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Norepinephrine ignites local hotspots of neuronal excitation: How arousal amplifies selectivity in perception and memory

Published online by Cambridge University Press:  01 July 2015

Mara Mather
Affiliation:
Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089-0191mara.mather@usc.edu
David Clewett
Affiliation:
Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089-2520clewett@usc.edu
Michiko Sakaki
Affiliation:
School of Psychology and Clinical Language Sciences, University of Reading, Reading RGX 7BE, United Kingdom; and Kochi University of Technology, Kami, 782-0003, Japanm.sakaki@reading.ac.uk
Carolyn W. Harley
Affiliation:
Professor Emeritus, Memorial University of Newfoundland, St. John's, NL A1C 5S7, Canadacharley@play.psych.mun.ca
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Abstract

Emotional arousal enhances perception and memory of high-priority information but impairs processing of other information. Here, we propose that, under arousal, local glutamate levels signal the current strength of a representation and interact with norepinephrine (NE) to enhance high priority representations and out-compete or suppress lower priority representations. In our "glutamate amplifies noradrenergic effects" (GANE) model, high glutamate at the site of prioritized representations increases local NE release from the locus coeruleus (LC) to generate “NE hotspots.” At these NE hotspots, local glutamate and NE release are mutually enhancing and amplify activation of prioritized representations. In contrast, arousal-induced LC activity inhibits less active representations via two mechanisms: 1) Where there are hotspots, lateral inhibition is amplified; 2) Where no hotspots emerge, NE levels are only high enough to activate low-threshold inhibitory adrenoreceptors. Thus, LC activation promotes a few hotspots of excitation in the context of widespread suppression, enhancing high priority representations while suppressing the rest. Hotspots also help synchronize oscillations across neural ensembles transmitting high-priority information. Furthermore, brain structures that detect stimulus priority interact with phasic NE release to preferentially route such information through large-scale functional brain networks. A surge of NE before, during, or after encoding enhances synaptic plasticity at NE hotspots, triggering local protein synthesis processes that enhance selective memory consolidation. Together, these noradrenergic mechanisms promote selective attention and memory under arousal. GANE not only reconciles apparently contradictory findings in the emotion-cognition literature but also extends previous influential theories of LC neuromodulation by proposing specific mechanisms for how LC-NE activity increases neural gain.

Type
Target Article
Copyright
Copyright © Cambridge University Press 2016 

1. Introduction

When jolted by a rough skydiving landing, psychologist James Easterbrook observed that his sense of space and time shrank and slowly re-expanded (Easterbrook Reference Easterbrook1982). This sparked his curiosity about how arousal influences attention. Later he published a review article in which he argued that under arousal, people rely more on central or immediately relevant information and less on peripheral information (Easterbrook Reference Easterbrook1959). Since his seminal paper, researchers have accumulated many more observations that arousal evoked by emotional events enhances some aspects of perception and memory but impairs others (for reviews, see Mather & Sutherland Reference Mather and Sutherland2011; Reisberg & Heuer Reference Reisberg, Heuer, Reisberg and Hertel2004). For example, victims of a crime tend to remember the weapon vividly but forget the perpetrator's face (Steblay Reference Steblay1992). People also pay attention to emotional information at the expense of neutral information (Dolcos & McCarthy Reference Dolcos and McCarthy2006; Knight et al. Reference Knight, Seymour, Gaunt, Baker, Nesmith and Mather2007). These examples fit with Easterbrook's formulation that arousal impairs attention to peripheral information. But arousing stimuli can sometimes enhance memory of peripheral neutral information (Kensinger et al. Reference Kensinger, Garoff-Eaton and Schacter2007; Knight & Mather Reference Knight and Mather2009). Thus, although it is clear that arousal shapes attention and memory, knowing that something is neutral or spatially peripheral is not enough to predict how it will fare under emotional conditions.

So, then, how does arousal influence the brain's selection of features to highlight versus suppress? An initial answer to this puzzle was provided by the arousal-biased competition (ABC) model, which posits that arousal does not have fixed rules about which types of stimuli to enhance or suppress. Instead, arousal amplifies the stakes of ongoing selection processes, leading to “winner-take-more” and “loser-take-less” effects in perception and memory (Mather & Sutherland Reference Mather and Sutherland2011). The ABC model builds on biased competition models proposing that stimuli must compete for limited mental resources (Beck & Kastner Reference Beck and Kastner2009; Desimone & Duncan Reference Desimone and Duncan1995; Duncan Reference Duncan2006). As conceptualized by Desimone and Duncan (Reference Desimone and Duncan1995), both bottom-up and top-down neural mechanisms help resolve competition.

Bottom-up processes are largely automatic, determined by the perceptual properties of a stimulus, and do not depend on top-down attention or task demands. For example, stimuli that contrast with their surroundings, such as a bright light in a dark room, engage attention automatically even if they are currently goal irrelevant (Itti & Koch Reference Itti and Koch2000; Parkhurst et al. Reference Parkhurst, Law and Niebur2002; Reynolds & Desimone Reference Reynolds and Desimone2003). Top-down goals can also bias competition in favor of particular stimuli that otherwise would not stand out. Although not included in the original biased competition models, past history with particular stimuli is also a source of selection bias (Awh et al. Reference Awh, Belopolsky and Theeuwes2012; Hutchinson & Turk-Browne Reference Hutchinson and Turk-Browne2012). For example, one's name or a novel stimulus tends to engage attention (Moray Reference Moray1959; Reicher et al. Reference Reicher, Snyder and Richards1976). In addition, faces, text, and emotionally salient stimuli all grab attention (e.g., Cerf et al. Reference Cerf, Frady and Koch2009; Knight et al. Reference Knight, Seymour, Gaunt, Baker, Nesmith and Mather2007; MacKay et al. Reference MacKay, Shafto, Taylor, Marian, Abrams and Dyer2004; Niu et al. Reference Niu, Todd and Anderson2012).

A core aspect of most current theories of visual attention is that these different signals are integrated into maps of the environment that indicate the priority or salience of stimuli across different locations (Itti & Koch Reference Itti and Koch2000; Soltani & Koch Reference Soltani and Koch2010; Treisman Reference Treisman1998). Regions in frontoparietal cortex integrating sensory and top-down signals help represent such priority maps (Ptak Reference Ptak2012). Moreover, having both feedforward and feedback connections between sensory regions and cortical priority maps enables distributed representations of prioritized information to modulate their own processing (e.g., lower-level visual features) even further (Klink et al. Reference Klink, Jentgens and Lorteije2014; Ptak Reference Ptak2012; Serences & Yantis Reference Serences and Yantis2007; Soltani & Koch Reference Soltani and Koch2010). Thus, priority signals are self-biasing to enhance efficient information processing in the brain.

In the ABC model, arousal further biases mental processing to favor high- over low-priority representations, regardless of whether initial priority is determined by bottom-up salience, emotional salience, or top-down goals. Thus, because spatially peripheral information is usually lower priority than central information, arousal usually impairs memory for it (Steblay Reference Steblay1992; Waring & Kensinger Reference Waring and Kensinger2011). Yet, when peripheral information is perceptually salient or goal relevant, arousal instead enhances memory for it (e.g., Kensinger et al. Reference Kensinger, Garoff-Eaton and Schacter2007, Experiment 4). But the ABC model does not tackle how this works in the brain. Previous brain-based models of emotion and cognition also do not account for the dual role of arousal. Most models posit that the amygdala enhances perception and memory consolidation of emotionally salient stimuli, but fail to address how arousal sometimes enhances and sometimes impairs information processing.

In this article we propose the glutamate amplifies noradrenergic effects (GANE) model, in which arousal amplifies the activation difference between high- and low-priority representations via local synaptic self-regulation of the locus coeruleus–norepinephrine (LC–NE) system. According to the GANE model, hearing an alarming sound or seeing something exciting leads to a surge in NE release, which, in turn, enhances activity of neurons transmitting high-priority mental representations and suppresses activity of neurons transmitting lower-priority mental representations. As already outlined, priority is determined by top-down goals, bottom-up factors, and high-level stimulus features (Beck & Kastner Reference Beck and Kastner2009; Desimone & Duncan Reference Desimone and Duncan1995; Fecteau & Munoz Reference Fecteau and Munoz2006).

According to the GANE model, the brain's primary excitatory neurotransmitter, glutamate, signals priority. Under arousal, elevated glutamate associated with highly active neural representations stimulates greater NE release, which then further increases glutamate via positive feedback loops. Thus, in these local “NE hotspots,” glutamate signals are amplified. At the same time, wherever NE is released and fails to ignite a local hotspot, inhibitory adrenoreceptors with lower thresholds of activation suppress activity. Higher NE concentration at hotspots also enhances delivery of energy resources to the site of active cognition, synchronizes brain oscillations, and modulates activity in large-scale functional networks. Thus, under arousal, local NE hotspots contrast with widespread NE suppression to amplify priority effects in perception and memory, regardless of how priority was instantiated.

2. Arousal-biased competition in perception and memory

We start by reviewing recent findings supporting Mather and Sutherland's (Reference Mather and Sutherland2011) ABC model and its novel predictions. Next, we turn to the question of how these arousal effects operate in the brain. A fundamental challenge in understanding how arousal influences cognition is that it sometimes enhances and sometimes impairs information processing. Although most emotion research focuses on how processing of emotional stimuli is enhanced compared with neutral stimuli, emotional arousal can also influence processing of neutral stimuli, and across studies, opposing effects are often seen. How can emotionally salient stimuli sometimes enhance memory for what just happened, but other times impair it? When do arousing stimuli enhance perception and when do they impair perception of subsequent stimuli? Many studies report that emotion increases selectivity (for reviews, see Levine & Edelstein Reference Levine and Edelstein2009; Mather & Sutherland Reference Mather and Sutherland2011; Murray et al. Reference Murray, Holland, Kensinger, Robinson, Watkins and Harmon-Jones2013), but how do we predict what gets selected?

2.1. Arousal enhances perception of salient stimuli, but impairs perception of inconspicuous stimuli

In previous research on how arousal influences subsequent perception, two types of findings were hard to reconcile. First, arousing stimuli impair perception of subsequent stimuli. For example, people preferentially perceive arousing stimuli (e.g., Anderson Reference Anderson2005; Keil & Ihssen Reference Keil and Ihssen2004) but fail to perceive or encode neutral stimuli close to arousing stimuli either in time (e.g., embedded in a rapid series of images after an arousing image) (Smith et al. Reference Smith, Most, Newsome and Zald2006) or in space (Kensinger et al. Reference Kensinger, Garoff-Eaton and Schacter2007; Tooley et al. Reference Tooley, Brigham, Maass and Bothwell1987). Second, hearing or seeing an arousing stimulus enhances visual perception of a subsequent Gabor patch (Lee et al. Reference Lee, Baek, Lu and Mather2014a; Padmala & Pessoa Reference Padmala and Pessoa2008; Phelps et al. Reference Phelps, Ling and Carrasco2006).

How can we explain both the enhancing and impairing effects of arousing stimuli on perception of stimuli that appear close in time or space? Initial evidence supports the ABC hypothesis that inducing arousal should have two opposing effects on perception: Arousal should enhance processing of high-priority (more salient) stimuli but impair processing of lower-priority (less salient) stimuli. When asked to report as many letters as they could from a briefly flashed array (Fig. 1), participants reported more of the high-salience letters and fewer of the low-salience letters after hearing an arousing emotionally negative sound than after hearing a neutral sound (Sutherland & Mather Reference Sutherland and Mather2012). Similar results were obtained when arousal was induced by emotionally positive sounds (Sutherland & Mather, under review). These results indicate that arousal makes salient stimuli stand out more than they would otherwise.

Figure 1. Participants heard an arousing or neutral sound before a letter array was flashed briefly. They then reported as many of the letters as they could. Some of the letters were shown in dark gray (high contrast and, therefore, salient) and some in light gray (low contrast and less salient). Participants reported a greater proportion of the salient letters than the nonsalient letters, but this advantage for salient letters was significantly greater on arousing trials than on neutral trials, and the disadvantage for the nonsalient letters was significantly greater on arousing than on neutral trials (Sutherland & Mather Reference Sutherland and Mather2012).

The ABC model also explains the enhanced processing of emotional stimuli, the focus of most previous theoretical accounts (e.g., Kensinger Reference Kensinger2004; LaBar & Cabeza Reference LaBar and Cabeza2006; Mather Reference Mather2007; Murty et al. Reference Murty, Ritchey, Adcock and LaBar2010; Phelps Reference Phelps2004). People tend to prioritize emotional stimuli due to top-down goals (e.g., increasing pleasure and avoiding pain), their emotional saliency (e.g., associations with reward/punishment), and/or bottom-up salience (e.g., a gunshot is loud as well as a threat to safety [Markovic et al. Reference Markovic, Anderson and Todd2014]). Thus, arousing stimuli should dominate competition for representation at their particular spatiotemporal position (Wang et al. Reference Wang, Kennedy and Most2012).

If the arousing stimulus appears in the exact same location as a neutral stimulus presented less than a second later, it will impair perception of that neutral stimulus, an effect known as emotion-induced blindness (Kennedy & Most Reference Kennedy and Most2012; Most et al. Reference Most, Chun, Widders and Zald2005). On the other hand, arousing stimuli tend to enhance the dominance of high-priority stimuli that are nearby but not competing for the same spatiotemporal spot. An emotionally salient word that impairs perception of a subsequent target word flashed in the same location 50 or 500 ms later can instead enhance perception of a target word flashed 1,000 ms later (Bocanegra & Zeelenberg Reference Bocanegra and Zeelenberg2009), because after the longer interval, the priority of the target word is no longer overshadowed by the emotionally salient word.

2.2. Arousal enhances perceptual learning about salient stimuli but impairs learning about nonsalient stimuli

Interspersing emotional or neutral pictures with a visual search task had opposite effects on perceptual learning of salient and nonsalient targets (Lee et al. Reference Lee, Itti and Mather2012). In this study, the targets were always the same, but in one condition they were salient because they differed from the distractors, and in the other condition they were not salient because they were quite similar to distractors. Emotional images enhanced perceptual learning about the salient target lines but impaired learning of nonsalient targets (Fig. 2). Thus, whether arousal enhanced or impaired learning depended on the target's salience.

Figure 2. Estimated tuning curves for averaged “target” responses as a function of emotion in the high-salience condition (A) and low-salience condition (B). In the high-salience condition, having interspersed emotional pictures enhanced perceptual learning of the exact tilt of the target (55°), whereas in the low-salience condition, emotion impaired learning of the exact tilt of the same target. Figure adapted from Lee et al. (Reference Lee, Itti and Mather2012).

2.3. How arousal modulates neural representations depends on salience

A recent study took advantage of the fact that faces and scenes activate distinct representational regions in the brain to test the ABC hypothesis that arousal increases brain activation associated with processing of salient stimuli, whereas it decreases brain activation associated with processing of less salient stimuli (Lee et al. Reference Lee, Sakaki, Cheng, Velasco and Mather2014b). On each trial, one yellow-framed face and one scene image appeared briefly side-by-side and then a dot appeared in the former location of one of the images (Fig. 3A). The participants' task was to indicate the side on which the dot appeared. Participants responded fastest to dots that appeared behind the salient faces on trials preceded by a tone conditioned to predict shock and thereby induce arousal. In a follow-up functional magnetic resonance imaging (fMRI) study, there was an arousal×saliency interaction in visual category-specific brain regions, such that arousal enhanced brain activation in the region processing the salient stimulus (i.e., fusiform face area) but suppressed brain activation in the region processing the nonsalient stimulus (i.e., parahippocampal place area) (Fig. 3B) (Lee et al. Reference Lee, Sakaki, Cheng, Velasco and Mather2014b).

Figure 3. In the functional magnetic resonance imaging study by Lee et al. (Reference Lee, Sakaki, Cheng, Velasco and Mather2014b), tones conditioned to predict shock (CS+ tones) played before the display of a salient face, and a less salient scene (A) increased activity in the left fusiform face area (FFA) associated with face processing, while decreasing activity in the left parahippocampal place area (PPA) associated with scene processing, compared with tones conditioned not to predict shock (CS– tones) (B). *p < 0.05, **p < 0.005. CS = conditioned stimulus; ISI = interstimulus interval.

2.4. Arousal enhances or impairs memory consolidation of representations depending on their priority

So far, we have focused on how arousal enhances processing of subsequent inputs; however, arousal should have similar effects on mental representations currently active at the moment arousal is induced. Previous research has indicated that arousal induced after initial encoding sometimes impairs and sometimes enhances memory of preceding information (Knight & Mather Reference Knight and Mather2009). The critical ABC hypothesis is that experimental manipulation of priority of information should alter the effect of subsequent arousal on memory consolidation.

In the first study testing this hypothesis, participants viewed lists of objects one object at a time, with one perceptual oddball in each list (Fig. 4) (Sakaki et al. Reference Sakaki, Fryer and Mather2014a). The oddball was either emotionally salient or neutral. Some participants were asked to recall the name of the oddball picture as soon as the list presentation ended. In this condition, the object shown just before the oddball (e.g., the cabbage in Fig. 4) was low priority. Other participants were asked to recall the name of the object shown just before the oddball (oddball-minus-1 object). Thus, in this condition, the oddball-minus-1 object (e.g., the cabbage) was high priority. After a series of lists, memory for details of all oddball-minus-1 objects was tested. As predicted, positively or negatively emotionally salient oddball pictures enhanced memory for prioritized oddball-minus-1 objects and impaired memory for nonprioritized oddball-minus-1 objects.

Figure 4. Schematic representations of a neutral trial in the prioritize-oddball condition (A) and a negative trial in the prioritize-oddball-minus-1 condition (B). Memory performance for oddball-minus-1 objects differed as a function of their priority and the valence of oddball pictures (C). Oddball pictures depicted here were obtained from iStockPhoto for illustration purposes and differ from those used in the experiments. Figures from Sakaki et al. (Reference Sakaki, Fryer and Mather2014a).

Although the brain mechanisms underlying this priority×arousal interaction in memory have yet to be tested, fMRI evidence indicates that arousal enhances activity in regions processing a high-priority stimulus. For example, pairing shock with certain high-priority (i.e., standalone) neutral scenes enhances successful encoding-related activity in the parahippocampal place area (PPA), the brain region specialized to process scene information (Schwarze et al. Reference Schwarze, Bingel and Sommer2012). Thus, arousal-induced enhancement of brain activity processing prioritized information not only occurs during perception (e.g., Lee et al. Reference Lee, Sakaki, Cheng, Velasco and Mather2014b), but also predicts memory for such items.

2.5. Summary

Mather and Sutherland's (Reference Mather and Sutherland2011) ABC model accounts for both the enhancement and impairment effects of arousal on neutral stimuli across a wide variety of experimental contexts. It makes novel predictions: (1) Arousal before exposure to stimuli should amplify the effects of salience on perception and memory encoding; and (2) Arousal shortly after encoding information should amplify the effects of its goal relevance on memory consolidation. Both effects result from arousal's differential modulation of representations depending on priority. Other models also highlight the importance of interactions between arousal, attention, and goals (Kaplan et al. Reference Kaplan, Van Damme and Levine2012; Levine & Edelstein Reference Levine and Edelstein2009; Montagrin et al. Reference Montagrin, Brosch and Sander2013; Talmi Reference Talmi2013). However, so far there has been no account of how arousal amplifies the effects of priority in the brain.

3. Current brain-based models of arousal's modulatory effects

Before we present our account of how arousal can modulate neural representations differently depending on their priority, we outline how existing brain-based models of arousal and cognition fail to adequately address how arousal has opposite effects depending on representational priority (see Table 1 for an overview).

Table 1. Brain-based emotion-cognition theories.

3.1. Modular vs. “multiple waves” of emotion enhancement in perception

Noticing things like snakes and guns can increase the odds of survival. Consistent with this adaptive importance, emotionally salient stimuli are often detected more rapidly than neutral stimuli (Leclerc & Kensinger Reference Leclerc and Kensinger2008; Mather & Knight Reference Mather and Knight2006; Öhman et al. Reference Öhman, Flykt and Esteves2001). Explaining the privileged status of emotional stimuli has been the focus of brain models of emotion perception. One common assumption is that the evolutionary value of noticing emotional stimuli led to a specialized emotion module or pathway to evaluate emotional salience (Tamietto & de Gelder Reference Tamietto and de Gelder2010). For example, in their multiple attention gain control (MAGiC) model, Pourtois et al. (Reference Pourtois, Schettino and Vuilleumier2013) argue that emotional salience shapes perception via amplification mechanisms independent of other attentional processes. In the MAGiC model, the amygdala and other modulatory brain regions amplify neural responses to emotional relative to neutral stimuli along sensory pathways. The model also posits that these modulations occur parallel to and sometimes in competition with signals from bottom-up (exogenous) and top-down (endogenous) attentional control systems (see also Vuilleumier Reference Vuilleumier2005b).

In contrast, Pessoa and Adolphs (Reference Pessoa and Adolphs2010) argue against a modular approach to emotion enhancement in perception. In their multiple waves model, affectively and motivationally significant visual stimuli rapidly engage multiple brain sites, including the amygdala, orbitofrontal cortex, anterior insula, and anterior cingulate cortex, that then bias processing to favor these stimuli. From their perspective, the amygdala helps prioritize emotional aspects of information processing by coordinating activity in other regions involved in selective attention. Thus, in the multiple waves model, emotion influences general-purpose perceptual and attention systems rather than harnessing independent brain mechanisms to enhance perception of emotional items.

The latter perspective is more compatible with our findings than are separate-system models; if emotional stimuli were processed via a system separate from that processing neutral stimuli, it is not clear how emotional arousal could have both enhancing and impairing effects on neutral stimuli depending on their priority. However, even this modulatory multiple waves approach to emotion–cognition interactions fails to explain the full picture of how emotional arousal influences cognitive processing, as it focuses only on the enhanced perception of arousing stimuli and ignores how arousal affects perceptual selectivity more generally.

3.2. Canonical amygdala modulation model of emotional memory enhancement

The act of noticing something creates initial trace representations that require additional resources over the next few minutes, hours, and days to consolidate into a longer-lasting memory. Much research indicates that emotional arousal experienced before, during, or after an event can enhance these memory consolidation processes (Hermans et al. Reference Hermans, Battaglia, Atsak, de Voogd, Fernández and Roozendaal2014). The prevailing view of how emotion affects memory consolidation is that the amygdala enhances processes in the hippocampus and other memory-related brain regions in the medial temporal lobes, such that memory for emotional events is enhanced compared with memory for neutral events (e.g., McGaugh Reference McGaugh2004). Consistent with this idea, activity in the amygdala during encoding predicts later memory for emotional items, but not memory for neutral items, as does greater amygdala functional connectivity with medial temporal brain regions (Dolcos et al. Reference Dolcos, LaBar and Cabeza2004; Kilpatrick & Cahill Reference Kilpatrick and Cahill2003; Richardson et al. Reference Richardson, Strange and Dolan2004; Ritchey et al. Reference Ritchey, Dolcos and Cabeza2008).

Converging rodent and human research indicates that NE facilitates the amygdala-mediated enhancement of emotional information. For example, NE released in the amygdala during arousal is associated with enhanced memory for the emotionally arousing event (McIntyre et al. Reference McIntyre, Hatfield and McGaugh2002). Infusion of noradrenergic agonists into the basolateral amygdala after training also enhances memory for emotionally arousing events (Hatfield & McGaugh Reference Hatfield and McGaugh1999; LaLumiere et al. Reference LaLumiere, Buen and McGaugh2003). In humans, administration of the β-adrenergic antagonist propranolol impairs emotional memories, whereas pharmacological agents that increase NE levels, such as a selective NE reuptake inhibitor, tend to enhance them (Chamberlain & Robbins Reference Chamberlain and Robbins2013), and enhanced amygdala activity during encoding emotional stimuli is reduced by propranolol (Strange & Dolan Reference Strange and Dolan2004). Thus, NE–amygdala interactions enhance memory for emotional events.

Activation of the amygdala by NE can also impair memory for neutral information encountered near something emotional. For example, as already described earlier in the context of the Sakaki et al. (Reference Sakaki, Fryer and Mather2014a) study, people often have worse memory for neutral-low priority information shown immediately before an emotional compared with a neutral “oddball” stimulus. Patients with amygdalar damage do not exhibit decrements in memory for neutral words preceding emotional oddball words, and in normal individuals, a β-adrenergic antagonist prevents this retrograde memory impairment (Strange et al. Reference Strange, Hurlemann and Dolan2003).

Although not usually articulated, the amygdala modulation hypothesis presumably explains these impairment effects for neutral stimuli in terms of a trade-off in which the amygdala focuses resources on emotional stimuli, leaving fewer resources available to process and consolidate the neutral stimuli. However, this trade-off explanation fails to explain how NE–amygdala interactions can also enhance memory for nonarousing information (e.g., Barsegyan et al. Reference Barsegyan, McGaugh and Roozendaal2014; Roozendaal et al. Reference Roozendaal, Castello, Vedana, Barsegyan and McGaugh2008).

