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Reward Processing in Alcohol-Dependent Patients and First-Degree Relatives: Functional Brain Activity During Anticipation of Monetary Gains and Losses

  • Milena P.M. Musial
    Correspondence
    Address correspondence to Milena P.M. Musial, M.Sc.
    Affiliations
    Charité - Universitätsmedizin Berlin, corporate member of Freie and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences| CCM, Berlin, Germany
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  • Anne Beck
    Affiliations
    Charité - Universitätsmedizin Berlin, corporate member of Freie and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences| CCM, Berlin, Germany

    Health and Medical University, Campus Potsdam, Faculty of Health, Potsdam, Germany
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  • Annika Rosenthal
    Affiliations
    Charité - Universitätsmedizin Berlin, corporate member of Freie and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences| CCM, Berlin, Germany
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  • Katrin Charlet
    Affiliations
    Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland
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  • Patrick Bach
    Affiliations
    Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany
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  • Falk Kiefer
    Affiliations
    Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany

    Mannheim Center for Translational Neurosciences, Medical Faculty of Mannheim, Heidelberg University, Mannheim, Germany
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  • Sabine Vollstädt-Klein
    Affiliations
    Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany

    Mannheim Center for Translational Neurosciences, Medical Faculty of Mannheim, Heidelberg University, Mannheim, Germany
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  • Henrik Walter
    Affiliations
    Charité - Universitätsmedizin Berlin, corporate member of Freie and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences| CCM, Berlin, Germany
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  • Andreas Heinz
    Affiliations
    Charité - Universitätsmedizin Berlin, corporate member of Freie and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences| CCM, Berlin, Germany
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  • Marcus Rothkirch
    Affiliations
    Charité - Universitätsmedizin Berlin, corporate member of Freie and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences| CCM, Berlin, Germany
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Open AccessPublished:May 27, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.05.024

      Abstract

      Background

      According to the reward deficiency syndrome and allostatic hypotheses, hyposensitivity of mesocorticolimbic regions to non–alcohol-related stimuli predisposes to dependence or is long-lastingly enhanced by chronic substance use. To date, no study has directly compared mesocorticolimbic brain activity during non–drug reward anticipation between alcohol-dependent, at risk, and healthy subjects.

      Methods

      Seventy-five abstinent alcohol-dependent human subjects (mean abstinence duration 957.66 days), 62 healthy first-degree relatives of alcohol-dependent individuals, and 76 healthy control subjects without family history of alcohol dependence performed a monetary incentive delay task. Functional magnetic resonance imaging data of the anticipation phase were analyzed, during which visual cues predicted that fast response to a target would result in monetary gain, avoidance of monetary loss, or a neutral outcome.

      Results

      During gain anticipation, there were no significant group differences. During loss anticipation, abstinent alcohol-dependent subjects showed lower activity in the left anterior insula compared with healthy control subjects without family history of alcohol dependence only (Montreal Neurological Institute [MNI] −25 19 –5; t206 = 4.17, familywise error corrected p = .009). However, this effect was no longer significant when age was included as a covariate. There were no group differences between abstinent alcohol-dependent subjects and healthy first-degree relatives or between healthy first-degree relatives and healthy control subjects during loss anticipation, respectively.

      Conclusions

      Neither the neural reward deficiency syndrome nor the allostatic hypotheses are supported by the results. Future studies should investigate whether the incentive salience hypothesis allows for more accurate predictions regarding mesocorticolimbic brain activity of subjects with alcohol dependence and healthy individuals during reward and loss anticipation and further examine the neural substrates underlying a predisposition to dependence.

      Keywords

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      ). During gain and loss anticipation, healthy subjects show robust activations in the VS, an important receptor region of dopaminergic afferents from the midbrain, as well as in the ACC and anterior insula (AI) (
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      Reward deficiency syndrome: A biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors.
      ). While most MIDT studies in FH+ subjects (
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      Striatal dopaminergic reward response relates to age of first drunkenness and feedback response in at-risk youth.
      ) found no functional differences to healthy subjects during the anticipation phase, other studies showed comparatively increased (
      • Andrews M.M.
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      • Potenza M.N.
      • Krystal J.H.
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      Individuals family history positive for alcoholism show functional magnetic resonance imaging differences in reward sensitivity that are related to impulsivity factors.
      ,
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      ) or decreased (
      • Yau W.Y.
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      • Samudra P.G.
      • Zucker R.A.
      • Heitzeg M.M.
      Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use.
      ) activity in relatives. Differences existed in the VS, ACC, and insula, among others (
      • Andrews M.M.
      • Meda S.A.
      • Thomas A.D.
      • Potenza M.N.
      • Krystal J.H.
      • Worhunsky P.
      • et al.
      Individuals family history positive for alcoholism show functional magnetic resonance imaging differences in reward sensitivity that are related to impulsivity factors.
      ,
      • Villafuerte S.
      • Heitzeg M.M.
      • Foley S.
      • Yau W.Y.
      • Majczenko K.
      • Zubieta J.K.
      • et al.
      Impulsiveness and insula activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism.
      ,
      • Yau W.Y.
      • Zubieta J.K.
      • Weiland B.J.
      • Samudra P.G.
      • Zucker R.A.
      • Heitzeg M.M.
      Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use.
      ).
      Findings from previous studies only allow for limited conclusions regarding the role of neural responses to non–drug reward cues as a risk factor for alcohol dependence as they 1) mainly had small sample sizes (
      • Romanczuk-Seiferth N.
      • Koehler S.
      • Dreesen C.
      • Wüstenberg T.
      • Heinz A.
      Pathological gambling and alcohol dependence: Neural disturbances in reward and loss avoidance processing.
      ,
      • Wrase J.
      • Schlagenhauf F.
      • Kienast T.
      • Wüstenberg T.
      • Bermpohl F.
      • Kahnt T.
      • et al.
      Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics.
      ,
      • Bjork J.M.
      • Smith A.R.
      • Chen G.
      • Hommer D.W.
      Mesolimbic recruitment by nondrug rewards in detoxified alcoholics: Effort anticipation, reward anticipation, and reward delivery.
      ,
      • Beck A.
      • Schlagenhauf F.
      • Wüstenberg T.
      • Hein J.
      • Kienast T.
      • Kahnt T.
      • et al.
      Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics.
      ,
      • Hägele C.
      • Schlagenhauf F.
      • Rapp M.
      • Sterzer P.
      • Beck A.
      • Bermpohl F.
      • et al.
      Dimensional psychiatry: Reward dysfunction and depressive mood across psychiatric disorders.
      ,
      • Nestor L.J.
      • Murphy A.
      • McGonigle J.
      • Orban C.
      • Reed L.
      • Taylor E.
      • et al.
      Acute naltrexone does not remediate frontostriatal disturbances in alcoholic and alcoholic polysubstance-dependent populations during a monetary incentive delay task.
      ,
      • Andrews M.M.
      • Meda S.A.
      • Thomas A.D.
      • Potenza M.N.
      • Krystal J.H.
      • Worhunsky P.
      • et al.
      Individuals family history positive for alcoholism show functional magnetic resonance imaging differences in reward sensitivity that are related to impulsivity factors.
      ,
      • Bjork J.M.
      • Knutson B.
      • Hommer D.W.
      Incentive-elicited striatal activation in adolescent children of alcoholics.
      ,
      • Weiland B.J.
      • Zucker R.A.
      • Zubieta J.K.
      • Heitzeg M.M.
      Striatal dopaminergic reward response relates to age of first drunkenness and feedback response in at-risk youth.
      ,
      • Villafuerte S.
      • Heitzeg M.M.
      • Foley S.
      • Yau W.Y.
      • Majczenko K.
      • Zubieta J.K.
      • et al.
      Impulsiveness and insula activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism.
      ,
      • Yau W.Y.
      • Zubieta J.K.
      • Weiland B.J.
      • Samudra P.G.
      • Zucker R.A.
      • Heitzeg M.M.
      Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use.
      ,
      • Button K.S.
      • Ioannidis J.P.A.
      • Mokrysz C.
      • Nosek B.A.
      • Flint J.
      • Robinson E.S.J.
      • Munafò M.R.
      Power failure: Why small sample size undermines the reliability of neuroscience.
      ) and 2) did not directly compare neural responses between FH+ and AD subjects. It thus remains to be elucidated whether similar functional abnormalities occur in both groups.
      To this end, AD subjects with alcohol dependence according to the DSM-IV (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).
      ), FH+ subjects, and healthy control subjects without a family history of alcohol dependence (family history–negative [FH] subjects) were examined using fMRI during an MIDT. To our knowledge, this is the first direct comparison of AD, FH+, and FH subjects during the anticipation of monetary gains and losses. In addition to whole-brain analyses, region of interest (ROI) analyses were also performed. Based on the theoretical importance of the mesocorticolimbic dopamine system and the insula in reward processing and on a recent meta-analysis on anticipatory functional activity in healthy subjects (
      • Wilson R.P.
      • Colizzi M.
      • Bossong M.G.
      • Allen P.
      • Kempton M.
      • Bhattacharyya S.
      MTAC
      The neural substrate of reward anticipation in health: A meta-analysis of fMRI findings in the monetary incentive delay task.
      ), the VTA, VS, ACC, and AI were defined as ROIs.
      Departing from the reward deficiency syndrome (
      • Blum K.
      • Braverman E.R.
      • Holder J.M.
      • Lubar J.F.
      • Monastra V.J.
      • Miller D.
      • et al.
      Reward deficiency syndrome: A biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors.
      ) and the allostatic hypotheses (
      • Koob G.F.
      • Ahmed S.H.
      • Boutrel B.
      • Chen S.A.
      • Kenny P.J.
      • Markou A.
      • et al.
      Neurobiological mechanisms in the transition from drug use to drug dependence.
      ,
      • Koob G.F.
      • Le Moal M.
      Plasticity of reward neurocircuitry and the “dark side” of drug addiction.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug abuse: Hedonic homeostatic dysregulation.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug addiction, dysregulation of reward, and allostasis.
      ), we expected that 1) AD subjects would show lower neuronal activity than FHsubjects, 2) FH+ subjects would show lower activity than FH subjects, and 3) AD subjects would show lower activity than FH+ subjects in the ROIs during gain and loss anticipation, compared with anticipation of neutral outcomes, respectively.

