck and Clark, 1997). Dorsal regions of the PFC 2015). For instance, rodent work has shown that these regions (dorsomedial PFC (dmPFC) and dACC) seem to be associated have structurally similar projections to the amygdala (Cho et al., with this behavioural response at the neural level; these regions 2013; Gutman et al., 2012; Hübner et al., 2014; Pinard et al., are activated during conscious threat appraisal in healthy indi- 2012), and functionally dichotomous distinctions between these viduals and have been shown to be overactive during threat regions have been shown in the opposite direction than was ini- appraisal in pathological anxiety (for review, see Kalisch and tially postulated (Chang et al., 2010). Gerlicher, 2014). This rodent research nevertheless highlights a key potential Consistent with the animal work highlighted above, patho- mechanism of negative bias; namely that the overall expression logical negative bias in humans may in fact result in part from an of aversive responding may be held in the balance of opposing inability to extinguish conditioned fear responses driven in turn circuitry. Thus, whether negative bias is expressed or dampened by this altered, worry-related PFC responding (Milad et al., 2007; may depend on whether one of these circuits is able to override Rothbaum and Davis, 2003). Healthy subjects show increased the other, with sub-regions of the medial prefrontal cortex activation in the vmPFC during acquisition and retrieval of (mPFC) playing a key role in arbitrating this response. Indeed, extinction (Kalisch et al., 2006; Milad and Rauch, 2007; Phelps across a range of paradigms in rodents, the PFC has been shown et al., 2004), which has led to the suggestion that this region in to play a regulatory role over BLA activation during fear expres- the human brain might be functionally (albeit perhaps not struc- sion, social interaction and anxiety-related behaviours (Bickart turally) homologous to the rodent IL. On the other hand, patients et al., 2014; Bremner, 2004; Davis, 1998; Felix-Ortiz et al., 2013; with anxiety disorders have shown reduced activation in the Janak and Tye, 2015; Kling and Steklis, 1976). vmPFC along with increased activation in the dACC, leading to That said, the implicit implication that the PFC is a ‘top- the suggestion that the dACC might be functionally homologous down’ regulator of the amygdala during fear extinction should be to the rodent PL (Milad et al., 2009). challenged. Optogenetics research in rodents has shown bi-direc- Consistent with this proposition, at the neural level, the dACC tional effects of modulating BLA projections to the IL and PL and adjacent dmPFC have been implicated in the appraisal and during a number of behavioural assays assessing anxiety-like expression of fear (Etkin et al., 2011; Vogt, 2005), as well as the behaviour (Courtin et al., 2014; Felix-Ortiz et al., 2016; Herry anticipation of emotional stimuli (Erk et al., 2006). Moreover, et al., 2008; Laviolette et al., 2005). Specifically, BLA activity Kalisch and Gherlicher (2014) argue that the dACC/dmPFC can projecting up to PL regions is increased during fear conditioning be further subdivided into an anterior part, the rostral dACC/ (Senn et al., 2014), while fear extinction also enhances activity in dmPFC and a posterior part, with the rostral but not posterior part BLA projections up to the IL (Milad and Quirk, 2002; Senn et al., implicated in conscious threat appraisal and worry (Kalisch and 2014). Moreover, recordings from non-human primates further Gerlicher, 2014; Mechias et al., 2010). Thus, activity in dorsal support this bi-directional effect during fear learning (Klavir PFC regions is broadly associated with increased negative bias. et al., 2013). In other words, it is not so much that the PFC ‘regu- The dACC has also been linked to the adaptive control of behav- lates’ the amygdala but rather the reciprocal relationship of iour as well as the risk of development of anxiety disorders 4 Brain and Neuroscience Advances (Cavanagh et al., 2017; Goodkind et al., 2015; McTeague et al., responding (as indexed by a threat-by-valence interaction in reac- 2017; Meyer, 2017). tion times, driven by a valence-specific reduced reaction time to Regarding ventral regions, studies in healthy adults (Bishop, fearful faces under threat vs safe conditions), suggesting a key 2007) as well as adults (Etkin and Wager, 2007; Milad et al., mediating role for dACC/dmPFC–amygdala circuitry in driving 2007; Price and Drevets, 2012) and children and adolescents negative bias. Critically, coupling within this same circuitry was (Guyer et al., 2008; Monk et al., 2006; Strawn et al., 2012) with shown to be elevated at baseline in individuals with an anxiety anxiety disorders have shown abnormal function in orbitofrontal disorder (Robinson et al., 2014; in the absence of induced anxi- cortex (OFC) and ventrolateral PFC (VLPFC). The cause and ety), suggesting that the same circuitry which can be selectively effect of such abnormalities have been studied in non-human pri- engaged and disengaged in healthy controls is more persistently mates through lesions