3.3. Biased attention via norepinephrine model

In the biased attention via norepinephrine (BANE) model, Markovic et al. (Reference Markovic, Anderson and Todd2014) propose that affectively salient stimuli activate the LC–NE system to optimize their own processing. Like the ABC model (Mather & Sutherland Reference Mather and Sutherland2011), the BANE model builds on biased competition models of attention (Markovic et al. Reference Markovic, Anderson and Todd2014). The BANE model proposes that affect-biased attention “is distinct from both ‘classic’ executive top-down and bottom-up visual attention and is at least in part circumscribed by a different set of neural mechanisms” (Markovic et al. Reference Markovic, Anderson and Todd2014, p. 230). In the BANE model, emotional salience is detected by an “anterior affective system,” including the amygdala and the orbitofrontal cortex, based on the recent history of reward and punishment. In turn, the amygdala's recruitment of the LC–NE system serves as an additional specialized pathway that further biases attention and memory in favor of the affectively relevant information that triggered NE release. However, like other models of emotion and cognition, the BANE model focuses exclusively on how affectively salient stimuli outcompete less salient stimuli and does not address how arousal induced by these stimuli sometimes enhances and sometimes impairs processing of proximal neutral information.

3.4. Emotional attention competes with executive attention for limited mental resources

Another line of work focuses on how emotional stimuli compete for executive resources (Bishop Reference Bishop2007; Choi et al. Reference Choi, Padmala and Pessoa2012; Eysenck et al. Reference Eysenck, Derakshan, Santos and Calvo2007), with some researchers positing that a ventral affective system competes with a dorsal executive system (Bush et al. Reference Bush, Luu and Posner2000; Dolcos et al. Reference Dolcos, Iordan and Dolcos2011). For example, when task-irrelevant emotional stimuli capture attention, they diminish dorsal executive brain region function and therefore disrupt working memory for neutral faces that were just seen (Dolcos & McCarthy Reference Dolcos and McCarthy2006; Dolcos et al. Reference Dolcos, Diaz-Granados, Wang and McCarthy2008). However, meta-analyses indicate that emotional responses are associated with both the ventral and dorsal prefrontal cortical regions (Phan et al. Reference Phan, Wager, Taylor and Liberzon2002; Shackman et al. Reference Shackman, Salomons, Slagter, Fox, Winter and Davidson2011), and so the notion that emotional distractors lead the ventral prefrontal cortical region to inhibit the dorsolateral prefrontal cortical region (Dolcos et al. Reference Dolcos, Diaz-Granados, Wang and McCarthy2008) is unlikely to be universal across different contexts.

Instead of a ventral/dorsal antagonism model, the dual competition model posits that emotional stimuli compete for resources at both perceptual and executive levels of processing (Pessoa Reference Pessoa2009; Reference Pessoa2013). For example, when participants heard tones predicting shock, regions within the frontoparietal network were activated (Lim et al. Reference Lim, Padmala and Pessoa2009). Recruitment of these regions during intense emotional arousal should make them less available for concurrent neutral task-related processing and lead to behavioral impairments. At the perceptual level of the dual competition model, both cortical and subcortical structures help amplify visual cortex responses to emotional stimuli, again leading to the impaired perception of other concurrent stimuli.

As in the ABC framework, competition is a core feature of these models. These models, however, consider only one type of competition: that between arousing and neutral stimuli/tasks. Critically, our empirical results indicate that arousal also influences competition between two neutral stimuli, such that processing of high-priority stimuli is enhanced, whereas processing of lower-priority stimuli is impaired. It is not clear how, in competition models that focus on competition between arousing and neutral stimuli, arousal would interact differently with low- and high-priority neutral information. For example, such models cannot account for the differential effects of arousing sounds on subsequent perceptually salient versus nonsalient letters (Fig. 1).

3.5. Competition between items for memory consolidation

In a different type of competition account, Diamond et al. (Reference Diamond, Park, Campbell and Woodson2005) propose that there is “ruthless competition” between novel and existing memory representations, such that encoding a new emotional experience suppresses recently potentiated synapses, creating memory for emotional events at the cost of memory for information learned just before the emotional event (Diamond et al. Reference Diamond, Park, Campbell and Woodson2005).

This ruthless competition hypothesis argues that the acquisition of new information via the hippocampus depotentiates the most recently activated synapses and that this suppression of recently formed memories is greater when the new information induces emotion or stress. Thus, inducing arousal should impair memory for a preceding sequence of items, regardless of whether those preceding items were themselves emotional or not. That is not the case, however. Inducing arousal via emotional or cold-pressor stress immediately after participants study a mixed list of emotional and neutral pictures selectively enhances memory for preceding emotional, but not neutral, pictures (Cahill et al. Reference Cahill, Gorski and Le2003; Liu et al. Reference Liu, Graham and Zorawski2008).

3.6. An arousing stimulus sometimes impairs and sometimes enhances memory of what just happened

How can inducing arousal enhance memory for preceding emotional items but not neutral items? Investigators proposed that emotional arousal “tags” synapses associated with representations of emotional items, making these synapses the selective target of protein synthesis-dependent long-term potentiation (Bergado et al. Reference Bergado, Lucas and Richter-Levin2011; Richter-Levin & Akirav Reference Richter-Levin and Akirav2003; Segal & Cahill Reference Segal and Cahill2009; Tully & Bolshakov Reference Tully and Bolshakov2010). The emotional tagging hypothesis predicts that emotionally salient stimuli are remembered better than neutral stimuli because emotional tags allow those particular synapses to capture the plasticity-related proteins released with subsequent inductions of arousal.

A problem for the emotional tagging model is that inducing emotional arousal sometimes enhances memory for preceding neutral stimuli (Anderson et al. Reference Anderson, Wais and Gabrieli2006; Dunsmoor et al. Reference Dunsmoor, Murty, Davachi and Phelps2015; Knight & Mather Reference Knight and Mather2009; Nielson & Powless Reference Nielson and Powless2007; Sakaki et al. Reference Sakaki, Fryer and Mather2014a). Neither the emotional tagging hypothesis nor any of the other hypotheses outlined earlier can account for this retrograde enhancement of something neutral. In contrast to the emotional tagging hypothesis, behavioral studies demonstrate that whether something arousing will yield retrograde enhancement or impairment depends on the priority of the preceding information (sect. 2.5) (Ponzio & Mather Reference Ponzio and Mather2014; Sakaki et al. Reference Sakaki, Fryer and Mather2014a).

3.7. Summary

Although there are many models describing how emotion enhances perception, attention, and memory in the brain, these theories fail to account for both the enhancing and impairing effects of emotional arousal (see Table 1 for a summary). In the following sections, we make the case for GANE, a model of how NE released under arousal can impact high- and low-priority representations differently despite its diffuse release across the brain.

4. Locus coeruleus, NE, and arousal

Like the GANE model, other theories also argue that the LC–NE system is important for emotion–cognition interactions (Markovic et al. Reference Markovic, Anderson and Todd2014; McGaugh Reference McGaugh2000; Reference McGaugh2004; McIntyre et al. Reference McIntyre, McGaugh and Williams2012). However, they have focused mostly on how NE interacts with the amygdala to enhance processing and consolidation of emotional stimuli at the expense of processing neutral stimuli (e.g., Strange & Dolan Reference Strange and Dolan2004; Strange et al. Reference Strange, Hurlemann and Dolan2003). In contrast, we argue that the LC–NE system promotes selectivity for any prioritized stimuli, irrespective of whether they are emotional or nonemotional.

In this section, we review the functional anatomy of the LC–NE system. A small nucleus in the brainstem known as the locus coeruleus (LC) releases NE when people are aroused – whether by a reward or punishment, a loud noise, or a disturbing image. LC axons are distributed throughout most of the brain (Gaspar et al. Reference Gaspar, Berger, Febvret, Vigny and Henry1989; Javoy-Agid et al. Reference Javoy-Agid, Scatton, Ruberg, L'heureux, Cervera, Raisman, Maloteaux, Beck and Agid1989; Levitt et al. Reference Levitt, Rakic and Goldman-Rakic1984; Swanson & Hartman Reference Swanson and Hartman1975), enabling NE to modify neural processing both locally and more globally in large-scale functional brain networks. How does the LC influence information processing in most cortical and subcortical regions? One might think that a hormone released under conditions of arousal would amp up brain activity. But instead, NE quiets most neuronal activity. In turn, this quiet backdrop makes those select few representations that NE amplifies stand out even more.

4.1. Functional neuroanatomy of the LC–NE system

The LC is the primary source of cortical NE and helps determine arousal levels (Berridge & Waterhouse Reference Berridge and Waterhouse2003; Berridge et al. Reference Berridge, Schmeichel and Espana2012; Samuels & Szabadi Reference Samuels and Szabadi2008a; Reference Samuels and Szabadi2008b). Tonic, or background, levels of LC activity help regulate levels of wakefulness (Carter et al. Reference Carter, Yizhar, Chikahisa, Nguyen, Adamantidis, Nishino, Deisseroth and de Lecea2010). Phasic, or transient, bursts of LC activity occur in response to novel, stressful, or salient stimuli (Aston-Jones & Bloom Reference Aston-Jones and Bloom1981; Foote et al.Reference Foote, Aston-Jones and Bloom1980; Grant et al. Reference Grant, Aston-Jones and Redmond1988; Sara & Bouret Reference Sara and Bouret2012; Sara & Segal Reference Sara and Segal1991; Vankov et al. Reference Vankov, Hervé-Minvielle and Sara1995) or to top-down signals associated with decision outcomes or goal relevance (Aston-Jones & Cohen Reference Aston-Jones and Cohen2005; Aston-Jones et al. Reference Aston-Jones, Rajkowski and Cohen1999). Emotionally salient stimuli also induce LC phasic activity irrespective of whether stimuli are positive (Bouret & Richmond Reference Bouret and Richmond2015; Grant et al. Reference Grant, Aston-Jones and Redmond1988) or aversive (Chen & Sara Reference Chen and Sara2007; Grant et al. Reference Grant, Aston-Jones and Redmond1988).

With highly divergent branching axons, the LC projects to every major region of cortex, despite its relatively small number of neurons (13,000 per hemisphere in humans) (Foote & Morrison Reference Foote and Morrison1987). Subcortical regions that underlie memory, attention, and emotional processing, including the hippocampus, frontoparietal cortex, and amygdala, are also innervated by the LC (Berridge & Waterhouse Reference Berridge and Waterhouse2003). LC axon varicosities release NE into extracellular space, allowing it to activate a broad swath of receptors within a diffusion zone (Beaudet & Descarries Reference Beaudet and Descarries1978; Descarries et al. Reference Descarries, Watkins and Lapierre1977; O'Donnell et al. Reference O'Donnell, Zeppenfeld, McConnell, Pena and Nedergaard2012).

In target brain sites, NE binds to multiple receptor subtypes (i.e., α1, α2, and β receptors) that are located both pre- and postsynaptically on neurons and astrocytes (Berridge & Waterhouse Reference Berridge and Waterhouse2003; O'Donnell et al. Reference O'Donnell, Zeppenfeld, McConnell, Pena and Nedergaard2012; Terakado Reference Terakado2014; Tully & Bolshakov Reference Tully and Bolshakov2010). Whereas α2-adrenoreceptors limit global and local NE release by acting as autoreceptors and decrease cell excitability, β-adrenoreceptor activation generally increases cell excitability, network activity, and synaptic plasticity (Berridge & Waterhouse Reference Berridge and Waterhouse2003; Marzo et al. Reference Marzo, Bai and Otani2009; Nomura et al. Reference Nomura, Bouhadana, Morel, Faure, Cauli, Lambolez and Hepp2014; Starke Reference Starke2001). α1-Adrenoreceptors recruit phospholipase activation and typically increase cell excitability via the inhibition of potassium channels (Wang & McCormick Reference Wang and McCormick1993). Thus, the relative density and localization of adrenoreceptor subtypes help determine how arousal-induced NE release will affect neural processing in different brain regions.

4.2. NE decreases neuronal noise in sensory regions during arousal

In the 1970s, researchers proposed that LC–NE activity enhances signal-to-noise ratios in target neurons in sensory regions (Foote et al. Reference Foote, Freedman and Oliver1975; Freedman et al. Reference Freedman, Hoffer, Woodward and Puro1977; Segal & Bloom Reference Segal and Bloom1976; Waterhouse & Woodward Reference Waterhouse and Woodward1980). For example, recording from individual neurons in awake squirrel monkeys revealed that NE application reduced spontaneous activity more than it reduced activity evoked by species-specific vocalizations (Foote et al. Reference Foote, Freedman and Oliver1975). Noradrenergic regulation of signal-to-noise ratios is characterized by two simultaneous effects: (1) most neurons in a population decrease spontaneous firing, and (2) the few neurons that typically respond strongly to the specific current sensory stimuli either show no decrease or an increase in firing, unlike the majority of neurons for which the stimuli typically evoke weak responses (Foote et al. Reference Foote, Freedman and Oliver1975; Freedman et al. Reference Freedman, Hoffer, Woodward and Puro1977; Hasselmo et al. Reference Hasselmo, Linster, Patil, Ma and Cekic1997; Kuo & Trussell Reference Kuo and Trussell2011; Livingstone & Hubel Reference Livingstone and Hubel1981; O'Donnell et al. Reference O'Donnell, Zeppenfeld, McConnell, Pena and Nedergaard2012; Oades Reference Oades1985; Waterhouse & Woodward Reference Waterhouse and Woodward1980).

Intracellular recording data in awake animals support and extend these early observations. Both inhibitory and excitatory neurons are depolarized in aroused cortex when mice run (Polack et al. Reference Polack, Friedman and Golshani2013). Yet, consistent with earlier reports of a quieter cortex under arousal, inhibitory neurons are more depolarized than excitatory neurons (Polack et al. Reference Polack, Friedman and Golshani2013). Moreover, surround inhibition dominates sensory responses during wakefulness compared with anesthesia, increasing the speed and selectivity of responses to stimuli in the center of the receptive field (Haider et al. Reference Haider, Häusser and Carandini2013). NE mediates the increase in widespread depolarization and the increase in inhibitory activity in visual cortex that together increase the signal-to-noise ratio (Polack et al. Reference Polack, Friedman and Golshani2013). The effect of NE has also been characterized as increasing the gain on the activation function of neural networks (Fig. 5) (Aston-Jones & Cohen Reference Aston-Jones and Cohen2005).

Figure 5. Norepinephrine gain modulation makes the non-linear input–output function more extreme, increasing the activity of units receiving excitatory input and decreasing the activity of units receiving inhibitory input. Adapted from Aston-Jones and Cohen (Reference Aston-Jones and Cohen2005).

Arousal is also characterized by cortical desynchronization, both globally when comparing wakefulness with anesthesia (Constantinople & Bruno Reference Constantinople and Bruno2011) or locomotion with being stationary (Polack et al. Reference Polack, Friedman and Golshani2013) and locally among neurons corresponding to attended representations (Fries et al. Reference Fries, Reynolds, Rorie and Desimone2001). Such decreases in cortical slow wave synchrony under arousal are likely mediated by LC activity (Berridge & Foote Reference Berridge and Foote1991; Berridge et al. Reference Berridge, Page, Valentino and Foote1993). Synchronous slow wave neural activity may gate sensory inputs, whereas desynchronized activity permits communication of cortical representations of stimuli across the brain (Luczak et al. Reference Luczak, Bartho and Harris2013). Cortical cell depolarization, desynchronization, and increased responsiveness to external input also occur with pupil dilation (Reimer et al. Reference Reimer, Froudarakis, Cadwell, Yatsenko, Denfield and Tolias2014; Vinck et al. Reference Vinck, Batista-Brito, Knoblich and Cardin2014), and pupil dilation tracks LC activity (Murphy et al. Reference Murphy, O'Connell, O'Sullivan, Robertson and Balsters2014).

4.3 Summary

Years of research indicate that NE suppresses weak or random neuronal activity, but not strong activity. This is consistent with the increased selectivity seen under arousal (sect. 2). In the next section, we outline a model of how NE has such different outcomes depending on activity level.

5. Glutamate amplifies noradrenergic effects: The core noradrenergic selectivity mechanism under arousal

Now we turn to our GANE model, a novel brain-based account of how arousal amplifies priority effects in perception and memory. We propose that local glutamate–NE interactions increase gain under arousal. Glutamate is the most prevalent excitatory neurotransmitter in the brain (Meldrum Reference Meldrum2000). Glutamate receptors such as α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-D-aspartate (NMDA) receptors mediate rapid excitatory synaptic transmission, neural network connectivity, and long-term memory (Bliss & Collingridge Reference Bliss and Collingridge1993; Lynch Reference Lynch2004; Traynelis et al. Reference Traynelis, Wollmuth, McBain, Menniti, Vance, Ogden, Hansen, Yuan, Myers and Dingledine2010).

In addition to point-to-point transmission across a synapse, some glutamate escapes the synaptic cleft, resulting in “glutamate spillover” (Okubo et al. Reference Okubo, Sekiya, Namiki, Sakamoto, Iinuma, Yamasaki, Watanabe, Hirose and Iino2010). In this section, we outline evidence that glutamate spillover attracts and amplifies local NE release via positive feedback loops. These self-regulating NE hotspots generate even greater excitatory activity in the vicinity of synapses transmitting high-priority representations, in contrast with NE's suppressive effects in the more widespread non–hotspot regions.

5.1. The NE hotspot: How local NE–glutamate positive feedback loops amplify processing of high-priority information

5.1.1 High glutamate activity stimulates adjacent NE varicosities to release more NE

The first demonstrations of glutamate-evoked effects on NE found that glutamate increased NE release via NMDA and non-NMDA glutamate receptors on LC axons (Fink et al. Reference Fink, Göthert, Molderings and Schlicker1989; Göthert & Fink Reference Göthert, Fink, Bönisch, Graefe, Langer and Schömig1991; Lalies et al. Reference Lalies, Middlemiss and Ransom1988; Nelson et al. Reference Nelson, Zaczek and Coyle1980; Pittaluga & Raiteri Reference Pittaluga and Raiteri1990; Reference Pittaluga and Raiteri1992; Vezzani et al. Reference Vezzani, Wu and Samanin1987; Wang et al. Reference Wang, Andrews and Thukral1992; see also Jones et al. Reference Jones, Snell and Johnson1987). In these studies, glutamate-evoked NE release occurred for NE varicosities in all cortical structures investigated in vitro: olfactory bulb, hippocampus, and throughout neocortex. In vivo experiments replicated the effect with targeted glutamate in rodent prefrontal cortex (Lehmann et al. Reference Lehmann, Valentino and Robine1992). Other neurotransmitters associated with arousal, such as histamine (Burban et al. Reference Burban, Faucard, Armand, Bayard, Vorobjev and Arrang2010) and orexin (Tose et al. Reference Tose, Kushikata, Yoshida, Kudo, Furukawa, Ueno and Hirota2009), enhance glutamate-evoked NE release. Central to our hypothesis, glutamate-evoked NE release occurs in human neocortex (Fink et al. Reference Fink, Schultheiß and Göthert1992; Luccini et al. Reference Luccini, Musante, Neri, Brambilla Bas, Severi, Raiteri and Pittaluga2007; Pittaluga et al. Reference Pittaluga, Pattarini, Andrioli, Viola, Munari and Raiteri1999).

How do these glutamate–NE interactions occur? LC axon varicosities rarely make direct synaptic contacts (e.g., only ~5% in rat cortex) (Vizi et al. Reference Vizi, Fekete, Karoly and Mike2010), but the distribution of these varicosities suggests they should often be found near glutamate terminals at excitatory synapses in neocortex (Benavides-Piccione et al. Reference Benavides-Piccione, Arellano and DeFelipe2005; Gaspar et al. Reference Gaspar, Berger, Febvret, Vigny and Henry1989). Another critical point is that LC neurons produce the NMDA receptor subunits needed for glutamate to modulate the release of NE from LC axon varicosities (Chandler et al. Reference Chandler, Gao and Waterhouse2014; Grilli et al. Reference Grilli, Zappettini, Zanardi, Lagomarsino, Pittaluga, Zoli and Marchi2009; Petralia et al. Reference Petralia, Yokotani and Wenthold1994; Zhu et al. Reference Zhu, Brodsky, Gorman and Inturrisi2003).

New technologies enable the visualization of glutamate spillover in cerebellum, neocortex, and hippocampus (Okubo et al. Reference Okubo, Sekiya, Namiki, Sakamoto, Iinuma, Yamasaki, Watanabe, Hirose and Iino2010; Okubo & Iino Reference Okubo and Iino2011). Multiple action potentials in a row yield sufficient spillover glutamate to activate nonsynaptic NMDA and group I metabotropic glutamate receptors (mGluRs) (which are co-expressed on NE varicosities and enhance glutamate-evoked NE release in rodent and human cortices [Luccini et al. Reference Luccini, Musante, Neri, Brambilla Bas, Severi, Raiteri and Pittaluga2007]), but probably yield insufficient glutamate to recruit lower-affinity AMPA receptors (Okubo et al. Reference Okubo, Sekiya, Namiki, Sakamoto, Iinuma, Yamasaki, Watanabe, Hirose and Iino2010). Extracellular concentrations of the spillover rapidly decrease as distance from the synaptic cleft increases (Vizi et al. Reference Vizi, Fekete, Karoly and Mike2010), and the upper limit of glutamate spillover effects is estimated to be no greater than a few micrometers (Okubo & Iino Reference Okubo and Iino2011).

That spillover glutamate is sufficient to activate NMDA, but not AMPA receptors is another key factor. Unlike AMPA receptors, NMDA receptors require synchronized glutamate stimulation and neuron depolarization to activate (Lüscher & Malenka Reference Lüscher and Malenka2012). Thus, local glutamate spillover must co-occur with phasic depolarizing bursts of activity in LC neurons to recruit additional local NE release. Furthermore, a unique feature of NMDA receptors is that they require a co-agonist, which could be either glycine or D-serine (Wolosker Reference Wolosker2007). Glutamate stimulates astrocytes to release these co-agonists (Harsing & Matyus Reference Harsing and Matyus2013; Van Horn et al. Reference Van Horn, Sild and Ruthazer2013), and both glutamate and NE stimulate astrocytes to release glutamate (Parpura & Haydon Reference Parpura and Haydon2000). These additional glutamate interactions should further enhance NMDA receptor-mediated NE release (Fig. 6) (Paukert et al. Reference Paukert, Agarwal, Cha, Doze, Kang and Bergles2014). Together, these local glutamate–NE interactions support the emergence and sustainment of hotspots in the vicinity of the most activated synapses when arousal is induced.

Figure 6. Norepinephrine (NE) “hotspot” mechanism. (1A) Spillover glutamate (green dots) from highly active neurons interacts with nearby depolarized NE varicosities in a positive feedback loop involving N-methyl-D-aspartate (NMDA) and other glutamate receptors that leads to greater local NE release (maroon dots). The glutamatergic NMDA receptors require concomitant depolarization of noradrenergic axons (lightning symbol). Thus, hotspots amplify prioritized inputs most effectively under phasic arousal. (1B) Glutamate also recruits nearby astrocytes to release serine, glycine (orange dots), and additional glutamate. (2) Greater NE release creates concentration levels sufficient to activate low-affinity β-adrenoreceptors, which enhances neuron excitability. (3) Via activation of β- and α2A-auto-receptors, NE can stimulate and inhibit additional NE release, respectively. (4) Within hotspots, NE engages β-adrenoreceptors on pre-synaptic glutamate terminals to increase glutamate release. (5) Finally, NE binding to postsynaptic β-adrenoreceptors also inhibits the slow after-hyperpolarization, enabling the neuron to fire even longer. AMPA=α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid; mGluR=metabotropic glutamate receptor.

Consistent with the existence of glutamate–NE interactions, local NE release in the region of an activated novel representation depends on the coincident timing of the novel event and an arousing event (Rangel & Leon Reference Rangel and Leon1995). For example, when footshock was administered to a rat while it explored a novel environment, NE levels rose substantially higher and remained elevated longer than when footshock was administered to the rat in its holding cage (Fig. 7) (McIntyre et al. Reference McIntyre, Hatfield and McGaugh2002). The amygdala presumably activated in response to the novelty of the new environment (Weierich et al. Reference Weierich, Wright, Negreira, Dickerson and Barrett2010), and glutamate associated with that representational network amplified the NE release initiated by the shock.

Figure 7. A rat receiving a foot shock (FS) in its home cage exhibits a brief increase in norepinephrine (NE) levels (gray triangles). A novel training environment does not increase NE on its own (black squares), but NE levels increase dramatically when shock is combined with that novel training environment (black diamonds). Figure reprinted with permission from McIntyre et al. (Reference McIntyre, Hatfield and McGaugh2002).