      Methods and Materials

      Subjects and Procedures

      A total of 235 subjects (87 AD, 66 FH+, 82 FH) participated in the study. Twenty-two subjects (12 AD, 4 FH+, 6 FH) were excluded from data analyses due to missing or poor-quality MRI data. In total, we thus included data of 213 subjects (75 AD, 62 FH+, 76 FH). Subjects gave written informed consent prior to participation, and the study was conducted in accordance with the Declaration of Helsinki and approved by all local ethics committees.
      AD subjects reported a diagnosis of alcohol dependence according to DSM-IV (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).
      ) and at least 7 days of abstinence at the time of screening. With an average abstinence duration of 957.66 days (n = 64, SD = 1436.87), the majority of the AD sample was in protracted abstinence. FH+ subjects reported not being affected by DSM-IV (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).
      ) alcohol dependence themselves but having at least one affected first-degree relative. In case of uncertainty about the relative’s diagnosis, we administered the Family History Assessment Module (
      Collaborative study on the genetics of alcoholism (n.d.)
      COGA Instruments.
      ,
      • Rice J.P.
      • Reich T.
      • Bucholz K.K.
      • Neuman R.J.
      • Fishman R.
      • Rochberg N.
      • et al.
      Comparison of direct interview and family history diagnoses of alcohol dependence.
      ). FH subjects reported the absence of alcohol dependence according to DSM-IV (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).
      ) in themselves and any first-degree relative.
      Participants were excluded if they had any DSM-IV (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).
      ) Axis I psychiatric disorder other than alcohol dependence (AD group), nicotine dependence or abuse, single past depressive episodes, or anxiety and adjustment disorders, as assessed via the German screening version of the Structured Clinical Interview for DSM-IV Axis I (
      • Fydrich T.
      • Wittchen H.-U.
      • Zaudig M.
      SKID: Strukturiertes Klinisches Interview für DSM-IV; Achse I und II. SKID-II SKID-II.
      ). As verified by drug urine tests, subjects had not consumed psychotropic substances (cannabinoids, amphetamines, cocaine, opioids) within 1 month prior to participation. Further exclusion criteria were current antidepressant or neuroleptic medication, claustrophobia, physical diseases potentially interfering with the examinations or having an influence on the parameters of interest, MRI contraindications, insufficient knowledge of German language, and current breastfeeding or pregnancy. Significant age differences were present between AD and FH+ subjects and between AD and FH subjects, respectively (Table 1).
      Table 1Sociodemographic and Psychometric Data
      VariableGroupGroup DifferencesPost Hoc Tests
      AD, n = 75FH+, n = 62FH, n = 76Test Statisticp ValueEffectTest Statisticp Value
      Recruitment Site and Scanner, Berlin Trio/Berlin Prisma/Mannheim Trio, n
      Trio refers to MAGNETOM Trio, Siemens; Prisma refers to MAGNETOM Prisma, Siemens.
      50/1/2434/9/1938/3/35χ24 = 15.37.004
      p < .05.
      Sex, F/M, %28.00%/72.00%59.68%/40.32%27.63%/72.37%χ22 = 19.13<.001
      p < .05.
      Age, Years, Mean ± SD50.03 ± 10.8538.89 ± 13.3543.62 ± 12.58F2,210 = 14.37<.001
      p < .05.
      AD vs. FHWelch test: t146.38 = 3.35.001
      p < .017 (Bonferroni corrected).
      AD vs. FH+Welch test: t117.03 = 5.29<.001
      p < .017 (Bonferroni corrected).
      FH+ vs. FHt136 = −2.14.034
      Handedness, R/L/Bi, %88.00%/6.67%/5.33%88.71%/9.68%/1.61%96.05%/0%/3.95%χ24 = 8.28.082
      BMI, Mean ± SD25.46 ± 4.8724.43 ± 3.5224.45 ± 3.73F2,209 = 1.49.229
      German as Native Language, True/Not True, %98.67%/1.33%93.55%/6.45%96.05%/3.95%χ22 = 2.47.291
      Impaired Vision, True/Not True, %53.33%/44.00%54.84%/43.55%48.68%/47.37%χ22 = 0.4.817
      Adoption, True/Not True, %4.00%/96.00%1.61%/96.77%5.26%/94.74%χ22 = 1.24.538
      School-Leaving Qualification, %χ26 = 10.26.114
       Higher education entrance qualification45.33%61.29%60.53%
       School leaving certificate after grade 1037.33%32.26%25.00%
       School leaving certificate after grade 917.33%4.84%11.84%
       None0%0%1.32%
      Vocational Qualification, %χ26 = 14.38.026
      p < .05.
       University degree18.67%27.42%34.21%
       Polytechnic degree6.67%8.06%17.11%
       Vocational training66.67%50.00%39.47%
       None8.00%12.90%7.89%
      Birth or Pregnancy Complications, Present/Absent, %17.33%/80.00%17.74%/80.65%11.84%/84.21%χ22 = 1.1.577
      Smoking Status, %χ24 = 27.95<.001
      p < .05.
       Smoker52.00%24.194%21.05%
       Ex-smoker16.00%12.903%6.58%
       Nonsmoker32.00%62.903%72.37%
      Number of Cigarettes/Day, Mean ± SD
      Based on smokers.
      18.79 ± 10.978.57 ± 4.312.53 ± 7.71F2,65 = 7.05.002
      p < .05.
      AD vs. FHt52 = 2.02.048
      AD vs. FH+Welch test: t50.47 = 4.87<.001
      p < .017 (Bonferroni corrected).
      FH+ vs. FHWelch test: t22.22 = −1.72.099
      AUQ Sum Score, Mean ± SD
      Due to missing values, data in this row refer to 188 subjects (71 AD, 48 FH+, 69 FH−).
