(Neuro)therapeutic Approaches in the Field of Alcohol Use Disorders


Purpose of Review

Alcohol use disorder (AUD) is a burdening chronic condition that is characterized by high relapse rates despite severe negative consequences. There has been a recent emergence of interest in (neuro)therapeutic intervention strategies that largely involve the detrimental change in mechanisms linked to addiction disorders. Most prominently, the latter include habitual decision-making, cue-induced behavioral tendencies, as well as the amplifying effects of stressful events on drinking behavior. This article discusses these learning mechanisms and modification thereof as possible targets of (neuro)therapeutic interventions for AUD.

Recent Findings

Psychological therapies that target dysregulated neurocognitive processes underlying addictive behavior may hold promise as effective treatments for AUD.


Despite the progression in psychological and neuroscience research in the field of AUD, many behavioral interventions fail to systematically integrate and apply such findings into treatment development. Future research should focus on the targeted modification of the aforementioned processes.


Substance use disorders (SUD) are defined as problematic patterns of use associated with clinical impairment and persistent relapse overtime [1, 2]. Alcohol use disorder (AUD) is remarkable because it causes immense global health burden and financial costs and its treatment is problematic [1, 3]. Currently, the psychological treatment of post-acute AUD mainly comprises of motivational enhancement therapy, cognitive behavioral therapy, contingency management, as well as supervised patient group therapy [4].

However, effectiveness of current therapeutic approaches would benefit from better taking into account improved understanding of AUD underlying processes and their modification. Overall, addiction disorders in which substance intake is dysregulated [2] can be referred to as “pronounced preference disorders.” Regardless of the addiction condition, substance intake is somewhat similar to other automatic actions based on preferences and reinforcing everyday experiences, but due to the pharmacological effect of the substance, the stimulus is particularly pronounced [5] and as such may contribute to the dysregulation of substance intake.

AUD and other addiction disorders are characterized by a gradual shift from initial goal-directed drug use mediated by the reinforcing and hedonic effects of the drug (regulated substance intake) to an increasing loss of control over drug intake (dysregulation), which thus becomes habitual, that is, automated and disconnected from its consequence, one behavioral outcome of which is compulsive use. One unique characteristic of addictive disorders is persistent relapse rates over time despite awareness of severe negative consequences [6]. Even in the face of severely aversive consequences, it can be nearly impossible to stop drug intake in spite of conscious decisions to reduce consumption or to remain abstinent [7, 8].

Despite that habitual and compulsive drug intake are to be differentiated, they can be triggered by drug-associated cues, acute stress events, or a priming dose of the drug [9]. In chronic problem drug users, conditioned drug cues gain incentive salience, whereas alternative reinforcers become less important [10,11,12,13]. Finally, drug addiction also involves negative reinforcement during withdrawal distress and early and long-term abstinence, which is defined as drug-taking that alleviates a distress-associated aversive emotional state [14], which may be a characteristic of craving as a diagnostic criterion [15].

However, some AUD patients are able to regain control over their alcohol intake, suggesting that the above-named mechanisms can be partly reversed or compensated [16••]. There has been a recent emergence of interest in (neuro)therapeutic intervention strategies that largely involve the detrimental change in learning mechanisms linked to addiction disorders [17]. This report aims at reviewing potential therapeutic targets that have been highlighted in recent AUD research. Most prominently, they include habitual decision-making, cue-induced behavioral tendencies, as well as the amplifying effects of stressful events on drinking behavior (see Fig. 1). We will also review intervention strategies that are designed to target the aforementioned processes.

Fig. 1

Processes underlying the development and maintenance of alcohol use disorders

Habitual Decision-Making

According to dual-process theories of addiction, alcohol consumption in addiction can be driven by an attentional bias as well as by a habitual approach toward drug cues in expense of initially goal-directed control [18]. Goal-directed action control supports flexible planning to promote desirable choices when facing potential actions and probabilistic consequences. On the other hand, habitual control encompasses the mere repetition of previously rewarded action without taking into account that the outcome value might have changed [19]. In context of this, preclinical research and human neuroimaging studies have related goal-directed and habitual action control to two separable neuronal systems [20, 21]. Habitual control mainly relies on the putamen, while the goal-directed system has been suggested to involve the ventromedial prefrontal cortex as well as the ventral striatum [22,23,24]; however, see Deserno and colleagues [25] for indication of ventral striatal activation in habitual control.

The shift from goal-directed to habitual behavior that is seen with disease progression might render individuals with AUD to be insensitive to aversive outcomes associated with alcohol use [26]. Furthermore, subjects with AUD might also be more insensitive to aversive events in general, as, e.g., could be shown in terms of a reduced loss aversion in comparison with healthy subjects [27]. In addition to this, craving and acquired (learned) habitual patterns of alcohol intake could aggravate the phases of early abstinence [28]. Regarding a controlled selection of goals in contrast to habitual drug intake, it was recently observed that an increased general tendency for habitual responding predicted poor treatment outcome only in the presence of high alcohol expectancies [24••]. Such biases can be modified by, e.g., systematically training habitual rejection of alcohol-related stimuli (e.g., pictures) [29]. Following training, a good treatment outcome was associated with reduced limbic activation elicited by alcohol-related pictures [30••].

In addition to lack of goal-directed reward choices, avoidance behavior is proposed to be impaired as well. Ersche and colleagues showed that patients with cocaine use disorder (CUD) show similarly increased habitual responding as AUD cohorts and furthermore failed to avoid aversive outcomes in a punitive learning task [26]. In terms of therapeutic implications, the authors state that impaired avoidance behavior, as well as habitual drug intake should be targeted and replaced by healthy habits. This approach is extended by Stock in a perspective article [31] arguing for the establishment of habit reversal therapy (HRT) in AUD. HRT embodies multiple components that aim at altering dysfunctional habits and has been proven to be efficacious in repetitive behavioral disorders [32]. Briefly, HRT encompasses an awareness training phase in which automatic behavioral tendencies are identified to then be replaced by competing (healthy) habits in the therapy phase. These factors are accompanied by generalization to relevant contexts and motivational techniques such as social support training or the review of inconvenience caused by the habit [33]. The establishment of adequate motivational support, as well as concrete long-term perspectives, has been shown to be essential in any treatment strategy that addresses automatisms in AUD [34].