Hotspot effects have also been observed in the bed nucleus of the stria terminalis immediately after training rats on an inhibitory avoidance task (Liu et al. Reference Liu, Chen and Liang2009). When infused separately at low doses, glutamate and NE each had no effect. But when infused together at the same low doses, they produced marked memory enhancements. Infusion of a higher dose of glutamate led to memory enhancements that were blocked by propranolol, indicating that the glutamate effect required β-adrenergic activity, which, as we describe next, is another key feature of our hotspot model.

5.1.2. α- and β-adrenoreceptors exert different effects on neuronal excitability and require different NE concentrations to be activated

To be engaged, β-adrenoreceptors require relatively high NE concentrations, α1-adrenoreceptors more moderate levels, and α2-adrenoreceptors the lowest NE concentrations (Ramos & Arnsten Reference Ramos and Arnsten2007). Thus, under arousal, α2-adrenoreceptor effects should be widespread, whereas β-adrenoreceptors should be activated only at hotspot regions because the local glutamate-evoked NE release there results in higher NE levels. Next, we describe the importance of this distinction for adrenergic autoreceptors.

5.1.3 Adrenergic autoreceptors inhibit or amplify their own NE release

Autoreceptors at NE varicosities serve as neural gain amplifiers by taking opposing action at low and high local levels of NE. The predominant presynaptic noradrenergic autoreceptor in humans is the α2A-adrenoreceptor (Starke Reference Starke2001), which inhibits NE release when it detects low or moderate levels of NE (Delaney et al. Reference Delaney, Crane and Sah2007; Gilsbach & Hein Reference Gilsbach, Hein, Südhof and Starke2008; Langer Reference Langer2008; Starke Reference Starke2001). In contrast, presynaptic β-adrenoreceptors amplify NE release when activated by high levels of NE (Chang et al. Reference Chang, Goshima and Misu1986; Misu & Kubo Reference Misu and Kubo1986; Murugaiah & O'Donnell Reference Murugaiah and O'Donnell1995a; Reference Murugaiah and O'Donnell1995b; Ueda et al. Reference Ueda, Goshima, Kubo and Misu1985). In addition, α2A-adrenoreceptors may lose affinity for NE when neurons are depolarized (Rinne et al. Reference Rinne, Birk and Bünemann2013), which would remove their inhibitory influence as a region becomes highly active. However, this loss of affinity recovers at saturating levels of NE (Rinne et al. Reference Rinne, Birk and Bünemann2013), which should help prevent the runaway excitation that could otherwise emerge because of the NE–glutamate feedback loop. Together with glutamate-evoked NE release (see sect. 5.1.1), the opposing effects of these different autoreceptors at low and high levels of NE provide an elegant way for the LC to modulate signal gain depending on the degree of local excitation.

5.1.4 Elevated local NE at hotspots engages β-adrenoreceptors on the glutamate terminals transmitting the prioritized representation

This stimulates an even greater release of glutamate, thereby amplifying the high-priority excitatory signal (Ferrero et al. Reference Ferrero, Alvarez, Ramirez-Franco, Godino, Bartolome-Martin, Aguado, Torres, Lujan, Ciruela and Sanchez-Prieto2013; Gereau & Conn Reference Gereau and Conn1994; Herrero & Sánchez-Prieto Reference Herrero and Sánchez-Prieto1996; Ji et al. Reference Ji, Cao, Zhang, Feng, Zhang, Ma and Li2008; Kobayashi et al. Reference Kobayashi, Kojima, Koyanagi, Adachi, Imamura and Koshikawa2009; Mobley & Greengard Reference Mobley and Greengard1985). That β-adrenoreceptors require relatively high NE concentrations to be engaged further biases this form of cortical autoregulation towards the most active synapses. Through these feedback processes, high-priority representations are “self-selected” to produce a stronger glutamate message and excite their connections more effectively under arousal. This stronger glutamate message should also promote selective memory of such stimuli (see sect. 6.1). In contrast, activation of lower threshold α2-adrenoreceptors inhibits glutamate release (Bickler & Hansen Reference Bickler and Hansen1996; Egli et al. Reference Egli, Kash, Choo, Savchenko, Matthews, Blakely and Winder2005), providing a mechanism for inhibiting lower-priority neural activity under arousal.

5.1.5 Higher NE levels at hotspots help prolong the period of neuronal excitation by temporarily inhibiting processes that normalize neuron activity

Under normal conditions, the slow after-hyperpolarization current habituates a postsynaptic neuron's responses following prolonged depolarization (Alger & Nicoll Reference Alger and Nicoll1980). However, even here, NE seems to benefit prioritized inputs by prolonging neuronal excitation via β-adrenoreceptors inhibiting the slow after-hyperpolarization (Madison & Nicoll Reference Madison and Nicoll1982; Nicoll Reference Nicoll1988).

In summary, different receptor subtypes enable NE to ignite hotspots in regions with high glutamate levels while inhibiting activity elsewhere. As we outline later, this diversity in NE receptor subtypes also plays an important role in shaping synaptic plasticity to favor prioritized representations under phasic arousal.

5.2. NE hotspots modulate interneurons and GABAergic transmission to increase lateral inhibition of competing representations

Increases in glutamate and NE at hotspots should also enhance inhibitory activity that mediates competition among neurons. γ-Aminobutyric acid (GABA) is the most widespread inhibitory transmitter from neurons that suppress the responses of other neurons or neuronal circuits (Petroff Reference Petroff2002). Strong glutamate activity in cortical circuits stimulates local GABAergic activity, which increases the inhibitory effects of highly active regions on neighboring, competing neural circuits (Xue et al. Reference Xue, Atallah and Scanziani2014). Increases in NE also activate inhibition directly, with intermediate concentrations engaging maximal suppression (Nai et al. Reference Nai, Dong, Hayar, Linster and Ennis2009).

Subtypes of interneurons respond differently to NE in ways that should further increase neural gain. Although LC–NE activity activates interneurons that mediate lateral inhibition (Salgado et al. Reference Salgado, Garcia-Oscos, Martinolich, Hall, Restom, Tseng and Atzori2012a), it can also suppress interneurons with feedforward connections (Brown et al. Reference Brown, Walling, Milway and Harley2005), such that a strong signal will inhibit competing representations while enhancing activity in other neurons within its processing pathway.

5.3. NE directs metabolic resources to where they are most needed

To optimize processing of salient events, NE also helps coordinate the delivery of the brain's energy supplies, allowing it to mobilize resources quickly when needed (e.g., Toussay et al. Reference Toussay, Basu, Lacoste and Hamel2013). The brain's most essential energy supplies, oxygen and glucose, are delivered via the bloodstream. One key way that NE coordinates energy delivery is by increasing the spatial and temporal synchronization of blood delivery to oxygen demand within the brain. For example, in mice, as NE levels increase, overall blood vessel diameter in the brain decreases, but the spatial and temporal selectivity of blood distribution to active task-relevant regions increases (Bekar et al. Reference Bekar, Wei and Nedergaard2012).

In addition to distributing blood flow, NE also interacts with astrocytes locally to mobilize energy resources throughout the cortex. When a particular area of the brain needs more energy, it can obtain fuel not only from glucose, but also from glycogen in astrocytes (Pellerin & Magistretti Reference Pellerin and Magistretti2012). NE speeds up the process of obtaining energy from glycogen (Magistretti et al. Reference Magistretti, Morrison, Shoemaker, Sapin and Bloom1981; Sorg & Magistretti Reference Sorg and Magistretti1991; Walls et al. Reference Walls, Heimbürger, Bouman, Schousboe and Waagepetersen2009). While α1- and α2-adrenoreceptors mediate glutamate uptake and glycogen production in astrocytes, β-adrenoreceptors stimulate the breakdown of glycogen to provide rapid energy support in highly active local regions (O'Donnell et al. Reference O'Donnell, Zeppenfeld, McConnell, Pena and Nedergaard2012), further amplifying NE hotspot activity.

5.4. Summary

At the local neuronal level, NE suppresses most activity, but amplifies the strongest activity as a result of the differential effects of NE on different adrenoreceptor subtypes. The amplification of strong activity occurs via “NE hotspots,” where positive feedback loops between local NE and glutamate release increase the strength of activated representations. To sustain higher levels of activity, hotspots also recruit limited metabolic resources. At the circuit level, the increased glutamate and NE produced at hotspots recruit nearby astrocytes that supply additional energy to active neurons. On a broader scale, NE facilitates the redistribution of blood flow towards hotspots and away from areas of lower activity. Thus, by influencing multiple levels of brain function, NE selectively amplifies self-regulating processes that bias processing in favor of prioritized information.

6. Roles of the LC–NE system in memory

So far we have focused on how arousal increases the gain on prioritization processes in perception, attention, and initial memory encoding. Now we turn to memory consolidation processes. Experiencing an emotionally intense event influences the vividness and longevity of recent memory traces, enhancing or impairing them based on their priority (e.g., Fig. 4) (Knight & Mather Reference Knight and Mather2009; Sakaki et al. Reference Sakaki, Fryer and Mather2014a). Much research has indicated that NE is involved in memory consolidation effects (for a review, see McGaugh [Reference McGaugh2013]), but there has been little focus on the interplay between NE's enhancing and impairing effects during memory consolidation.

The durability of memories depends on adjustments in the strength of communication across synapses via processes known as long-term potentiation (LTP) and long-term depression (LTD). Whether neural activity triggers LTP or LTD depends on the relative timing of spikes in pre- and postsynaptic neurons (Nabavi et al. Reference Nabavi, Fox, Proulx, Lin, Tsien and Malinow2014), and whether LTP and LTD are maintained depends on protein synthesis processes (Abraham & Williams Reference Abraham and Williams2008). We propose that two main NE mechanisms modulate LTP and LTD, leading to “winner-take-more” and “loser-take-less” outcomes in long-term memory: (1) hotspot modulation of the probability of LTP (higher NE levels engaging LTP) and LTD (relatively lower NE levels promoting LTD), and (2) NE-enhanced protein synthesis supporting long-term maintenance of LTP and LTD.

6.1. NE gates spike-timing-dependent LTD and LTP

Long-term potentiation and long-term depression are often studied in brain slices in a petri dish using high-frequency electric stimulation to induce LTP and repeated slow stimulation to induce LTD. But in the brain's natural context involving constant barrages of pre-synaptic activity generating postsynaptic spikes, the relative timing of pre- and postsynaptic activity helps determine whether LTP or LTD occurs. Furthermore, to avoid constant up-and-down adjustment of synapses based on random firing patterns, neuromodulators such as NE and dopamine signal when the relationship between pre-synaptic and postsynaptic activity is likely to be meaningful (Pawlak et al. Reference Pawlak, Wickens, Kirkwood and Kerr2010). In vivo studies indicate that spike-timing-dependent LTP or LTD requires these neuromodulators (Huang et al. Reference Huang, Rozas, Treviño, Contreras, Yang, Song, Yoshioka, Lee and Kirkwood2014; Johansen et al. Reference Johansen, Diaz-Mataix, Hamanaka, Ozawa, Ycu, Koivumaa, Kumar, Hou, Deisseroth and Boyden2014). In particular, by binding to G-coupled receptors, NE modulates kinases and phosphatases that determine whether LTP or LTD induction occurs (Treviño et al. Reference Treviño, Huang, He, Ardiles, De Pasquale, Guo, Palacios, Huganir and Kirkwood2012b; Tully & Bolshakov Reference Tully and Bolshakov2010).

Different adrenoreceptor subtypes appear to mediate NE's regulation of spike-timing-dependent LTP and LTD. Spike-timing-dependent LTP is initiated primarily by β-adrenoreceptor activation, whereas α1-adrenoreceptors promote spike-timing-dependent LTD (Salgado et al. Reference Salgado, Kohr and Trevino2012b). Critically, Salgado and colleagues reported that the LTP promoting activation of β-adrenoreceptors requires concentrations of NE ~25-fold higher (8.75 µM) than the NE concentration that promotes α1-adrenoreceptor-mediated spike-timing-dependent LTD (0.3 µM) in vitro. This agrees with an in vivo estimate of a 30-fold increase in NE associated with LTP in dentate gyrus (Harley et al. Reference Harley, Lalies and Nutt1996). The increase in NE required to support spike-timing-dependent LTP is substantially higher than the increases in NE levels seen when experimenters stimulate LC and measure NE in cortex or hippocampus using microdialysis (e.g., approximately twice baseline [Florin-Lechner et al. Reference Florin-Lechner, Druhan, Aston-Jones and Valentino1996], ~0.5 µM [Palamarchouk et al. Reference Palamarchouk, Zhang, Zhou, Swiergiel and Dunn2000]). Thus, there is a discrepancy between the NE levels needed for spike-timing-dependent LTP to occur and the levels measured in laboratory studies. Our GANE model accounts for this difference, as it posits that LC activation interacts with prioritized representations to elicit much higher NE release in a select few local hotspots than elsewhere, such that the average cortical sampling location would not detect the NE levels needed to support LTP.

The NE hotspot model supports a range of simultaneous NE modulatory actions. At high-priority hotspots, NE levels should be sufficiently high to engage β-adrenoreceptors and initiate spike-timing-dependent LTP (Salgado et al. Reference Salgado, Kohr and Trevino2012b; Treviño et al. Reference Treviño, Huang, He, Ardiles, De Pasquale, Guo, Palacios, Huganir and Kirkwood2012b). Conversely, areas with lower glutamate activity, where NE levels are by comparison modestly increased, would undergo LTD as a result of the engagement of relatively higher affinity α1-adrenergic receptors (Huang et al. Reference Huang, Rozas, Treviño, Contreras, Yang, Song, Yoshioka, Lee and Kirkwood2014; Salgado et al. Reference Salgado, Kohr and Trevino2012b; Treviño et al. Reference Treviño, Frey and Köhr2012a). Variations in NE levels in the alert brain thereby support bidirectional plasticity (Salgado et al. Reference Salgado, Kohr and Trevino2012b; Treviño et al. Reference Treviño, Huang, He, Ardiles, De Pasquale, Guo, Palacios, Huganir and Kirkwood2012b).

6.2. NE increases protein synthesis processes that promote memory consolidation: Critical role of β-adrenoreceptors

Arousal levels in the minutes and hours before or after an event also influence later memory for it. Here we review evidence that these wider time window effects of arousal depend on NE's enhancement of protein synthesis processes that determine the long-term durability of salient memories. Critically, such regulation of memory processes by NE appears to be mediated by β-adrenoreceptors, which we propose are selectively activated in high-priority representational networks.

The role of NE in gating the synthesis of plasticity-related proteins has been recognized for more than a decade (Cirelli et al. Reference Cirelli, Pompeiano and Tononi1996; Cirelli & Tononi Reference Cirelli and Tononi2000). For example, plasticity-related proteins promoted by an LC–NE novelty signal can enhance long-term memory consolidation of another salient, but otherwise poorly consolidated event (i.e., learning that stepping off of a platform leads to a weak shock) that occurs 1 hour later or even 1 hour prior to the novelty experience (Moncada & Viola Reference Moncada and Viola2007; Moncada et al. Reference Moncada, Ballarini, Martinez, Frey and Viola2011).

Blocking β-adrenoreceptors or protein synthesis prior to novelty exposure prevents novelty facilitation of LTP (Straube et al. Reference Straube, Korz, Balschun and Frey2003). What is particularly striking is that β-adrenoreceptor activation at time 1 primes synapses to induce LTP at time 2 an hour later, even when β-adrenoreceptors are blocked by propranolol during time 2 (Tenorio et al. Reference Tenorio, Connor, Guévremont, Abraham, Williams, O'Dell and Nguyen2010). However, if protein synthesis processes are blocked during time 2, the time 1 priming event does not lead to enhancement. The plasticity marker, Arc protein, is recruited by β-adrenoreceptor activation in the presence of NMDA receptor activation (Bloomer et al. Reference Bloomer, VanDongen and VanDongen2008). Hotspots are characterized by high levels of glutamate release and β-adrenoreceptor activation; thus, emotional arousal should elevate Arc selectively in NE hotspots.

β-Adrenergic activation after learning or weak LTP induction can also convert short-term LTP to more lasting protein synthesis-dependent late LTP (Gelinas & Nguyen Reference Gelinas and Nguyen2005; Gelinas et al. Reference Gelinas, Tenorio, Lemon, Abel and Nguyen2008). Likewise, stimulating the basolateral amygdala either before or after tetanization of the hippocampus converts early LTP to late LTP via a β-adrenoreceptor mechanism (Frey et al. Reference Frey, Bergado-Rosado, Seidenbecher, Pape and Frey2001). Activation of β-adrenoreceptors also shields late LTP from subsequent depotentiation (Gelinas & Nguyen Reference Gelinas and Nguyen2005; Katsuki et al. Reference Katsuki, Izumi and Zorumski1997).

Creation of long-lasting memories depends on the protein synthesis cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/cAMP response element-binding protein (CREB) pro-signaling cascade (Kandel Reference Kandel2012; O'Dell et al. Reference O'Dell, Connor, Gelinas and Nguyen2010). Neuronal ensembles in which the cAMP/PKA/CREB cascade has been activated, as happens with the engagement of β-adrenoreceptors, have been found to be selectively allocated to the engram representing a memory (Han et al. Reference Han, Kushner, Yiu, Cole, Matynia, Brown, Neve, Guzowski, Silva and Josselyn2007). Furthermore, increasing excitability via different methods mimics the effects of CREB overexpression, suggesting that neurons are recruited to an engram based on their neural excitability (Frankland & Josselyn Reference Frankland and Josselyn2015; Zhou et al. Reference Zhou, Won, Karlsson, Zhou, Rogerson, Balaji, Neve, Poirazi and Silva2009). Thus, by modulating CREB and other aspects of neural excitability, NE hotspots should help determine which neurons are allocated to an engram and stabilized in long-term memory.

6.3. Summary

Local NE concentration is the key to understanding how NE mediates arousal's dichotomous effects on memory. Previous research has indicated that different NE levels regulate different forms of spike-timing-dependent plasticity by engaging distinct adrenoreceptors. Whereas NE binding to moderate-affinity α1-adrenergic receptors leads to LTD and memory suppression, NE binding to lower-affinity β-adrenoreceptors leads to LTP and memory enhancement. We propose that local discrepancies in NE levels arise from self-regulating NE–glutamate interactions. Where NE concentrations become high enough to engage low-affinity β-adrenoreceptors, a cascade of intracellular events triggers protein synthesis processes that enable long-term memory consolidation of the high-priority trace. In contrast, more modest increases in NE levels at less active regions lead to LTD, ensuring less important events are forgotten. Before or after encoding, the confluence of protein synthesis and β-adrenoreceptor activation selectively strengthens memory consolidation when these mechanisms are recruited close in time.

7. Beyond local GANE: Broader noradrenergic circuitry involved in increased selectivity under arousal

Beyond local effects, NE increases biased competition processes by altering how different brain structures interact. With its widely distributed afferents, the LC–NE system influences neural processing in many brain regions when an arousing event occurs. NE release can translate local hotspot effects to more global winner-take-more effects by modulating neuronal oscillations. Furthermore, cortical and subcortical priority signals modulate glutamate release in sensory regions and the hippocampus as mental representations are formed and sustained. As previously reviewed (see sect. 5.1), glutamate is essential for NE release to selectively amplify the processing of significant information. Thus, by stimulating local glutamate release and recruiting LC firing, key brain structures can optimize synaptic conditions for arousal to ignite hotspots.

7.1. Activation of inhibitory networks by NE primes neuronal synchronization among high-priority neural ensembles

So far, we have reviewed evidence that NE hotspots amplify the effects of priority, enhancing salient features while suppressing noisy background activity. In this section, we discuss the possibility that neuronal oscillations communicate activity in local hotspots more globally (Singer Reference Singer1993).

The first candidate is gamma synchrony (30–80 Hz). Conceptual frameworks of neural oscillations posit that gamma synchrony supports gain modulation in local networks (Fries Reference Fries2009), such that a target area can oscillate in phase with only one of two competing inputs. As a result, the synaptic input that more successfully synchronizes its activity with the target region is amplified, whereas the less synchronized input is suppressed. Gamma synchrony is likely a key component of selective attention (Baluch & Itti Reference Baluch and Itti2011; Fries Reference Fries2009; Fries et al. Reference Fries, Reynolds, Rorie and Desimone2001).

Gamma oscillations are generated by a feedback loop between excitatory pyramidal cells and fast-spiking parvalbumin-positive inhibitory interneurons (Buzsáki & Wang Reference Buzsáki and Wang2012; Cardin et al. Reference Cardin, Carlen, Meletis, Knoblich, Zhang, Deisseroth, Tsai and Moore2009; Carlen et al. Reference Carlen, Meletis, Siegle, Cardin, Futai, Vierling-Claassen, Ruhlmann, Jones, Deisseroth, Sheng, Moore and Tsai2012; Sohal et al. Reference Sohal, Zhang, Yizhar and Deisseroth2009). Noradrenergic release activates these interneurons (Cox et al. Reference Cox, Racca and Lebeau2008; Huang et al. Reference Huang, Huganir and Kirkwood2013; Toussay et al. Reference Toussay, Basu, Lacoste and Hamel2013) and increases gamma synchrony in these target regions (Gire & Schoppa Reference Gire and Schoppa2008; Haggerty et al. Reference Haggerty, Glykos, Adams and LeBeau2013; Marzo et al. Reference Marzo, Totah, Neves, Logothetis and Eschenko2014). Emotional arousal also modulates gamma oscillations in regions that process motivational significance, such as the amygdala, sensory cortex, and prefrontal cortex (Headley & Weinberger Reference Headley and Weinberger2013). These results suggest that arousal-induced NE release selectively biases gamma oscillations in favor of the most activated representations in local neuronal ensembles.

Consistent with the hotspot model, increases in local gamma power during cognitive processing in humans are associated with increases in glutamate levels (Lally et al. Reference Lally, Mullins, Roberts, Price, Gruber and Haenschel2014). Increases in local gamma power are also associated with successful memory encoding in humans (Burke et al. Reference Burke, Zaghloul, Jacobs, Williams, Sperling, Sharan and Kahana2013). Likewise, in rats, fear conditioning increases gamma synchronization in sensory cortex (Headley & Pare Reference Headley and Pare2013). Increased gamma power predicts retention of tone–shock associations and enhanced representations of the tone associated with shock in the primary auditory cortex (Headley & Weinberger Reference Headley and Weinberger2011).

Recent research indicates that β-adrenoreceptors recruit in-phase oscillations with gamma activity, whereas α1-adrenoreceptors recruit out-of-phase oscillations (Haggerty et al. Reference Haggerty, Glykos, Adams and LeBeau2013). Given the higher threshold for activating β-adrenergic than α1-adrenergic receptors (see sect. 5.1), these results suggest that high NE levels at hotspots engage β-adrenoreceptors, recruit in-phase oscillations, and increase local network connectivity for prioritized representations. Elsewhere, lower NE levels should only be sufficient to engage α1-adrenoreceptors and thereby reduce local gamma power and diminish local synchronization.

In addition to modulating oscillations in local neuronal ensembles, NE also facilitates oscillatory coupling across regions. Current frameworks of neural synchrony posit that long-range/interregional communication between areas is modulated by oscillation in low-frequency bands, such as theta (4–8 Hz), whereas communication within local networks is modulated by high frequencies, including gamma synchrony (Canolty & Knight Reference Canolty and Knight2010; Von Stein & Sarnthein Reference Von Stein and Sarnthein2000). New research further suggests that optimal network function occurs when gamma is embedded in, and phasically facilitated by, slower theta (or even delta [Lakatos et al. Reference Lakatos, Karmos, Mehta, Ulbert and Schroeder2008]) oscillations (Canolty & Knight Reference Canolty and Knight2010; but see Burke et al. Reference Burke, Zaghloul, Jacobs, Williams, Sperling, Sharan and Kahana2013). This theta–gamma coupling seems to provide a mechanism for interregional communication and cross-location phase coupling across regions to help translate local NE hotspots into global effects.

Activation of the LC–NE system promotes hippocampal theta (e.g., Berridge & Foote Reference Berridge and Foote1991; Walling et al. Reference Walling, Brown, Milway, Earle and Harley2011) and is linked to enhancement of novelty-related hippocampal theta (Kocsis et al. Reference Kocsis, Li and Hajos2007). In humans, the phase coupling of gamma with slower oscillations has been described primarily for neocortex (Canolty et al. Reference Canolty, Edwards, Dalal, Soltani, Nagarajan, Kirsch, Berger, Barbaro and Knight2006), where the role of LC–NE in slower rhythms is less well studied. However, hippocampal theta entrains prefrontal cortical theta (Paz et al. Reference Paz, Bauer and Paré2008). Recently, selective LC–NE activation was found to increase neocortical theta in anesthetized animals (Vazey & Aston-Jones Reference Vazey and Aston-Jones2014). The parvalbumin neurons modulated by NE participate in setting not only gamma, but also theta rhythms (Varga et al. Reference Varga, Oijala, Lish, Szabo, Bezaire, Marchionni, Golshani and Soltesz2014; Wulff et al. Reference Wulff, Ponomarenko, Bartos, Korotkova, Fuchs, Bähner, Both, Tort, Kopell, Wisden and Monyer2009); thus, parvalbumin interneurons provide a mechanism for LC–NE support of phase-coupled rhythms. Indeed, lesions of NMDA receptors in the parvalbumin neurons result in decreased power of theta oscillations and reduced modulations of gamma oscillation by theta (Korotkova et al. Reference Korotkova, Fuchs, Ponomarenko, von Engelhardt and Monyer2010). NE modulation of the hyperpolarization-associated Ih current has also been proposed to support thalamocortical driving of slower neocortical oscillations (Yue & Huguenard Reference Yue and Huguenard2001). Thus, by modulating gamma and theta, the LC–NE system can amplify the winner-take-more effects of hotspots.