      12.03 ± 5.4012.10 ± 5.3010.88 ± 3.90F2,185 = 1.27.283
      OCDS Sum Score, Mean ± SD
      Due to missing values, data in this row refer to 112 subjects (65 AD, 17 FH+, 30 FH−).
      16.28 ± 11.2212.24 ± 0.6612.87 ± 2.22F2,109 = 2.43.093
      Alcohol Dependence in Parents, %
      Related to AD and FH+.
      χ23 = 28.14<.001
      p < .05.
       Father32.00%54.84%0%
       Mother5.33%20.97%0%
       Both5.33%9.68%0%
       Neither57.33%14.52%100%
      Alcohol Dependence in Siblings, Present/Absent, %
      Related to AD and FH+.
      10.67%/82.67%20.97%/72.58%0%/100%χ21 = 2.79.095
      Alcohol Dependence in Children, n = 136, Present/Absent, %
      Related to AD and FH+.
      ,
      At one site, information on alcohol dependence according to DSM-IV (54) was assessed in children, but not coded separately. Data in this row therefore refer to 135 subjects (51 AD, 43 FH+, 41 FH−).
      0.00%/100.00%2.33%/97.67%0%/100%χ21 = 1.2.274
      Smoking Status of Mother During Pregnancy, Smoker/Nonsmoker, %5.33%/21.33%14.52%/32.26%1.32%/26.32%χ24 = 10.51.033
      p < .05.
      Verbal IQ, Mean ± SD106.14 ± 9.86104.55 ± 10.81107.19 ± 10.19F2,203 = 1.1.334
      Group differences were determined by χ2 tests and univariate ANOVAs, respectively. If the percentages within a field do not add up to 100%, the remaining percentages are made up by missing values. If only the percentage of true cases is reported, the remaining percentage is made up by nontrue cases.
      AD, abstinent alcohol-dependent subjects; ANOVAs, analyses of variance; AUQ, Alcohol Urge Questionnaire; Bi, bilateral; BMI, body mass index; F, female; FH+, healthy first-degree relatives of alcohol-dependent individuals; FH, healthy control subjects without family history of alcohol dependence; L, left; OCDS, Obsessive Compulsive Drinking Scale; R, right.
      a Trio refers to MAGNETOM Trio, Siemens; Prisma refers to MAGNETOM Prisma, Siemens.
      b p < .05.
      c p < .017 (Bonferroni corrected).
      d Based on smokers.
      e Due to missing values, data in this row refer to 188 subjects (71 AD, 48 FH+, 69 FH).
      f Due to missing values, data in this row refer to 112 subjects (65 AD, 17 FH+, 30 FH).
      g Related to AD and FH+.
      h At one site, information on alcohol dependence according to DSM-IV (
      American Psychiatric Association
      Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).
      ) was assessed in children, but not coded separately. Data in this row therefore refer to 135 subjects (51 AD, 43 FH+, 41 FH).

      Monetary Incentive Delay Task

      We administered a modified version of the MIDT (
      • Knutson B.
      • Westdorp A.
      • Kaiser E.
      • Hommer D.
      FMRI visualization of brain activity during a monetary incentive delay task.
      ) using Presentation (Neurobehavioral Systems) (Supplemental Methods and Figure S1). In each trial, subjects saw a cue indicating a potential gain, potential loss, or neutral trial, respectively (anticipation phase). After a variable delay, they had to respond to a target as quickly as possible. Subsequent feedback (outcome phase) indicated whether a trial was successful (i.e., +1€ in gain trials, 0€ in loss trials) or unsuccessful (i.e., 0€ in gain trials, −1€ in loss trials). The outcome for neutral trials was always 0€. The MIDT had a total duration of approximately 12 minutes and consisted of 25 gain, 25 loss, and 25 neutral trials presented in a pseudo-randomized order, with the same condition occurring maximally twice in succession.
      To avoid decreased anticipatory activity at the beginning of the MIDT (
      • Balodis I.M.
      • Potenza M.N.
      Anticipatory reward processing in addicted populations: A focus on the monetary incentive delay task.
      ) and to ensure learning of the association between the cues and their respective outcomes, participants performed one practice session each outside and inside the scanner. An adaptive algorithm was applied in the in-scanner practice session and during the main task, leading to each subject being successful in approximately 70% of trials (Supplemental Methods).

      Functional Magnetic Resonance Imaging

      fMRI was performed in three 3T scanners (Berlin and Mannheim: MAGNETOM Trio, Siemens; Berlin: MAGNETOM Prisma, Siemens). T2-weighted images were obtained using echo-planar imaging (repetition time = 2.2 seconds, echo time = 30 ms, flip angle = 75°, matrix = 64 × 64, voxel size = 3.44 × 3.44 × 3.41 mm3, 40 slices, 323 or 360 images). A T1-weighted structural image was acquired as an anatomical reference (repetition time = 2.3 seconds, echo time = 3.03 ms, flip angle = 9°, matrix = 256 × 256, voxel size = 1 × 1 × 1 mm3, 192 slices).

      Data Analysis

      Demographic and behavioral MIDT data were analyzed using SPSS Statistics 23 and 28 (IBM Corp.), while fMRI analyses were performed with SPM12 (The Wellcome Centre for Human Neuroimaging) in MATLAB (versions 2013b and 2021a; The MathWorks, Inc.). Data analyses were preregistered in the Open Science Framework (https://osf.io/3gnk9) (
      • Musial M.
      Reward processing in alcohol-dependent patients and first-degree relatives: Functional brain activity during anticipation of monetary gains and losses.
      ).