In light of this, it was also observed that goal-directed decision-making is affected by increased life stressors [35], underlining the strong potential of interventions aimed at altering stress-related effects on AUD. Overall, there is a prominent lack of therapeutic interventions that target the overreliance on habitual control in AUD directly. However, habitual alcohol use largely depends on instrumental conditioning with learned stimulus-response associations. This indicates that alcohol-associated environmental stimuli act as powerful motivators for recurrent alcohol intake [36].

Cue Reactivity

As an individual diagnostic criterion, craving is the most selective and specific across addictive disorders [15], but it is also an individual experience that varies over time and is elicited by exposure to different drug-related cues and autonomic responses, which can manifest as higher heart rate as well as higher skin conductance [37, 38] but predominantly trigger aversive responses such as tension, restlessness, and trembling [39]. In line with that, higher heart rate variability (HRV) during exposure to stress-primed alcohol cues was linked to relapse rates [40], while HRV might reflect increased active suppression of appetitive motivational responses in highly cue-reactive individuals [41, 42]. Others argue that physiological responses to alcohol cues are mainly unconsciously processed, as they were not correlated to self-ratings of arousal, valence, and craving [43]. The same study also linked shorter abstinence duration in AUD patients to an attenuated startle response toward alcohol cues [43]. Studies furthermore reported a positive link between display of alcohol cues and subsequent craving as well as alcohol consumption in heavy social drinkers [44, 45]. A promising approach to assess craving in a valid manner without biases associated with retrospective reporting is the so-called Ecological Momentary Assessment (EMA, e.g., see 46 for review). This method offers the opportunity to examine both craving and substance use with strong ecological validity by collecting real-time data in daily life and to identify relevant moderators of craving [47•].

On a neural level, relapse in AUD patients was also directly linked to cue-induced functional activation in the striatum, anterior cingulate cortex, as well as medial prefrontal cortex [48, 49]. Furthermore, examining functional correlation between brain regions active during a cue-reactivity task, Stroche and colleagues [50] found that prefrontal-striatal connectivity during cue reactivity was negatively related to craving and alcohol consumption, indicating a potential top-down control effect that limits craving.

It has been argued that alcohol cues affect subsequent drinking via a direct stimulus-response association [51,52,53]. In extension of this, neutral environmental stimuli can become associated with drinking and also reinforce alcohol intake [36].

One hypothesis is that such contextual stimuli directly stimulate the motivation to drink via Pavlovian-to-Instrumental Transfer (PIT): a behavioral phenomenon describing how Pavlovian-conditioned cues enhance instrumental behavior for rewarding outcomes (e.g., alcohol seeking and intake) [54, 55]. In a current study in alcohol-dependent patients, it was shown that there is an enhanced PIT effect on instrumental behavior using Pavlovian cues that have been passively associated with monetary reward and loss [56]. Furthermore, enhanced PIT was negatively related to goal-directed control, suggesting a strong common basis of Pavlovian inference and habitual control that might affect the course and maintenance of AUD [57].

Using alcohol-related stimuli in a PIT task, Schad and colleagues [58] showed that alcohol-related background stimuli inhibited the approach behavior in prospective abstinent alcohol-dependent patients, but not in healthy controls or prospective relapsing patients. This observation could indicate that subsequent abstainers acquired a successful way to deal with alcohol cues and that such behavior inhibition may be specifically trained in therapeutic interventions.

Despite these promising findings, the mechanism underlying approach avoidance behavior related to different Pavlovian stimuli is not known to date. However, it is known that in patients with AUD there is a bias toward action tendencies to approach alcohol and alcohol-related stimuli [59]. One method to assess such an approach bias, i.e., automated instrumental behavior in reaction to Pavlovian stimuli, is the so-called Approach-Avoidance Task (AAT, see 60). Here, patients are explicitly or implicitly asked to initiate approach or avoidance movements (pulling/pushing a joystick) in response to alcohol-related pictures. Wiers and colleagues [61] observed that heavy drinkers indeed showed strong automatic approach tendencies for alcohol (approach bias). Here, the so-called Approach Bias Modification (ABM), which uses a training version of the AAT, has been shown to be effective in the treatment of AUD: By using AAT training, patients’ initial approach bias could be changed into an avoidance bias for alcohol-related stimuli [62]. This effect even generalized to untrained pictures, and patients showed better treatment outcomes in terms of reduced relapse rates 1 year later [60]. However, the underlying mechanisms of ABM are not fully understood (see 29).

Another psychological treatment approach that aims at diminishing the impact of cues (both contextual and specific) on drug intake and relapse is based on animal extinction research [17, 63]. The so-called cue exposure therapy (CET) operationalizes the prediction that conditioned responses to drug cues can be extinguished by prolonged non-reinforced drug cue exposure [64, 65]. One meta-analysis examined the effect of CET on abstinence or drug-use reduction across several studies and concluded the intervention to be ineffective for other substance use disorders [66], but significant effect sizes indicated clinical efficacy in AUD [67,68,69]. In line with this, a more recent meta-analytic review indicated that AUD patients do benefit from CET, specifically on secondary outcomes such as drinking score, latency to relapse, and alcohol-induced cravings [70].