7.2. Key brain regions help evaluate priority and modulate NE hotspots

Here we review how several key brain regions help enhance GANE selectivity mechanisms under arousal. These regions help detect saliency and interact with the LC to fine-tune priority signals via their own hotspot-like effects (e.g., amygdala) and/or other NE mechanisms (e.g., prefrontal cortex and thalamus).

The amygdala plays a central role in enhancing selectivity under arousal. It helps notice and track salient information (Sander et al. Reference Sander, Grafman and Zalla2003) and recruits the LC when activated (e.g., Bouret et al. Reference Bouret, Duvel, Onat and Sara2003; Fallon et al. Reference Fallon, Koziell and Moore1978; Jones & Moore Reference Jones and Moore1977; Price & Amaral Reference Price and Amaral1981; Van Bockstaele et al. Reference Van Bockstaele, Colago and Valentino1998). The LC, in turn, modulates amygdala activity via NE to further enhance the saliency signal (Sears et al. Reference Sears, Fink, Wigestrand, Farb, de Lecea and LeDoux2013). Through its strong anatomical projections to sensory cortices (Amaral et al. Reference Amaral, Behniea and Kelly2003), the amygdala amplifies cortical processing of behaviorally relevant events (Chau & Galvez Reference Chau and Galvez2012; Pessoa & Adolphs Reference Pessoa and Adolphs2010). Such modulation of other regions may be mediated by amplification of saliency signals by glutamate–NE interactions within the amygdala (Fig. 7) (see Liu et al. Reference Liu, Chen and Liang2009), thereby enhancing the amygdala's selective modulatory influence on other regions. In addition, as reviewed previously (see sect. 3.2), β-adrenoreceptors in the amygdala mediate the selective effects of arousal on memory.

The thalamus helps control the communication of sensory information across the brain (Sherman Reference Sherman2005). Within the thalamus, there are dense NE fibers and high levels of NE in the pulvinar posteriolateral/posteriomedial complex, but very few in the lateral geniculate nucleus (Morrison & Foote Reference Morrison and Foote1986; Oke et al. Reference Oke, Keller, Mefford and Adams1978). Through its widespread reciprocal connections with cortical and subcortical structures (Shipp Reference Shipp2003), the pulvinar helps filter inputs based on behavioral relevance (Fischer & Whitney Reference Fischer and Whitney2012), promotes communication across brain regions (Saalmann & Kastner Reference Saalmann and Kastner2009; Saalmann et al. Reference Saalmann, Pinsk, Wang, Li and Kastner2012), modulates gamma oscillations (Shumikhina & Molotchnikoff Reference Shumikhina and Molotchnikoff1999), and controls the gain of sensory processing (Purushothaman et al. Reference Purushothaman, Marion, Li and Casagrande2012). In addition, the pulvinar is sensitive to emotional saliency (Liddell et al. Reference Liddell, Brown, Kemp, Barton, Das, Peduto, Gordon and Williams2005; Padmala et al. Reference Padmala, Lim and Pessoa2010; Troiani & Schultz Reference Troiani and Schultz2013). Thus, anatomically, NE is set up to modulate thalamic signals of priority.

Furthermore, in rats, NE increases signal-to-noise processing within the thalamus. When directly infused with NE, rat ventral posteriomedial thalamus exhibits reduced spontaneous firing, but enhanced firing in response to whisker stimulation (Hirata et al. Reference Hirata, Aguilar and Castro-Alamancos2006). When stimulated by phasic or tonic LC activation, ventral posteriomedial thalamus also exhibited increased firing in response to whisker stimulation (Devilbiss & Waterhouse Reference Devilbiss and Waterhouse2011). However, an intriguing observation was that in sensory barrel field cortex, phasic stimulation of LC enhanced firing to strong whisker stimulation, but slightly impaired firing to weak whisker stimulation, an outcome consistent with the NE hotspot model. This differential response based on stimulus intensity did not, however, occur within the ventral posteriomedial thalamus, where both strong and weak sensory inputs increased firing (Devilbiss & Waterhouse Reference Devilbiss and Waterhouse2011). This initial finding suggests that NE influences in sensory thalamus may occur through mechanisms other than NE hotspots. Thus, further work is needed to examine NE's modulatory role in the thalamus. In any case, the thalamus plays a key role in amplifying selectivity under arousal by coordinating responses to salient stimuli across the brain. Such local representations of salient stimuli are then subject to NE modulatory influences.

The prefrontal cortex (PFC), including the orbital frontal cortex (OFC) and anterior cingulate cortex (ACC), has reciprocal connections with the LC (Arnsten & Goldman-Rakic Reference Arnsten and Goldman-Rakic1984; Jodo et al. Reference Jodo, Chiang and Aston-Jones1998) and is an important regulator of LC output. PFC regions help appraise sensory information and recruit the LC based on goal relevance (Aston-Jones & Cohen Reference Aston-Jones and Cohen2005), motivational relevance (Mohanty et al. Reference Mohanty, Gitelman, Small and Mesulam2008), reward (for the OFC; Schoenbaum & Roesch Reference Schoenbaum and Roesch2005), conflict (Botvinick et al. Reference Botvinick, Nystrom, Fissell, Carter and Cohen1999; Sheth et al. Reference Sheth, Mian, Patel, Asaad, Williams, Dougherty, Bush and Eskandar2012), monetary loss (Gehring & Willoughby Reference Gehring and Willoughby2002), and pain (Rainville et al. Reference Rainville, Duncan, Price, Carrier and Bushnell1997). The ACC is also a key site for integrating task-relevant and arousal inputs (Pessoa Reference Pessoa2009; Shackman et al. Reference Shackman, Salomons, Slagter, Fox, Winter and Davidson2011). In humans, LC innervation of the PFC is relatively sparse, especially in anterior regions (Gaspar et al. Reference Gaspar, Berger, Febvret, Vigny and Henry1989; Javoy-Agid et al. Reference Javoy-Agid, Scatton, Ruberg, L'heureux, Cervera, Raisman, Maloteaux, Beck and Agid1989), but NE modulates working memory processes in PFC (Arnsten Reference Arnsten2011; Wang et al. Reference Wang, Ramos, Paspalas, Shu, Simen, Duque, Vijayraghavan, Brennan, Dudley, Nou, Mazer, McCormick and Arnsten2007).

These PFC noradrenergic influences on working memory have different mechanisms than the NE hotspot. First, in our model, β-adrenoreceptors support positive feedback loops at NE–glutamate hotspots, but α2-adrenoreceptors suppress those feedback loops (see sect. 5.1). However, the facilitatory versus inhibitory role of these adrenoreceptors reverses in the context of working memory. β-Adrenoreceptors stimulate cAMP, whereas α2-adrenoreceptors inhibit it (Duman & Enna Reference Duman and Enna1986; Nomura et al. Reference Nomura, Bouhadana, Morel, Faure, Cauli, Lambolez and Hepp2014; Robinson & Siegelbaum Reference Robinson and Siegelbaum2003). Inhibition of cAMP via stimulation of postsynaptic α2-adrenoreceptors increases input resistance and enhances recurrent network activity and working memory performance (Wang et al. Reference Wang, Ramos, Paspalas, Shu, Simen, Duque, Vijayraghavan, Brennan, Dudley, Nou, Mazer, McCormick and Arnsten2007). Thus, by activating via α2-adrenoreceptors, moderate levels of arousal should enhance working memory processes that maintain goal-relevant information in mind, whereas by activating β-adrenoreceptors, high levels of arousal should impair these processes (Arnsten Reference Arnsten2011; Kuhbandner & Zehetleitner Reference Kuhbandner and Zehetleitner2011). Such impairments may, in turn, disrupt initiation of top-down prioritization goals after exposure to emotionally salient stimuli (Sutherland et al., Reference Sutherland, McQuiggan, Ryan and Matherin press).

One interesting question is what might occur when top-down priority and bottom-up priority conflict. The insula plays a key role in this aspect and integrates salience signals from internal and external stimuli (Craig Reference Craig2009; Uddin Reference Uddin2015). The insula is involved in various types of saliency processing, including error detection (Ullsperger et al. Reference Ullsperger, Harsay, Wessel and Ridderinkhof2010), interoception (Craig Reference Craig2009), oddball detection (Harsay et al. Reference Harsay, Spaan, Wijnen and Ridderinkhof2012), aversive memory (Miranda & McGaugh Reference Miranda and McGaugh2004), and detection of events that require cognitive resources (Cai et al. Reference Cai, Chen, Ryali, Kochalka, Li and Menon2015). Although not much is known about LC–insula interactions, the LC and other NE brainstem sites project to the insula (at least in rats) Robertson et al. Reference Robertson, Plummer, de Marchena and Jensen2013). Neuroimaging studies also suggest that elevated LC–NE activity is associated with encoding-related activity in the insula in response to aversive stimuli (Clewett et al. Reference Clewett, Schoeke and Mather2014; Rasch et al. Reference Rasch, Spalek, Buholzer, Luechinger, Boesiger, Papassotiropoulos and Quervain2009). Consistent with GANE, motivated (higher-priority) versus passive viewing of emotional faces enhances functional connectivity within face processing networks, including the insula and LC (Skelly & Decety Reference Skelly and Decety2012; but see Astafiev et al. Reference Astafiev, Snyder, Shulman and Corbetta2010 for caution when interpreting results from LC fMRI).

7.3. NE amplifies activity in behaviorally relevant functional brain networks

Along with the dorsal ACC, the insula is a key node in a broader “salience network” (Eckert et al. Reference Eckert, Menon, Walczak, Ahlstrom, Denslow, Horwitz and Dubno2009; Hermans et al. Reference Hermans, van Marle, Ossewaarde, Henckens, Qin, van Kesteren, Schoots, Cousijn, Rijpkema, Oostenveld and Fernandez2011) that helps integrate different sources of saliency (Seeley et al. Reference Seeley, Menon, Schatzberg, Keller, Glover, Kenna, Reiss and Greicius2007), guide adaptive behavior (Bressler & Menon Reference Bressler and Menon2010; Cocchi et al. Reference Cocchi, Zalesky, Fornito and Mattingley2013), and regulate shifts from rest to task-oriented behavior (Sidlauskaite et al. Reference Sidlauskaite, Wiersema, Roeyers, Krebs, Vassena, Fias, Brass, Achten and Sonuga-Barke2014). On the basis of these findings, recent models of the salience network propose that it mediates competitive interactions between antagonistic attention networks that prioritize internal versus external stimuli (Bressler & Menon Reference Bressler and Menon2010; Menon & Uddin Reference Menon and Uddin2010). Current data suggest that the LC–NE system modulates salience network activity. For example, β-adrenoreceptor blockade during stress reduces salience network activity (Hermans et al. Reference Hermans, van Marle, Ossewaarde, Henckens, Qin, van Kesteren, Schoots, Cousijn, Rijpkema, Oostenveld and Fernandez2011), and salience network activity is associated with pupil and autonomic responses to errors (Critchley et al. Reference Critchley, Tang, Glaser, Butterworth and Dolan2005) and overall arousal (Sadaghiani & D'Esposito Reference Sadaghiani and D'Esposito2014). In neuroimaging studies, the LC co-activates with the dorsal anterior cingulate during the detection of novel stimuli (Krebs et al. Reference Krebs, Fias, Achten and Boehler2013) and during task switching (von der Gablentz et al. Reference Von der Gablentz, Tempelmann, Münte and Heldmann2015), a proposed function of the salience network.

Anatomically, activation of the LC–NE system is well positioned to modulate activity based on priority, as some of the most dense NE innervation is to frontoparietal regions (Gaspar et al. Reference Gaspar, Berger, Febvret, Vigny and Henry1989; Javoy-Agid et al. Reference Javoy-Agid, Scatton, Ruberg, L'heureux, Cervera, Raisman, Maloteaux, Beck and Agid1989; Morrison & Foote Reference Morrison and Foote1986) that coordinate attention to salient stimuli via priority maps (Ptak Reference Ptak2012). Indeed, phasic LC responses, as indexed by pupil dilation, correlate with activity in a dorsal frontoparietal network during focused attention (Alnæs et al. Reference Alnæs, Sneve, Espeseth, Endestad, van de Pavert and Laeng2014). However, more generally, according to the GANE model, activation of the LC–NE system should amplify activity in whichever functional network is currently dominant. Consistent with a role for NE in mediating this process, while subjects rest, pupil dilation increases as activity in the functional network associated with resting state activity increases and activity in a competing motor network activity is suppressed (Yellin et al. Reference Yellin, Berkovich-Ohana and Malach2015). In addition, NE preferentially enhances ventral frontoparietal attention network activity during the detection of salient events that trigger re-orienting (Corbetta et al. Reference Corbetta, Patel and Shulman2008; Strange & Dolan Reference Strange and Dolan2007). Thus, NE's influence on gain modulation also manifests at the whole-brain level.

7.4. Summary

Arousal's dual effects on cognition pervade multilevel brain systems to amplify the priority of important information. By modulating theta and gamma oscillations, NE preferentially synchronizes activity between high-glutamate regions, leading to “winner-take-more” effects in perception and memory. Like some earlier emotion–cognition theories (e.g., Pessoa & Adolphs Reference Pessoa and Adolphs2010), the GANE model favors the perspective that the amygdala coordinates information transfer within broader networks that influence salience processing and is not the only route by which NE enhances processing of prioritized stimuli. Brain regions that evaluate saliency modulate LC activity either directly via afferent inputs or indirectly via broader networks. Without contextual signals from these central structures and the periphery, the LC would be blind to salient events that demand attention (Sara & Bouret Reference Sara and Bouret2012). In turn, the resulting increase in NE release activates these modulatory structures to further bias neural processing in favor of high-priority stimuli. On a larger scale, NE modulates activity in a salience network that mediates competitive interactions between frontoparietal attention networks supporting higher-level representations of priority. Thus, according to the GANE model, reciprocal interactions between the LC and hierarchical brain networks help strengthen and reinforce priority-biasing signals under phasic arousal (see Fig. 8).

Figure 8. Summary of the glutamate amplifies noradrenergic effects (GANE) model. (A) An example of how arousal biases perception and memory to favor prioritized information. High perceptual contrast (bottom-up) and top-down attention prioritize processing of the cow stimulus in the brain over a less salient hay bale. The sound of booming thunder induces arousal and triggers phasic norepinephrine (NE) release. (B) Salience-evaluating structures, such as the amygdala and prefrontal cortex, recruit locus coeruleus (LC) firing to enable NE to modulate ongoing processing at multiple levels of brain function. In the high-priority processing pathway, NE interacts with high local glutamate levels to create “hotspots” that increase the “cow” representational activity even further. These local hotspots recruit energetic resources, synchronize oscillations, lead to enhanced activity in high priority large-scale networks, and increase synaptic plasticity. Local glutamate–NE effects occur parallel to more broad-scale suppression, as NE recruits lateral and auto-inhibitory processes that suppress weaker glutamate signals in lower-priority processing pathways. Together, these noradrenergic mechanisms lead to “winner-take-more” and “loser-take-less” outcomes in perception and memory under arousal, such that the cow is even more likely to be remembered, whereas the hay bale is even more likely to be forgotten. ACC = anterior cingulate cortex; INS = insular cortex.

8. Existing models of LC modulation of cognition

In this section, we discuss how the GANE model relates to existing theories of LC neuromodulation of cognition that we have not already discussed.

8.1. Adaptive gain theory

The adaptive gain theory (Aston-Jones & Cohen Reference Aston-Jones and Cohen2005) posits that two different modes of LC activity (phasic vs. tonic) adaptively adjust the gain of cortical information processing to optimize behavioral performance. Phasic LC activity serves as a temporal attentional filter to selectively process task-relevant stimuli and filter out task-irrelevant stimuli, whereas tonic LC activity regulates overall arousal level in the brain. Phasic LC responses to target detection are constrained by background LC activity and occur most frequently during moderate levels of tonic activity (Usher et al. Reference Usher, Cohen, Servan-Schreiber, Rajkowski and Aston-Jones1999). Adaptive gain theory provides predictions similar to those of the GANE model in terms of the role of the phasic LC mode: phasic LC activity should increase the gain of task-relevant inputs over noisy or task-irrelevant activity. Our GANE model provides a neuromechanism for these effects by proposing that low to moderate NE levels create ideal conditions to ignite and sustain local NE hotspots via greater phasic LC responses. In support of this notion, a recent fMRI study used baseline pupil dilation before trials of a reward-learning task as a measure of tonic LC–NE activity (Eldar et al. Reference Eldar, Cohen and Niv2013). Both low baseline pupil diameter before the trial and high pupil dilation response during the trial were associated with stronger brain activation in response to task-relevant, but not task-irrelevant stimuli.

8.2. Network reset theory

The LC–NE system activates in response to various salient stimuli, including novel, uncertain, or emotionally salient stimuli (Sara Reference Sara2009; Yu & Dayan Reference Yu and Dayan2005). The network reset theory proposes that when these stimuli are detected, the LC issues a phasic “reset” signal that reorganizes neural networks to facilitate behavioral and cognitive shifts accordingly (Bouret & Sara Reference Bouret and Sara2005; Sara & Bouret Reference Sara and Bouret2012). This theory explains why emotionally salient stimuli and the sudden onset of goal-relevant or perceptually salient stimuli are preferentially perceived and remembered: these events activate the LC, which then reconfigures functional brain networks to process new sources of priority while impairing ongoing processing of other stimuli. This model, however, does not offer a clear explanation of why phasic arousal induced when encountering emotional stimuli can enhance processing of preceding stimuli when they have high priority.

To explain both the facilitative and impairing effects of emotional arousal on preceding stimuli, the GANE model posits that the incidental release of NE by something emotional can instead maintain – or even enhance – ongoing functional network connectivity when those networks are highly activated. Stimulating the LC can inhibit feedforward inhibition by interneurons, thereby increasing the throughput of coincident sensory (glutamatergic) inputs (Brown et al. Reference Brown, Walling, Milway and Harley2005). Although this “loosening” of neurotransmission enables network flexibility and the building of new representations, the GANE model's prediction that strong glutamatergic signals transmitting a prioritized representation will benefit from sudden LC activation explains how the “reset” signal triggered by phasic LC activity can still enhance processing of preceding high-priority stimuli.

8.3. Summary

The GANE model both complements and extends previous models of how cognition is influenced by the LC–NE system. According to adaptive gain theory, high phasic LC activity promotes exploitation of the current focus of attention over exploration of other options. In contrast, the network reset theory proposes that phasic LC activity promotes a global reset of attention. The GANE model reconciles these two theories by highlighting the role of priority. According to the GANE model, if the current focus of attention has sufficient priority to yield high glutamate release in synapses transmitting those stimuli, then a phasic increase in LC activity should enhance processing of those representations. Otherwise, increases in LC activity should shift attention and neural resource allocation towards new sources of priority.

The GANE model extends current models of LC function by positing that under arousal, local glutamate–NE interactions will amplify activity of high-priority representations regardless of how those representations initially became highly active. Thus, although the GANE model provides neural mechanisms that account for arousal increasing biased competition outcomes, it can also accommodate other models or modes of information prioritization (Friston Reference Friston2010; Keitel et al. Reference Keitel, Andersen, Quigley and Müller2013; Reynolds & Heeger Reference Reynolds and Heeger2009; Wieser et al. Reference Wieser, McTeague and Keil2011).

9. Potential boundary conditions and questions for future research

In this article, we have argued that arousal leads to winner-take-more and loser-take-less effects in perception and memory via local and global noradrenergic mechanisms in the brain. Yet, although the GANE model explains many findings observed in the emotion–cognition literature, there are a number of important questions for future research.

Arousal may not increase selectivity as effectively among older adults because of age-related changes in the LC–NE system, including loss of LC neurons (Manaye et al. Reference Manaye, McIntire, Mann and German1995; Mather & Harley Reference Mather and Harley2016; Sladek & Sladek Reference Sladek, Sladek, Finch, Potter and Kenny1978; Vijayashankar & Brody Reference Vijayashankar and Brody1979). Recent autopsy evidence indicates that lower LC neuron density is related to the rate of cognitive decline prior to death, even after controlling for decline in other aminergic nuclei (e.g., dorsal raphe, ventral tegmental area) (Wilson et al. Reference Wilson, Nag, Boyle, Hizel, Yu, Buchman, Schneider and Bennett2013). β- and α2-adrenoreceptors may also be affected in aging (e.g., Bigham & Lidow Reference Bigham and Lidow1995; Kalaria et al. Reference Kalaria, Andorn, Tabaton, Whitehouse, Harik and Unnerstall1989). Decreases in α2-adrenoreceptor activity may contribute to age-related cognitive declines because agonists that engage α2A-adrenoreceptors can improve age-related deficits in working memory (Arnsten & Cai Reference Arnsten and Cai1993; Arnsten & Goldman-Rakic Reference Arnsten and Goldman-Rakic1985; Ramos et al. Reference Ramos, Stark, Verduzco, van Dyck and Arnsten2006), potentially via α2A-induced improvements in the ability to maintain focused attention (Decamp et al. Reference Decamp, Clark and Schneider2011). Aging also affects how effectively glutamate triggers additional NE release (Gonzales et al. Reference Gonzales, Brown, Jones, Trent, Westbrook and Leslie1991; Pittaluga et al. Reference Pittaluga, Fedele, Risiglione and Raiteri1993), which would disrupt the emergence and/or efficacy of NE hotspots in older adults.

Another question involves sleep, which plays a crucial role in selectively consolidating salient memory traces (Diekelmann & Born Reference Diekelmann and Born2010), including emotional stimuli (Hu et al. Reference Hu, Stylos-Allan and Walker2006; Payne et al. Reference Payne, Stickgold, Swanberg and Kensinger2008; Reference Payne, Chambers and Kensinger2012) and top-down prioritized information (Rauchs et al. Reference Rauchs, Feyers, Landeau, Bastin, Luxen, Maquet and Collette2011; Saletin et al. Reference Saletin, Goldstein and Walker2011). Emerging research suggests that the LC–NE system may enhance memory consolidation during slow wave sleep (non–rapid eye movement [NREM]), a period when high-priority neural ensembles reactivate (for a review, see Dang-Vu et al. Reference Dang-Vu, Schabus, Desseilles, Albouy, Boly, Darsaud, Gais, Rauchs, Sterpenich and Vandewalle2008; Eschenko et al. Reference Eschenko, Magri, Panzeri and Sara2012; Sara Reference Sara2010). For example, a learning-dependent increase in LC activity occurs during slow wave sleep (Eschenko & Sara Reference Eschenko and Sara2008), and depleting NE prior to encoding reduces slow wave sleep that night (Cirelli et al. Reference Cirelli, Huber, Gopalakrishnan, Southard and Tononi2005). Pharmacologically enhancing LC–NE system activity during slow wave sleep improves recognition of odors learned within the previous 3 hours, whereas blocking LC–NE activity impairs odor recognition (Gais et al. Reference Gais, Rasch, Dahmen, Sara and Born2011). Blocking NE during sleep also leads to greater memory impairment for emotional than for neutral stimuli (Groch et al. Reference Groch, Wilhelm, Diekelmann, Sayk, Gais and Born2011). The timing of transient LC activity coincides with the slow wave grouping of hippocampal sharp wave ripple complexes and sleep spindles that promote NMDA-mediated cellular plasticity (Diekelmann & Born Reference Diekelmann and Born2010; Rosanova & Ulrich Reference Rosanova and Ulrich2005). NE may interact with these processes, given evidence that pharmacological activation of β-adrenoreceptors facilitates the emergence of sharp waves and the induction of LTP (Ul Haq et al. Reference Ul Haq, Liotta, Kovacs, Rösler, Jarosch, Heinemann and Behrens2012). Together these findings raise the intriguing possibility that the precise timing of NE release interacts with the reactivation of high-priority memory networks to facilitate GANE effects during slow wave sleep.