      Behavioral Data

      Group differences regarding sociodemographic and psychometric data were examined using χ2 tests or analyses of variance, respectively. For MIDT reaction time (RT), success rate, and outcome analyses, we used univariate analyses of variance. Significant main effects of group were followed by post hoc independent-samples t tests or, in case of variance inequality, Welch tests. In case of significant main effects of anticipation condition, we performed post hoc t tests for dependent samples. For exploratory analyses including age as a covariate, see the Supplement.

      fMRI Data

      After preprocessing (Supplemental Methods), we performed first- and second-level analyses in the context of the general linear model. At the first level of the event-related design, 3 anticipation conditions (gain, loss, neutral), target presentation, and 5 feedback conditions (gain, unsuccessful gain attempt, neutral feedback, loss avoidance, unsuccessful loss avoidance attempt) were modeled using stick functions convolved with the hemodynamic response function implemented in SPM. In addition, we included 3 regressors each for translational and rotational head motion.
      For each participant, the linear contrast images gain > neutral anticipation and loss > neutral anticipation were computed and subsequently used in the random-effects second-level analysis. Here, the 3 groups (AD, FH+, FH) were included as regressors. MRI scanner, handedness, and smoking status were included as covariates of no interest (Supplemental Methods). To rule out the possibility that neural group differences were caused or masked by systematically different success rates (
      • Balodis I.M.
      • Potenza M.N.
      Anticipatory reward processing in addicted populations: A focus on the monetary incentive delay task.
      ) and thus by systematically different motivation to actively achieve a positive outcome, we included success rate in MIDT gain or loss trials as a covariate (Supplemental Discussion).
      Besides whole-brain analyses, we conducted ROI analyses using small-volume correction for each group comparison (AD vs. FH, FH+ vs. FH, AD vs. FH+) for the contrasts gain > neutral anticipation and loss > neutral anticipation. Based on previous MIDT studies (
      • Wilson R.P.
      • Colizzi M.
      • Bossong M.G.
      • Allen P.
      • Kempton M.
      • Bhattacharyya S.
      MTAC
      The neural substrate of reward anticipation in health: A meta-analysis of fMRI findings in the monetary incentive delay task.
      ) and using an in-house tool (
      • Schubert R.
      • Ritter P.
      • Wüstenberg T.
      • Preuschhof C.
      • Curio G.
      • Sommer W.
      • Villringer A.
      Spatial attention related SEP amplitude modulations covary with BOLD signal in S1—A simultaneous EEG—fMRI study.
      ), the VS, AI, ACC, and VTA were defined as ROIs (Supplemental Methods, Figure S2, and Tables S1 and S2).
      A familywise error (FWE)–corrected significance level of p = .05 was used in all whole-brain and ROI analyses. Activations within AD, FH+, and FH groups during gain and loss anticipation were assessed using one-sample t tests, group differences (AD vs. FH, FH+ vs. FH, AD vs. FH+) for both contrasts (gain > neutral anticipation, loss > neutral anticipation) using independent-samples t tests. For exploratory analyses related to success rate, age, craving measures, abstinence duration, and the MIDT outcome phase, see the Supplement.

      Results

      Behavioral Data

      We found a main effect of anticipation condition on RT (Tables 2 and 3): consistent with a basic assumption underlying MIDT (
      • Balodis I.M.
      • Potenza M.N.
      Anticipatory reward processing in addicted populations: A focus on the monetary incentive delay task.
      ), participants responded faster in gain and loss compared with neutral trials, respectively. In addition, RT across groups was lower in gain than in loss trials. Moreover, the main effect of groups was significant (Figure 1A), with AD and FH+ subjects responding more slowly than FH subjects across anticipation conditions, respectively. AD and FH+ subjects did not differ regarding RT. The interaction between group and condition was not significant.
      Table 2Descriptive Behavioral Performance in the Monetary Incentive Delay Task
      VariableAD, n = 75FH+, n = 62FH, n = 76All, N = 213
      RT Gain, ms
      Values refer to a subsample of 208 subjects (72 AD, 61 FH+, 75 FH−) (Supplemental Methods).
      297.75 ± 89.13283.82 ± 72.43245.98 ± 68.99275.00 ± 80.34
      RT Neutral, ms
      Values refer to a subsample of 208 subjects (72 AD, 61 FH+, 75 FH−) (Supplemental Methods).
      319.31 ± 89.47312.67 ± 86.32264.86 ± 75.95297.73 ± 87.10
      RT Loss, ms
      Values refer to a subsample of 208 subjects (72 AD, 61 FH+, 75 FH−) (Supplemental Methods).
      303.33 ± 86.5289.93 ± 80.8248.34 ± 70.38279.57 ± 82.53
      Total RT, ms
      Values refer to a subsample of 208 subjects (72 AD, 61 FH+, 75 FH−) (Supplemental Methods).
      306.80 ± 85.12295.47 ± 77.69253.06 ± 70.46284.10 ± 81.09
      Success Rate Gain, %63.31 ± 12.9667.16 ± 14.5467.11 ± 11.9765.78 ± 13.17
      Success Rate Loss, %59.84 ± 16.4263.42 ± 14.2366.21 ± 11.7563.15 ± 14.44
      Total Success Rate, %61.57 ± 11.9165.29 ± 12.2166.66 ± 10.3064.47 ± 11.61
      Total Outcome, €7.65 ± 7.7610.66 ± 8.3612.86 ± 7.4710.38 ± 8.10
      Values are presented as mean ± SD.
      AD, abstinent alcohol-dependent subjects; FH+, healthy first-degree relatives of alcohol-dependent individuals; FH, healthy control subjects without family history of alcohol dependence; Gain, in gain trials; Loss, in loss trials; Neutral, in neutral trials; RT, reaction time.
      a Values refer to a subsample of 208 subjects (72 AD, 61 FH+, 75 FH) (Supplemental Methods).
      Table 3Behavioral Performance Differences in the Monetary Incentive Delay Task
      Behavioral MeasureF TestsPost Hoc Tests
      EffectFp ValueEffecttp Value
      RT
      Values refer to a subsample of 208 subjects (72 AD, 61 FH+, 75 FH−) (Supplemental Methods).
      GroupF2,205 = 9.66<.001
      p < .05.
      ,
      Significant when age was included as a covariate. See Supplemental Methods and Results for further information.
      AD vs. FHt145 = 4.18<.001
      Significant when age was included as a covariate. See Supplemental Methods and Results for further information.
      ,
      p < .017 (Bonferroni corrected).
      AD vs. FH+t131 = 0.80.428
      FH+ vs. FHt134 = 3.33.001
      Significant when age was included as a covariate. See Supplemental Methods and Results for further information.
      ,
      p < .017 (Bonferroni corrected).
      Anticipation condition
      Greenhouse-Geisser corrected.
      F1.44,295.99 = 54.62<.001
      p < .05.
      Gain vs. neutralt207 = −8.44<.001
      p < .017 (Bonferroni corrected).
      Loss vs. neutralt207 = −6.9<.001
      p < .017 (Bonferroni corrected).
      Gain vs. losst207 = −3.19.002
      p < .017 (Bonferroni corrected).
      Group × anticipation condition
      Greenhouse-Geisser corrected.
      F2.89,295.99 = 0.87.452
      Success RateGroupF2,210 = 3.95.021
      p < .05.
      AD vs. FHt149 = −2.81.006
      p < .017 (Bonferroni corrected).
      AD vs. FH+t135 = −1.80.074
      FH+ vs. FHt136 = −0.71.477
      Anticipation conditionF1,210 = 6.85.010
      p < .05.
      Group × anticipation conditionF2,210 = 0.80.453
      Total OutcomeGroupF2,210 = 8.37<.001
      p < .05.
      ,
      Significant when age was included as a covariate. See Supplemental Methods and Results for further information.
      AD vs. FHt149 = −4.20<.001
      Significant when age was included as a covariate. See Supplemental Methods and Results for further information.
      ,
      p < .017 (Bonferroni corrected).
      AD vs. FH+t135 = −2.18.031
      FH+ vs. FHt136 = −1.63.106
      AD, abstinent alcohol-dependent subjects; FH+, healthy first-degree relatives of alcohol-dependent individuals; FH, healthy control subjects without family history of alcohol dependence; Gain, in gain trials; Loss, in loss trials; Neutral, in neutral trials; RT, reaction time.
      a Values refer to a subsample of 208 subjects (72 AD, 61 FH+, 75 FH) (Supplemental Methods).
      b p < .05.
      c Significant when age was included as a covariate. See Supplemental Methods and Results for further information.
      d p < .017 (Bonferroni corrected).
      e Greenhouse-Geisser corrected.
      Figure thumbnail gr1
      Figure 1Behavioral performance of healthy control subjects without family history of alcohol dependence (FH), healthy first-degree relatives of alcohol-dependent individuals (FH+), and abstinent alcohol-dependent subjects (AD) in the monetary incentive delay task. (A) Reaction time (RT) across gain, loss, and neutral trials. (B) Success rate across gain and loss trials. Asterisks represent means, lines within violin plots represent interquartile range, and width of violin plots represents distribution density.
      A similar pattern emerged regarding success rate (Tables 2 and 3): across groups, the success rate was higher in gain than in loss trials. In addition, we found a main effect of group (Figure 1B). Post hoc t tests revealed that AD subjects were less successful than FH subjects, while AD and FH+ subjects as well as FH+ and FH subjects did not differ, respectively. The interaction between group and condition was not significant.