In neuroimaging studies, it was shown that CET reduced neural cue reactivity in the ventral and dorsal striatum [71, 72]. In extension to traditional CET, the so-called memory retrieval-extinction has been proposed as an augmentation. Based on fear extinction research in animals, it was shown that memories can be diminished if non-reinforced exposure takes place during memory consolidation [73, 74]. In a study combining human and rodent research, Xue and colleagues [75] showed that retrieval of drug-associated memories before the extinction phase led to decreased cue-induced craving as well as increased abstinence rates. Recently, research into CET has been extended by technological advances to improve the effects and accessibility of CET for AUD [76]. A gamified version of CET in combination with virtual reality (VR) was well received in a cohort of AUD patients [77], and another study could show that VR CET reduced craving [78]. A video-enabled live action CET showed a promising treatment outcome as well [79]. One study assessed if cognitive behavioral therapy was more effective when combined with CET and training of urge-specific coping skills or with aftercare as usual [80]. In addition, the deliverance of CET between group sessions and via a smartphone app was tested, and no difference in efficacy was found; however, also no additional benefit to cognitive behavioral therapy was indicated [80].

An additional line of research aims to enhance the efficacy of CET using pharmacological adjuncts like D-cycloserine, a partial N-methyl-D-aspartate (NMDA) receptor agonist shown to facilitate extinction learning in animal models of drug addiction [81]. While translating these findings to cue exposure in addiction revealed very heterogeneous results [82], at least some preliminary evidence indicate that DCS-augmented cue exposure reduced cue-induced BOLD activation in the ventral striatum [71] and subjective craving [83] in subjects with AUD.

Overall, there is ample information on the effects of drug-related cues on various addiction-related processes, and different interventional approaches such as ABM and cue exposure therapy have been developed based on this evidence. However, to date it remains unclear whether these approaches enhance treatment effects beyond standard interventions such as CBT.


Besides contextual (Pavlovian drug-associated) cues, stress has also been shown to induce drug-seeking behavior [84, 85]. Moreover, in a study by Seo et al. [86], stressful life events were associated with heavy drinking in early adolescence. Thus, the experience of stress may act as an internal cue for drug-seeking behavior in addictions, comparable with effects of drug-associated external cues. Further, stress exposure has a strong moderating influence on cognitive abilities, reward learning, risk-taking, reward responsivity, and decision-making [35, 87,88,89,90]. In particular, a recent study assessed the influence of stress exposure on behavior (i.e., button presses to earn junk food) induced by Pavlovian stimuli in a group of healthy controls [91]. Here, it was found that high levels of stress were associated with elevated responding in the presence of a cue associated with a non-rewarding outcome, whereas low levels of stress were associated with appropriate suppression of responding (inhibition) during presentation of this cue. Noteworthy, gender and age effects were not found so far in previous studies [56, 58, 92].

Moreover, stress has a high propensity to shift goal-directed behavior to more habitual behavior (e.g., 93). This phenomenon plays a key role in addictive behaviors: Although initially drugs are consumed to, for example, avoid discomfort or to relax (i.e., to achieve a certain goal), drug-taking behavior can become habitual without considering the outcome when intake is regularly repeated. Habitual behavior is then performed automated and largely independent of its consequences by simply repeating actions associated with past reward, while goal-directed actions are performed because they are expected to produce a certain (desirable) outcome [19]. Regarding the impact of stress, Schwabe and Wolf [93] observed that stress modulates the control of instrumental action in a manner that favors habitual over goal-directed action. In line with this finding, Friedel and colleagues [90•] observed that high chronic life stress reduced goal-directed and increased habitual decision-making in healthy subjects moderated by low cognitive capacity.

Although generally adaptive, these changes in the control of instrumental action under stress may promote dysfunctional behaviors and the development of mental disorders such as addiction.

Although there is evidence on the prominent role of stress in the course of addiction, surprisingly, only a few studies are focusing on stress reduction in this field of research. Based on mindfulness-based stress reduction, mindfulness-based interventions (MBIs) such as mindfulness-based relapse prevention (MBRP) [94, 95] or mindfulness-oriented recovery enhancement (MORE) [96], among others, target several pathological mechanisms in SUDs. Notably, there is support for the effectiveness of MBIs in reducing stress in the context of addiction: Measures of heart rate variability (HRV) are often used as an index of stress regulatory ability [97], while higher HRV in response to drug cues might indicate the need for higher regulatory effort [40]. In samples with SUDs, MBRP was associated with increased HRV in response to stress [98,99,100]. In AUD patients, participation in MORE compared with a control intervention led to increased HRV recovery from stress-primed alcohol cues in AUD patients [101]. Other studies observed that 2 weeks of meditation training (integrative body-mind training) produced a significant reduction in smoking among a group of smokers while progressive muscle relaxation as an active control condition did not [102, 103].

Furthermore, in smokers, decreased hair cortisol was associated with mindfulness training, indicating a decrease in chronic stress [104]. Brewer and colleagues [105] assessed mindfulness training (MT) compared with cognitive behavioral therapy (CBT) in individuals with alcohol and or cocaine use disorder showing reduced psychological and physiological indices of stress during stress provocation in MT compared with CBT.

Likewise, Back and colleagues [106] observed significantly less stress-induced craving and stress-related responses (during stress provocation) and greater ability to resist urges to consume in subjects with SUD receiving a cognitive-behavioral stress management intervention in contrast to the comparison group. In pathological gamblers, a stress management program including relaxation breathing revealed a significant reduction of stress, depression, and anxiety symptoms as well as an increase of life satisfaction and a better daily routine compared with a waiting list control group [107]. A neuroimaging study associated mindfulness training in smokers with decreased neural activity within insula and amygdala during exposure to stress, which was in turn associated with the amount of cigarettes smoked at follow-up [108].

Another interesting instrumental learning approach for patients with AUD—which might also be used in stress regulation—is real-time fMRI neurofeedback (rtfMRI NF). By feeding back the neural activity in circumscribed brain regions to the patient while presenting alcohol cues, its goal is to enhance control over brain activation and related cognitive processes [109]. First study results indeed indicate a reduction of neuronal activity and craving in patients with AUD [109] and of striatal cue-reactivity in heavy drinkers by rtfMRI NF [110].