In this article, we focused on perception, encoding, and consolidation processes, but another important question for future research is how NE modulates memory retrieval (e.g., Sterpenich et al. Reference Sterpenich, D'Argembeau, Desseilles, Balteau, Albouy, Vandewalle, Degueldre, Luxen, Collette and Maquet2006). For example, when encountering a new experience, our memory system can either store this novel information as a distinct memory (i.e., requiring pattern separation) or use it to reactivate existing memories (i.e., requiring pattern completion) (Bakker et al. Reference Bakker, Kirwan, Miller and Stark2008). Previous research indicated that arousal facilitates pattern separation (Segal et al. Reference Segal, Stark, Kattan, Stark and Yassa2012) and that NE facilitates retrieval or pattern completion (Devauges & Sara Reference Devauges and Sara1991). But it has been unclear how NE/arousal modulates competition between these two hippocampal processing modes. Glutamate amplification of noradrenergic effects might also affect the stability of a salient memory after it is retrieved, or reconsolidated, because this process involves β-adrenoreceptor and NMDA receptor activation (Lee et al. Reference Lee, Milton and Everitt2006; Przybyslawski et al. Reference Przybyslawski, Roullet and Sara1999).

Another open question concerns the timing of these effects. Behavioral data indicate that presenting an emotionally salient item influences memory of items appearing in the past few seconds (e.g., Sakaki et al. Reference Sakaki, Fryer and Mather2014a) and memory of items appearing in the next few seconds, as well (e.g., Sutherland & Mather Reference Sutherland and Mather2012). It is plausible that the phasic release of NE would have effects on this time scale, but research examining NE–glutamate interactions is needed to address this question.

On the tonic side of the equation, events that induce stress activate both the LC–NE system and the hypothalamic pituitary adrenal (HPA) axis (Pacak & Palkovits Reference Pacak and Palkovits2001; Sved et al. Reference Sved, Cano, Passerin and Rabin2002), and these two systems interact in many ways, especially via the actions of corticotropin releasing factor (CRF). Released by the hypothalamus under stress, CRF helps to initiate the HPA axis response while also targeting the LC (Carrasco & Van de Kar Reference Carrasco and Van de Kar2003; Valentino & Van Bockstaele Reference Valentino and Van Bockstaele2001; Van Bockstaele et al. Reference Van Bockstaele, Bajic, Proudfit and Valentino2001). CRF influences both tonic LC activity and sensory-evoked phasic discharge, either enhancing or impairing sensory-evoked phasic responses depending on waking state and CRF levels administered (Bangasser & Valentino Reference Bangasser and Valentino2012; Devilbiss et al. Reference Devilbiss, Waterhouse, Berridge and Valentino2012; Zitnik et al. Reference Zitnik, Clark and Waterhouse2014). One possibility is that by modulating tonic levels of LC activity, stress also enhances or constrains the impact of phasic arousal responses (see sect. 8.1).

Human genetic studies suggest that different NE polymorphisms moderate the strength of arousal's influence on memory and perceptual processing. To date, much of this research has focused on the ADRA2B deletion variant in which there is reduced NE inhibitory signaling. In human ADRA2B deletion carriers, there is greater activity in the amygdala and insula during the viewing or encoding of emotional versus neutral images (Cousijn et al. Reference Cousijn, Rijpkema, Qin, van Marle, Franke, Hermans, van Wingen and Fernández2010; Rasch et al. Reference Rasch, Spalek, Buholzer, Luechinger, Boesiger, Papassotiropoulos and Quervain2009). Such patterns of NE-related activity are believed to underlie the larger advantage of emotionally salient over neutral stimuli in memory (de Quervain et al. Reference de Quervain, Kolassa, Ertl, Onyut, Neuner, Elbert and Papassotiropoulos2007) and perception (Todd et al. Reference Todd, Palombo, Levine and Anderson2011; Reference Todd, Müller, Lee, Robertson, Eaton, Freeman, Palombo, Levine and Anderson2013) observed in these individuals.

It is, however, unclear how these genetic effects relate to the NE hotspot mechanisms outlined in the GANE model. Whereas α2A-adrenoreceptors are found throughout much of the brain and have been clearly identified as autoreceptors regulating NE release, the α2B-adrenoreceptors associated with this genetic polymorphism have a different profile (Brede et al. Reference Brede, Philipp, Knaus, Muthig and Hein2004). They are most dense in striatum, globus pallidus, and thalamus (De Vos et al. Reference De Vos, Vauquelin, Keyser, Backer and Liefde1992; Saunders & Limbird Reference Saunders and Limbird1999) and are essential for regulating the fetal blood supply (Brede et al. Reference Brede, Philipp, Knaus, Muthig and Hein2004). Thus, although it is possible that these genetic effects alter the feedback cycle in NE hotspots, the genetic differences could also be mediated by different developmental pathways, thalamic modulation of emotional input, or some other factor.

Related to this point about the differential brain localization of α2B-adrenoreceptors is the more general question of how regional variation in receptor density (e.g., Zilles & Amunts Reference Zilles and Amunts2009) modulates hotspot effects. Modeling and direct comparisons of NE–glutamate interactions across regions could help address this question. In addition, although we have focused on how the LC–NE system influences cognition, other neuromodulators such as serotonin, dopamine, and acetylcholine share many mechanisms of action with NE (Hurley et al. Reference Hurley, Devilbiss and Waterhouse2004) and interact with NE to regulate attention, memory, and arousal (Arnsten Reference Arnsten2011; Briand et al. Reference Briand, Gritton, Howe, Young and Sarter2007; Sara Reference Sara2009). Such interactions are likely to modulate the NE–glutamate interactions highlighted here (some examples already described in sect. 5.1 are interactions with orexin, histamine, glycine, and serine). These interactions may allow for more nuanced effects and some redundancy within the arousal system. However, given NE's core role in arousal and broad innervation of much of the brain, including source nuclei of other neuromodulators (e.g., ventral tegmental area and basal forebrain) (Jones Reference Jones2004; Sara Reference Sara2009), we expect that it plays the lead role in modulating cognitive selectivity as arousal levels fluctuate.

10. Conclusion

Selection is at the core of what allows our cognitive systems to function effectively, enabling us to process the constant influx of information and retrieve the experiences most relevant for adaptive behavior and maintenance of well-being. The ability to focus on salient information is especially important during situations that induce arousal, such as during exposure to threatening or exciting sounds or objects and the pressure to perform a challenging task. For more than 50 years, there has been robust behavioral evidence that arousal often simultaneously enhances and impairs processing of different types of neutral information (Easterbrook Reference Easterbrook1959). Yet brain-based accounts of how arousal influences cognition failed to address how such dual effects could arise.

Our GANE model fills this critical gap. In this framework, we propose that increases in NE levels under arousal enhance the selectivity of information processing. GANE builds on the previous ABC model (Mather & Sutherland Reference Mather and Sutherland2011) to provide neural mechanisms of how NE leads to winner-take-more and loser-take-less effects in perception, attention, and memory. Unlike the ABC model, however, the GANE model does not require competition to be a fundamental mechanism. Instead, the GANE model selectively amplifies the activity of whatever priority mechanisms are operating.

Under phasic arousal, local glutamate signals corresponding to a highly activated percept interact with NE to create a hotspot of even higher levels of activity, whereas lower-priority representations are either neglected or further suppressed. These self-regulating hotspots are further aided by NE's recruitment of brain structures and large-scale functional networks that determine which stimuli deserve attention. NE directs blood flow and energetic resources to brain regions transmitting prioritized information. It supports selective memory consolidation via initiation of LTP and LTD. Through all of these processes, NE increases the gain of prioritized information in the brain, such that things that matter stand out even more and are remembered even better, while the mundane and irrelevant recede even farther into the background and are ignored or forgotten.

