Factors modulating neural reactivity to drug cues in addiction: a survey of human neuroimaging studies (2013)

Agnes J. Jasinska,^1,* Elliot A. Stein,¹ Jochen Kaiser,² Marcus J. Naumer,² and Yavor Yalachkov^2,*

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3.1. Mesocorticolimbic system and brain circuits of reward, motivation, and goal-directed behavior

A common characteristic, and arguably a shared neurobiological mechanism, of most if not all drugs of abuse is that they increase extracellular dopamine (DA) concentration in the mesocorticolimbic system, including the ventral striatum (VS), extended amygdala, hippocampus, anterior cingulate (ACC), prefrontal cortex (PFC), and insula, which are innervated by dopaminergic projections predominantly from the ventral tegmental area (VTA) (Hyman et al., 2006; Nestler, 2005). Such directly or indirectly drug-induced increases in DA have been demonstrated for different classes of drugs that target different neurotransmitter systems, including nicotine (acetylcholine), cocaine and amphetamine (dopamine, norepinephrine, and serotonin), heroin (opioids), marijuana (endocannabinoids), and alcohol (GABA). For example, nicotine enhances DA release by binding to nicotinic acetylcholine receptors (nAChRs) located on the DA neurons projecting from the VTA to NAc (Clarke and Pert, 1985; Deutch et al., 1987), as well as on glutamatergic and GABAergic neurons that modulate these DA neurons (Mansvelder et al., 2002; Wooltorton et al., 2003). Nicotine increases the firing rates of VTA DA neurons (Calabresi et al., 1989), leading to increased DA release in the NAc (Imperato et al., 1986).

Although the mesocorticolimbic system also responds to natural rewards such as food, water, and sex, drugs of abuse precipitate a larger amplitude and longer duration of DA response than a normal physiological response (Jay, 2003; Kelley, 2004; Nestler, 2005). Thus, drugs of abuse are characterized as “hijacking” the neurobiological mechanisms by which the brain responds to reward, establishes reward-associated memories, and consolidates action repertoires leading to the reward (Everitt and Robbins, 2005b; Kalivas and O’Brien, 2008). Repeated drug intake, serving as an unconditioned stimulus, allows drug-related cues to become conditioned stimuli predictive of a drug response, and thus elicit DA release and craving (Volkow et al., 2006, 2008; Wong et al., 2006). Consequently, the incentive salience of drug cues and associated contexts increases over time (Robinson and Berridge, 1993), producing physiological arousal and robust attentional biases, and acting as a potent trigger of drug-seeking and drug-taking behaviors.

Such increased incentive salience of drug cues, as reflected by their impact on mesocorticolimbic circuitry, has been repeatedly demonstrated in human neuroimaging studies (for recent meta-analyses, see (Chase et al., 2011; Engelmann et al., 2012; Kuhn and Gallinat, 2011; Schacht et al., 2012)). Taken together, these studies strongly suggest that, compared to neutral control cues, drug-related cues elicit greater brain activations within the mesocorticolimbic circuits, including VTA, VS, amygdala, ACC, PFC, insula, and hippocampus in drug users (Brody et al., 2007; Childress et al., 2008; Childress et al., 1999; Claus et al., 2011; Due et al., 2002; Franklin et al., 2007; Grüsser et al., 2004; Kilts et al., 2001; Luijten et al., 2011; Smolka et al., 2006; Volkow et al., 2006; Vollstädt-Klein et al., 2010b; Yalachkov et al., 2009).

Much of our understanding of the essential functions of brain regions mediating drug cue reactivity in human drug users comes from preclinical research in rodent and non-human primates. This research has shown that phasic firing of DA neurons projecting from VTA to VS is crucial for behavioral conditioning (Tsai et al., 2009), and activity in these brain regions reflects the reward value predicted by discriminative cues (Schultz, 2007a, b; Schultz et al., 1997). Other brain structures that are important for associative learning are the amygdala and hippocampus. The amygdala and the hippocampus play distinct roles in conditioned learning (Robbins et al., 2008), which implies that their activation in neuroimaging experiments reflects the processing of learned reward values of conditioned cues and contexts. A part of the PFC, the orbitofrontal cortex (OFC), partly overlapping with the ventromedial PFC (VMPFC), is believed to play a key role in integrating sensory inputs, reward values, and homeostatic signals about the current state and needs of the organism, in order to guide motivated behavior (Lucantonio et al., 2012; Schoenbaum et al., 2006; Schoenbaum et al., 2009). Preclinical animal research has demonstrated that the amygdala and OFC project to the VS, and that the interplay between these three regions contributes to drug-seeking over long delays bridged by conditioned reinforcers (Everitt and Robbins, 2005a). Thus, the VS receives information about the respective motivational values and incentive drives of stimuli from a broad network of cortical and subcortical regions, and it plays a key role in governing the basal ganglia’s final action output (Haber and Knutson, 2010).