Several key mechanisms that control the course of AUD have been identified by a large body of research that has been continuously expanded over the last decade. Integration of this literature on habitual decision-making [20, 57], cue-induced behavioral tendencies [56, 92], as well as the amplifying effects of stressful events [86, 90] can provide a framework to identify and disentangle the relative contributions of these mechanisms to AUD. However, while there have been attempts to target these mechanisms to treat AUD, studies indicated mixed results. These discrepant results may be due to unidentified modifying variables, which may well be targeted using ambulatory assessment with EMA [16, 46, 47]. In light of these digital technologies and opportunities, mechanism-based interventions with the aim of (1) the promotion of goal-directed control, (2) modification of Pavlovian effects on instrumental behavior, and (3) reduction of stress to enhance cognitive control over drug urges can be tested within intense longitudinal datasets that also reflect differences in gender, age, social status, and cultural diversity within an essential future field of research [16••].


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.

    AmericanPsychiatricAssociation. Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Washington D.C. and London: American Psychiatric Publishing; 2013.

    Google Scholar 

  2. 2.

    Auriacombe M, Serre F, Denis C, Fatseas M. Diagnosis of addictions. The Routledge Handbook of the Philosophy and Science of Addiction. H. Pickard and S. Ahmed. London and New York, Routledge; 2018. pp. 132–144.

  3. 3.

    World Health Organization. Global status report on alcohol and health 2018; Geneva, World Health Organization; 2019.

  4. 4.

    Mann K, Batra A, Fauth-Bühler M, Hoch E. German guidelines on screening, diagnosis and treatment of alcohol use disorders. Eur Addict Res. 2017;23(1):45–60.

    PubMed  Google Scholar 

  5. 5.

    Garbusow M, Sebold M, Beck A, Heinz A. Too difficult to stop: mechanisms facilitating relapse in alcohol dependence. Neuropsychobiology. 2014;70(2):103–10.

    CAS  PubMed  Google Scholar 

  6. 6.

    Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016;3(8):760–73.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Everitt BJ, Robbins TW. From the ventral to the dorsal striatum: devolving views of their roles in drug addiction. Neuroscience and biobehavioral reviews. 2013;37(9 Pt A):1946–54.

    PubMed  Google Scholar 

  8. 8.

    Volkow ND, Morales M. The brain on drugs: from reward to addiction. Cell. 2015;162(4):712–25.

    CAS  PubMed  Google Scholar 

  9. 9.

    Bossert JM, Marchant NJ, Calu DJ, Shaham Y. The reinstatement model of drug relapse: recent neurobiological findings, emerging research topics, and translational research. Psychopharmacology. 2013;229(3):453–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Berridge KC, Robinson TE. Liking, wanting, and the incentive-sensitization theory of addiction. Am Psychol. 2016;71(8):670–9.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Beck A, Schlagenhauf F, Wustenberg T, Hein J, Kienast T, Kahnt T, et al. Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics. Biol Psychiatry. 2009;66(8):734–42.

    CAS  PubMed  Google Scholar 

  12. 12.

    Romanczuk-Seiferth N, Koehler S, Dreesen C, Wustenberg T, Heinz A. Pathological gambling and alcohol dependence: neural disturbances in reward and loss avoidance processing. Addict Biol. 2015;20(3):557–69.

    PubMed  Google Scholar 

  13. 13.

    Heinz A. Dopaminergic dysfunction in alcoholism and schizophrenia--psychopathological and behavioral correlates. Eur Psychiatry. 2002;17(1):9–16.

    CAS  PubMed  Google Scholar 

  14. 14.

    Koob GF. Negative reinforcement in drug addiction: the darkness within. Curr Opin Neurobiol. 2013;23(4):559–63.

    CAS  PubMed  Google Scholar 

  15. 15.

    Kervran C, Shmulewitz D, Serre F, Stohl M, Denis C, Hasin D, et al. Item response theory analyses of DSM-5 substance use disorder criteria in French outpatient addiction clinic participants. How much is craving special? Drug and alcohol dependence. 2020:108036.

  16. 16.

    •• Heinz A, Kiefer F, Smolka MN, Endrass T, Beste C, Beck A, et al. Addiction research consortium: losing and regaining control over drug intake (ReCoDe)—from trajectories to mechanisms and interventions. Addiction biology. 2020;25(2):e12866 The domains of this described collaborative research center aim at identification of key addiction mechanisms as well as interventions to specifically target these mechanisms in order to regain control over drug intake.

    PubMed  Google Scholar 

  17. 17.

    Everitt BJ, Robbins TW. Drug addiction: updating actions to habits to compulsions ten years on. Annu Rev Psychol. 2016;67:23–50.

    PubMed  Google Scholar 

  18. 18.

    Gladwin TE, Wiers CE, Wiers RW. Cognitive neuroscience of cognitive retraining for addiction medicine: from mediating mechanisms to questions of efficacy. Prog Brain Res. 2016;224:323–44.

    PubMed  Google Scholar 

  19. 19.

    Dolan RJ, Dayan P. Goals and habits in the brain. Neuron. 2013;80(2):312–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Balleine BW, O'Doherty JP. Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology. 2010;35(1):48–69.

    PubMed  Google Scholar 

  21. 21.

    Smittenaar P, FitzGerald TH, Romei V, Wright ND, Dolan RJ. Disruption of dorsolateral prefrontal cortex decreases model-based in favor of model-free control in humans. Neuron. 2013;80(4):914–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Valentin VV, Dickinson A, O'Doherty JP. Determining the neural substrates of goal-directed learning in the human brain. J Neurosci. 2007;27(15):4019–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Tanaka SC, Balleine BW, O'Doherty JP. Calculating consequences: brain systems that encode the causal effects of actions. J Neurosci. 2008;28(26):6750–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    •• Sebold M, Nebe S, Garbusow M, Guggenmos M, Schad DJ, Beck A, et al. When habits are dangerous: alcohol expectancies and habitual decision making predict relapse in alcohol dependence. Biol psychiatry. 2017;82(11):847–56 This important study indicates poor treatment outcome specifically resulting from an interaction between high drug expectancies and low model-based decision making.