to the anterior OFC and VLPFC (Agustín- engaged in patients with clinical anxiety, thereby providing a Pavón et al., 2012; Izquierdo and Murray, 2005; Kalin et al., route by which adaptive anxiety can transition into a maladaptive 2007; Machado and Bachevalier, 2008), with findings largely state. Across both studies, however, the correlation between the showing increased anxiety during fear conditioning paradigms amygdala and dorsal cortical regions was positive. In other words, when these regions are lesioned. Thus, broadly speaking, activity activity in the dorsal cortical regions increases as activity increases in ventral cortical regions is associated with reduced negative in the amygdala and vice versa. The role that this circuit seems to bias (although it should be noted that this association may not be play in threat responding therefore appears somewhat analogous as consistent as previously thought (cf. Shackman et al., 2011). to the role of the PL in rodents. Thus, a human functional homo- The hippocampus is another structure that has been hypothe- logue of the rodent PL–amygdala circuit may drive increased sised to play a critical role in the pathophysiology of anxiety. threat responding and negative affective biases in anxiety Specifically, this region is a key mediator of the acquisition and disorders. expression of learned fear, as demonstrated by a number of early However, rodent work has also highlighted the contrasting lesion studies showing that hippocampal lesions dampened fear role of the inhibitory IL circuit (Kim et al., 2011a). To this end, response to previous learned associations (Kim and Fanselow, another study in humans (Vytal et al., 2014) expanded this puta- 1992; Phillips and LeDoux, 1992; Selden et al., 1991). Studies in tive circuitry to encompass a reciprocal inhibitory circuit. both human and rodents suggest that this region integrates con- Specifically, inducing anxiety during an adapted resting-state textual information during fear conditioning and may regulate scan replicated positive dmPFC–amygdala coupling, but at the context-dependent recall after extinction (Giustino and Maren, same time enhanced negative coupling between a ventral medial 2015). The rodent PL and IL receive excitatory inputs from both prefrontal region and the amygdala. In other words, while the dorsal and ventral hippocampus (Little and Carter, 2013), and increased dorsal activation was associated with increased amyg- it has been suggested that, similar to the amygdala, these projec- dala activation, increased ventral activation was associated with tions may inhibit downstream mPFC outputs (Sotres-Bayon decreased amygdala activity (Vytal et al., 2014). Earlier positron et al., 2012). In humans, Linnman et al. (2011) demonstrated that emission tomography (PET) studies in humans have shown simi- fear (elicited by electric shock expectation) was associated with larly contrasting relationships between prefrontal and subcortical increased connectivity between the hippocampus and the vmPFC, regions (Linnman et al., 2012a, 2012b). For example, during and decreased connectivity between the hippocampus and the red extinction training, resting amygdala metabolism positively pre- nucleus midbrain region, suggesting that the hippocampus may dicted vmPFC activation and negatively predicted dACC activa- facilitate a switch between what they term a ‘fear’ network and a tion, but during extinction recall, these relationships were in the ‘resting’ network. opposite direction (Linnman et al., 2012a). In a rodent study However, as highlighted by the rodent literature above, nega- investigating the impact of early-life environmental stress, tive bias is not so much driven by regions acting in isolation. Johnson et al. (2018) showed that stress was related to increased Rather, it is the cortical–subcortical circuitry that is important for amygdala–PFC and amygdala–hippocampus coupling and that anxiety response. For example, Kalin et al. (2016) used a viral this connectivity was related to anxiety-like behaviours in a vector approach in primates to demonstrate a relationship between translational model of early-life stress. Thus, these studies dem- overexpression of corticotropin-releasing hormone (CRH) in the onstrate that the relationship between distinct mPFC regions may dorsal amygdala and increased defensive behaviour during expo- have opposing effects on aversive responding. Another study of sure to threat. Moreover, this link between metabolism and behav- resting-state functional connectivity in healthy humans showed iour has also been observed in rodents and was associated with that those individuals who reported high levels of anxiety were functional connectivity between the dorsal amygdala and OFC characterised by negatively correlated amygdala–vmPFC con- (Regev et al., 2012). To this end, connectivity between the dACC/ nectivity, while this connectivity was positively correlated in dmPFC and the amygdala has been implicated in the pathophysi- those reporting low levels of anxiety (Kim et al., 2011b). ology of anxiety in humans. Structurally, the integrity of white Moreover, amygdala–dmPFC