Critical roles in drug-cue reactivity and in drug addiction more generally have also been postulated for the ACC and the insula. The ACC is engaged in a range of cognitive tasks, particularly tasks that involve cognitive control, conflict, or error monitoring (e.g., (Dosenbach et al., 2006; Garavan et al., 2002; Nee et al., 2007); but the ACC is also activated by salient stimuli (e.g., (Liu et al., 2011)), including reward-related stimuli but also stimuli that elicit pain or negative affect (for a review on the integrative role of this region, see (Shackman et al., 2011)). The insula has been associated primarily with interoception, or the awareness of bodily states and internal homeostasis (for a review, see (Craig, 2003)). However, in a close parallel to the ACC, the insula and the adjacent inferior frontal gyrus are also often engaged during tasks requiring cognitive control (e.g., (Wager et al., 2005) and in response to salient external stimuli (e.g., (Liu et al., 2011)). Indeed, the ACC and the insula are commonly regarded as parts of a common large-scale brain network, variously referred to as the cingulo-opercular, fronto-insular, or salience network (Dosenbach et al., 2006; Seeley et al., 2007), and whose function may be to integrate internal and external signals of salience and to initiate interactions between large-scale brain networks to best meet the current demands for control (Menon and Uddin, 2010; Sridharan et al., 2008; Sutherland et al., 2012).

The impact of drug-related modulation of the mesocorticolimbic circuitry also extends to sensory representations of drug cues. Rewards enhance the sensory representations of cues associated with these rewards in the occipital, temporal, and parietal regions (Serences, 2008; Yalachkov et al., 2010). In particular, due to their acute reinforcing effects mediated by increases in DA and other neurotransmitter signaling, drugs of abuse are thought to facilitate the sensory processing of drug cues and to promote a range of learning and plasticity processes (Devonshire et al., 2004; Devonshire et al., 2007). Arguably, such drug-induced enhancement of sensory processing of drug cue is an early manifestation of increased incentive salience of these cues. Because of this enhanced early processing, the sensory representations of drug cues are easily activated and trigger robust attentional biases in drug users, and these processing biases may then be propagated to the decision-making and motor control systems, increasing the chances of drug-seeking behavior. These mechanisms may explain the strong response in sensory and perceptual cortices often observed in human neuroimaging studies of drug cue reactivity (Due et al., 2002; Luijten et al., 2011; Yalachkov et al., 2010).

3.2. Nigrostriatal system and brain circuits related to habit learning, automaticity, and tool use

In parallel to the mesocorticolimbic system which connects the VTA with the VS, amygdala, hippocampus, ACC, PFC, and insula, drug-induced DA increases also affect another, parallel ascending DA system: the nigrostriatal system. The nigrostriatal DA system consists primarily of DA projections from the substantia nigra (SN) to the caudate and putamen (also referred to as the dorsal striatum; DS) and globus pallidus. These structures are thought to underlie habit learning and automaticity, and growing evidence suggests that they are also more strongly activated in response to drug cues compared to neutral stimuli in drug users.

The DS, which has been extensively studied in the rodent, can be divided anatomically and functionally into the dorsomedial striatum (DMS, corresponding to the dorsal caudate nucleus in humans) and dorsolateral striatum (DLS, corresponding to the dorsal putamen in humans). While the DMS has a more prominent role in action-outcome learning and the acquiring of instrumental responding (Belin et al., 2009), the DLS is involved in the development and expression of habits. Habits are a product of stimulus-response learning where reinforcers primarily strengthen the stimulus-response associations. However, after extensive training the behavior does not remain under the control of the goal but rather shifts towards the influence of the stimulus. Thus, devaluing the reinforcer at this stage of learning has no consequence for the behavioral responses which are now conducted automatically upon the presentation of the stimulus and their future performance is maintained solely by the cue presentation (Belin et al., 2009; Everitt and Robbins, 2005a). This change from goal-oriented actions to automatized habits is reflected by a shift of the neural control of behavior from the ventral to dorsolateral striatum (Belin et al., 2009; Everitt and Robbins, 2005a).