References

Abraham, W. C. & Williams, J. M. (2008) LTP maintenance and its protein synthesis-dependence. Neurobiology of Learning and Memory 89(3):260–68.CrossRefGoogle ScholarPubMed
Alger, B. E. & Nicoll, R. A. (1980) Epileptiform burst afterhyperolarization: Calcium-dependent potassium potential in hippocampal CA1 pyramidal cells. Science 210(4474):1122–24.CrossRefGoogle ScholarPubMed
Alnæs, D., Sneve, M. H., Espeseth, T., Endestad, T., van de Pavert, S. H. P. & Laeng, B. (2014) Pupil size signals mental effort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus. Journal of Vision 14(4). doi: 10.1167/14.4.1.CrossRefGoogle ScholarPubMed
Amaral, D. G., Behniea, H. & Kelly, J. L. (2003) Topographic organization of projections from the amygdala to the visual cortex in the macaque monkey. Neuroscience 118(4):1099–120.CrossRefGoogle Scholar
Anderson, A. K. (2005) Affective influences on the attentional dynamics supporting awareness. Journal of Experimental Psychology: General 134(2):258–81. doi: 10.1037/0096-3445.134.2.258.CrossRefGoogle ScholarPubMed
Anderson, A. K., Wais, P. E. & Gabrieli, J. D. E. (2006) Emotion enhances remembrance of neutral events past. Proceedings of the National Academy of Sciences of the United States of America 103(5):1599–604. doi: 10.1073/pnas.0506308103.Google Scholar
Arnsten, A. F. & Cai, J. (1993) Postsynaptic alpha-2 receptor stimulation improves memory in aged monkeys: Indirect effects of yohimbine versus direct effects of clonidine. Neurobiology of Aging 14(6):597603.Google Scholar
Arnsten, A. F. & Goldman-Rakic, P. S. (1985) Alpha 2-adrenergic mechanisms in prefrontal cortex associated with cognitive decline in aged nonhuman primates. Science 230(4731):1273–76.CrossRefGoogle ScholarPubMed
Arnsten, A. F. T. (2011) Catecholamine influences on dorsolateral prefrontal cortical networks. Biological Psychiatry 69(12):e89e99. Available at: http://www.sciencedirect.com/science/article/pii/S0006322311001193.CrossRefGoogle ScholarPubMed
Arnsten, A. F. T. & Goldman-Rakic, P. S. (1984) Selective prefrontal cortical projections to the region of the locus coeruleus and raphe nuclei in the rhesus monkey. Brain Research 306(1/2):918. Available at: http://dx.doi.org/10.1016/0006-8993(84)90351-2.Google Scholar
Astafiev, S. V., Snyder, A. Z., Shulman, G. L. & Corbetta, M. (2010) Comment on “Modafinil shifts human locus coeruleus to low-tonic, high-phasic activity during functional MRI” and “Homeostatic sleep pressure and responses to sustained attention in the suprachiasmatic area.Science 328(5976):309.Google Scholar
Aston-Jones, G. & Bloom, F. E. (1981) Nonrepinephrine-containing locus coeruleus neurons in behaving rats exhibit pronounced responses to non-noxious environmental stimuli. The Journal of Neuroscience 1(8):887900.Google Scholar
Aston-Jones, G. & Cohen, J. D. (2005) An integrative theory of locus coeruleus–norepinephrine function: Adaptive gain and optimal performance. Annual Review of Neuroscience 28:403–50. http://doi.org/10.1146/annurev.neuro.28.061604.135709.CrossRefGoogle ScholarPubMed
Aston-Jones, G., Rajkowski, J. & Cohen, J. (1999) Role of locus coeruleus in attention and behavioral flexibility. Biological Psychiatry 46(9):1309–20.CrossRefGoogle ScholarPubMed
Awh, E., Belopolsky, A. V. & Theeuwes, J. (2012) Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Sciences 16(8):437–43.CrossRefGoogle ScholarPubMed
Bakker, A., Kirwan, C. B., Miller, M. & Stark, C. E. L. (2008) Pattern separation in the human hippocampal CA3 and dentate gyrus. Science 319(5870):1640–42.Google Scholar
Baluch, F. & Itti, L. (2011) Mechanisms of top-down attention. Trends in Neurosciences 34(4):210–24.CrossRefGoogle ScholarPubMed
Bangasser, D. & Valentino, R. (2012) Sex differences in molecular and cellular substrates of stress. Cellular and Molecular Neurobiology 32(5):709–23. doi: 10.1007/s10571-012-9824-4.CrossRefGoogle ScholarPubMed
Barsegyan, A., McGaugh, J. L. & Roozendaal, B. (2014) Noradrenergic activation of the basolateral amygdala modulates the consolidation of object-in-context recognition memory. Frontiers in Behavioral Neuroscience 8:Article 160.Google Scholar
Beaudet, A. & Descarries, L. (1978) The monoamine innervation of rat cerebral cortex: Synaptic and nonsynaptic axon terminals. Neuroscience 3(10):851–60.Google Scholar
Beck, D. M. & Kastner, S. (2009) Top-down and bottom-up mechanisms in biasing competition in the human brain. Vision Research 49(10):1154–65. doi: 10.1016/j.visres.2008.07.012.CrossRefGoogle ScholarPubMed
Bekar, L. K., Wei, H. S. & Nedergaard, M. (2012) The locus coeruleus–norepinephrine network optimizes coupling of cerebral blood volume with oxygen demand. Journal of Cerebral Blood Flow and Metabolism 32:2135–45.Google Scholar
Benavides-Piccione, R., Arellano, J. I. & DeFelipe, J. (2005) Catecholaminergic innervation of pyramidal neurons in the human temporal cortex. Cerebral Cortex 15(10):1584–91. doi: 10.1093/cercor/bhi036.Google Scholar
Bergado, J. A., Lucas, M. & Richter-Levin, G. (2011) Emotional tagging: A simple hypothesis in a complex reality. Progress in Neurobiology 94(1):6476. doi: 10.1016/j.pneurobio.2011.03.004.Google Scholar
Berridge, C. & Foote, S. (1991) Effects of locus coeruleus activation on electroencephalographic activity in neocortex and hippocampus. The Journal of Neuroscience 11(10):3135–45.CrossRefGoogle ScholarPubMed
Berridge, C., Page, M., Valentino, R. & Foote, S. (1993) Effects of locus coeruleus inactivation on electroencephalographic activity in neocortex and hippocampus. Neuroscience 55(2):381–93.Google Scholar
Berridge, C. W., Schmeichel, B. E. & Espana, R. A. (2012) Noradrenergic modulation of wakefulness/arousal. Sleep Medicine Reviews 16(2):187–97. doi: 10.1016/j.smrv.2011.12.003.Google Scholar
Berridge, C. W. & Waterhouse, B. D. (2003) The locus coeruleus–noradrenergic system: Modulation of behavioral state and state-dependent cognitive processes. Brain Research Reviews 42(1):3384. doi: 10.1016/s0165-0173(03)00143-7.Google Scholar
Bickler, P. E. & Hansen, B. M. (1996) α2-Adrenergic agonists reduce glutamate release and glutamate receptor-mediated calcium changes in hippocampal slices during hypoxia. Neuropharmacology 35(6):679–87.Google Scholar
Bigham, M. H. & Lidow, M. S. (1995) Adrenergic and serotonergic receptors in aged monkey neocortex. Neurobiology of Aging 16(1):91104.Google Scholar
Bishop, S. J. (2007) Neurocognitive mechanisms of anxiety: An integrative account. Trends in Cognitive Sciences 11(7):307–16.CrossRefGoogle ScholarPubMed
Bliss, T. V. P. & Collingridge, G. L. (1993) A synaptic model of memory: Long-term potentiation in the hippocampus. Nature 361(6407):3139.Google Scholar
Bloomer, W. A., VanDongen, H. M. & VanDongen, A. M. (2008) Arc/Arg3.1 translation is controlled by convergent N-methyl-D-aspartate and Gs-coupled receptor signaling pathways. Journal of Biological Chemistry 283(1):582–92.CrossRefGoogle ScholarPubMed
Bocanegra, B. R. & Zeelenberg, R. (2009) Dissociating emotion-induced blindness and hypervision. Emotion 9(6):865–73. doi: 10.1037/a0017749.Google Scholar
Botvinick, M. M., Nystrom, L. E., Fissell, K., Carter, C. S. & Cohen, J. D. (1999) Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature 402(6758):179–81.Google Scholar
Bouret, S., Duvel, A., Onat, S. & Sara, S. J. (2003) Phasic activation of locus ceruleus neurons by the central nucleus of the amygdala. The Journal of Neuroscience 23(8):3491–97.Google Scholar
Bouret, S. & Richmond, B. J. (2015) Sensitivity of locus ceruleus neurons to reward value for goal-directed actions. The Journal of Neuroscience 35(9):4005–14. doi: 10.1523/jneurosci.4553-14.2015.Google Scholar
Bouret, S. & Sara, S. J. (2005) Network reset: A simplified overarching theory of locus coeruleus noradrenaline function. Trends in Neurosciences 28(11):574–82. Available at: http://dx.doi.org/10.1016/j.tins.2005.09.002.CrossRefGoogle Scholar
Brede, M., Philipp, M., Knaus, A., Muthig, V. & Hein, L. (2004) α2-Adrenergic receptor subtypes – Novel functions uncovered in gene-targeted mouse models. Biology of the Cell 96(5):343–48.Google Scholar
Bressler, S. L. & Menon, V. (2010) Large-scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences 14(6):277–90.CrossRefGoogle ScholarPubMed
Briand, L. A., Gritton, H., Howe, W. M., Young, D. A. & Sarter, M. (2007) Modulators in concert for cognition: Modulator interactions in the prefrontal cortex. Progress in Neurobiology 83(2):6991.Google Scholar
Brown, R. A. M., Walling, S. G., Milway, J. S. & Harley, C. W. (2005) Locus ceruleus activation suppresses feedforward interneurons and reduces beta-gamma electroencephalogram frequencies while it enhances theta frequencies in rat dentate gyrus. Journal of Neuroscience 25(8):1985–91. doi: 10.1053/jneurosci.4307-04-2005.Google Scholar
Burban, A., Faucard, R., Armand, V., Bayard, C., Vorobjev, V. & Arrang, J.-M. (2010) Histamine potentiates N-methyl-D-aspartate receptors by interacting with an allosteric site distinct from the polyamine binding site. Journal of Pharmacology and Experimental Therapeutics 332(3):912–21.Google Scholar
Burke, J. F., Zaghloul, K. A., Jacobs, J., Williams, R. B., Sperling, M. R., Sharan, A. D. & Kahana, M. J. (2013) Synchronous and asynchronous theta and gamma activity during episodic memory formation. The Journal of Neuroscience 33(1):292304.Google Scholar
Bush, G., Luu, P. & Posner, M. I. (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences 4(6):215–22.CrossRefGoogle ScholarPubMed
Buzsáki, G. & Wang, X.-J. (2012) Mechanisms of gamma oscillations. Annual Review of Neuroscience 35(1):203–25. doi: 10.1146/annurev-neuro-062111-150444.Google Scholar
Cahill, L., Gorski, L. & Le, K. (2003) Enhanced human memory consolidation with post-learning stress: Interaction with the degree of arousal at encoding. Learning and Memory 10(4):270–74. doi: 10.1101/lm.62403.CrossRefGoogle ScholarPubMed
Cai, W., Chen, T., Ryali, S., Kochalka, J., Li, C.-S. R. & Menon, V. (2015) Causal interactions within a frontal-cingulate-parietal network during cognitive control: Convergent evidence from a multisite–multitask investigation. Cerebral Cortex 26(5):2140–53. doi: 10.1093/cercor/bhv046.CrossRefGoogle ScholarPubMed
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Berger, M. S., Barbaro, N. M. & Knight, R. T. (2006) High gamma power is phase-locked to theta oscillations in human neocortex. Science 313(5793):1626–28. doi: 10.1126/science.1128115.Google Scholar
Canolty, R. T. & Knight, R. T. (2010) The functional role of cross-frequency coupling. Trends in Cognitive Sciences 14(11):506–15. Available at: http://dx.doi.org/10.1016/j.tics.2010.09.001.Google Scholar
Cardin, J. A., Carlen, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.-H. & Moore, C. I. (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459(7247):663–67. doi: 10.1038/nature08002.CrossRefGoogle ScholarPubMed
Carlen, M., Meletis, K., Siegle, J. H., Cardin, J. A., Futai, K., Vierling-Claassen, D., Ruhlmann, C., Jones, S. R., Deisseroth, K., Sheng, M., Moore, C. I. & Tsai, L. H. (2012) A critical role for NMDA receptors in parvalbumin interneurons for gamma rhythm induction and behavior. Molecular Psychiatry 17(5):537–48. Available at: http://www.nature.com/mp/journal/v17/n5/suppinfo/mp201131s1.html.Google Scholar
Carrasco, G. A. & Van de Kar, L. D. (2003) Neuroendocrine pharmacology of stress. European Journal of Pharmacology 463(1–3):235–72. Available at: http://dx.doi.org/10.1016/S0014-2999(03)01285-8.Google Scholar
Carter, M. E., Yizhar, O., Chikahisa, S., Nguyen, H., Adamantidis, A., Nishino, S., Deisseroth, K. & de Lecea, L. (2010) Tuning arousal with optogenetic modulation of locus coeruleus neurons. Nature Neuroscience 13(12):1526–33. Available at: http://doi.org/10.1038/nn.2682.Google Scholar
Cerf, M., Frady, E. P. & Koch, C. (2009) Faces and text attract gaze independent of the task: Experimental data and computer model. Journal of Vision 9(12):10.Google Scholar
Chamberlain, S. R. & Robbins, T. W. (2013) Noradrenergic modulation of cognition: Therapeutic implications. Journal of Psychopharmacology 27(8):694718.CrossRefGoogle ScholarPubMed
Chandler, D. J., Gao, W.-J. & Waterhouse, B. D. (2014) Heterogeneous organization of the locus coeruleus projections to prefrontal and motor cortices. Proceedings of the National Academy of Sciences of the United States of America 111(18):6816–21.Google Scholar
Chang, C. C., Goshima, Y. & Misu, Y. (1986) Evidence for the existence of stereoselective presynaptic β1-adrenoceptors on noradrenergic and dopaminergic neurons in the rat hypothalamus. The Japanese Journal of Pharmacology 42(3):447–49.Google Scholar
Chau, L. S. & Galvez, R. (2012) Amygdala's involvement in facilitating associative learning-induced plasticity: A promiscuous role for the amygdala in memory acquisition. Frontiers in Integrative Neuroscience 6:92. doi: 10.3389/fnint.2012.00092.Google Scholar
Chen, F. J. & Sara, S. J. (2007) Locus coeruleus activation by foot shock or electrical stimulation inhibits amygdala neurons. Neuroscience 144(2):472–81.CrossRefGoogle ScholarPubMed
Choi, J. M., Padmala, S. & Pessoa, L. (2012) Impact of state anxiety on the interaction between threat monitoring and cognition. NeuroImage 59(2):1912–23.Google Scholar
Cirelli, C., Huber, R., Gopalakrishnan, A., Southard, T. L. & Tononi, G. (2005) Locus ceruleus control of slow-wave homeostasis. The Journal of Neuroscience 25(18):4503–11. doi: 10.1523/jneurosci.4845-04.2005.Google Scholar
Cirelli, C., Pompeiano, M. & Tononi, G. (1996) Neuronal gene expression in the waking state: A role for the locus coeruleus. Science 274(5290):1211–15.Google Scholar
Cirelli, C. & Tononi, G. (2000) Differential expression of plasticity-related genes in waking and sleep and their regulation by the noradrenergic system. The Journal of Neuroscience 20(24):9187–94.CrossRefGoogle ScholarPubMed
Clewett, D., Schoeke, A. & Mather, M. (2014) Locus coeruleus neuromodulation of memories encoded during negative or unexpected action outcomes. Neurobiology of Learning and Memory 111:6570.Google Scholar
Cocchi, L., Zalesky, A., Fornito, A. & Mattingley, J. B. (2013) Dynamic cooperation and competition between brain systems during cognitive control. Trends in Cognitive Sciences 17(10):493501.Google Scholar
Constantinople, C. M. & Bruno, R. M. (2011) Effects and mechanisms of wakefulness on local cortical networks. Neuron 69(6):1061–68. Available at: http://dx.doi.org/10.1016/j.neuron.2011.02.040.Google Scholar
Corbetta, M., Patel, G. & Shulman, G. L. (2008) The reorienting system of the human brain: From environment to theory of mind. Neuron 58(3):306–24.Google Scholar
Cousijn, H., Rijpkema, M., Qin, S., van Marle, H. J., Franke, B., Hermans, E. J., van Wingen, G. & Fernández, G. (2010) Acute stress modulates genotype effects on amygdala processing in humans. Proceedings of the National Academy of Sciences of the United States of America (21):9867–72.Google Scholar
Cox, D. J., Racca, C. & Lebeau, F. E. N. (2008) β-Adrenergic receptors are differentially expressed in distinct interneuron subtypes in the rat hippocampus. The Journal of Comparative Neurology 509(6):551–65. doi: 10.1002/cne.21758.Google Scholar
Craig, A. D. (2009) How do you feel – now? The anterior insula and human awareness. Nature Reviews Neuroscience 10(1):5970. doi: 10.1038/nrn2555.Google Scholar
Critchley, H. D., Tang, J., Glaser, D., Butterworth, B. & Dolan, R. J. (2005) Anterior cingulate activity during error and autonomic response. NeuroImage 27(4):885–95.Google Scholar
Dang-Vu, T. T., Schabus, M., Desseilles, M., Albouy, G., Boly, M., Darsaud, A., Gais, S., Rauchs, G., Sterpenich, V. & Vandewalle, G. (2008) Spontaneous neural activity during human slow wave sleep. Proceedings of the National Academy of Sciences of the United States of America 105(39):15160–65.Google Scholar
de Quervain, D. J., Kolassa, I.-T., Ertl, V., Onyut, P. L., Neuner, F., Elbert, T. & Papassotiropoulos, A. (2007) A deletion variant of the α2b-adrenoceptor is related to emotional memory in Europeans and Africans. Nature Neuroscience 10(9):1137–39.Google Scholar
De Vos, H., Vauquelin, G., Keyser, J., Backer, J. P. & Liefde, I. (1992) Regional distribution of α2A-and α2B-Adrenoceptor subtypes in postmortem human brain. Journal of Neurochemistry 58(4):1555–60.Google Scholar
Decamp, E., Clark, K. & Schneider, J. S. (2011) Effects of the alpha-2 adrenoceptor agonist guanfacine on attention and working memory in aged non-human primates. European Journal of Neuroscience 34(6):1018–22.Google Scholar
Delaney, A. J., Crane, J. W. & Sah, P. (2007) Noradrenaline modulates transmission at a central synapse by a presynaptic mechanism. Neuron 56(5):880–92. Available at: http://dx.doi.org/10.1016/j.neuron.2007.10.022.Google Scholar
Descarries, L., Watkins, K. C. & Lapierre, Y. (1977) Noradrenergic axon terminals in the cerebral cortex of rat: III. Topometric ultrastructural analysis. Brain Research 133(2):197222.Google Scholar
Desimone, R. & Duncan, J. (1995) Neural mechanisms of selective visual attention. Annual Review of Neuroscience 18:193222.Google Scholar
Devauges, V. & Sara, S. J. (1991) Memory retrieval enhancement by locus coeruleus stimulation: Evidence for mediation by β-receptors. Behavioural Brain Research 43(1):9397.CrossRefGoogle ScholarPubMed
Devilbiss, D. M. & Waterhouse, B. D. (2011) Phasic and tonic patterns of locus coeruleus output differentially modulate sensory network function in the awake rat. Journal of Neurophysiology 105(1):6987. doi: 10.1152/jn.00445.2010.Google Scholar
Devilbiss, D. M., Waterhouse, B. D., Berridge, C. W. & Valentino, R. (2012) Corticotropin-releasing factor acting at the locus coeruleus disrupts thalamic and cortical sensory-evoked responses. Neuropsychopharmacology 37(9):2020–30.Google Scholar
Diamond, D. M., Park, C. R., Campbell, A. M. & Woodson, J. C. (2005) Competitive interactions between endogenous LTD and LTP in the hippocampus underlie the storage of emotional memories and stress-induced amnesia. Hippocampus 15(8):1006–25.Google Scholar
Diekelmann, S. & Born, J. (2010) The memory function of sleep. Nature Reviews Neuroscience 11(2):114–26.CrossRefGoogle ScholarPubMed
Dolcos, F., Diaz-Granados, P., Wang, L. H. & McCarthy, G. (2008) Opposing influences of emotional and non-emotional distracters upon sustained prefrontal cortex activity during a delayed-response working memory task. Neuropsychologia 46(1):326–35. doi: 10.1016/j.neuropsychologia.2007.07.010.Google Scholar
Dolcos, F., Iordan, A. D. & Dolcos, S. (2011) Neural correlates of emotion–cognition interactions: A review of evidence from brain imaging investigations. Journal of Cognitive Psychology 23:669–94.Google Scholar
Dolcos, F., LaBar, K. S. & Cabeza, R. (2004) Interaction between the amygdala and the medial temporal lobe memory system predicts better memory for emotional events. Neuron 42(5):855–63.Google Scholar
Dolcos, F. & McCarthy, G. (2006) Brain systems mediating cognitive interference by emotional distraction. Journal of Neuroscience 26(7):2072–79. doi: 10.1523/JNEUROSCI.5042-05.2006.Google Scholar
Duman, R. S. & Enna, S. (1986) A procedure for measuring α2-adrenergic receptor-mediated inhibition of cyclic AMP accumulation in rat brain slices. Brain Research 384(2):391–94.Google Scholar
Duncan, J. (2006) EPS Mid-Career Award 2004 – Brain mechanisms of attention. Quarterly Journal of Experimental Psychology 59(1):227. doi: 10.1080/17470210500260674.Google Scholar
Dunsmoor, J. E., Murty, V. P., Davachi, L. & Phelps, E. A. (2015) Emotional learning selectively and retroactively strengthens memories for related events. Nature 520(7547): 345–48.Google Scholar
Easterbrook, J. A. (1959) The effect of emotion on cue utilization and the organization of behavior. Psychological Review 66(3):183201. doi: 10.1037/h0047707.Google Scholar
Easterbrook, J. A. (1982) This week's citation classic. Current Contents (12):20.Google Scholar
Eckert, M. A., Menon, V., Walczak, A., Ahlstrom, J., Denslow, S., Horwitz, A. & Dubno, J. R. (2009) At the heart of the ventral attention system: The right anterior insula. Human Brain Mapping 30(8):2530–41. doi: 10.1002/hbm.20688.Google Scholar
Egli, R. E., Kash, T. L., Choo, K., Savchenko, V., Matthews, R. T., Blakely, R. D. & Winder, D. G. (2005) Norepinephrine modulates glutamatergic transmission in the bed nucleus of the stria terminalis. Neuropsychopharmacology 30(4):657–68.Google Scholar
Eldar, E., Cohen, J. D. & Niv, Y. (2013) The effects of neural gain on attention and learning. Nature Neuroscience 16(8):1146–53.Google Scholar
Eschenko, O., Magri, C., Panzeri, S. & Sara, S. J. (2012) Noradrenergic neurons of the locus coeruleus are phase locked to cortical up–down states during sleep. Cerebral Cortex 22(2):426–35.Google Scholar
Eschenko, O. & Sara, S. J. (2008) Learning-dependent, transient increase of activity in noradrenergic neurons of locus coeruleus during slow wave sleep in the rat: Brain stem–cortex interplay for memory consolidation? Cerebral Cortex 18(11):2596–603. doi: 10.1093/cercor/bhn020.Google Scholar
Eysenck, M. W., Derakshan, N., Santos, R. & Calvo, M. G. (2007) Anxiety and cognitive performance: Attentional control theory. Emotion 7(2):336.Google Scholar
Fallon, J. H., Koziell, D. A. & Moore, R. Y. (1978) Catecholamine innervation of the basal forebrain: II. Amygdala, suprarhinal cortex and entorhinal cortex. The Journal of Comparative Neurology 180(3):509–31. doi: 10.1002/cne.901800308.Google Scholar
Fecteau, J. H. & Munoz, D. P. (2006) Salience, relevance, and firing: A priority map for target selection. Trends in Cognitive Sciences 10(8):382–90. doi: 10.1016/j.tics.2006.06.011.Google Scholar
Ferrero, J. J., Alvarez, A. M., Ramirez-Franco, J., Godino, M. C., Bartolome-Martin, D., Aguado, C., Torres, M., Lujan, R., Ciruela, F. & Sanchez-Prieto, J. (2013) β-Adrenergic receptors activate Epac, translocate Munc13-1 and enhance the Rab3A–Rim1α interaction to potentiate glutamate release at cerebrocortical nerve terminals. Journal of Biological Chemistry 288(43):31370–85.Google Scholar
Fink, K., Göthert, M., Molderings, G. & Schlicker, E. (1989) N-Methyl-D-aspartate (NMDA) receptor-mediated stimulation of noradrenaline release, but not release of other neurotransmitters, in the rat brain cortex: Receptor location, characterization and desensitization. Naunyn-Schmiedeberg's Archives of Pharmacology 339(5):514–21.Google Scholar
Fink, K., Schultheiß, R. & Göthert, M. (1992) Stimulation of noradrenaline release in human cerebral cortex mediated by N-methyl-D-aspartate (NMDA) and non-NMDA receptors. British Journal of Pharmacology 106(1):6772.Google Scholar
Fischer, J. & Whitney, D. (2012) Attention gates visual coding in the human pulvinar. Nature Communications 3:1051.Google Scholar
Florin-Lechner, S. M., Druhan, J. P., Aston-Jones, G. & Valentino, R. J. (1996) Enhanced norepinephrine release in prefrontal cortex with burst stimulation of the locus coeruleus. Brain Research 742(1/2):8997.Google Scholar
Foote, S. L., Aston-Jones, G. & Bloom, F. E. (1980) Impulse activity of locus coeruleus neurons in awake rats and monkeys is a function of sensory stimulation and arousal. Proceedings of the National Academy of Sciences of the United States of America 77(5):3033–37.Google Scholar
Foote, S. L., Freedman, R. & Oliver, A. P. (1975) Effects of putative neurotransmitters on neuronal activity in monkey auditory cortex. Brain Research 86(2):229–42.Google Scholar
Foote, S. L. & Morrison, J. H. (1987) Extrathalamic modulation of cortical function. Annual Review of Neuroscience 10:6795. doi: 10.1146/annurev.neuro.10.1.67.Google Scholar
Frankland, P. W. & Josselyn, S. A. (2015) Memory allocation. Neuropsychopharmacology 40(1):243–43.CrossRefGoogle ScholarPubMed
Freedman, R., Hoffer, B. J., Woodward, D. J. & Puro, D. (1977) Interaction of norepinephrine with cerebellar activity evoked by mossy and climbing fibers. Experimental Neurology 55(1):269–88.Google Scholar
Frey, S., Bergado-Rosado, J., Seidenbecher, T., Pape, H.-C. & Frey, J. U. (2001) Reinforcement of early long-term potentiation (early-LTP) in dentate gyrus by stimulation of the basolateral amygdala: Heterosynaptic induction mechanisms of late-LTP. The Journal of Neuroscience 21(10):3697–703.Google Scholar
Fries, P. (2009) Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annual Review of Neuroscience 32(1):209–24. doi: 10.1146/annurev.neuro.051508.135603.Google Scholar
Fries, P., Reynolds, J. H., Rorie, A. E. & Desimone, R. (2001) Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291(5508):1560–63. doi: 10.2307/3082512.CrossRefGoogle ScholarPubMed
Friston, K. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11(2):127–38.Google Scholar
Gais, S., Rasch, B., Dahmen, J. C., Sara, S. & Born, J. (2011) The memory function of noradrenergic activity in non-REM sleep. Journal of Cognitive Neuroscience 23(9):2582–92. doi: 10.1162/jocn.2011.21622.Google Scholar
Gaspar, P., Berger, B., Febvret, A., Vigny, A. & Henry, J. P. (1989) Catecholamine innervation of the human cerebral cortex as revealed by comparative immunohistochemistry of tyrosine hydroxylase and dopamine-beta-hydroxylase. Journal of Comparative Neurology 279(2):249–71.Google Scholar
Gehring, W. J. & Willoughby, A. R. (2002) The medial frontal cortex and the rapid processing of monetary gains and losses. Science 295(5563):2279–82. doi: 10.2307/3076349.Google Scholar
Gelinas, J. N. & Nguyen, P. V. (2005) Beta-adrenergic receptor activation facilitates induction of a protein synthesis-dependent late phase of long-term potentiation. Journal of Neuroscience 25(13):3294–303. doi: 10.1523/jneurosci.4175-04.2005.CrossRefGoogle ScholarPubMed
Gelinas, J. N., Tenorio, G., Lemon, N., Abel, T. & Nguyen, P. V. (2008) β-Adrenergic receptor activation during distinct patterns of stimulation critically modulates the PKA-dependence of LTP in the mouse hippocampus. Learning and Memory 15(5):281–89.CrossRefGoogle ScholarPubMed
Gereau, R. & Conn, P. J. (1994) A cyclic AMP-dependent form of associative synaptic plasticity induced by coactivation of beta-adrenergic receptors and metabotropic glutamate receptors in rat hippocampus. The Journal of Neuroscience 14(5):3310–18.Google Scholar
Gilsbach, R. & Hein, L. (2008) Presynaptic metabotropic receptors for acetylcholine and adrenaline/noradrenaline. In: Pharmacology of neurotransmitter release, ed. Südhof, T. C. & Starke, K., pp. 261–88. Springer.CrossRefGoogle ScholarPubMed
Gire, D. H. & Schoppa, N. E. (2008) Long-term enhancement of synchronized oscillations by adrenergic receptor activation in the olfactory bulb. Journal of Neurophysiology 99(4):2021–25. doi: 10.1152/jn.01324.2007.Google Scholar
Gonzales, R. A., Brown, L. M., Jones, T. W., Trent, R. D., Westbrook, S. L. & Leslie, S. W. (1991) N-Methyl-D-aspartate mediated responses decrease with age in Fischer 344 rat brain. Neurobiology of Aging 12(3):219–25.Google Scholar
Göthert, M. & Fink, K. (1991) Stimulation of noradrenaline release in the cerebral cortex via presynaptic N-Methyl-D-aspartate (NMDA) receptors and their pharmacological characterization. In: Recent advances in neuropharmacology, ed. Bönisch, H., Graefe, K.-H., Langer, S. Z. & Schömig, E., pp. 121–27. Springer.Google Scholar
Grant, S. J., Aston-Jones, G. & Redmond, D. E. Jr. (1988) Responses of primate locus coeruleus neurons to simple and complex sensory stimuli. Brain Research Bulletin 21(3):401–10.Google Scholar