connectivity was negatively cor- matter tracts between the amygdala and the PFC has been shown related only in those reporting low anxiety. More dorsal regions to predict individual differences in trait anxiety (Kim and Whalen, of the PFC (like the PL in rodents) may increase aversive 2009). Functionally, Robinson et al. (2012a) studied the role of responding, while more ventral regions (like the IL in rodents) these regions in negative bias during induced anxiety in healthy may reduce aversive responding. Moreover, the nature of these individuals. They found that connectivity increased during the functional imaging connectivity analyses means that they are processing of threatening stimuli (fearful faces) selectively in the non-directional. In other words, it is not possible to say whether context of induced anxiety. Moreover, the strength of this connec- one region is driving the other – it is simply a correlation. Given tivity was positively correlated with participants’ subjective rat- the bi-directional nature of the rodent work highlighted above, ings of anxiety, as well as the extent of negative bias in behavioural these circuits therefore should not be considered ‘top-down’ or Carlisi and Robinson 5 ‘bottom-up’; rather, the overall reciprocal cortical–subcortical One influential idea common to the human and rodent devel- interaction likely drives the ultimate behavioural expression. opmental literature is that learned fear associations (i.e. memo- Together, these findings highlight the value of translational ries) from early life are important contributing factors to the research. A model of cortical–subcortical interactions during subsequent development of anxiety disorders (Britton et al., negative bias inspired by rodent work provides a framework 2011; Glenn et al., 2012; Jacobs and Nadel, 1985). Cross-species within which to consider the role of neural circuitry in negative animal work (Harlow and Harlow, 1965; Hess et al., 1962) has bias in humans. shown that fear learning is characterised by approach behaviour (such as maternal attachment or odour approach) in infants, but is characterised by almost diametrically opposed avoidance behav- Development iour (such as maternal or odour avoidance or avoidance of ele- vated/open areas in typical rodent paradigms) in adults (for an The work reviewed above therefore suggests that medial prefron- extensive review, see Ganella and Kim, 2014). This also suggests tal–amygdala interactions may drive the negative bias symptoms that at some point during development, there is a change in the that are a core feature of anxiety disorders. However, how these underlying neurobiology promoting this behaviour. mechanisms develop and persist across the lifespan remains Integrating this within the circuitry framework of the pre- unclear. If we want to target these symptoms and intervene early, sent review, Chan et al. (2011) inactivated PL in juvenile, pre- it is important to determine when and how alterations to these adolescent and adult rats and found that PL inactivation circuits emerge. significantly reduced freezing behaviour, as would be pre- Within the general population, pathological anxiety com- dicted by the above reviewed evidence, but that it only did so monly emerges during childhood or adolescence and reflects a in adolescent and adult rats, suggesting that the role of differ- combination of genetic factors and early-life experiences (Pine, ent medial prefrontal regions in negative bias changes across 2007). ‘Anxious temperament’ is considered to be a stable trait development. In other words, differences in fear responding, across time, and those with extreme levels of such traits are at a mediated by amygdala–medial prefrontal pathways, may par- higher risk for developing clinical or pathological anxiety tially be a result of a more protracted course of development (Arnaudova et al., 2013; Jones, 2013; Nugent et al., 2011). and reorganisation in these cortical–subcortical pathways Similarly, the stable traits of ‘behavioural inhibition’, a tempera- (Arruda-Carvalho et al., 2017; Ganella and Kim, 2014; ment characterised by a tendency to withdraw from new situa- LeDoux, 2000; Pattwell et al., 2016). Similar developmental tions (Kagan et al., 1987; Svihra and Katzman, 2004) and, more changes in prefrontal–subcortical negative bias circuitry are broadly, ‘dispositional negativity’ (Shackman et al., 2016) are also seen in humans; in typically developing humans, mPFC– thought to be early phenotypes of anxiety disorders. There is evi- amygdala connections are immature during childhood and dence that anxiety-related amygdala abnormalities and affected strengthen to adult levels during adolescence (Gee et al., top-down prefrontal regulation originate early in development 2013a, 2013b), and structural changes in white matter have (Clauss and Blackford, 2012; Kalin, 2017). Moreover, it has been been shown to mediate amygdala function in adolescents estimated that 50% of children showing increased behavioural (Swartz et al., 2014). Moreover, early perturbations in medial inhibition in childhood will later develop stress-related