Recent findings revealed that the mechanisms leading to the development and expression of such habitual behaviors in drug addiction are more complex than initially thought. Drug-seeking habits seem to be mediated not by a single brain region such as the DLS but rather by spiraling striato-nigro-striatal interconnections between the VTA, VS and DS. Thus, bilateral DA blockade in the DLS (Vanderschuren et al., 2005) or bilateral glutamate receptor blockade/lesions in the NAc core (i.e., VS) (Di Ciano and Everitt, 2001; Ito et al., 2004) have essentially the same effects as the disconnection of the ventral from the dorsolateral striatum (Belin and Everitt, 2008; Belin et al., 2009). Volkow et al. (2006) reported cocaine cue-induced increases in DA release in the dorsal but not ventral striatum. This might reflect glutamatergic rather than dopaminergic involvement of the VS, although some studies have also demonstrated dopaminergic increases in the NAc after presentation of drug cues (Ito et al., 2000).

A number of studies have shown increases in DS activity in response to drug cues relative to neutral cues in drug users (Claus et al., 2011; Schacht et al., 2011; Vollstädt-Klein et al., 2010b; Wilson et al., 2013). A recent, well-powered study in 326 heavy drinkers (Claus et al., 2011) demonstrated a particularly robust cue-induced activation in the DS, as well as the expected activation in the VS, among other regions, in response to gustatory alcohol cues. The cue-induced activation in the DS, as well as in the VS, was stable over short periods of time, as assessed with scans 14 days apart in alcohol-dependent individuals (Schacht et al., 2011). Vollstadt-Klein and colleagues (2010) reported that heavy drinkers (5.0 ± 1.5 drinks/day) showed higher cue-induced activations in the DS compared to light social drinkers (0.4 ± 0.4 drinks/day), although light drinkers showed higher cue-induced activation in the VS and PFC compared to heavy drinkers. In that study, the DS activation to drug cues was positively correlated with drug craving in all participants, whereas the VS activation was negatively correlated with such craving in heavy drinkers. Consistent with animal research and theoretical accounts, the authors (Vollstädt-Klein et al., 2010b) interpreted the results in terms of a transition from the initial hedonic, controlled drug use (mediated by the VS and PFC) to habit-driven and eventually uncontrolled and compulsive drug abuse and dependence (mediated by the DS). In addition, nicotine-dependent smokers who subsequently slipped in their quit attempt showed a greater cue-induced activity in the DS (putamen), among other regions, but not in the VS compared to smokers who remained abstinent (Janes et al., 2010a).

Several studies have also highlighted the role of further cortical and subcortical structures in automatized behavior and motor planning. The DS circuits are known to project to, and interact with, thalamic-cortical circuits involved in planning and execution of motor responses. A more extended neural circuitry comprising the premotor cortex (PMC) and motor cortex (MC), as well as supplementary motor area (SMA), superior and inferior parietal cortices, posterior middle temporal gyrus (pMTG) and inferior temporal cortex (ITC), is known to store and process action knowledge and tool use skills (Buxbaum et al., 2007; Calvo-Merino et al., 2005; Calvo-Merino et al., 2006; Chao and Martin, 2000; Creem-Regehr and Lee, 2005; Johnson-Frey, 2004; Johnson-Frey et al., 2005; Lewis, 2006). Subjects with lesions in one or several of these brain regions usually exhibit different kinds of apraxia or general action planning and executing difficulties (Lewis, 2006). Moreover, behavioral tasks designed to reveal the neural correlates of tool use skills and object manipulation knowledge typically activate the abovementioned circuitry (Grezes and Decety, 2002; Grezes et al., 2003; Yalachkov et al., 2009). Interestingly, a number of studies have reported higher activation in this brain network for drug cues compared to neutral cues (Kosten et al., 2006; Smolka et al., 2006; Wagner et al., 2011; Yalachkov et al., 2009, 2010). Drug-taking skills have been suggested to constitute the core of drug acquisition and consumption behavior, which becomes highly automatized after repeated practice (Tiffany, 1990). However, the neural representations of drug-taking skills in the PMC, MC, SMA, SPL, IPL, pMTG, ITC and cerebellum have only recently attracted the interest of the addiction field (Wagner et al., 2011; Yalachkov et al., 2013; Yalachkov et al., 2009, 2010; Yalachkov and Naumer, 2011).