    PubMed  Google Scholar 

  25. 25.

    Deserno L, Huys QJM, Boehme R, Buchert R, Heinze H-J, Grace AA, et al. Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making. Proc Natl Acad Sci. 2015;112(5):1595–600.

    CAS  PubMed  Google Scholar 

  26. 26.

    Ersche KD, Gillan CM, Jones PS, Williams GB, Ward LH, Luijten M, et al. Carrots and sticks fail to change behavior in cocaine addiction. Science. 2016;352(6292):1468–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Genauck A, Quester S, Wüstenberg T, Mörsen C, Heinz A, Romanczuk-Seiferth N. Reduced loss aversion in pathological gambling and alcohol dependence is associated with differential alterations in amygdala and prefrontal functioning. Sci Rep. 2017;7(1):16306.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Tiffany ST, Conklin CA. A cognitive processing model of alcohol craving and compulsive alcohol use. Addiction. 2000;95(8s2):145–53.

    PubMed  Google Scholar 

  29. 29.

    Eberl C, Wiers RW, Pawelczack S, Rinck M, Becker ES, Lindenmeyer J. Approach bias modification in alcohol dependence: do clinical effects replicate and for whom does it work best? Dev Cogn Neurosci. 2013;4:38–51.

    PubMed  Google Scholar 

  30. 30.

    •• Wiers CE, Stelzel C, Gladwin TE, Park SQ, Pawelczack S, Gawron CK, et al. Effects of cognitive bias modification training on neural alcohol cue reactivity in alcohol dependence. The American journal of psychiatry. 2015;172(4):335–43 Remarkably, the authors show that Approach Avoidance Training is effective in reduction of craving as well as decreasing cue-induced mesolimbic brain activity in patients with AUD.

    PubMed  Google Scholar 

  31. 31.

    Stock A-K. Barking up the wrong tree: why and how we may need to revise alcohol addiction therapy. Front Psychol. 2017;8(884). https://doi.org/10.3389/fpsyg.2017.00884.

  32. 32.

    Bate KS, Malouff JM, Thorsteinsson ET, Bhullar N. The efficacy of habit reversal therapy for tics, habit disorders, and stuttering: a meta-analytic review. Clin Psychol Rev. 2011;31(5):865–71.

    PubMed  Google Scholar 

  33. 33.

    Yang C, Hao Z, Zhu C, Guo Q, Mu D, Zhang L. Interventions for tic disorders: an overview of systematic reviews and meta analyses. Neurosci Biobehav Rev. 2016;63:239–55.

    PubMed  Google Scholar 

  34. 34.

    Gladwin TE, Wiers CE, Wiers RW. Interventions aimed at automatic processes in addiction: considering necessary conditions for efficacy. Curr Opin Behav Sci. 2017;13:19–24.

    Google Scholar 

  35. 35.

    Friedel E, Schlagenhauf F, Beck A, Dolan RJ, Huys QJ, Rapp MA, et al. The effects of life stress and neural learning signals on fluid intelligence. Eur Arch Psychiatry Clin Neurosci. 2015;265(1):35–43.

    PubMed  Google Scholar 

  36. 36.

    Berridge KC. From prediction error to incentive salience: mesolimbic computation of reward motivation. Eur J Neurosci. 2012;35(7):1124–43.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Carter BL, Tiffany ST. Meta-analysis of cue-reactivity in addiction research. Addiction. 1999;94(3):327–40.

    CAS  PubMed  Google Scholar 

  38. 38.

    Sinha R, Talih M, Malison R, Cooney N, Anderson GM, Kreek MJ. Hypothalamic-pituitary-adrenal axis and sympatho-adreno-medullary responses during stress-induced and drug cue-induced cocaine craving states. Psychopharmacology. 2003;170(1):62–72.

    CAS  PubMed  Google Scholar 

  39. 39.

    Bergquist KL, Fox HC, Sinha R. Self-reports of interoceptive responses during stress and drug cue-related experiences in cocaine-and alcohol-dependent individuals. Exp Clin Psychopharmacol. 2010;18(3):229–37.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Garland EL, Franken IH, Howard MO. Cue-elicited heart rate variability and attentional bias predict alcohol relapse following treatment. Psychopharmacology. 2012;222(1):17–26.

    CAS  PubMed  Google Scholar 

  41. 41.

    Ingjaldsson JT, Laberg JC, Thayer JF. Reduced heart rate variability in chronic alcohol abuse: relationship with negative mood, chronic thought suppression, and compulsive drinking. Biol Psychiatry. 2003;54(12):1427–36.

    CAS  PubMed  Google Scholar 

  42. 42.

    Segerstrom SC, Nes LS. Heart rate variability reflects self-regulatory strength, effort, and fatigue. Psychol Sci. 2007;18(3):275–81.

    PubMed  Google Scholar 

  43. 43.

    Leménager T, Hill H, Reinhard I, Hoffmann S, Zimmermann US, Hermann D, et al. Association between alcohol-cue modulated startle reactions and drinking behaviour in alcohol dependent patients—results of the PREDICT study. Int J Psychophysiol. 2014;94(3):263–71.

    PubMed  Google Scholar 

  44. 44.

    Baines L, Field M, Christiansen P, Jones A. The effect of alcohol cue exposure and acute intoxication on inhibitory control processes and ad libitum alcohol consumption. Psychopharmacology. 2019;236(7):2187–99.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Van Dyke N, Fillmore MT. Operant responding for alcohol following alcohol cue exposure in social drinkers. Addict Behav. 2015;47:11–6.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Serre F, Fatseas M, Swendsen J, Auriacombe M. Ecological momentary assessment in the investigation of craving and substance use in daily life: a systematic review. Drug Alcohol Depend. 2015;148:1–20.

    PubMed  Google Scholar 

  47. 47.