psycho- prefrontal circuitry have been implicated in the development of pathology (Clauss and Blackford, 2012). This is paralleled by anxiety and depression. For example, a preliminary study in findings of reduced amygdala–dorsolateral prefrontal cortex adolescents with depression found that patients had decreased (dlPFC) coupling in preadolescent children diagnosed with an functional connectivity in a subgenual (ventral) anterior cingu- anxiety disorder as well as in young non-human primates with late (ACC)-based network compared to healthy adolescents elevated levels of traits related to anxious temperament (includ- (Cullen et al., 2009). Moreover, negative coupling within ing heightened behavioural inhibition; Birn et al., 2014). vmPFC–amygdala circuitry during fear extinction was recently Although more longitudinal studies are needed to confirm this, shown only in adults and not adolescents (Ganella et al., 2017). this evidence suggests that rapid changes in the mPFC and the These ventral regions may reflect overlapping human homo- later maturation of amygdala–cortical connections during adoles- logues of the rodent IL. Thus, a developmental delay in the cence, a period recently suggested to encompass 10–24 years of ability to engage the circuitry that can dampen negative bias age (Sawyer et al., 2018), may contribute to the emergence of might explain the emergence of anxiety disorders during ado- anxiety during a specific developmental window (Andersen, lescence. This work is in its infancy, but the concept of recipro- 2003; Casey et al., 2008). Indeed, prospective studies in humans cal cortical–subcortical circuits again provides a framework (Giedd et al., 1999; Jones et al., 2017; Kalin, 2017; Swartz and with which to consider the emergence of anxiety and negative Monk, 2013) as well as rodent studies (Arruda-Carvalho et al., bias during development. 2017; Cohen et al., 2013; Gee et al., 2013a; Pattwell et al., 2016) have shown that this period constitutes a window of heightened risk for the development of anxiety. However, vast structural Treatment brain changes have also been observed during childhood, sug- gesting that children are subject to a heightened vulnerability to If prefrontal–subcortical circuitry is critical in the development environmental impacts which may influence the development of and manifestation of negative bias, then modulation of this cir- anxiety even before the onset of adolescence. Indeed, behaviour- cuitry should serve to modify negative affective biases and hence ally inhibited temperament has been observed in young children treat symptoms. The first-line treatments for clinical anxiety are who later develop anxiety, with similar neural circuitry altera- serotonergic medication and psychological therapy. Emerging tions linking these phenotypes (Buzzell et al., 2017; Gold et al., evidence suggests that successful response to both types of treat- 2016; Sylvester et al., 2016). ment may also depend on this prefrontal–subcortical circuitry. 6 Brain and Neuroscience Advances assessed whether CBT combined with attention bias modifica- The role of serotonin in pharmacological tion therapy (ABMT) was more clinically effective than CBT treatment alone and whether this treatment response could be predicted Serotonin (5-hydroxytryptamine (5-HT)) has long been impli- through pre-treatment amygdala-based functional connectivity cated in the neuropsychopharmacology of anxiety (Dayan and (White et al., 2017). This study found that patients differed from Huys, 2009; Harmer et al., 2009, 2011), largely because selective controls in amygdala–insula connectivity on a threat attention serotonin reuptake inhibitors (SSRIs) are the most common and task. Moreover, while both CBT groups showed clinical improve- effective pharmacological treatment for anxiety disorders ment, the combined CBT + ABMT group showed the greatest (Harmer et al., 2009, 2011). It is thought that serotonin plays a reduction in symptoms and that baseline amygdala functional particular role in maintaining the balance between the processing connectivity differentially predicted the level of treatment of appetitive and aversive information (Cools et al., 2008; response in patients. Crockett et al., 2009; Robinson et al., 2012b) and more precisely However, whether these changes in cortical–subcortical cir- in the inhibition of PFC-linked neural circuitry important for cuits are driven by CBT, or whether they simply reflect reduced driving negative bias (Crockett et al., 2009; Robinson et al., overall anxiety and negative bias per se, is unclear. To this end, 2013b). basic research has attempted to determine causality. Specifically, The impact of serotonin in healthy humans can be studied by it has been shown that in healthy individuals, simple attentional acute tryptophan depletion – a dietary manipulation that tempo- instruction can alter the engagement of affective-bias-related rarily reduces serotonin levels (Crockett et al., 2012). Reduced dmPFC–amygdala circuitry (Robinson et al., 2016). When sub- serotonin has been shown to increase positive coupling within jects are instructed to