3.3 Inter- and intra-study variability in neural correlates of drug cue reactivity

Thus, the existing neuroimaging evidence suggests that, relative to neutral control stimuli, salient drug cues presented to drug users elicit increases in activity throughout the mesocorticolimbic system, including the VTA, VS, amygdala, ACC, PFC (including OFC and DLPFC), insula, and hippocampus, as well as in sensory and motor cortices (for recent meta-analyses, see (Chase et al., 2011; Engelmann et al., 2012; Kuhn and Gallinat, 2011; Schacht et al., 2012; Tang et al., 2012; Yalachkov et al., 2012)). These drug cue-evoked responses likely reflect the neural representations of reward values of drug cues and the motivational processes of incentive salience that guide drug-seeking behavior (Chase et al., 2011; Engelmann et al., 2012; Kuhn and Gallinat, 2011; Yalachkov et al., 2012). This notion is supported by the often-reported positive correlations between activation of these regions and measurements of drug-induced urges, attentional bias, eye movements, severity of dependence, and relapse (for reviews see (Kuhn and Gallinat, 2011; Yalachkov et al., 2012)).

Similar increases in neural activity in response to drug cues have been demonstrated within the parallel nigrostriatal DA system. The nigrostriatal system is critical to habit learning and a transition from controlled to automatic behavior, and drug cue-induced activation of this system in chronic, dependent drug users has been reported across different drugs of abuse (Claus et al., 2011; Schacht et al., 2011; Vollstädt-Klein et al., 2010b; Wilson et al., 2013). In addition to the subcortical regions, drug cues presented to drug users engage the cortical circuits underlying motor planning and execution, action knowledge, and tool use skills, which encompass the PMC, MC, SMA, SPL, IPL, pMTG, ITC and cerebellum (Kosten et al., 2006; Smolka et al., 2006; Wagner et al., 2011; Yalachkov et al., 2009, 2010). Furthermore, the responses in these regions are correlated with the severity of dependence and the degree of automaticity of the behavioral responses towards drug cues (Smolka et al., 2006; Yalachkov et al., 2009). These observations have been interpreted as evidence that, in addition to reward, motivational and goal-directed mechanisms, drug cues may trigger drug taking by activating the corresponding drug-taking skills in drug users (Yalachkov et al., 2009).

However, considerable inter- and intra-study variability in the patterns of brain response to drug cues exists, suggesting modulation by other factors. This is not surprising, since drug cue reactivity is a complex phenomenon, and as such it is likely to be modulated by a large number of both study-specific and individual-specific factors as well as their interactions. Nevertheless, an important goal is to synthesize the existing knowledge of such modulatory factors and their respective influences on the neural responses to drug cues in drug users, building on existing models (Field and Cox, 2008; Franken, 2003; Wilson et al., 2004). Several previous reviews and meta-analyses of neural cue reactivity have been published (Chase et al., 2011; Engelmann et al., 2012; Kuhn and Gallinat, 2011; Schacht et al., 2012; Sinha and Li, 2007; Tang et al., 2012; Yalachkov et al., 2012) but typically focused on a small number of modulatory factors acting in isolation, either study-specific (i.e., type of drug cue) or individual-specific (i.e., treatment status), in part due to scarcity of experimental evidence on the actions and interactions of multiple modulatory factors on the brain’s response to drug cues. Our goal was to build upon and extend these previous efforts towards a more comprehensive model, including multiple study-specific and individual-specific factors that modulate neural cue reactivity. Towards that goal, we survey the evidence on a subset of factors that have been demonstrated to modulate neural cue reactivity in the human neuroimaging literature: length and intensity of use and measures of addiction severity, craving, and relapse/treatment outcome (section 4.1); current treatment status and drug availability/expectancy (section 4.2); abstinence and withdrawal symptoms (section 4.3); sensory modality and length of presentation of drug cues (section 4.4); explicit and implicit regulation of drug cue reactivity (section 4.5); and stressor exposure (section 4.6). Building on previous model-building reviews on the topic (Field and Cox, 2008; Franken, 2003; Wilson et al., 2004), we then summarize these data with a simplified model that incorporates the major modulatory factors and we offer a tentative ranking of their relative impact on neural drug-cue reactivity (section 5). We conclude with a discussion of outstanding challenges, suggested future research directions, and the potential relevance of this research both to the neuroimaging research on substance use disorders and to translation of this research to treatment and prevention in the clinic (section 6).