Grilli, M., Zappettini, S., Zanardi, A., Lagomarsino, F., Pittaluga, A., Zoli, M. & Marchi, M. (2009) Exposure to an enriched environment selectively increases the functional response of the pre-synaptic NMDA receptors which modulate noradrenaline release in mouse hippocampus. Journal of Neurochemistry 110(5):1598–606. doi: 10.1111/j.1471-4159.2009.06265.x.Google Scholar
Groch, S., Wilhelm, I., Diekelmann, S., Sayk, F., Gais, S. & Born, J. (2011) Contribution of norepinephrine to emotional memory consolidation during sleep. Psychoneuroendocrinology 36(9):1342–50.Google Scholar
Haggerty, D. C., Glykos, V., Adams, N. E. & LeBeau, F. E. N. (2013) Bidirectional modulation of hippocampal gamma (20–80 Hz) frequency activity in vitro via alpha(α)- and beta(β)-adrenergic receptors (AR). Neuroscience 253(0):142–54. Available at: http://dx.doi.org/10.1016/j.neuroscience.2013.08.028.Google Scholar
Haider, B., Häusser, M. & Carandini, M. (2013) Inhibition dominates sensory responses in the awake cortex. Nature 493(7430):97100.Google Scholar
Han, J.-H., Kushner, S. A., Yiu, A. P., Cole, C. J., Matynia, A., Brown, R. A., Neve, R. L., Guzowski, J. F., Silva, A. J. & Josselyn, S. A. (2007) Neuronal competition and selection during memory formation. Science 316(5823):457–60.Google Scholar
Harley, C. W., Lalies, M. D. & Nutt, D. J. (1996) Estimating the synaptic concentration of norepinephrine in dentate gyrus which produces β-receptor mediated long-lasting potentiation in vivo using microdialysis and intracerebroventricular norepinephrine. Brain Research 710(1):293–98.Google Scholar
Harsay, H. A., Spaan, M., Wijnen, J. G. & Ridderinkhof, K. R. (2012) Error awareness and salience processing in the oddball task: Shared neural mechanisms. Frontiers in Human Neuroscience 6:246. doi: 10.3389/fnhum.2012.00246.Google Scholar
Harsing, L. G. & Matyus, P. (2013) Mechanisms of glycine release, which build up synaptic and extrasynaptic glycine levels: The role of synaptic and non-synaptic glycine transporters. Brain Research Bulletin 93:110–19.Google Scholar
Hasselmo, M. E., Linster, C., Patil, M., Ma, D. & Cekic, M. (1997) Noradrenergic suppression of synaptic transmission may influence cortical signal-to-noise ratio. Journal of Neurophysiology 77(6):3326–39.Google Scholar
Hatfield, T. & McGaugh, J. L. (1999) Norepinephrine infused into the basolateral amygdala posttraining enhances retention in a spatial water maze task. Neurobiology of Learning and Memory 71(2):232–39.Google Scholar
Headley, D. B. & Pare, D. (2013) In sync: Gamma oscillations and emotional memory. Frontiers in Behavioral Neuroscience 7. doi: 10.3389/fnbeh.2013.00170.Google Scholar
Headley, D. B. & Weinberger, N. M. (2011) Gamma-band activation predicts both associative memory and cortical plasticity. The Journal of Neuroscience 31(36):12748–58. doi: 10.1523/jneurosci.2528-11.2011.Google Scholar
Headley, D. B. & Weinberger, N. M. (2013) Fear conditioning enhances gamma oscillations and their entrainment of neurons representing the conditioned stimulus. The Journal of Neuroscience 33(13):5705–17. doi: 10.1523/jneurosci.4915-12.2013.Google Scholar
Hermans, E. J., Battaglia, F. P., Atsak, P., de Voogd, L. D., Fernández, G. & Roozendaal, B. (2014) How the amygdala affects emotional memory by altering brain network properties. Neurobiology of Learning and Memory 112:216.CrossRefGoogle ScholarPubMed
Hermans, E. J., van Marle, H. J. F., Ossewaarde, L., Henckens, M., Qin, S. Z., van Kesteren, M. T. R., Schoots, V. C., Cousijn, H., Rijpkema, M., Oostenveld, R. & Fernandez, G. (2011) Stress-related noradrenergic activity prompts large-scale neural network reconfiguration. Science 334(6059):1151–53. doi: 10.1126/science.1209603.Google Scholar
Herrero, I. & Sánchez-Prieto, J. (1996) cAMP-dependent facilitation of glutamate release by β-adrenergic receptors in cerebrocortical nerve terminals. Journal of Biological Chemistry 271(48):30554–60.Google Scholar
Hirata, A., Aguilar, J. & Castro-Alamancos, M. A. (2006) Noradrenergic activation amplifies bottom-up and top-down signal-to-noise ratios in sensory thalamus. Journal of Neuroscience 26(16):4426–36. doi: 10.1523/jneurosci.5298-05.2006.Google Scholar
Hu, P., Stylos-Allan, M. & Walker, M. P. (2006) Sleep facilitates consolidation of emotional declarative memory. Psychological Science 17(10):891–98.Google Scholar
Huang, S., Huganir, R. L. & Kirkwood, A. (2013) Adrenergic gating of Hebbian spike-timing-dependent plasticity in cortical interneurons. The Journal of Neuroscience 33(32):13171–78. doi: 10.1523/jneurosci.5741-12.2013.Google Scholar
Huang, S., Rozas, C., Treviño, M., Contreras, J., Yang, S., Song, L., Yoshioka, T., Lee, H.-K. & Kirkwood, A. (2014) Associative Hebbian synaptic plasticity in primate visual cortex. The Journal of Neuroscience 34(22):7575–79.Google Scholar
Hurley, L., Devilbiss, D. & Waterhouse, B. (2004) A matter of focus: Monoaminergic modulation of stimulus coding in mammalian sensory networks. Current Opinion in Neurobiology 14(4):488–95.Google Scholar
Hutchinson, J. B. & Turk-Browne, N. B. (2012) Memory-guided attention: Control from multiple memory systems. Trends in Cognitive Sciences 16(12):576–79.Google Scholar
Itti, L. & Koch, C. (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40(10–12):1489–506. doi: 10.1016/S0042-6989(99)00163-7.CrossRefGoogle ScholarPubMed
Javoy-Agid, F., Scatton, B., Ruberg, M., L'heureux, R., Cervera, P., Raisman, R., Maloteaux, J.-M., Beck, H. & Agid, Y. (1989) Distribution of monoaminergic, cholinergic, and GABAergic markers in the human cerebral cortex. Neuroscience 29(2):251–59.CrossRefGoogle ScholarPubMed
Ji, X.-H., Cao, X.-H., Zhang, C.-L., Feng, Z.-J., Zhang, X.-H., Ma, L. & Li, B.-M. (2008) Pre- and postsynaptic β-adrenergic activation enhances excitatory synaptic transmission in layer V/VI pyramidal neurons of the medial prefrontal cortex of rats. Cerebral Cortex 18(7):1506–20. doi: 10.1093/cercor/bhm177.Google Scholar
Jodo, E., Chiang, C. & Aston-Jones, G. (1998) Potent excitatory influence of prefrontal cortex activity on noradrenergic locus coeruleus neurons. Neuroscience 83(1):6379. Available at: http://dx.doi.org/10.1016/S0306-4522(97)00372-2.Google Scholar
Johansen, J. P., Diaz-Mataix, L., Hamanaka, H., Ozawa, T., Ycu, E., Koivumaa, J., Kumar, A., Hou, M., Deisseroth, K. & Boyden, E. S. (2014) Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation. Proceedings of the National Academy of Sciences of the United States of America 111(51):E5584–92.Google Scholar
Jones, B. E. (2004) Activity, modulation and role of basal forebrain cholinergic neurons innervating the cerebral cortex. Progress in Brain Research 145:157–69.Google Scholar
Jones, B. E. & Moore, R. Y. (1977) Ascending projections of the locus coeruleus in the rat: II. Autoradiographic study. Brain Research 127(1):2353. Available at: http://dx.doi.org/10.1016/0006-8993(77)90378-X.Google Scholar
Jones, S. M., Snell, L. D. & Johnson, K. M. (1987) Phencyclidine selectively inhibits N-methyl-D-aspartate-induced hippocampal [3H] norepinephrine release. Journal of Pharmacology and Experimental Therapeutics 240(2):492–97.Google Scholar
Kalaria, R., Andorn, A., Tabaton, M., Whitehouse, P., Harik, S. & Unnerstall, J. (1989) Adrenergic receptors in aging and Alzheimer's Disease: Increased β2-receptors in prefrontal cortex and hippocampus. Journal of Neurochemistry 53(6):1772–81.Google Scholar
Kandel, E. R. (2012) The molecular biology of memory: cAMP, PKA, CRE, CREB-1, CREB-2, and CPEB. Molecular Brain 5(1):14.Google Scholar
Kaplan, R. L., Van Damme, I. & Levine, L. J. (2012) Motivation matters: Differing effects of pre-goal and post-goal emotions on attention and memory. Frontiers in Psychology 3: 404.Google Scholar
Katsuki, H., Izumi, Y. & Zorumski, C. F. (1997) Noradrenergic regulation of synaptic plasticity in the hippocampal CA1 region. Journal of Neurophysiology 77(6):3013–20.Google Scholar
Keil, A. & Ihssen, N. (2004) Identification facilitation for emotionally arousing verbs during the attentional blink. Emotion 4(1):2335. doi: 10.1037/1528-3542.4.1.23.CrossRefGoogle ScholarPubMed
Keitel, C., Andersen, S. K., Quigley, C. & Müller, M. M. (2013) Independent effects of attentional gain control and competitive interactions on visual stimulus processing. Cerebral Cortex 23(4):940–46.Google Scholar
Kennedy, B. L. & Most, S. B. (2012) Perceptual, not memorial, disruption underlies emotion-induced blindness. Emotion 12(2):199202.Google Scholar
Kensinger, E. A. (2004) Remembering emotional experiences: The contribution of valence and arousal. Reviews in the Neurosciences 15(4):241–51.Google Scholar
Kensinger, E. A., Garoff-Eaton, R. J. & Schacter, D. L. (2007) Effects of emotion on memory specificity: Memory trade-offs elicited by negative visually arousing stimuli. Journal of Memory and Language 56(4):575–91. doi: 10.1016/j.jml.2006.05.004.Google Scholar
Kilpatrick, L. & Cahill, L. (2003) Amygdala modulation of parahippocampal and frontal regions during emotionally influenced memory storage. NeuroImage 20(4):2091–99.Google Scholar
Klink, P. C., Jentgens, P. & Lorteije, J. A. (2014) Priority maps explain the roles of value, attention, and salience in goal-oriented behavior. The Journal of Neuroscience 34(42):13867–69.Google Scholar
Knight, M. & Mather, M. (2009) Reconciling findings of emotion-induced memory enhancement and impairment of preceding items. Emotion 9(6):763–81. doi: 10.1037/a0017281.Google Scholar
Knight, M., Seymour, T. L., Gaunt, J. T., Baker, C., Nesmith, K. & Mather, M. (2007) Aging and goal-directed emotional attention: Distraction reverses emotional biases. Emotion 7(4):705–14. doi: 10.1037/1528-3542.7.4.705.Google Scholar
Kobayashi, M., Kojima, M., Koyanagi, Y., Adachi, K., Imamura, K. & Koshikawa, N. (2009) Presynaptic and postsynaptic modulation of glutamatergic synaptic transmission by activation of α1- and β-adrenoceptors in layer V pyramidal neurons of rat cerebral cortex. Synapse 63(4):269–81. doi: 10.1002/syn.20604.Google Scholar
Kocsis, B., Li, S. & Hajos, M. (2007) Behavior-dependent modulation of hippocampal EEG activity by the selective norepinephrine reuptake inhibitor reboxetine in rats. Hippocampus 17(8):627–33. doi: 10.1002/hipo.20299.Google Scholar
Korotkova, T., Fuchs, E. C., Ponomarenko, A., von Engelhardt, J. & Monyer, H. (2010) NMDA receptor ablation on parvalbumin-positive interneurons impairs hippocampal synchrony, spatial representations, and working memory. Neuron 68(3):557–69. Available at: http://dx.doi.org/10.1016/j.neuron.2010.09.017.Google Scholar
Krebs, R. M., Fias, W., Achten, E. & Boehler, C. N. (2013) Picture novelty attenuates semantic interference and modulates concomitant neural activity in the anterior cingulate cortex and the locus coeruleus. NeuroImage 74(0):179–87. Available at: http://dx.doi.org/10.1016/j.neuroimage.2013.02.027.Google Scholar
Kuhbandner, C. & Zehetleitner, M. (2011) Dissociable effects of valence and arousal in adaptive executive control. PLoS ONE 6(12):e29287.Google Scholar
Kuo, S. P. & Trussell, L. O. (2011) Spontaneous spiking and synaptic depression underlie noradrenergic control of feed-forward inhibition. Neuron 71(2):306–18.Google Scholar
LaBar, K. S. & Cabeza, R. (2006) Cognitive neuroscience of emotional memory. Nature Reviews Neuroscience 7(1):5464.Google Scholar
Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I. & Schroeder, C. E. (2008) Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 320(5872):110–13.Google Scholar
Lalies, M., Middlemiss, D. N. & Ransom, R. (1988) Stereoselective antagonism of NMDA-stimulated noradrenaline release from rat hippocampal slices by MK-801. Neuroscience Letters 91(3):339–42.Google Scholar
Lally, N., Mullins, P. G., Roberts, M. V., Price, D., Gruber, T. & Haenschel, C. (2014) Glutamatergic correlates of gamma-band oscillatory activity during cognition: A concurrent ER-MRS and EEG study. NeuroImage 85:823–33.Google Scholar
LaLumiere, R. T., Buen, T. V. & McGaugh, J. L. (2003) Post-training intra-basolateral amygdala infusions of norepinephrine enhance consolidation of memory for contextual fear conditioning. Journal of Neuroscience 23(17):6754–58.Google Scholar
Langer, S. Z. (2008) Presynaptic autoreceptors regulating transmitter release. Neurochemistry International 52(1/2):2630. Available at: http://dx.doi.org/10.1016/j.neuint.2007.04.031.Google Scholar
Leclerc, C. M. & Kensinger, E. A. (2008) Effects of age on detection of emotional information. Psychology and Aging 23(1):209–15.Google Scholar
Lee, J. L., Milton, A. L. & Everitt, B. J. (2006) Reconsolidation and extinction of conditioned fear: Inhibition and potentiation. The Journal of Neuroscience 26(39):10051–56.Google Scholar
Lee, T. H., Baek, J., Lu, Z. L. & Mather, M. (2014a) How arousal modulates the contrast sensitivity function. Emotion 5:978–84.Google Scholar
Lee, T. H., Itti, L. & Mather, M. (2012) Evidence for arousal-biased competition in perceptual learning. Frontiers in Emotion Science 3:241.Google Scholar
Lee, T. H., Sakaki, M., Cheng, R., Velasco, R. & Mather, M. (2014b) Emotional arousal amplifies the effects of biased competition in the brain. Social Cognitive and Affective Neuroscience 9(12):2067–77. doi: 10.1093/scan/nsu015.Google Scholar
Lehmann, J., Valentino, R. & Robine, V. (1992) Cortical norepinephrine release elicited in situ by N-methyl-D-aspartate (NMDA) receptor stimulation: A microdialysis study. Brain Research 599(1):171–74. Available at: http://dx.doi.org/10.1016/0006-8993(92)90868-A.CrossRefGoogle ScholarPubMed
Levine, L. J. & Edelstein, R. S. (2009) Emotion and memory narrowing: A review and goal-relevance approach. Cognition and Emotion 23(5):833–75. doi: 10.1080/02699930902738863.Google Scholar
Levitt, P., Rakic, P. & Goldman-Rakic, P. (1984) Region-specific distribution of catecholamine afferents in primate cerebral cortex: A fluorescence histochemical analysis. The Journal of Comparative Neurology 227(1):2336. doi: 10.1002/cne.902270105.Google Scholar
Liddell, B. J., Brown, K. J., Kemp, A. H., Barton, M. J., Das, P., Peduto, A., Gordon, E. & Williams, L. M. (2005) A direct brainstem–amygdala–cortical “alarm” system for subliminal signals of fear. NeuroImage 24(1):235–43.Google Scholar
Lim, S. L., Padmala, S. & Pessoa, L. (2009) Segregating the significant from the mundane on a moment-to-moment basis via direct and indirect amygdala contributions. Proceedings of the National Academy of Sciences of the United States of America 106(39):16841–46. doi: 10.1073/pnas.0904551106.Google Scholar
Liu, D. L. J., Graham, S. & Zorawski, M. (2008) Enhanced selective memory consolidation following post-learning pleasant and aversive arousal. Neurobiology of Learning and Memory 89(1):3646. doi: 10.1016/j.nlm.2007.09.001.CrossRefGoogle ScholarPubMed
Liu, T.-L., Chen, D.-Y. & Liang, K. (2009) Post-training infusion of glutamate into the bed nucleus of the stria terminalis enhanced inhibitory avoidance memory: An effect involving norepinephrine. Neurobiology of Learning and Memory 91(4):456–65.Google Scholar
Livingstone, M. S. & Hubel, D. H. (1981) Effects of sleep and arousal on the processing of visual information in the cat. Nature 291(5816):554–61.Google Scholar
Luccini, E., Musante, V., Neri, E., Brambilla Bas, M., Severi, P., Raiteri, M. & Pittaluga, A. (2007) Functional interactions between presynaptic NMDA receptors and metabotropic glutamate receptors co-expressed on rat and human noradrenergic terminals. British Journal of Pharmacology 151(7):1087–94.CrossRefGoogle ScholarPubMed
Luczak, A., Bartho, P. & Harris, K. D. (2013) Gating of sensory input by spontaneous cortical activity. The Journal of Neuroscience 33(4):1684–95.Google Scholar
Lüscher, C. & Malenka, R. C. (2012) NMDA receptor-dependent long-term potentiation and long-term depression (LTP/LTD). Cold Spring Harbor Perspectives in Biology 4(6). doi: 10.1101/cshperspect.a005710.Google Scholar
Lynch, M. (2004) Long-term potentiation and memory. Physiological Reviews 84(1):87136.Google Scholar
MacKay, D. G., Shafto, M., Taylor, J. K., Marian, D. E., Abrams, L. & Dyer, J. R. (2004) Relations between emotion, memory, and attention: Evidence from taboo Stroop, lexical decision, and immediate memory tasks. Memory and Cognition 32(3):474–88.Google Scholar
Madison, D. & Nicoll, R. (1982) Noradrenaline blocks accommodation of pyramidal cell discharge in the hippocampus. Nature 299:636–38.Google Scholar
Magistretti, P. J., Morrison, J. H., Shoemaker, W. J., Sapin, V. & Bloom, F. E. (1981) Vasoactive intestinal polypeptide induces glycogenolysis in mouse cortical slices: A possible regulatory mechanism for the local control of energy metabolism. Proceedings of the National Academy of Sciencesof the United States of America 78(10):6535–39.Google Scholar
Manaye, K. F., McIntire, D. D., Mann, D. M. A. & German, D. C. (1995) Locus-coeruleus cell loss in the aging human brain: A nonrandom process. Journal of Comparative Neurology 358(1):7987. doi: 10.1002/cne.903580105.Google Scholar
Markovic, J., Anderson, A. K. & Todd, R. M. (2014) Tuning to the significant: Neural and genetic processes underlying affective enhancement of visual perception and memory. Behavioural Brain Research 259:229–41.Google Scholar
Marzo, A., Bai, J. & Otani, S. (2009) Neuroplasticity regulation by noradrenaline in mammalian brain. Current Neuropharmacology 7(4):286.Google Scholar
Marzo, A., Totah, N. K., Neves, R. M., Logothetis, N. K. & Eschenko, O. (2014) Unilateral electrical stimulation of rat locus coeruleus elicits bilateral response of norepinephrine neurons and sustained activation of medial prefrontal cortex. Journal of Neurophysiology 111(12):2570–88. doi: 10.1152/jn.00920.2013.Google Scholar
Mather, M. (2007) Emotional arousal and memory binding: An object-based framework. Perspectives on Psychological Science 2(1):3352. doi: 10.1111/j.1745-6916.2007.00028.x.Google Scholar
Mather, M. & Harley, C. W. (2016) The locus coeruleus: Essential for maintaining cognitive function and the aging brain. Trends in Cognitive Sciences 20:214–26.Google Scholar
Mather, M. & Knight, M. R. (2006) Angry faces get noticed quickly: Threat detection is not impaired among older adults. Journals of Gerontology Series B: Psychological Sciences and Social Sciences 61:P54P57.Google Scholar
Mather, M. & Sutherland, M. R. (2011) Arousal-biased competition in perception and memory. Perspectives on Psychological Science 6(2):114–33. doi: 10.1177/1745691611400234.Google Scholar
McGaugh, J. L. (2000). Memory: A century of consolidation. Science 287:248–51.Google Scholar
McGaugh, J. L. (2004) The amygdala modulates the consolidation of memories of emotionally arousing experiences. Annual Review of Neuroscience 27:128.Google Scholar
McGaugh, J. L. (2013) Making lasting memories: Remembering the significant. Proceedings of the National Academy of Sciences of the United States of America 110(Suppl. 2):10402–407. doi: 10.1073/pnas.1301209110.Google Scholar
McIntyre, C. K., Hatfield, T. & McGaugh, J. L. (2002) Amygdala norepinephrine levels after training predict inhibitory avoidance retention performance in rats. European Journal of Neuroscience 16(7):1223–26.Google Scholar
McIntyre, C. K., McGaugh, J. L. & Williams, C. L. (2012) Interacting brain systems modulate memory consolidation. Neuroscience and Biobehavioral Reviews 36:1750–62.Google Scholar
Meldrum, B. S. (2000) Glutamate as a neurotransmitter in the brain: Review of physiology and pathology. The Journal of Nutrition 130(4):1007S15S.Google Scholar
Menon, V. & Uddin, L. Q. (2010) Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function 214(5/6):655–67. doi: 10.1007/s00429-010-0262-0.Google Scholar
Miranda, M. I. & McGaugh, J. L. (2004) Enhancement of inhibitory avoidance and conditioned taste aversion memory with insular cortex infusions of 8-Br-cAMP: Involvement of the basolateral amygdala. Learning and Memory 11(3):312–17. doi: 10.1101/lm.72804.Google Scholar
Misu, Y. & Kubo, T. (1986) Presynaptic β-adrenoceptors. Medicinal Research Reviews 6(2):197225.Google Scholar
Mobley, P. & Greengard, P. (1985) Evidence for widespread effects of noradrenaline on axon terminals in the rat frontal cortex. Proceedings of the National Academy of Sciences of the United States of America 82(3):945–47.Google Scholar
Mohanty, A., Gitelman, D. R., Small, D. M. & Mesulam, M. M. (2008) The spatial attention network interacts with limbic and monoaminergic systems to modulate motivation-induced attention shifts. Cerebral Cortex 18(11):2604–13. doi: 10.1093/cercor/bhn021.Google Scholar
Moncada, D., Ballarini, F., Martinez, M. C., Frey, J. U. & Viola, H. (2011) Identification of transmitter systems and learning tag molecules involved in behavioral tagging during memory formation. Proceedings of the National Academy of Sciences of the United States of America 108(31):12931–36. doi: 10.1073/pnas.1104495108.Google Scholar
Moncada, D. & Viola, H. (2007) Induction of long-term memory by exposure to novelty requires protein synthesis: Evidence for a behavioral tagging. The Journal of Neuroscience 27(28):7476–81. doi: 10.1523/jneurosci.1083-07.2007.Google Scholar
Montagrin, A., Brosch, T. & Sander, D. (2013) Goal conduciveness as a key determinant of memory facilitation. Emotion 13(4):622–28. doi: 10.1037/a0033066.Google Scholar
Moray, N. (1959) Attention in dichotic listening: Affective cues and the influence of instructions. Quarterly Journal of Experimental Psychology 11(1):5660.Google Scholar
Morrison, J. H. & Foote, S. L. (1986) Noradrenergic and serotoninergic innervation of cortical, thalamic, and tectal visual structures in Old and New World monkeys. Journal of Comparative Neurology 243(1):117–38. doi: 10.1002/cne.902430110.Google Scholar
Most, S. B., Chun, M. M., Widders, D. M. & Zald, D. H. (2005) Attentional rubbernecking: Cognitive control and personality in emotion-induced blindness. Psychonomic Bulletin and Review 12(4):654–61.Google Scholar
Murphy, P. R., O'Connell, R. G., O'Sullivan, M., Robertson, I. H. & Balsters, J. H. (2014) Pupil diameter covaries with BOLD activity in human locus coeruleus. Human Brain Mapping 35(8):4140–54.Google Scholar
Murray, B. D., Holland, A. C. & Kensinger, E. A. (2013) Episodic memory and emotion. In: Handbook of cognition and emotion, ed. Robinson, M. D., Watkins, E. & Harmon-Jones, E., pp. 156–75. Guilford Press.Google Scholar
Murty, V. P., Ritchey, M., Adcock, R. A. & LaBar, K. S. (2010) fMRI studies of successful emotional memory encoding: A quantitative meta-analysis. Neuropsychologia 48(12):3459–69. doi: 10.1016/j.neuropsychologia.2010.07.030.Google Scholar
Murugaiah, K. D. & O'Donnell, J. M. (1995a) Beta adrenergic receptors facilitate norepinephrine release from rat hypothalamic and hippocampal slices. Research Communications in Molecular Pathology and Pharmacology 90(2):179–90.Google Scholar
Murugaiah, K. D. & O'Donnell, J. M. (1995b) Facilitation of noradrenaline release from rat brain slices by β-adrenoceptors. Naunyn-Schmiedeberg's Archives of Pharmacology 351(5):483–90.Google Scholar
Nabavi, S., Fox, R., Proulx, C. D., Lin, J. Y., Tsien, R. Y. & Malinow, R. (2014) Engineering a memory with LTD and LTP. Nature 511(7509):348–52. doi: 10.1038/nature13294.Google Scholar
Nai, Q., Dong, H.-W., Hayar, A., Linster, C. & Ennis, M. (2009) Noradrenergic regulation of GABAergic inhibition of main olfactory bulb mitral cells varies as a function of concentration and receptor subtype. Journal of Neurophysiology 101(5):2472–84.Google Scholar
Nelson, M. F., Zaczek, R. & Coyle, J. T. (1980) Effects of sustained seizures produced by intrahippocampal injection of kainic acid on noradrenergic neurons: Evidence for local control of norepinephrine release. Journal of Pharmacology and Experimental Therapeutics 214(3):694702.Google Scholar
Nicoll, R. A. (1988) The coupling of neurotransmitter receptors to ion channels in the brain. Science 241(4865):545–51.Google Scholar
Nielson, K. A. & Powless, M. (2007) Positive and negative sources of emotional arousal enhance long-term word-list retention when induced as long as 30 min after learning. Neurobiology of Learning and Memory 88(1):4047. doi: 10.1016/j.nlm.2007.03.005.Google Scholar
Niu, Y., Todd, R. & Anderson, A. K. (2012) Affective salience can reverse the effects of stimulus-driven salience on eye movements in complex scenes. Frontiers in Psychology 3:336. doi: 10.3389/fpsyg.2012.00336.Google Scholar
Nomura, S., Bouhadana, M., Morel, C., Faure, P., Cauli, B., Lambolez, B. & Hepp, R. (2014) Noradrenalin and dopamine receptors both control cAMP-PKA signaling throughout the cerebral cortex. Frontiers in Cellular Neuroscience 8:247.Google Scholar
Oades, R. D. (1985) The role of noradrenaline in tuning and dopamine in switching between signals in the CNS. Neuroscience and Biobehavioral Reviews 9(2):261–82.Google Scholar
O'Dell, T. J., Connor, S. A., Gelinas, J. N. & Nguyen, P. V. (2010) Viagra for your synapses: Enhancement of hippocampal long-term potentiation by activation of beta-adrenergic receptors. Cellular Signalling 22(5):728–36. doi: 10.1016/j.cellsig.2009.12.004.Google Scholar
O'Donnell, J., Zeppenfeld, D., McConnell, E., Pena, S. & Nedergaard, M. (2012) Norepinephrine: A neuromodulator that boosts the function of multiple cell types to optimize CNS performance. Neurochemical Research 37(11):2496–512. doi: 10.1007/s11064-012-0818-x.Google Scholar
Öhman, A., Flykt, A. & Esteves, F. (2001) Emotion drives attention: Detecting the snake in the grass. Journal of Experimental Psychology: General 130(3):466–78.Google Scholar
Oke, A., Keller, R., Mefford, I. & Adams, R. N. (1978) Lateralization of norepinephrine in human thalamus. Science 200(4348):1411–13.Google Scholar
Okubo, Y. & Iino, M. (2011) Visualization of glutamate as a volume transmitter. The Journal of Physiology 589(3):481–88.Google Scholar
Okubo, Y., Sekiya, H., Namiki, S., Sakamoto, H., Iinuma, S., Yamasaki, M., Watanabe, M., Hirose, K. & Iino, M. (2010) Imaging extrasynaptic glutamate dynamics in the brain. Proceedings of the National Academy of Sciences of the United States of America 107(14):6526–31.Google Scholar
Pacak, K. & Palkovits, M. (2001) Stressor specificity of central neuroendocrine responses: Implications for stress-related disorders. Endocrine Reviews 22(4):502–48.Google Scholar
Padmala, S., Lim, S.-L. & Pessoa, L. (2010) Pulvinar and affective significance: Responses track moment-to-moment stimulus visibility. Frontiers in Human Neuroscience 4. doi: 10.3389/fnhum.2010.00064.Google Scholar
Padmala, S. & Pessoa, L. (2008) Affective learning enhances visual detection and responses in primary visual cortex. Journal of Neuroscience 28(24):6202–10. doi: 10.1523/jneurosci.1233-08.2008.Google Scholar
Palamarchouk, V. S., Zhang, J.-J., Zhou, G., Swiergiel, A. H. & Dunn, A. J. (2000) Hippocampal norepinephrine-like voltammetric responses following infusion of corticotropin-releasing factor into the locus coeruleus. Brain Research Bulletin 51(4):319–26.Google Scholar
Parkhurst, D., Law, K. & Niebur, E. (2002) Modeling the role of salience in the allocation of overt visual attention. Vision Research 42(1):107–23.Google Scholar
Parpura, V. & Haydon, P. G. (2000) Physiological astrocytic calcium levels stimulate glutamate release to modulate adjacent neurons. Proceedings of the National Academy of Sciences of the United States of America 97(15):8629–34.Google Scholar
Paukert, M., Agarwal, A., Cha, J., Doze, V. A., Kang, J. U. & Bergles, D. E. (2014) Norepinephrine controls astroglial responsiveness to local circuit activity. Neuron 82(6):1263–70.Google Scholar
Pawlak, V., Wickens, J. R., Kirkwood, A. & Kerr, J. N. (2010) Timing is not everything: Neuromodulation opens the STDP gate. Frontiers in Synaptic Neuroscience 2:146.Google Scholar