pay attention to neutral aspects of com- the same circuit shown to be elevated by induced anxiety (Vytal pound cues (rather than the affectively salient components of the et al., 2014) and at baseline in individuals with an anxiety disor- same cues), anxiety-induced amygdala–dmPFC coupling (as der (Robinson et al., 2014), suggesting that serotonergic drugs seen above; Robinson et al., 2012a) is down-regulated. This sug- (which putatively elevate serotonin availability) may work by gests that psychological treatments such as CBT may reduce reducing activity within this dorsal prefrontal circuit (Robinson negative bias by down-regulating the dorsal PFC–amygdala cir- et al., 2013b), thus reducing negative bias. By contrast, a study cuitry that promotes negative bias. In the context of threat pro- using a different paradigm showed that tryptophan depletion can cessing, there has been limited work showing whole-brain also decrease coupling between the amygdala and a more ventral increased ventrolateral prefrontal activation in anxious youth prefrontal region (Passamonti et al., 2012). Moreover, direct who underwent CBT relative to controls (Maslowsky et al., reductions in ventrally located orbitofrontal serotonin in the mar- 2010), as well as reduced dorsomedial prefrontal activation post- moset can increase negative bias (Rygula et al., 2015). Within the CBT relative to pre-CBT in individuals with social anxiety framework described above, this suggests that serotonin can also (Klumpp et al., 2013). Nevertheless, the role that CBT plays in serve to promote ventral PFC circuits that inhibit aversive pro- dorsal versus ventral prefrontal–amygdala circuitry in humans cessing while inhibiting dorsal PFC circuits that promote aver- has not been systematically studied. Moreover, recovery rates of sive responding. As such, pharmacological treatments may work patients with anxiety undergoing psychological treatment are less by restoring the balance between the circuits that, respectively, than 50% (Community and Mental Health team, 2014), so it is promote and inhibit negative bias. Recent work also suggests that plausible that these mechanisms are again only relevant in a sub- the influence of serotonergic drugs on this circuitry might be set of patients. mediated by genetic factors (Perna et al., 2005; Santangelo et al., 2016), which may in turn explain why such medications only work for a subset of anxious patients. Conclusions and future directions In this review, we have outlined evidence across animals and humans suggesting that bi-directional prefrontal–subcortical cir- Psychological treatment cuits and their interactions may drive elevated aversive process- CBT is the most common psychological intervention used to treat ing, or negative bias, in anxiety. Specifically, we point to anxiety and is based on the premise that negative biases in converging evidence within the Negative Valance Systems thoughts and actions can be shifted through cognitive reappraisal domain of the RDoC which suggests that ventral PFC–subcortical and emotion regulation strategies (Beck and Clark, 1997). There circuitry in humans may be associated with reduced negative have been numerous studies (see review by Brooks and Stein, bias, while more dorsal PFC–subcortical circuitry may be associ- 2015) which suggest that CBT modulates prefrontal–subcortical ated with increased negative bias. Moreover, we provide evi- interactions. Indeed, baseline medial prefrontal and amygdala dence suggesting that the emergence of anxiety in adolescence activity might even predict treatment response to CBT in anxiety may be a result of differential developmental trajectories of these (Klumpp et al., 2017). For instance, Shou et al. (2017) showed circuits and that both pharmacological and psychological inter- that functional connectivity between the amygdala and the ventions might be effective by modulating the overall balance of fronto-parietal network increased in patients with major depres- these circuits in driving negative affective bias. These findings sive disorder (MDD) or PTSD who underwent a course of CBT are summarised in Figure 1. compared to controls, supporting a mechanism by which this cir- Nevertheless, it is still unclear exactly how we bridge the gap cuitry may interact with psychological intervention (although it between brain and behaviour. Although we can associate these should be noted that this study did not include a patient group that circuits with negative bias, we cannot yet say how exactly the did not undergo CBT, so the specificity of these results is difficult underlying neuronal activity is translated into observable behav- to quantify). Similarly, a study of adolescents with anxiety iour. One particularly promising avenue in this regard is the Carlisi and Robinson 7 data (Thompson et al., 2014). Moreover, the UK Biobank (Sudlow et al., 2015) is a consortium across 22 research centres in the United Kingdom with genetic and longitudinal physical health and behavioural data on over 500,000 participants, all of which has been made open access. These are early efforts, par- ticularly in the field of anxiety