The purpose of this review is also to draw the field’s attention to the growing number of factors that have been shown to affect brain responses to drug-related cues. Our hope is that this will encourage researchers to assess and report as many of the reviewed factors as feasible. Additionally, we tried to highlight both the need for—and the considerable challenge of— controlling and manipulating the known factors that modulate cue reactivity as well as their interactions in future research.

4.1 Addiction severity, craving, and treatment outcome

The clinical relevance of drug cue reactivity is well documented by behavioral studies (Field and Cox, 2008). Drug cue reactivity is associated with, and in some cases predictive of, a number of clinical measures of drug use and dependence, including length and intensity of drug use, addiction severity, risk of relapse, treatment outcomes, and use-associated problems. However, it should be emphasized that the direction of influence, or cause and effect, are less clear. On the one hand, chronic drug consumption may lead to a heightened incentive salience of drug cues and a compulsion to continue using and even accelerate drug use, despite negative consequences. On the other hand, heightened neural reactivity to drug cues within the mesocorticolimbic and nigrostriatal systems, as well as in the sensory and motor control circuits, might repeatedly trigger drug consumption. Most likely the two processes co-exist in the addicted brain: repeated drug taking increases neural reactivity to drug cues, while increased neural reactivity to drug cues promotes drug taking, leading to a vicious cycle of escalating use and dependence.

4.2 Current treatment status and drug availability/expectancy

The importance of current abstinence and treatment-seeking status as factors influencing the neural reactivity to drug cues has been previously argued (Wilson et al., 2004) and supported by recent meta-analyses of neuroimaging data (Chase et al., 2011). The role of drug availability and expectancy as an independent factor modulating neural cue reactivity has also been suggested (Wertz and Sayette, 2001b). In addition, drug availability and expectancy has been proposed to mediate at least some of the influence of abstinence and treatment-seeking status on neural cue reactivity (Wertz and Sayette, 2001a, b; Wilson et al., 2004).

Focusing on the PFC, Wilson and colleagues (Wilson et al., 2004) reviewed 18 fMRI and PET studies of drug cue reactivity, and concluded that drug-related cues activate the DLPFC and (more variably) the OFC in individuals who are actively using drugs and not seeking treatment at the time of the study, but not in treatment-seeking drug users. Similarly, Hayashi and colleagues found that when cigarettes were immediately available, subjective craving was greater (Hayashi et al., 2013). Using fMRI, the authors showed that the information about inter-temporal drug availability was encoded in the DLPFC. Furthermore, the strong craving elicited by the immediate availability of cigarettes was diminished by transiently inactivating the DLPFC with transcranial magnetic stimulation. Thus, the DLPFC appears to be of particular importance in establishing and dynamically modulating value signals based on one’s knowledge of drug availability (Hayashi et al., 2013).

4.3 Abstinence and withdrawal symptoms

Abstinence and associated withdrawal symptoms (including irritable, anxious, or depressed mood, difficulty concentrating, motor disturbances, disturbances in appetite and sleep, as well as changes in heart rate, blood pressure, and body temperature) are also likely to modulate neural reactivity to drug cues in drug users. Craving for a drug is sometimes considered a symptom of drug withdrawal as well. In fact, drug seeking during abstinence-induced withdrawal has been postulated to be at least in part motivated by alleviating unpleasant withdrawal symptoms (negative reinforcement), although it is also known that drug-related cues can precipitate relapse to drug taking even after prolonged abstinence and in the absence of any withdrawal symptoms. Thus, we would expect that abstinence and the presence of withdrawal symptoms would potentiate both craving for a drug and neural reactivity to drug cues, whereas satiety and the absence of such withdrawal symptoms would reduce both craving and cue reactivity (David et al., 2007; McClernon et al., 2005; McClernon et al., 2008).