    • Fatseas M, Serre F, Swendsen J, Auriacombe M. Effects of anxiety and mood disorders on craving and substance use among patients with substance use disorder: an ecological momentary assessment study. Drug and alcohol dependence. 2018;187:242–8 Notably, by monitoring of AUD patients with ecological momentary assessment, the authors found that substance use was predicted by craving intensity, while higher craving intensity was associated with comorbid mood or anxiety disorders.

    PubMed  Google Scholar 

  48. 48.

    Beck A, Wüstenberg T, Genauck A, Wrase J, Schlagenhauf F, Smolka MN, et al. Effect of brain structure, brain function, and brain connectivity on relapse in alcohol-dependent patients. Arch Gen Psychiatry. 2012;69(8):842–52.

    PubMed  Google Scholar 

  49. 49.

    Grüsser SM, Wrase J, Klein S, Hermann D, Smolka MN, Ruf M, et al. Cue-induced activation of the striatum and medial prefrontal cortex predicts relapse in abstinent alcoholics. Psychopharmacology. 2004;175(3):296–302.

    PubMed  Google Scholar 

  50. 50.

    Strosche A, Zhang X, Kirsch M, Hermann D, Ende G, Kiefer F, et al. Investigation of brain functional connectivity to assess cognitive control over cue-processing in alcohol use disorder. Addict Biol. 2020:e12863.

  51. 51.

    Belin D, Jonkman S, Dickinson A, Robbins TW, Everitt BJ. Parallel and interactive learning processes within the basal ganglia: relevance for the understanding of addiction. Behav Brain Res. 2009;199(1):89–102.

    PubMed  Google Scholar 

  52. 52.

    Hogarth L, Dickinson A, Duka T. The associative basis of cue-elicited drug taking in humans. Psychopharmacology. 2010;208(3):337–51.

    CAS  PubMed  Google Scholar 

  53. 53.

    Everitt BJ, Dickinson A, Robbins TW. The neuropsychological basis of addictive behaviour. Brain Res Brain Res Rev. 2001;36(2–3):129–38.

    CAS  PubMed  Google Scholar 

  54. 54.

    Talmi D, Seymour B, Dayan P, Dolan RJ. Human pavlovian-instrumental transfer. J Neurosci. 2008;28(2):360–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Cartoni E, Balleine B, Baldassarre G. Appetitive Pavlovian-instrumental transfer: a review. Neurosci Biobehav Rev. 2016;71:829–48.

    PubMed  Google Scholar 

  56. 56.

    Garbusow M, Schad DJ, Sebold M, Friedel E, Bernhardt N, Koch SP, et al. Pavlovian-to-instrumental transfer effects in the nucleus accumbens relate to relapse in alcohol dependence. Addict Biol. 2016;21(3):719–31.

    CAS  PubMed  Google Scholar 

  57. 57.

    Sebold M, Schad DJ, Nebe S, Garbusow M, Jünger E, Kroemer NB, et al. Don’t think, just feel the music: individuals with strong Pavlovian-to-instrumental transfer effects rely less on model-based reinforcement learning. J Cogn Neurosci. 2016;28(7):985–95.

    PubMed  Google Scholar 

  58. 58.

    Schad DJ, Garbusow M, Friedel E, Sommer C, Sebold M, Hägele C, et al. Neural correlates of instrumental responding in the context of alcohol-related cues index disorder severity and relapse risk. Eur Arch Psychiatry Clin Neurosci. 2019;269(3):295–308.

  59. 59.

    Palfai TP, Ostafin BD. Alcohol-related motivational tendencies in hazardous drinkers: assessing implicit response tendencies using the modified-IAT. Behav Res Ther. 2003;41(10):1149–62.

    PubMed  Google Scholar 

  60. 60.

    Wiers RW, Eberl C, Rinck M, Becker ES, Lindenmeyer J. Retraining automatic action tendencies changes alcoholic patients’ approach bias for alcohol and improves treatment outcome. Psychol Sci. 2011;22(4):490–7.

    PubMed  Google Scholar 

  61. 61.

    Wiers RW, Rinck M, Dictus M, van den Wildenberg E. Relatively strong automatic appetitive action-tendencies in male carriers of the OPRM1 G-allele. Genes Brain Behav. 2009;8(1):101–6.

    CAS  PubMed  Google Scholar 

  62. 62.

    Wiers RW, Rinck M, Kordts R, Houben K, Strack F. Retraining automatic action-tendencies to approach alcohol in hazardous drinkers. Addiction. 2010;105(2):279–87.

    PubMed  Google Scholar 

  63. 63.

    Berridge KC, Kringelbach ML. Affective neuroscience of pleasure: reward in humans and animals. Psychopharmacology. 2008;199(3):457–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Boening JAL. Neurobiology of an addiction memory. J Neural Transm. 2001;108(6):755–65.

    CAS  PubMed  Google Scholar 

  65. 65.

    Drummond DC, Cooper T, Glautier SP. Conditioned learning in alcohol dependence: implications for cue exposure treatment. Br J Addict. 1990;85(6):725–43.

    CAS  PubMed  Google Scholar 

  66. 66.

    Conklin CA, Tiffany ST. Applying extinction research and theory to cue-exposure addiction treatments. Addiction. 2002;97(2):155–67.

    PubMed  Google Scholar 

  67. 67.

    Monti PM, Rohsenow DJ, Rubonis AV, Niaura RS, Sirota AD, Colby SM, et al. Cue exposure with coping skills treatment for male alcoholics: a preliminary investigation. J Consult Clin Psychol. 1993;61(6):1011–9.

    CAS  PubMed  Google Scholar 

  68. 68.

    Rohsenow DJ, Monti PM, Rubonis AV, Gulliver SB, Colby SM, Binkoff JA, et al. Cue exposure with coping skills training and communication skills training for alcohol dependence: 6-and 12-month outcomes. Addiction. 2001;96(8):1161–74.

    CAS  PubMed  Google Scholar 

  69. 69.

    Sitharthan T, Sitharthan G, Hough MJ, Kavanagh DJ. Cue exposure in moderation drinking: a comparison with cognitive–behavior therapy. J Consult Clin Psychol. 1997;65(5):878–82.