      fMRI Data

      As expected (
      • Wilson R.P.
      • Colizzi M.
      • Bossong M.G.
      • Allen P.
      • Kempton M.
      • Bhattacharyya S.
      MTAC
      The neural substrate of reward anticipation in health: A meta-analysis of fMRI findings in the monetary incentive delay task.
      ,
      • Dugré J.R.
      • Dumais A.
      • Bitar N.
      • Potvin S.
      Loss anticipation and outcome during the Monetary Incentive Delay Task: A neuroimaging systematic review and meta-analysis.
      ), confirming MIDT effectiveness, FH subjects showed significant activation in the VTA, VS, ACC, and AI, among others, during both gain and loss anticipation contrasted against neutral trials, respectively (Figure 2; Tables S5 and S8).
      Figure thumbnail gr2
      Figure 2Whole-brain activity (p < .05, familywise error [FWE] corrected) within healthy control subjects without family history of alcohol dependence (FH), healthy first-degree relatives of alcohol-dependent individuals (FH+), and abstinent alcohol-dependent subjects (AD), respectively, during gain (gain > neutral anticipation; warm colors) and loss (loss > neutral anticipation; cold colors) anticipation in the monetary incentive delay task. The respective z coordinate is indicated above each layer.

      Gain Anticipation

      During gain anticipation (gain > neutral anticipation), we found no differences in activity between AD, FH+, and FH subjects, neither within the ROIs (Figure 3) nor in whole-brain analyses (Figure 2; Tables S3–S5).
      Figure thumbnail gr3
      Figure 3Estimated contrast weights for mean activity in the bilateral ventral striatum (VS) region of interest during gain anticipation (gain > neutral anticipation) in the monetary incentive delay task for healthy control subjects without family history of alcohol dependence (FH), healthy first-degree relatives of alcohol-dependent individuals (FH+), and abstinent alcohol-dependent subjects (AD), respectively. Asterisks represent means, lines within violin plots represent interquartile range, and width of violin plots represents distribution density.
      Exploratory ROI and whole-brain analyses 1) including age as covariate, with and without success rate as covariate, respectively, and 2) on subsamples of age-matched AD versus FH+ subjects or AD versus FH subjects, respectively, did not reveal any group differences. Instead, we found significant reductions in neural activity with increasing age in the VS ROI (right: MNI 9 15 −5; t205 = 4.46, pFWE = .003; left: MNI −11 12 −5; t205 = 4.02, pFWE = .013) and left AI ROI (MNI −29 26 –2; t205 = 3.64, pFWE = .043) in the full sample (Supplement).
      When exploring the role of the substantial variation in abstinence duration in AD subjects, we found no significant correlation (n = 64, rs = −0.19, p = .14) between mean activity in the bilateral VS ROI during gain anticipation and abstinence duration (Supplemental Results and Figure S4).

      Loss Anticipation

      During loss anticipation (loss > neutral anticipation), AD subjects exhibited significantly decreased neuronal activation in the left AI ROI compared with FH subjects (MNI −25 19 −5; t206 = 4.17, pFWE = .009) (Figure 4). There were no significant differences in activity within the ROIs between AD and FH+ subjects or between FH+ and FH subjects, respectively. Whole-brain analyses did not reveal any significant group differences, although, descriptively, neural activity spanned fewer brain regions in AD than in FH+ and FHsubjects, respectively (Figure 2; Tables S6–S8).
      Figure thumbnail gr4
      Figure 4Group difference in brain activity in the left anterior insula between abstinent alcohol-dependent subjects (AD) and healthy control subjects without family history of alcohol dependence (FH) during loss anticipation (loss > neutral anticipation) in the monetary incentive delay task. (A) Three-dimensional plot of activity differences (AD < FH) within regions of interest at a significance level of p < .001, uncorrected. (B) Estimated contrast weights for activity in the peak voxel of the group difference, for FH, healthy first-degree relatives of alcohol-dependent individuals (FH+), and AD, respectively. Asterisks represent means, lines within violin plots represent interquartile range, and width of plots represents distribution density.
      When exploratively 1) including age as covariate in whole-brain and ROI analyses, with and without success rate as covariate, respectively, and 2) repeating whole-brain and ROI analyses on subsamples of age-matched AD versus FH+ subjects or AD versus FH subjects, we neither found a difference between AD and FH subjects in left AI nor any other group difference. Instead, with increasing age, BOLD signals decreased in left putamen (whole-brain, MNI −18 15 −5; t205 = 4.70, pFWE = .026) and in the ROIs of left VS (MNI −15 12 –5; t205 = 4.52, pFWE = .002), right VS (MNI 9 15 −9; t205 = 3.87, pFWE = .024) and left AI (MNI −25 19 −5; t205 = 4.03, pFWE = .014) (Supplemental Methods).
      In a post hoc analysis exploring the substantial variation in abstinence duration in AD subjects, we found no significant Spearman's rank correlation between mean activity in the bilateral AI ROI during loss anticipation and abstinence duration (n = 64, rs = −0.05, p = .68) (Supplement and Figure S7).