A number of studies examined the impact of abstinence on smoking cue reactivity in smokers. McClernon and colleagues (2005) directly compared neural reactivity to smoking cues in the same group of nicotine-dependent smokers scanned twice: once after ad libitum smoking (satiety condition), and once after an overnight abstinence. Across both satiety and abstinence conditions, smoking cues relative to neutral cues activated the ventral ACC and PFC (superior frontal gyrus), with no differences between sessions (although the response to neutral cues decreased in the thalamus, dorsal ACC, and insula in the satiated state relative to the abstinent state) (McClernon et al., 2005). However, as expected, self-reported craving increased in the abstinence condition relative to the satiated condition, and these abstinence-induced changes in craving were positively correlated with smoking cue-induced responses in the DLPFC (middle frontal gyrus), IFG, superior frontal gyrus, ventral and dorsal ACC, and thalamus (McClernon et al., 2005). Another study (David et al., 2007) also assessed the effects of overnight smoking abstinence and found a decrease in smoking cue-induced response in the VS/NAc relative to the satiated condition. Extending the length of abstinence to 24 hours, McClernon et al. (2009) showed that smoking abstinence increased craving, increased negative affect, hunger, somatic symptoms, and habit withdrawal, and decreased arousal relative to a satiated condition in moderately dependent smokers. Relative to satiety, 24-hour smoking abstinence increased smoking cue-induced responses in the PFC (superior frontal gyrus), superior parietal lobule, PCC, occipital cortex, precentral and postcentral gyri, and caudate, whereas no regions showed a decreased cue-induced response in the abstinent relative to satiated condition (McClernon et al., 2009).

Janes and colleagues (2009) contrasted the neural reactivity to smoking cues in a group of nicotine-dependent smokers prior to a quit attempt and after extended abstinence (~50 days). Of note, smokers in this study were using a transdermal nicotine patch and were allowed to supplement it with nicotine gum and lozenges, as part of a clinical trial. This study found that extended smoking abstinence was associated with increases in smoking cue-induced responses in the caudate nucleus, ACC, PFC (including DLPFC and IFG), and precentral gyrus, as well as in the temporal, parietal, and primary somatosensory cortices, relative to the pre-quit assessment. In contrast, the response to smoking cues in the hippocampus decreased after extended abstinence relative to the pre-quit scan. Finally, a recent meta-analysis (Engelmann et al., 2012) demonstrated that neural responses to smoking cues in the DLPFC and occipital cortex were more reliably detected in deprived/abstinent smokers relative to non-deprived smokers.

The impact of abstinence on neural reactivity to drug cues has also been assessed in alcohol users. A recent study (Fryer et al., 2012) compared three groups of one-time alcoholics (current drinkers, recent abstainers, and long-term abstainers) and healthy controls (social drinkers), and reported that long-time abstainers showed an increased reactivity to alcohol-related distracters relative to neutral distracters in the dorsal ACC and the IPL regions, compared to both recent abstainers and current users.

4.4 Sensory modality and length of presentation of drug cues

The sensory modality of the cues can also influence the behavioral and brain cue reactivity itself. Behavioral experiments have demonstrated pronounced differences in the ability of drug cues to elicit behavioral and psychophysiological reactions depending on the sensory modality (Johnson et al., 1998; Reid et al., 2006; Shadel et al., 2001; Wray et al., 2011). For instance, a recent fMRI study revealed that haptic smoking cues activate the DS more strongly than visual smoking cues (Yalachkov et al., 2013). In this study, the preference for haptic over visual smoking stimuli correlated positively with the severity of nicotine dependence (see also 4.1.1) in the inferior parietal cortex, somatosensory cortex, FG, inferior temporal cortex, cerebellum, hippocampus/parahippocampal gyrus, PCC, and SMA.