    CAS  PubMed  Google Scholar 

  70. 70.

    Mellentin AI, Skøt L, Nielsen B, Schippers GM, Nielsen AS, Stenager E, et al. Cue exposure therapy for the treatment of alcohol use disorders: a meta-analytic review. Clin Psychol Rev. 2017;57:195–207.

    PubMed  Google Scholar 

  71. 71.

    Kiefer F, Kirsch M, Bach P, Hoffmann S, Reinhard I, Jorde A, et al. Effects of D-cycloserine on extinction of mesolimbic cue reactivity in alcoholism: a randomized placebo-controlled trial. Psychopharmacology. 2015;232(13):2353–62.

    CAS  PubMed  Google Scholar 

  72. 72.

    Vollstädt-Klein S, Loeber S, Kirsch M, Bach P, Richter A, Buhler M, et al. Effects of cue-exposure treatment on neural cue reactivity in alcohol dependence: a randomized trial. Biol Psychiatry. 2011;69(11):1060–6.

    PubMed  Google Scholar 

  73. 73.

    Schiller D, Monfils M-H, Raio CM, Johnson DC, LeDoux JE, Phelps EA. Preventing the return of fear in humans using reconsolidation update mechanisms. Nature. 2010;463(7277):49–53.

    CAS  PubMed  Google Scholar 

  74. 74.

    Monfils M-H, Cowansage KK, Klann E, LeDoux JE. Extinction-reconsolidation boundaries: key to persistent attenuation of fear memories. Science. 2009;324(5929):951–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Xue Y-X, Luo Y-X, Wu P, Shi H-S, Xue L-F, Chen C, et al. A memory retrieval-extinction procedure to prevent drug craving and relapse. Science. 2012;336(6078):241–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Byrne SP, Haber P, Baillie A, Giannopolous V, Morley K. Cue exposure therapy for alcohol use disorders: what can be learned from exposure therapy for anxiety disorders? Substance Use & Misuse. 2019;54(12):2053–63.

    Google Scholar 

  77. 77.

    Vollstädt-Klein S, Mildner P, Bumb JM, Karl D, Ueberle C, Shevchenko Y, et al. The training game SALIENCE for the therapy of alcohol use disorder. Health Informatics Journal. 2020;26(1):499–512.

  78. 78.

    Lee SH, Han DH, Oh S, Lyoo IK, Lee YS, Renshaw PF, et al. Quantitative electroencephalographic (qEEG) correlates of craving during virtual reality therapy in alcohol-dependent patients. Pharmacol Biochem Behav. 2009;91(3):393–7.

    CAS  PubMed  Google Scholar 

  79. 79.

    Nattala P, Murthy P, Leung KS, Rentala S, Ramakrishna J. Video-enabled cue-exposure-based intervention improves postdischarge drinking outcomes among alcohol-dependent men: a prospective study at a government addiction treatment setting in India. J Ethn Subst Abus. 2018;17(4):532–47.

    Google Scholar 

  80. 80.

    Mellentin AI, Nielsen B, Nielsen AS, Yu F, Mejldal A, Nielsen DG, et al. A Mobile phone app featuring cue exposure therapy as aftercare for alcohol use disorders: an investigator-blinded randomized controlled trial. JMIR Mhealth Uhealth. 2019;7(8):e13793.

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Dhonnchadha BAN, Kantak KM. Cognitive enhancers for facilitating drug cue extinction: insights from animal models. Pharmacol Biochem Behav. 2011;99(2):229–44.

    Google Scholar 

  82. 82.

    Otto MW, Kredlow MA, Smits JA, Hofmann SG, Tolin DF, de Kleine RA, et al. Enhancement of psychosocial treatment with d-cycloserine: models, moderators, and future directions. Biol Psychiatry. 2016;80(4):274–83.

    CAS  PubMed  Google Scholar 

  83. 83.

    MacKillop J, Few L, Stojek M, Murphy C, Malutinok S, Johnson F, et al. D-cycloserine to enhance extinction of cue-elicited craving for alcohol: a translational approach. Translational psychiatry. 2015;5(4):e544-e.

    Google Scholar 

  84. 84.

    Shalev U, Grimm JW, Shaham Y. Neurobiology of relapse to heroin and cocaine seeking: a review. Pharmacol Rev. 2002;54(1):1–42.

    CAS  PubMed  Google Scholar 

  85. 85.

    Sinha R. How does stress lead to risk of alcohol relapse? Alcohol Res. 2012;34(4):432–40.

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Seo S, Beck A, Matthis C, Genauck A, Banaschewski T, Bokde ALW, et al. Risk profiles for heavy drinking in adolescence: differential effects of gender. Addict Biol. 2019;24(4):787–801.

  87. 87.

    Petzold A, Plessow F, Goschke T, Kirschbaum C. Stress reduces use of negative feedback in a feedback-based learning task. Behav Neurosci. 2010;124(2):248–55.

    PubMed  Google Scholar 

  88. 88.

    Cavanagh JF, Frank MJ, Allen JJ. Social stress reactivity alters reward and punishment learning. Soc Cogn Affect Neurosci. 2011;6(3):311–20.

    PubMed  Google Scholar 

  89. 89.

    Otto AR, Raio CM, Chiang A, Phelps EA, Daw ND. Working-memory capacity protects model-based learning from stress. Proc Natl Acad Sci U S A. 2013;110(52):20941–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    • Friedel E, Sebold M, Kuitunen-Paul S, Nebe S, Veer IM, Zimmermann US, et al. How accumulated real life stress experience and cognitive speed interact on decision-making processes. Frontiers in human neuroscience. 2017;11:302 Although done in healthy subjects, this important study highlights the effect of stressful events on decision making, as stress significantly decreases model-based control in subjects with slow cognitive speed.

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Quail SL, Morris RW, Balleine BW. Stress associated changes in Pavlovian-instrumental transfer in humans. Q J Exp Psychol (Hove). 2017;70(4):675–85.