Payne, J. D., Chambers, A. M. & Kensinger, E. A. (2012) Sleep promotes lasting changes in selective memory for emotional scenes. Frontiers in Integrative Neuroscience 6:108. doi: 10.3389/fnint.2012.00108.Google Scholar
Payne, J. D., Stickgold, R., Swanberg, K. & Kensinger, E. A. (2008) Sleep preferentially enhances memory for emotional components of scenes. Psychological Science 19(8):781. doi: 10.1111/j.1467-9280.2008.02157.x.Google Scholar
Paz, R., Bauer, E. P. & Paré, D. (2008) Theta synchronizes the activity of medial prefrontal neurons during learning. Learning and Memory 15(7):524–31.Google Scholar
Pellerin, L. & Magistretti, P. J. (2012) Sweet sixteen for ANLS. Journal of Cerebral Blood Flow and Metabolism 32(7):1152–66.Google Scholar
Pessoa, L. (2009) How do emotion and motivation direct executive control? Trends in Cognitive Sciences 13(4):160–66.Google Scholar
Pessoa, L. (2013) The cognitive–emotional brain: From interactions to integration: MIT Press.Google Scholar
Pessoa, L. & Adolphs, R. (2010) Emotion processing and the amygdala: From a “low road” to “many roads” of evaluating biological significance. Nature Reviews Neuroscience 11(11):773–83.Google Scholar
Petralia, R., Yokotani, N. & Wenthold, R. (1994) Light and electron microscope distribution of the NMDA receptor subunit NMDAR1 in the rat nervous system using a selective anti-peptide antibody. Journal of Neuroscience 14(2):667–96.Google Scholar
Petroff, O. A. (2002) Book review: GABA and glutamate in the human brain. The Neuroscientist 8(6):562–73.Google Scholar
Phan, K. L., Wager, T., Taylor, S. F. & Liberzon, I. (2002) Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI. NeuroImage 16:331–48.Google Scholar
Phelps, E. A. (2004) Human emotion and memory: Interactions of the amygdala and hippocampal complex. Current Opinion in Neurobiology 14(2):198202.Google Scholar
Phelps, E. A., Ling, S. & Carrasco, M. (2006) Emotion facilitates perception and potentiates the perceptual benefits of attention. Psychological Science 17(4):292–99. doi: 10.1111/j.1467-9280.2006.01701.x.Google Scholar
Pittaluga, A. & Raiteri, M. (1990) Release-enhancing glycine-dependent presynaptic NMDA receptors exist on noradrenergic terminals of hippocampus. European Journal of Pharmacology 191(2):231–34.Google Scholar
Pittaluga, A. & Raiteri, M. (1992) N-Methyl-D-aspartic acid (NMDA) and non-NMDA receptors regulating hippocampal norepinephrine release: I. Location on axon terminals and pharmacological characterization. Journal of Pharmacology and Experimental Therapeutics 260(1):232–37.Google Scholar
Pittaluga, A., Fedele, E., Risiglione, C. & Raiteri, M. (1993) Age-related decrease of the NMDA receptor-mediated noradrenaline release in rat hippocampus and partial restoration by D-cycloserine. European Journal of Pharmacology 231(1):129–34.Google Scholar
Pittaluga, A., Pattarini, R., Andrioli, G. C., Viola, C., Munari, C. & Raiteri, M. (1999) Activity of putative cognition enhancers in kynurenate test performed with human neocortex slices. Journal of Pharmacology and Experimental Therapeutics 290(1):423–28.Google Scholar
Polack, P.-O., Friedman, J. & Golshani, P. (2013) Cellular mechanisms of brain state-dependent gain modulation in visual cortex. Nature Neuroscience 16(9):1331–39.Google Scholar
Ponzio, A. & Mather, M. (2014) Hearing something emotional affects memory for what was just seen: How arousal amplifies trade-off effects in memory consolidation. Emotion 14:1137–42.Google Scholar
Pourtois, G., Schettino, A. & Vuilleumier, P. (2013) Brain mechanisms for emotional influences on perception and attention: What is magic and what is not. Biological Psychology 92(3):492512.Google Scholar
Price, J. L. & Amaral, D. G. (1981) An autoradiographic study of the projections of the central nucleus of the monkey amygdala. The Journal of Neuroscience 1(11):1242–59.Google Scholar
Przybyslawski, J., Roullet, P. & Sara, S. J. (1999) Attenuation of emotional and nonemotional memories after their reactivation: Role of β adrenergic receptors. The Journal of Neuroscience 19(15):6623–28.Google Scholar
Ptak, R. (2012) The frontoparietal attention network of the human brain action, saliency, and a priority map of the environment. The Neuroscientist 18(5):502–15.Google Scholar
Purushothaman, G., Marion, R., Li, K. & Casagrande, V. A. (2012) Gating and control of primary visual cortex by pulvinar. Nature Neuroscience 15(6):905–12.Google Scholar
Rainville, P., Duncan, G. H., Price, D. D., Carrier, B. & Bushnell, M. C. (1997) Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science 277(5328):968–71. doi: 10.2307/2892916.Google Scholar
Ramos, B. P. & Arnsten, A. F. T. (2007) Adrenergic pharmacology and cognition: Focus on the prefrontal cortex. Pharmacology and Therapeutics 113(3):523–36.Google Scholar
Ramos, B. P., Stark, D., Verduzco, L., van Dyck, C. H. & Arnsten, A. F. (2006) α2A-adrenoceptor stimulation improves prefrontal cortical regulation of behavior through inhibition of cAMP signaling in aging animals. Learning and Memory 13(6):770–76.Google Scholar
Rangel, S. & Leon, M. (1995) Early odor preference training increases olfactory bulb norepinephrine. Developmental Brain Research 85(2):187–91. Available at: http://dx.doi.org/10.1016/0165-3806(94)00211-H.Google Scholar
Rasch, B., Spalek, K., Buholzer, S., Luechinger, R., Boesiger, P., Papassotiropoulos, A. & Quervain, D. J. F. d. (2009) A genetic variation of the noradrenergic system is related to differential amygdala activation during encoding of emotional memories. Proceedings of the National Academy of Sciences of the United States of America 106(45):19191–96. doi: 10.2307/25593165.Google Scholar
Rauchs, G., Feyers, D., Landeau, B., Bastin, C., Luxen, A., Maquet, P. & Collette, F. (2011) Sleep contributes to the strengthening of some memories over others, depending on hippocampal activity at learning. The Journal of Neuroscience 31(7):2563–68. doi: 10.1523/jneurosci.3972-10.2011.Google Scholar
Reicher, G. M., Snyder, C. R. & Richards, J. T. (1976) Familiarity of background characters in visual scanning. Journal of Experimental Psychology: Human Perception and Performance 2(4):522.Google Scholar
Reimer, J., Froudarakis, E., Cadwell, C. R., Yatsenko, D., Denfield, G. H. & Tolias, A. S. (2014) Pupil fluctuations track fast switching of cortical states during quiet wakefulness. Neuron 84(2):355–62.Google Scholar
Reisberg, D. & Heuer, F. (2004) Memory for emotional events. In: Memory and emotion, ed. Reisberg, D. & Hertel, P., pp. 341. Oxford University Press.Google Scholar
Reynolds, J. H. & Desimone, R. (2003) Interacting roles of attention and visual salience in V4. Neuron 37(5):853–63.Google Scholar
Reynolds, J. H. & Heeger, D. J. (2009) The normalization model of attention. Neuron 61(2):168–85.Google Scholar
Richardson, M. P., Strange, B. A. & Dolan, R. J. (2004) Encoding of emotional memories depends on amygdala and hippocampus and their interactions. Nature Neuroscience 7(3):278–85.Google Scholar
Richter-Levin, G. & Akirav, I. (2003) Emotional tagging of memory formation – in the search for neural mechanisms. Brain Research Reviews 43(3):247–56.Google Scholar
Rinne, A., Birk, A. & Bünemann, M. (2013) Voltage regulates adrenergic receptor function. Proceedings of the National Academy of Sciences of the United States of America 110(4):1536–41.Google Scholar
Ritchey, M., Dolcos, F. & Cabeza, R. (2008) Role of amygdala connectivity in the persistence of emotional memories over time: An event-related fMRI investigation. Cerebral Cortex 18(11):2494–504. doi: 10.1093/cercor/bhm262.Google Scholar
Robertson, S. D., Plummer, N. W., de Marchena, J. & Jensen, P. (2013) Developmental origins of central norepinephrine neuron diversity. Nature Neuroscience 16(8):1016–23.Google Scholar
Robinson, R. B. & Siegelbaum, S. A. (2003) Hyperpolarization-activated cation currents: From molecules to physiological function. Annual Review of Physiology 65(1):453–80.Google Scholar
Roozendaal, B., Castello, N. A., Vedana, G., Barsegyan, A. & McGaugh, J. L. (2008) Noradrenergic activation of the basolateral amygdala modulates consolidation of object recognition memory. Neurobiology of Learning and Memory 90(3):576–79. doi: 10.1016/j.nlm.2008.06.010.Google Scholar
Rosanova, M. & Ulrich, D. (2005) Pattern-specific associative long-term potentiation induced by a sleep spindle-related spike train. The Journal of Neuroscience 25(41):9398–405. doi: 10.1523/jneurosci.2149-05.2005.Google Scholar
Saalmann, Y. B. & Kastner, S. (2009) Gain control in the visual thalamus during perception and cognition. Current Opinion in Neurobiology 19(4):408–14.Google Scholar
Saalmann, Y. B., Pinsk, M. A., Wang, L., Li, X. & Kastner, S. (2012) The pulvinar regulates information transmission between cortical areas based on attention demands. Science 337(6095):753–56. doi: 10.1126/science.1223082.Google Scholar
Sadaghiani, S. & D'Esposito, M. (2014) Functional characterization of the cingulo-opercular network in the maintenance of tonic alertness. Cerebral Cortex. doi: 10.1093/cercor/bhu072 Google Scholar
Sakaki, M., Fryer, K. & Mather, M. (2014a) Emotion strengthens high priority memory traces but weakens low priority memory traces. Psychological Science 25(2):387–95. doi: 10.1177/0956797613504784 Google Scholar
Saletin, J. M., Goldstein, A. N. & Walker, M. P. (2011) The role of sleep in directed forgetting and remembering of human memories. Cerebral Cortex 21(11):2534–41. doi: 10.1093/cercor/bhr034.Google Scholar
Salgado, H., Garcia-Oscos, F., Martinolich, L., Hall, S., Restom, R., Tseng, K. Y. & Atzori, M. (2012a) Pre- and postsynaptic effects of norepinephrine on γ-aminobutyric acid-mediated synaptic transmission in layer 2/3 of the rat auditory cortex. Synapse 66(1):2028.Google Scholar
Salgado, H., Kohr, G. & Trevino, M. (2012b) Noradrenergic “tone” determines dichotomous control of cortical spike-timing-dependent plasticity. Scientific Reports 2:7. doi: 417 10.1038/srep00417.Google Scholar
Samuels, E. R. & Szabadi, E. (2008a) Functional neuroanatomy of the noradrenergic locus coeruleus: Its roles in the regulation of arousal and autonomic function. Part I. Principles of functional organisation. Current Neuropharmacology 6(3):235–53. doi: 10.2174/157015908785777229.CrossRefGoogle ScholarPubMed
Samuels, E. R. & Szabadi, E. (2008b) Functional neuroanatomy of the noradrenergic locus coeruleus: Its roles in the regulation of arousal and autonomic function. Part II. Physiological and pharmacological manipulations and pathological alterations of locus coeruleus activity in humans. Current Neuropharmacology 6(3):254–85. doi: 10.2174/157015908785777193.Google Scholar
Sander, D., Grafman, J. & Zalla, T. (2003) The human amygdala: An evolved system for relevance detection. Reviews in the Neurosciences 14(4):303–16.Google Scholar
Sara, S. J. (2009) The locus coeruleus and noradrenergic modulation of cognition. Nature Reviews Neuroscience 10(3):211–23.Google Scholar
Sara, S. J. (2010) Reactivation, retrieval, replay and reconsolidation in and out of sleep: Connecting the dots. Frontiers in Behavioral Neuroscience 4. doi: 10.3389/fnbeh.2010.00185.Google Scholar
Sara, S. J. & Bouret, S. (2012) Orienting and reorienting: The locus coeruleus mediates cognition through arousal. Neuron 76(1):130–41. doi: 10.1016/j.neuron.2012.09.011.Google Scholar
Sara, S. J. & Segal, M. (1991) Plasticity of sensory responses of locus coeruleus neurons in the behaving rat: Implications for cognition. Progress in Brain Research 88:571–85.Google Scholar
Saunders, C. & Limbird, L. E. (1999) Localization and trafficking of α2-adrenergic receptor subtypes in cells and tissues. Pharmacology and Therapeutics 84(2):193205.Google Scholar
Schoenbaum, G. & Roesch, M. R. (2005) Orbitofrontal cortex, associative learning, and expectancies. Neuron 47(5):633–36.Google Scholar
Schwarze, U., Bingel, U. & Sommer, T. (2012) Event-related nociceptive arousal enhances memory consolidation for neutral scenes. The Journal of Neuroscience 32(4):1481–87.Google Scholar
Sears, R. M., Fink, A. E., Wigestrand, M. B., Farb, C. R., de Lecea, L. & LeDoux, J. E. (2013) Orexin/hypocretin system modulates amygdala-dependent threat learning through the locus coeruleus. Proceedings of the National Academy of Sciences of the United States of America 110(50):20260–65.Google Scholar
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L. & Greicius, M. D. (2007) Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience 27(9):2349–56.Google Scholar
Segal, M. & Bloom, F. E. (1976) The action of norepinephrine in the rat hippocampus: IV. The effects of locus coeruleus stimulation on evoked hippocampal unit activity. Brain Research 107(3):513–25.Google Scholar
Segal, S. K. & Cahill, L. (2009) Endogenous noradrenergic activation and memory for emotional material in men and women. Psychoneuroendocrinology 34(9):1263–71. doi: 10.1016/j.psyneuen.2009.04.020.Google Scholar
Segal, S. K., Stark, S. M., Kattan, D., Stark, C. E. & Yassa, M. A. (2012) Norepinephrine-mediated emotional arousal facilitates subsequent pattern separation. Neurobiology of Learning and Memory 97(4):465–69.Google Scholar
Serences, J. T. & Yantis, S. (2007) Spatially selective representations of voluntary and stimulus-driven attentional priority in human occipital, parietal, and frontal cortex. Cerebral Cortex 17(2):284–93.Google Scholar
Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J. & Davidson, R. J. (2011) The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience 12(3):154–67.Google Scholar
Sherman, S. M. (2005) Thalamic relays and cortical functioning. Progress in Brain Research 149:107–26.Google Scholar
Sheth, S. A., Mian, M. K., Patel, S. R., Asaad, W. F., Williams, Z. M., Dougherty, D. D., Bush, G. & Eskandar, E. N. (2012) Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature 488(7410):218–21.Google Scholar
Shipp, S. (2003) The functional logic of cortico-pulvinar connections. Philosophical Transactions: Biological Sciences 358(1438):1605–24. doi: 10.2307/3558264.Google Scholar
Shumikhina, S. & Molotchnikoff, S. (1999) Pulvinar participates in synchronizing neural assemblies in the visual cortex, in cats. Neuroscience Letters 272(2):135–39.Google Scholar
Sidlauskaite, J., Wiersema, J. R., Roeyers, H., Krebs, R. M., Vassena, E., Fias, W., Brass, M., Achten, E. & Sonuga-Barke, E. (2014) Anticipatory processes in brain state switching – Evidence from a novel cued-switching task implicating default mode and salience networks. NeuroImage 98:359–65.Google Scholar
Singer, W. (1993) Synchronization of cortical activity and its putative role in information processing and learning. Annual Review of Physiology 55(1):349–74. doi: 10.1146/annurev.ph.55.030193.002025.Google Scholar
Skelly, L. R. & Decety, J. (2012) Passive and motivated perception of emotional faces: Qualitative and quantitative changes in the face processing network. PLoS ONE 7(6):e40371. doi: 10.1371/journal.pone.0040371.Google Scholar
Sladek, J. R. Jr. & Sladek, C. D. (1978) Relative quantitation of monoamine histofluorescence in young and old non-human primates. In: Parkinson's disease – II: Aging and neuroendocrine relationships, ed. Finch, C. E., Potter, D. & Kenny, A. D., pp. 231–39. Springer.Google Scholar
Smith, S. D., Most, S. B., Newsome, L. A. & Zald, D. H. (2006) An emotion-induced attentional blink elicited by aversively conditioned stimuli. Emotion 6(3):523–27.Google Scholar
Sohal, V. S., Zhang, F., Yizhar, O. & Deisseroth, K. (2009) Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature 459(7247):698702.Google Scholar
Soltani, A. & Koch, C. (2010) Visual saliency computations: Mechanisms, constraints, and the effect of feedback. The Journal of Neuroscience 30(38):12831–43.Google Scholar
Sorg, O. & Magistretti, P. J. (1991) Characterization of the glycogenolysis elicited by vasoactive intestinal peptide, noradrenaline and adenosine in primary cultures of mouse cerebral cortical astrocytes. Brain Research 563(1):227–33.Google Scholar
Starke, K. (2001). Presynaptic autoreceptors in the third decade: Focus on α2-adrenoceptors. Journal of Neurochemistry 78(4):685–93.Google Scholar
Steblay, N. M. (1992) A metaanalytic review of the weapon focus effect. Law and Human Behavior 16(4):413–24. doi: 10.1007/BF02352267.Google Scholar
Sterpenich, V., D'Argembeau, A., Desseilles, M., Balteau, E., Albouy, G., Vandewalle, G., Degueldre, C., Luxen, A., Collette, F. & Maquet, P. (2006) The locus ceruleus is involved in the successful retrieval of emotional memories in humans. The Journal of Neuroscience 26(28):7416–23. Available at: http://doi.org/10.1523/JNEUROSCI.1001-06.2006.Google Scholar
Strange, B. A. & Dolan, R. J. (2004) Beta-adrenergic modulation of emotional memory-evoked human amygdala and hippocampal responses. Proceedings of the National Academy of Sciences of the United States of America 101(31):11454–58.Google Scholar
Strange, B. A. & Dolan, R. J. (2007) Beta-adrenergic modulation of oddball responses in humans. Behavioral and Brain Functions 3:29.Google Scholar
Strange, B. A., Hurlemann, R. & Dolan, R. J. (2003) An emotion-induced retrograde amnesia in humans is amygdala- and beta-adrenergic-dependent. Proceedings of the National Academy of Sciences of the United States of America 100(23):13626–31. Available at: http://www.ncbi.nlm.nih.gov/pubmed/14595032.Google Scholar
Straube, T., Korz, V., Balschun, D. & Frey, J. U. (2003) Requirement of beta-adrenergic receptor activation and protein synthesis for LTP-reinforcement by novelty in rat dentate gyrus. The Journal of Physiology 552(3):953–60. doi: 10.1113/jphysiol.2003.049452.Google Scholar
Sutherland, M. R., Lee, T. H. & Mather, M. (under review) Arousal impairs top-down prioritization in selective attention.Google Scholar
Sutherland, M. R. & Mather, M. (2012) Negative arousal amplifies the effects of saliency in short-term memory. Emotion 12:1367–72. doi: 10.1037/a0027860.Google Scholar
Sutherland, M. R. & Mather, M. (under review) Both positive and negative arousing sounds increase the impact of visual salience.Google Scholar
Sutherland, M. R., McQuiggan, D. A., Ryan, J. D. & Mather, M. (in press) Perceptual salience does not influence emotional arousal's impairing effects on top-down attention. Emotion Google Scholar
Sved, A. F., Cano, G., Passerin, A. M. & Rabin, B. S. (2002) The locus coeruleus, Barrington's nucleus, and neural circuits of stress. Physiology and Behavior 77(4):737–42.Google Scholar
Swanson, L. W. & Hartman, B. K. (1975) The central adrenergic system: An immunofluorescence study of the location of cell bodies and their efferent connections in the rat utilizing dopamine-B-hydroxylase as a marker. The Journal of Comparative Neurology 163(4):467505. doi: 10.1002/cne.901630406.Google Scholar
Talmi, D. (2013) Enhanced emotional memory: Cognitive and neural mechanisms. Current Directions in Psychological Science 22(6):430–36.Google Scholar
Tamietto, M. & de Gelder, B. (2010) Neural bases of the non-conscious perception of emotional signals. Nature Reviews Neuroscience 11(10):697709.Google Scholar
Tenorio, G., Connor, S. A., Guévremont, D., Abraham, W. C., Williams, J., O'Dell, T. J. & Nguyen, P. V. (2010) “Silent” priming of translation-dependent LTP by β-adrenergic receptors involves phosphorylation and recruitment of AMPA receptors. Learning and Memory 17(12):627–38.Google Scholar
Terakado, M. (2014) Adrenergic regulation of GABA release from presynaptic terminals in rat cerebral cortex. Cortex 56:4957.Google Scholar
Todd, R. M., Müller, D. J., Lee, D. H., Robertson, A., Eaton, T., Freeman, N., Palombo, D. J., Levine, B. & Anderson, A. K. (2013) Genes for emotion-enhanced remembering are linked to enhanced perceiving. Psychological Science 24(11):2244–53. doi: 10.1177/0956797613492423.Google Scholar
Todd, R. M., Palombo, D. J., Levine, B. & Anderson, A. K. (2011) Genetic differences in emotionally enhanced memory. Neuropsychologia 49(4):734–44.Google Scholar
Tooley, V., Brigham, J. C., Maass, A. & Bothwell, R. K. (1987) Facial recognition: Weapon effect and attentional focus. Journal of Applied Social Psychology 17(10):845–59.Google Scholar
Tose, R., Kushikata, T., Yoshida, H., Kudo, M., Furukawa, K., Ueno, S. & Hirota, K. (2009) Interaction between orexinergic neurons and NMDA receptors in the control of locus coeruleus–cerebrocortical noradrenergic activity of the rat. Brain Research 1250:8187.Google Scholar
Toussay, X., Basu, K., Lacoste, B. & Hamel, E. (2013) Locus coeruleus stimulation recruits a broad cortical neuronal network and increases cortical perfusion. The Journal of Neuroscience 33(8):3390–401. doi: 10.1523/jneurosci.3346-12.2013.Google Scholar
Traynelis, S. F., Wollmuth, L. P., McBain, C. J., Menniti, F. S., Vance, K. M., Ogden, K. K., Hansen, K. B., Yuan, H., Myers, S. J. & Dingledine, R. (2010) Glutamate receptor ion channels: Structure, regulation, and function. Pharmacological Reviews 62(3):405–96.Google Scholar
Treisman, A. (1998) Feature binding, attention and object perception. Philosophical Transactions of the Royal Society of London Series B Biological Sciences 353(1373):1295–306.Google Scholar
Treviño, M., Frey, S. & Köhr, G. (2012a) Alpha-1 adrenergic receptors gate rapid orientation-specific reduction in visual discrimination. Cerebral Cortex 22(11):2529–41. doi: 10.1093/cercor/bhr333.Google Scholar
Treviño, M., Huang, S., He, K., Ardiles, A., De Pasquale, R., Guo, Y., Palacios, A., Huganir, R. L. & Kirkwood, A. (2012b) Pull–push neuromodulation of LTP and LTD enables bidirectional experience-induced synaptic scaling in visual cortex. Neuron 73(3):497510.Google Scholar
Troiani, V. & Schultz, R. T. (2013) Amygdala, pulvinar & inferior parietal cortex contribute to early processing of faces without awareness. Frontiers in Human Neuroscience 7:241. doi: 10.3389/fnhum.2013.00241.Google Scholar
Tully, K. & Bolshakov, V. Y. (2010) Emotional enhancement of memory: How norepinephrine enables synaptic plasticity. Molecular Brain 3(1):15.Google Scholar
Uddin, L. Q. (2015) Salience processing and insular cortical function and dysfunction. Nature Reviews Neuroscience 16(1):5561.Google Scholar
Ueda, H., Goshima, Y., Kubo, T. & Misu, Y. (1985) Involvement of epinephrine in the presynaptic beta adrenoceptor mechanism of norepinephrine release from rat hypothalamic slices. Journal of Pharmacology and Experimental Therapeutics 232(2):507–12.Google Scholar
Ul Haq, R., Liotta, A., Kovacs, R., Rösler, A., Jarosch, M. J., Heinemann, U. & Behrens, C. J. (2012) Adrenergic modulation of sharp wave-ripple activity in rat hippocampal slices. Hippocampus 22(3):516–33. doi: 10.1002/hipo.20918.Google Scholar
Ullsperger, M., Harsay, H., Wessel, J. & Ridderinkhof, K. R. (2010) Conscious perception of errors and its relation to the anterior insula. Brain Structure and Function 214(5/6):629–43. doi: 10.1007/s00429-010-0261-1.Google Scholar
Usher, M., Cohen, J. D., Servan-Schreiber, D., Rajkowski, J. & Aston-Jones, G. (1999) The role of locus coeruleus in the regulation of cognitive performance. Science 283(5401):549–54.Google Scholar
Valentino, R. J. & Van Bockstaele, E. (2001) Opposing regulation of the locus coeruleus by corticotropin-releasing factor and opioids. Psychopharmacology 158(4):331–42.Google Scholar
Van Bockstaele, E., Bajic, D., Proudfit, H. & Valentino, R. (2001) Topographic architecture of stress-related pathways targeting the noradrenergic locus coeruleus. Physiology and Behavior 73(3):273–83.Google Scholar
Van Bockstaele, E. J., Colago, E. E. O. & Valentino, R. J. (1998) Amygdaloid corticotropin-releasing factor targets locus coeruleus dendrites: Substrate for the co-ordination of emotional and cognitive limbs of the stress response. Journal of Neuroendocrinology 10(10):743–58. doi: 10.1046/j.1365-2826.1998.00254.x.Google Scholar
Van Horn, M. R., Sild, M. & Ruthazer, E. S. (2013) D-Serine as a gliotransmitter and its roles in brain development and disease. Frontiers in Cellular Neuroscience 7:39. doi: 10.3389/fncel.2013.00039.Google Scholar
Vankov, A., Hervé-Minvielle, A. & Sara, S. J. (1995) Response to novelty and its rapid habituation in locus coeruleus neurons of the freely exploring rat. European Journal of Neuroscience 7(6):1180–87. doi: 10.1111/j.1460-9568.1995.tb01108.x.Google Scholar
Varga, C., Oijala, M., Lish, J., Szabo, G. G., Bezaire, M., Marchionni, I., Golshani, P. & Soltesz, I. (2014) Functional fission of parvalbumin interneuron classes during fast network events. eLife 3:e04006. doi: 10.7554/eLife.04006.Google Scholar
Vazey, E. M. & Aston-Jones, G. (2014) Designer receptor manipulations reveal a role of the locus coeruleus noradrenergic system in isoflurane general anesthesia. Proceedings of the National Academy of Sciences of the United States of America 111(10):3859–64.Google Scholar
Vezzani, A., Wu, H. Q. & Samanin, R. (1987) [3H]Norepinephrine release from hippocampal slices is an in vitro biochemical tool for investigating the pharmacological properties of excitatory amino acid receptors. Journal of Neurochemistry 49(5):1438–42.Google Scholar
Vijayashankar, N. & Brody, H. (1979) Quantitative study of the pigmented neurons in the nuclei locus coeruleus and subcoeruleus in man as related to aging. Journal of Neuropathology and Experimental Neurology 38(5):490–97. doi: 10.1097/00005072-197909000-00004.Google Scholar
Vinck, M., Batista-Brito, R., Knoblich, U. & Cardin, J. A. (2014) Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron 86(3):740754.Google Scholar
Vizi, E., Fekete, A., Karoly, R. & Mike, A. (2010) Non-synaptic receptors and transporters involved in brain functions and targets of drug treatment. British Journal of Pharmacology 160(4):785809.Google Scholar
Von der Gablentz, J., Tempelmann, C., Münte, T. & Heldmann, M. (2015) Performance monitoring and behavioral adaptation during task switching: An fMRI study. Neuroscience 285:227–35.Google Scholar
Von Stein, A. & Sarnthein, J. (2000) Different frequencies for different scales of cortical integration: From local gamma to long range alpha/theta synchronization. International Journal of Psychophysiology 38(3):301–13.Google Scholar
Vuilleumier, P. (2005b) How brains beware: Neural mechanisms of emotional attention. Trends in Cognitive Sciences 9(12):585–94.Google Scholar
Walling, S. G., Brown, R. A. M., Milway, J. S., Earle, A. G. & Harley, C. W. (2011) Selective tuning of hippocampal oscillations by phasic locus coeruleus activation in awake male rats. Hippocampus 21(11):1250–62. doi: 10.1002/hipo.20816.Google Scholar
Walls, A. B., Heimbürger, C. M., Bouman, S. D., Schousboe, A. & Waagepetersen, H. S. (2009) Robust glycogen shunt activity in astrocytes: Effects of glutamatergic and adrenergic agents. Neuroscience 158(1):284–92.Google Scholar
Wang, J. K., Andrews, H. & Thukral, V. (1992) Presynaptic glutamate receptors regulate noradrenaline release from isolated nerve terminals. Journal of Neurochemistry 58(1):204–11.Google Scholar
Wang, L., Kennedy, B. L. & Most, S. B. (2012) When emotion blinds: A spatiotemporal competition account of emotion-induced blindness. Frontiers in Psychology 3:438. doi: 10.3389/fpsyg.2012.00438.Google Scholar
Wang, M., Ramos, B., Paspalas, C., Shu, Y., Simen, A., Duque, A., Vijayraghavan, S., Brennan, A., Dudley, A., Nou, E., Mazer, J. A., McCormick, D. A. & Arnsten, A. F. T. (2007) Alpha2A-adrenoceptor stimulation strengthens working memory networks by inhibiting cAMP–HCN channel signaling in prefrontal cortex. Cell 129:397410.