      Discussion

      During the anticipation of monetary gains or losses, FH+ subjects did not exhibit reduced or increased neural responses in mesocorticolimbic regions in comparison to FH and AD subjects, respectively. In contrast, AD subjects showed decreased activity in the left AI during loss anticipation compared with FH subjects. However, this finding was not significant when age was included as a covariate. Taken together, our findings are neither in accordance with the reward deficiency hypothesis (
      • Blum K.
      • Braverman E.R.
      • Holder J.M.
      • Lubar J.F.
      • Monastra V.J.
      • Miller D.
      • et al.
      Reward deficiency syndrome: A biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors.
      ) nor the allostatic hypothesis (
      • Koob G.F.
      • Ahmed S.H.
      • Boutrel B.
      • Chen S.A.
      • Kenny P.J.
      • Markou A.
      • et al.
      Neurobiological mechanisms in the transition from drug use to drug dependence.
      ,
      • Koob G.F.
      • Le Moal M.
      Plasticity of reward neurocircuitry and the “dark side” of drug addiction.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug abuse: Hedonic homeostatic dysregulation.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug addiction, dysregulation of reward, and allostasis.
      ), at least when the latter refers to persistent hyposensitivity of the reward system to non–drug-related stimuli during prolonged abstinence.

      Gain Anticipation

      We found no significant functional differences between FH+ and FH subjects in VTA, VS, ACC, or AI during gain anticipation. While this is in line with a number of previous studies (
      • Bjork J.M.
      • Knutson B.
      • Hommer D.W.
      Incentive-elicited striatal activation in adolescent children of alcoholics.
      ,
      • Müller K.U.
      • Gan G.
      • Banaschewski T.
      • Barker G.J.
      • Bokde A.L.W.
      • Büchel C.
      • et al.
      No differences in ventral striatum responsivity between adolescents with a positive family history of alcoholism and controls.
      ,
      • Weiland B.J.
      • Welsh R.C.
      • Yau W.Y.
      • Zucker R.A.
      • Zubieta J.K.
      • Heitzeg M.M.
      Accumbens functional connectivity during reward mediates sensation-seeking and alcohol use in high-risk youth.
      ,
      • Weiland B.J.
      • Zucker R.A.
      • Zubieta J.K.
      • Heitzeg M.M.
      Striatal dopaminergic reward response relates to age of first drunkenness and feedback response in at-risk youth.
      ), it stands in contrast to others reporting functional differences of inconsistent direction between FH+ and FH subjects during gain anticipation (
      • Andrews M.M.
      • Meda S.A.
      • Thomas A.D.
      • Potenza M.N.
      • Krystal J.H.
      • Worhunsky P.
      • et al.
      Individuals family history positive for alcoholism show functional magnetic resonance imaging differences in reward sensitivity that are related to impulsivity factors.
      ,
      • Filippi I.
      • Hoertel N.
      • Artiges E.
      • Airagnes G.
      • Guérin-Langlois C.
      • Seigneurie A.S.
      • et al.
      Family history of alcohol use disorder is associated with brain structural and functional changes in healthy first-degree relatives.
      ,
      • Villafuerte S.
      • Heitzeg M.M.
      • Foley S.
      • Yau W.Y.
      • Majczenko K.
      • Zubieta J.K.
      • et al.
      Impulsiveness and insula activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism.
      ,
      • Yau W.Y.
      • Zubieta J.K.
      • Weiland B.J.
      • Samudra P.G.
      • Zucker R.A.
      • Heitzeg M.M.
      Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use.
      ).
      Reinforced by a comparatively large sample size, our finding questions the validity of the reward deficiency syndrome hypothesis (
      • Blum K.
      • Braverman E.R.
      • Holder J.M.
      • Lubar J.F.
      • Monastra V.J.
      • Miller D.
      • et al.
      Reward deficiency syndrome: A biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors.
      ) and the concomitant assumption that hyposensitivity of the mesocorticolimbic reward system to non–substance-related rewards predisposes to alcohol dependence, at least when assuming a genetic contribution to reward deficiency shared between (subsequent) patients and relatives of alcohol-dependent patients. However, unaffected family history–positive individuals may differ from (subsequently) addicted individuals: Volkow et al. (
      • Volkow N.D.
      • Wang G.J.
      • Begleiter H.
      • Porjesz B.
      • Fowler J.S.
      • Telang F.
      • et al.
      High levels of dopamine D2 receptors in unaffected members of alcoholic families: Possible protective factors.
      ) observed increased dopamine D2 receptors in the VS of unaffected relatives of alcohol-dependent patients, who, in contrast, tended to display reduced dopamine D2 receptors at least during early abstinence (
      • Martinez D.
      • Gil R.
      • Slifstein M.
      • Hwang D.R.
      • Huang Y.
      • Perez A.
      • et al.
      Alcohol dependence is associated with blunted dopamine transmission in the ventral striatum.
      ,
      • Heinz A.
      • Siessmeier T.
      • Wrase J.
      • Hermann D.
      • Klein S.
      • Grüsser S.M.
      • et al.
      Correlation between dopamine D(2) receptors in the ventral striatum and central processing of alcohol cues and craving.
      ,
      • Gleich T.
      • Spitta G.
      • Butler O.
      • Zacharias K.
      • Aydin S.
      • Sebold M.
      • et al.
      Dopamine D2/3 receptor availability in alcohol use disorder and individuals at high risk: Towards a dimensional approach.
      ). Whether, instead, a hypersensitivity of mesocorticolimbic regions to alcohol-related stimuli (
      • Berridge K.C.
      • Robinson T.E.
      Liking, wanting, and the incentive-sensitization theory of addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      The neural basis of drug craving: An incentive-sensitization theory of addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      Review. The incentive sensitization theory of addiction: Some current issues.
      ) plays a role in relatives remains to be elucidated. So far, consistent with the incentive salience hypothesis (
      • Berridge K.C.
      • Robinson T.E.
      Liking, wanting, and the incentive-sensitization theory of addiction.
      ,
      • Droutman V.
      • Read S.J.
      • Bechara A.
      Revisiting the role of the insula in addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      The neural basis of drug craving: An incentive-sensitization theory of addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      Review. The incentive sensitization theory of addiction: Some current issues.
      ), increased alcohol cue–elicited activity in the insula, among other regions, has been found in heavy drinkers regardless of familial exposure (
      • Dager A.D.
      • Anderson B.M.
      • Stevens M.C.
      • Pulido C.
      • Rosen R.
      • Jiantonio-Kelly R.E.
      • et al.
      Influence of alcohol use and family history of alcoholism on neural response to alcohol cues in college drinkers.
      ).
      For AD and FH subjects, we found comparable neuronal activation during the anticipation of monetary gains. While in line with 3 previous studies (
      • Bjork J.M.
      • Smith A.R.
      • Hommer D.W.
      Striatal sensitivity to reward deliveries and omissions in substance dependent patients.
      ,
      • Bjork J.M.
      • Smith A.R.
      • Chen G.
      • Hommer D.W.
      Mesolimbic recruitment by nondrug rewards in detoxified alcoholics: Effort anticipation, reward anticipation, and reward delivery.
      ,
      • Murphy A.
      • Nestor L.J.
      • McGonigle J.
      • Paterson L.
      • Boyapati V.
      • Ersche K.D.
      • et al.
      Acute D3 antagonist GSK598809 selectively enhances neural response during monetary reward anticipation in drug and alcohol dependence.
      ), this is inconsistent with the majority of MIDT research in AD subjects (
      • Romanczuk-Seiferth N.
      • Koehler S.
      • Dreesen C.
      • Wüstenberg T.
      • Heinz A.
      Pathological gambling and alcohol dependence: Neural disturbances in reward and loss avoidance processing.
      ,
      • Wrase J.
      • Schlagenhauf F.
      • Kienast T.
      • Wüstenberg T.
      • Bermpohl F.
      • Kahnt T.
      • et al.
      Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics.
      ,
      • Beck A.
      • Schlagenhauf F.
      • Wüstenberg T.
      • Hein J.
      • Kienast T.
      • Kahnt T.
      • et al.
      Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics.
      ,
      • Hägele C.
      • Schlagenhauf F.
      • Rapp M.
      • Sterzer P.
      • Beck A.
      • Bermpohl F.
      • et al.
      Dimensional psychiatry: Reward dysfunction and depressive mood across psychiatric disorders.
      ,
      • Nestor L.J.
      • Murphy A.
      • McGonigle J.
      • Orban C.
      • Reed L.
      • Taylor E.
      • et al.
      Acute naltrexone does not remediate frontostriatal disturbances in alcoholic and alcoholic polysubstance-dependent populations during a monetary incentive delay task.