The notion that the sensory modality modulates brain responses to drug stimuli has been further corroborated by a recent meta-analysis including data from 44 functional neuroimaging studies with a total of 1168 participants (Yalachkov et al., 2012). Visual cues are easily employed in experiments, since their presentation parameters can be easily modified, e.g., full color or grey scale, length of presentation, and location on the screen. Visual cues are also relatively cheap and can be used repeatedly. In contrast, the employment of haptic cues (e.g., cigarettes) is more challenging, since their length and location of presentation are more difficult to control, and they have to be replaced after each participant. In fMRI experiments, haptic stimuli also have to be non-ferromagnetic, and touching haptic cues is correlated with increased head movements compared with viewing movies or pictures or listening to imagery scripts. In addition, the experimenter needs to be present in the scanner room in order to put the stimuli in the subject’s hand. Olfactory and gustatory cues present their own challenges. Multisensory drug cues may elicit more robust brain responses than the commonly employed visual drug cues, and significant correlations between neural cue reactivity and clinical covariates (e.g., craving) have been reported more often for multisensory than visual cues in the MC, insula, and PCC.

Another experimental parameter that may influence cue reactivity is the length of stimulus presentation. A meta-analysis investigating the neural substrates of smoking cue reactivity showed that short-duration cues (≤ 5 sec) presented in event-related designs produced more reliable responses in the bilateral FG than long-duration cues (≥ 18 sec) presented in blocked designs (Engelmann et al., 2012). No brain regions exhibited more reliable responses for long-duration as compared to short-duration cues.

In fact, even drug cues presented for such short durations that they remain below the perceptual threshold and are never consciously perceived, activate the neural circuits underlying cue reactivity. For example, cocaine-related cues presented for 33 msec, so subjects were not able to consciously identify them, elicited higher activations in the amygdala, VS, ventral pallidum, insula, temporal poles, and OFC, compared to subliminal neutral cues (Childress et al., 2008). Equally interesting was the observation that the “unconscious” activation of the ventral pallidum and amygdala was positively correlated with the subsequent positive affect to longer, consciously perceived presentation of the same cues in subsequent behavioral testing. However, in a fMRI study using a backward masking paradigm, the BOLD response in the amygdala decreased when smokers viewed but did not perceive masked smoking-related stimuli presented for 33 msec, while no significant differences were found in the non-smokers group (Zhang et al., 2009).

However, the impact of duration of stimulus presentation of drug cues may also be related to the question about which type of fMRI design (event-related or blocked) is better suited to examine cue reactivity in addiction (for discussion, see also (Yang et al., 2011)). The advantage of event-related fMRI designs is that they permit examination of the hemodynamic responses to individual drug cues rather than blocks of cues. In addition, in event-related designs, incorrect responses can be analysed separately or discarded, which increases the specificity of the analyses. On the other hand, blocked designs typically yield more robust fMRI signals sue to temporal summation of the hemodynamic responses to individual drug cues within a block. Thus, the advantage of blocked designs is that they offer greater sensitivity and thus a greater chance of detecting the effects of interest, particularly in brain regions in which these effects may be more subtle.

For instance, Bühler and colleagues (Bühler et al., 2008) investigated the impact of fMRI design on the neural responses to erotic cues in healthy males by directly comparing an event-related design (stimulus duration of 0.75 sec per event) and a blocked design (total block duration of 19.8 sec). In that study, the event-related design yielded a higher erotic-cue-elicited response than the blocked design in the SMA and auditory cortices, whereas the blocked design yielded a greater erotic-cue reactivity than the event-related design in the pre- and post-central gyri, IPC/SPC, and occipital regions. To the best of our knowledge, no study has directly compared the impact of event-related vs. blocked designs on drug cue reactivity.

Finally, although understudied, the neural reactivity to drug cues is also likely to be influenced by the degree of individualization of drug cues, i.e., whether the drug cues are tailored to each participant or not (e.g., each participant’s preferred brand of tobacco cigarettes or of alcoholic drink, rather than the same generic smoking- or alcohol-related cues used for all participants). The prediction would be that individualized drug cues should elicit a greater neural response than generic drug cues, although this hypothesis remains mostly untested.