    Google Scholar 

  92. 92.

    Sommer C, Garbusow M, Junger E, Pooseh S, Bernhardt N, Birkenstock J, et al. Strong seduction: impulsivity and the impact of contextual cues on instrumental behavior in alcohol dependence. Transl Psychiatry. 2017;7(8):e1183.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Schwabe L, Wolf OT. Stress prompts habit behavior in humans. J Neurosci. 2009;29(22):7191–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Bowen S, Chawla N, Witkiewitz K. Chapter 7 - Mindfulness-based relapse prevention for addictive behaviors. In: Baer RA, editor. Mindfulness-Based Treatment Approaches (Second Edition). San Diego: Academic Press; 2014. p. 141–57.

  95. 95.

    Witkiewitz K, Marlatt GA, Walker D. Mindfulness-based relapse prevention for alcohol and substance use disorders. J Cogn Psychother. 2005;19(3):211–28.

    Google Scholar 

  96. 96.

    Froeliger B, Mathew AR, McConnell P, Eichberg C, Saladin M, Carpenter M, et al. Restructuring reward mechanisms in nicotine addiction: a pilot fMRI study of mindfulness-oriented recovery enhancement for cigarette smokers. Evid Based Complement Alternat Med. 2017;2017:1–10.

    Google Scholar 

  97. 97.

    Castaldo R, Melillo P, Bracale U, Caserta M, Triassi M, Pecchia L. Acute mental stress assessment via short term HRV analysis in healthy adults: a systematic review with meta-analysis. Biomedical Signal Processing and Control. 2015;18:370–7.

    Google Scholar 

  98. 98.

    Brewer JA, Sinha R, Chen JA, Michalsen RN, Babuscio TA, Nich C, et al. Mindfulness training and stress reactivity in substance abuse: results from a randomized, controlled stage I pilot study. Subst Abus. 2009;30(4):306–17.

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Carroll H, Lustyk MKB. Mindfulness-based relapse prevention for substance use disorders: effects on cardiac vagal control and craving under stress. Mindfulness. 2018;9(2):488–99.

    Google Scholar 

  100. 100.

    Paz R, Zvielli A, Goldstein P, Bernstein A. Brief mindfulness training de-couples the anxiogenic effects of distress intolerance on reactivity to and recovery from stress among deprived smokers. Behav Res Ther. 2017;95:117–27.

    PubMed  Google Scholar 

  101. 101.

    Garland EL, Gaylord SA, Boettiger CA, Howard MO. Mindfulness training modifies cognitive, affective, and physiological mechanisms implicated in alcohol dependence: results of a randomized controlled pilot trial. J Psychoactive Drugs. 2010;42(2):177–92.

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Tang YY, Tang R, Posner MI. Mindfulness meditation improves emotion regulation and reduces drug abuse. Drug Alcohol Depend. 2016;163(Suppl 1):S13–8.

    PubMed  Google Scholar 

  103. 103.

    Tang YY, Tang R, Posner MI. Brief meditation training induces smoking reduction. Proc Natl Acad Sci U S A. 2013;110(34):13971–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Goldberg SB, Manley AR, Smith SS, Greeson JM, Russell E, Van Uum S, et al. Hair cortisol as a biomarker of stress in mindfulness training for smokers. J Altern Complement Med. 2014;20(8):630–4.

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Brewer JA, Sinha R, Chen JA, Michalsen RN, Babuscio TA, Nich C, et al. Mindfulness training and stress reactivity in substance abuse: results from a randomized, controlled stage I pilot study. Subst Abus. 2009;30(4):306–17.

    PubMed  PubMed Central  Google Scholar 

  106. 106.

    Back SE, Gentilin S, Brady KT. Cognitive-behavioral stress management for individuals with substance use disorders: a pilot study. J Nerv Ment Dis. 2007;195(8):662–8.

    PubMed  Google Scholar 

  107. 107.

    Linardatou C, Parios A, Varvogli L, Chrousos G, Darviri C. An 8-week stress management program in pathological gamblers: a pilot randomized controlled trial. J Psychiatr Res. 2014;56:137–43.

    CAS  PubMed  Google Scholar 

  108. 108.

    Kober H, Brewer JA, Height KL, Sinha R. Neural stress reactivity relates to smoking outcomes and differentiates between mindfulness and cognitive-behavioral treatments. NeuroImage. 2017;151:4–13.

    PubMed  Google Scholar 

  109. 109.

    Karch S, Keeser D, Hümmer S, Paolini M, Kirsch V, Karali T, et al. Modulation of craving related brain responses using real-time fMRI in patients with alcohol use disorder. PLoS One. 2015;10(7):e0133034-e.

    Google Scholar 

  110. 110.

    Kirsch M, Gruber I, Ruf M, Kiefer F, Kirsch P. Real-time functional magnetic resonance imaging neurofeedback can reduce striatal cue-reactivity to alcohol stimuli. Addict Biol. 2016;21(4):982–92.

    PubMed  Google Scholar 

Download references


Open Access funding provided by Projekt DEAL. This work was supported by the German Research Foundation (DFG, Project-ID 402170461 – TRR 265).

A.B. was moreover in part supported by the German Ministry for Education and Research (BMBF 01ZX1311E/1611E) and the German Ministry of Health (BMG, ZMVI1-2516DSM223).

Author information



Corresponding author

Correspondence to A Rosenthal.

Ethics declarations

Conflict of Interest

The authors declare no conflicts of interest in the production of this work.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Alcohol

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Beck, A., Rosenthal, A., Auriacombe, M. et al. (Neuro)therapeutic Approaches in the Field of Alcohol Use Disorders. Curr Addict Rep 7, 252–259 (2020). https://doi.org/10.1007/s40429-020-00324-w

Download citation


  • Alcohol use disorder
  • Treatment
  • Addiction
  • Habitual decision-making
  • Stress
  • Cue
  • EMA
  • Intervention