      ) and a meta-analysis (
      • Luijten M.
      • Schellekens A.F.
      • Kühn S.
      • Machielse M.W.J.
      • Sescousse G.
      Disruption of reward processing in addiction: An image-based meta-analysis of functional magnetic resonance imaging studies.
      ) reporting decreased activity in the VS and insula, among others. Importantly, most previous studies reporting group differences assessed patients with rather short abstinence durations [1–3 weeks; e.g., (
      • Romanczuk-Seiferth N.
      • Koehler S.
      • Dreesen C.
      • Wüstenberg T.
      • Heinz A.
      Pathological gambling and alcohol dependence: Neural disturbances in reward and loss avoidance processing.
      ,
      • Wrase J.
      • Schlagenhauf F.
      • Kienast T.
      • Wüstenberg T.
      • Bermpohl F.
      • Kahnt T.
      • et al.
      Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics.
      ,
      • Beck A.
      • Schlagenhauf F.
      • Wüstenberg T.
      • Hein J.
      • Kienast T.
      • Kahnt T.
      • et al.
      Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics.
      ,
      • Nestor L.J.
      • Murphy A.
      • McGonigle J.
      • Orban C.
      • Reed L.
      • Taylor E.
      • et al.
      Acute naltrexone does not remediate frontostriatal disturbances in alcoholic and alcoholic polysubstance-dependent populations during a monetary incentive delay task.
      )], while in the current study, average abstinence duration was about 3 years (Supplemental Results). Neuroadaptations elicited by chronic alcohol intake have, at least in subgroups (
      • Mon A.
      • Durazzo T.C.
      • Gazdzinski S.
      • Meyerhoff D.J.
      The impact of chronic cigarette smoking on recovery from cortical gray matter perfusion deficits in alcohol dependence: Longitudinal arterial spin labeling MRI.
      ,
      • Durazzo T.C.
      • Gazdzinski S.
      • Mon A.
      • Meyerhoff D.J.
      Cortical perfusion in alcohol-dependent individuals during short-term abstinence: Relationships to resumption of hazardous drinking after treatment.
      ), been shown to reverse within the first weeks or days of abstinence (
      • Heinz A.
      • Lichtenberg-Kraag B.
      • Baum S.S.
      • Gräf K.
      • Krüger F.
      • Dettling M.
      • Rommelspacher H.
      Evidence for prolonged recovery of dopaminergic transmission after detoxification in alcoholics with poor treatment outcome.
      ,
      • Hirth N.
      The endogenous opioid system in alcoholism: Translational studies in humans and rodents.
      ,
      • Heinz A.
      • Schmidt L.G.
      • Reischies F.M.
      Anhedonia in schizophrenic, depressed, or alcohol-dependent patients – Neurobiological correlates.
      ). Conversely, delayed recovery of dopaminergic neurotransmission following detoxification is associated with subsequent relapse (
      • Heinz A.J.
      • Beck A.
      • Meyer-Lindenberg A.
      • Sterzer P.
      • Heinz A.
      Cognitive and neurobiological mechanisms of alcohol-related aggression.
      ,
      • Heinz A.
      • Schmidt L.G.
      • Reischies F.M.
      Anhedonia in schizophrenic, depressed, or alcohol-dependent patients – Neurobiological correlates.
      ). In our long-term abstinent sample, dopamine neurotransmission and dopamine-dependent reward anticipation (
      • Schultz W.
      Predictive reward signal of dopamine neurons.
      ) might thus long have recovered from such neuroadaptations, and we cannot rule out reduced mesolimbic responses in acutely abstinent individuals. Our finding that abstinence duration was not correlated with mean reward anticipatory activity in the VS does not stand in contrast to this hypothesis, as only 2 subjects with a known abstinence duration of <1 month were included (Supplement).
      Conclusively, our findings in AD subjects support neither the reward deficiency syndrome hypothesis (
      • Blum K.
      • Braverman E.R.
      • Holder J.M.
      • Lubar J.F.
      • Monastra V.J.
      • Miller D.
      • et al.
      Reward deficiency syndrome: A biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors.
      ) nor the allostatic hypothesis (
      • Koob G.F.
      • Ahmed S.H.
      • Boutrel B.
      • Chen S.A.
      • Kenny P.J.
      • Markou A.
      • et al.
      Neurobiological mechanisms in the transition from drug use to drug dependence.
      ,
      • Koob G.F.
      • Le Moal M.
      Plasticity of reward neurocircuitry and the “dark side” of drug addiction.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug abuse: Hedonic homeostatic dysregulation.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug addiction, dysregulation of reward, and allostasis.
      ) insofar as the latter postulates decreased reward network sensitivity to non–drug-related stimuli during protracted abstinence (
      • Koob G.F.
      • Le Moal M.
      Plasticity of reward neurocircuitry and the “dark side” of drug addiction.
      ,
      • Koob G.F.
      • Le Moal M.
      Addiction and the brain antireward system.
      ,
      • Koob G.F.
      • Volkow N.D.
      Neurocircuitry of addiction.
      ,
      • George O.
      • Le Moal M.
      • Koob G.F.
      Allostasis and addiction: Role of the dopamine and corticotropin-releasing factor systems.
      ). Instead, AD subjects might rather be characterized by a hypersensitivity of mesocorticolimbic regions to alcohol-related cues (
      • Berridge K.C.
      • Robinson T.E.
      Liking, wanting, and the incentive-sensitization theory of addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      The neural basis of drug craving: An incentive-sensitization theory of addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      Review. The incentive sensitization theory of addiction: Some current issues.
      ). This would be consistent with the incentive salience hypothesis (
      • Romanczuk-Seiferth N.
      • Koehler S.
      • Dreesen C.
      • Wüstenberg T.
      • Heinz A.
      Pathological gambling and alcohol dependence: Neural disturbances in reward and loss avoidance processing.
      ,
      • Berridge K.C.
      • Robinson T.E.
      Liking, wanting, and the incentive-sensitization theory of addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      The neural basis of drug craving: An incentive-sensitization theory of addiction.
      ,
      • Robinson T.E.
      • Berridge K.C.
      Review. The incentive sensitization theory of addiction: Some current issues.
      ) and is supported by previous studies (
      • Wrase J.
      • Schlagenhauf F.
      • Kienast T.
      • Wüstenberg T.
      • Bermpohl F.
      • Kahnt T.
      • et al.
      Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics.
      ,
      • Grüsser S.M.
      • Wrase J.
      • Klein S.
      • Hermann D.
      • Smolka M.N.
      • Ruf M.
      • et al.
      Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics.
      ,
      • Beck A.
      • Wüstenberg T.
      • Genauck A.
      • Wrase J.
      • Schlagenhauf F.
      • Smolka M.N.
      • et al.
      Effect of brain structure, brain function, and brain connectivity on relapse in alcohol-dependent patients.
      ,
      • Hirth N.
      • Meinhardt M.W.
      • Noori H.R.
      • Salgado H.
      • Torres-Ramirez O.
      • Uhrig S.
      • et al.
      Convergent evidence from alcohol-dependent humans and rats for a hyperdopaminergic state in protracted abstinence.
      ). It should be noted, however, that the allostatic hypothesis is more complex than portrayed here (
      • Koob G.F.
      • Volkow N.D.
      Neurocircuitry of addiction.
      ,
      • George O.
      • Le Moal M.
      • Koob G.F.
      Allostasis and addiction: Role of the dopamine and corticotropin-releasing factor systems.
      ,
      • Kramer J.
      • Dick D.M.
      • King A.
      • Ray L.A.
      • Sher K.J.
      • Vena A.
      • et al.
      Mechanisms of alcohol addiction: Bridging human and animal studies.
      ) and emphasizes anti-reward network activation during protracted abstinence (
      • Koob G.F.
      • Ahmed S.H.
      • Boutrel B.
      • Chen S.A.
      • Kenny P.J.
      • Markou A.
      • et al.
      Neurobiological mechanisms in the transition from drug use to drug dependence.
      ,
      • Koob G.F.
      • Le Moal M.
      Plasticity of reward neurocircuitry and the “dark side” of drug addiction.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug abuse: Hedonic homeostatic dysregulation.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug addiction, dysregulation of reward, and allostasis.
      ,
      • Koob G.F.
      • Le Moal M.
      Addiction and the brain antireward system.
      ,
      • Koob G.F.
      Alcoholism: Allostasis and beyond.
      ). Currently lacking (
      • Parvaz M.A.
      • Rabin R.A.
      • Adams F.
      • Goldstein R.Z.
      Structural and functional brain recovery in individuals with substance use disorders during abstinence: A review of longitudinal neuroimaging studies.
      ), longitudinal studies should assess trajectories of drug- and non–drug-related primary and secondary reward anticipation over the course of abstinence and among subsequently abstaining versus relapsing alcohol-dependent individuals.