A related issue pertains to the choice of control stimuli to be contrasted with drug cues in neuroimaging analyses. These control stimuli vary from appetitive cues such as food cues, which arguably yield a more specific but less robust contrast (e.g., (Tang et al., 2012))—to neutral, non-drug-related cues such as everyday objects or scenes, which produce a greater effect but at a potential cost of reduced specificity. Importantly, precise matching of the control stimuli to the drug stimuli (e.g., in content, arousal, familiarity) may be essential for isolating drug-specific effects. While this implies inevitable pre-testing of a bigger pool of potential experimental stimuli and thus increases the time and efforts needed for the planning phase of a study, it also ensures a greater validity of the reported findings. A very helpful option is to consider employing well-established smoking and control stimulus sets, which have been tested for important parameters, such as the International Smoking Images Series (Gilbert and Rabinovich, 2006). In this stimulus set both smoking cues and their counterparts have been extensively rated for interest, valence, arousal, and urge to smoke, and have been used in several cue reactivity studies (e.g., David et al., 2007; Yalachkov et al., 2009; Westbrook et al., 2011)(Zhang et al., 2011). On the other hand, using an already existing stimulus set may pose limitations on the experimental questions to be asked. Thus, if one wants to test novel or highly specific hypotheses about cue-reactivity processes (e.g., response to images of people smoking vs. images of smoking paraphernalia only), one may have to use, and possibly develop and test, a novel set of stimuli. An interesting approach has been employed by Conklin and colleagues (Conklin et al., 2010), who instructed smokers to take pictures of the environments in which they do and do not smoke, to be used as smoking and control cues, respectively, in the laboratory. Consequently, both drug-related and non-drug-related (neutral or control) stimuli were highly personalized, increasing the ecological validity of the subsequent cue-reactivity measurements.

4.5 Explicit and implicit regulation of drug cue reactivity

Current theories of addiction posit that, with repeated drug use and associated DA processes in the mesocorticolimbic and nigrostriatal circuits, drug-related cues acquire incentive-motivational salience, which gives them the capacity to trigger craving and drug seeking (Robinson and Berridge, 1993). In the process, drug cues also acquire attentional salience, which is manifested as a powerful attentional bias for drug cues in drug-dependent individuals ((Field and Cox, 2008; Franken, 2003); see also (Hahn et al., 2007)). Through the combined mechanisms of attentional and motivational salience, drug cues both hijack perceptual, cognitive, and memory processes and produce a state of motor readiness for drug-seeking behaviors (Franken, 2003). Consistent with this view, recent theories of drug addiction stress the contribution of impaired cognitive control or executive function in addictive behaviors and in the progression from controlled, recreational drug use to drug abuse and drug dependence (Bechara, 2005; Feil et al., 2010; Goldstein and Volkow, 2011; Jentsch and Taylor, 1999; Volkow et al., 2003). Thus, we would expect that strategies and task attributes aimed at modulating (or regulating) the attentional salience of drug cues, either explicitly or implicitly, should also modulate the neural reactivity to drug cues.

4.7 Stressor exposure

Stressor exposure is known to interact with drug-related cues as a potent trigger of craving and relapse to drug-taking behavior following abstinence (for reviews, see (Koob, 2008; Sinha, 2008). Stressors and drug-related cues also engage partially overlapping brain systems, including the mesocorticolimbic system (for review, see (Sinha and Li, 2007)). Therefore, stressor exposure would be expected to impact neural reactivity to drug cues in drug users. Consistent with this view, when a smoking-cue reactivity task followed an acute psychosocial stress (the Montreal Imaging Stress Task), smokers showed increased responses to smoking-related videos (vs. control videos) in the caudate nucleus, MPFC, PCC/precuneus, dorsomedial thalamus, and hippocampus, relative to a separate scanning session in which the smoking-cue reactivity was assessed after a non-stress control task (Dagher et al., 2009). Furthermore, a significant correlation was found between the nucleus accumbens deactivation during stress and drug-cue-related activation in MPFC, ACC, caudate, PCC, dorsomedial thalamus, amygdala, hippocampus, and primary and association visual areas (Dagher et al., 2009). Using a different approach, a study in heavy alcohol users found significant positive correlations between depressive symptoms and neural responses to gustatory alcohol cues in the insula, cingulate, striatum, thalamus, and VTA, and between anxiety symptoms and neural responses to gustatory alcohol cues in the insula, cingulate, striatum, thalamus, IFG, and DLPFC, relative to control cues (Feldstein Ewing et al., 2010).

A.J.J. and E.A.S. are supported by the National Institute on Drug Abuse Intramural Research Program (NIDA-IRP). M.J.N., J.K. and Y.Y. are supported by the Hessisches Ministerium für Wissenschaft und Kultur (LOEWE Forschungsschwerpunkt Neuronale Koordination Frankfurt).

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