disorders, but promising mega- and meta-analyses have already come out of such efforts in other fields of psychiatry such as obsessive–compulsive disorder and schizophrenia (Boedhoe et al., 2016; De Wit et al., 2014; Van Erp et al., 2016). Finally, it is important to investigate how these effects change over time. That is, are these mechanisms stable, or do they change across development to influence symptom onset and persistence? Longitudinal studies are critical for understanding these ques- tions. There have been longitudinal studies investigating brain changes over time in adolescents (e.g. the IMAGEN study; Schumann et al., 2010), but this investigation needs to be scaled up to larger populations and multiple time points and age ranges if we are to truly understand the developmental changes that occur across the life course of anxiety disorders. One promising example of this work currently underway is the Adolescent Brain Cognitive Development study (ABCD; https://abcdstudy.org/ index.html), which is the largest long-term longitudinal study of brain development in the United States, currently in the process of collecting biological and behavioural data on over 10,000 chil- dren aged 9–10. Similarly, to gain an understanding of the under- lying genetic contributions of anxiety, it is important to investigate Figure 1. Schematic summarising findings and proposed simplified the extent to which certain features and symptoms are heritable. model of negative affective bias in anxiety. Bi-directional excitatory This can be achieved through longitudinal twin studies (e.g. the connections between dorsal regions of the mPFC/ACC and the amygdala Twins Early Development Study (TEDS; Oliver and Plomin, promote negative bias, while inhibitory connections between ventral 2007) and the Tennessee Twin Study (Lahey et al., 2008)), but regions coupled with the amygdala inhibit negative bias. The ventral many of the existing studies do not focus on brain imaging due to inhibitory circuit may only fully develop in adulthood, meaning limited time and resources and the high cost involved in neuroim- that adolescence is a period of high vulnerability to negative bias. aging research. Regardless, observational population-based stud- Successful treatments (SSRIs and CBT) may be effective via promotion ies are an important complimentary approach to the small-scale of the ventral circuit and inhibition of the dorsal circuit. case–control designs more frequently implemented in neuroim- ACC: anterior cingulate cortex; mPFC: medial prefrontal cortex; CBT: cognitive- aging research on anxiety. behavioural therapy; SSRI: selective serotonin reuptake inhibitor. In conclusion, work has begun to delineate overlapping neu- ral networks involving the PFC and subcortical regions includ- ing the amygdala that may drive aversive responding and nascent field of computational psychiatry, which attempts to negative bias in both animals and humans. There is also promis- bridge the gap between brain activation and observable symp- ing evidence that pharmacological and psychological interven- toms (Huys et al., 2016). Specifically, using mathematical theo- tions can shape this circuitry and hence ameliorate negative ries of cognition and human behaviour, computational psychiatry affective bias. Future research should expand these findings to aims to objectively quantify the calculations generated by neu- larger populations and investigate how these neural underpin- rons which shape behaviour (Huys et al., 2016). This work is in nings arise in childhood/adolescence and change over time to its infancy but has begun to delineate the computational basis of shape behaviour. common symptoms in anxiety linked to negative bias, such as avoidance (Mkrtchian et al., 2017), risk aversion (Charpentier et al., 2017) and goal-directed behaviour (Carlisi et al., 2017; Declaration of conflicting interests Gillan et al., 2014). The author(s) declared no potential conflicts of interest with respect to Furthermore, if we are to understand current findings in a the research, authorship and/or publication of this article. truly generalisable context, it is critical to investigate these mech- anisms in large-scale populations. Cohort studies are an ideal Funding way to examine these questions at the population level, often C.O.C. is supported by a Wellcome Trust Sir Henry Wellcome Postdoctoral sampling from a diverse community of individuals. Data sharing Fellowship (206459/Z/17/Z) and O.J.R. is supported by a Medical efforts have attempted to address this. For example, the ENIGMA Research Council Career Development Award (MR/K024280/1). consortium is an international collaboration of research centres which aims to combine neuroimaging and genetic datasets from ORCID iD sites around the world in an attempt to amass sample sizes large enough to detect very small effects in brain imaging and genetic Christina O. 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Brain and Neuroscience Advances – SAGE
Published: May 8, 2018
Keywords: Anxiety; circuit; negative bias; prefrontal cortex
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