      Loss Anticipation

      During loss anticipation, FH+ subjects did not show decreased activity in any ROI compared with FH subjects. This is inconsistent with 2 previous studies (
      • Villafuerte S.
      • Heitzeg M.M.
      • Foley S.
      • Yau W.Y.
      • Majczenko K.
      • Zubieta J.K.
      • et al.
      Impulsiveness and insula activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism.
      ,
      • Yau W.Y.
      • Zubieta J.K.
      • Weiland B.J.
      • Samudra P.G.
      • Zucker R.A.
      • Heitzeg M.M.
      Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use.
      ), but in line with most studies reporting no group differences in insula (
      • Bjork J.M.
      • Knutson B.
      • Hommer D.W.
      Incentive-elicited striatal activation in adolescent children of alcoholics.
      ,
      • Weiland B.J.
      • Welsh R.C.
      • Yau W.Y.
      • Zucker R.A.
      • Zubieta J.K.
      • Heitzeg M.M.
      Accumbens functional connectivity during reward mediates sensation-seeking and alcohol use in high-risk youth.
      ,
      • Yau W.Y.
      • Zubieta J.K.
      • Weiland B.J.
      • Samudra P.G.
      • Zucker R.A.
      • Heitzeg M.M.
      Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use.
      ) or VS activation (
      • Bjork J.M.
      • Knutson B.
      • Hommer D.W.
      Incentive-elicited striatal activation in adolescent children of alcoholics.
      ,
      • Weiland B.J.
      • Welsh R.C.
      • Yau W.Y.
      • Zucker R.A.
      • Zubieta J.K.
      • Heitzeg M.M.
      Accumbens functional connectivity during reward mediates sensation-seeking and alcohol use in high-risk youth.
      ) between relatives and individuals without family history of alcohol dependence. As loss anticipation in the MIDT may be best understood as anticipation of potential loss avoidance, this finding, again, argues against the reward deficiency syndrome hypothesis.
      However, AD subjects exhibited decreased activity in the left AI compared with FH subjects during loss anticipation. The AI has been linked to action initiation in response to subjectively salient stimuli (
      • Uddin L.Q.
      Salience processing and insular cortical function and dysfunction.
      ) and impairments in insula function have repeatedly been associated with addictive behavior (
      • Droutman V.
      • Read S.J.
      • Bechara A.
      Revisiting the role of the insula in addiction.
      ,
      • Belin-Rauscent A.
      • Daniel M.L.
      • Puaud M.
      • Jupp B.
      • Sawiak S.
      • Howett D.
      • et al.
      From impulses to maladaptive actions: The insula is a neurobiological gate for the development of compulsive behavior.
      ,
      • Naqvi N.H.
      • Bechara A.
      The hidden island of addiction: The insula.
      ). However, the decreased AI activation was most likely driven by AD subjects' being significantly older than FH subjects rather than by their diagnosis of alcohol dependence, as it was no longer significant when age was included as a covariate.
      Apart from the AI, we found no significant differences in neural activation between AD and FH subjects in any other ROI. This is inconsistent with 2 previous findings (
      • Romanczuk-Seiferth N.
      • Koehler S.
      • Dreesen C.
      • Wüstenberg T.
      • Heinz A.
      Pathological gambling and alcohol dependence: Neural disturbances in reward and loss avoidance processing.
      ,
      • Wrase J.
      • Schlagenhauf F.
      • Kienast T.
      • Wüstenberg T.
      • Bermpohl F.
      • Kahnt T.
      • et al.
      Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics.
      ), but in line with several MIDT studies in AD subjects (
      • Bjork J.M.
      • Smith A.R.
      • Hommer D.W.
      Striatal sensitivity to reward deliveries and omissions in substance dependent patients.
      ,
      • Bjork J.M.
      • Smith A.R.
      • Chen G.
      • Hommer D.W.
      Mesolimbic recruitment by nondrug rewards in detoxified alcoholics: Effort anticipation, reward anticipation, and reward delivery.
      ,
      • Beck A.
      • Schlagenhauf F.
      • Wüstenberg T.
      • Hein J.
      • Kienast T.
      • Kahnt T.
      • et al.
      Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics.
      ,
      • Hägele C.
      • Schlagenhauf F.
      • Rapp M.
      • Sterzer P.
      • Beck A.
      • Bermpohl F.
      • et al.
      Dimensional psychiatry: Reward dysfunction and depressive mood across psychiatric disorders.
      ) and suggests that the most robust finding is that of no group differences during loss anticipation.

      Limitations

      An important limitation of this study is that we cannot rule out the confounding influence of age. Indeed, exploratory analyses showed that activation in the VS and AI decreased with increasing age, which is in line with findings in healthy subjects (
      • Dhingra I.
      • Zhang S.
      • Zhornitsky S.
      • Le T.M.
      • Wang W.
      • Chao H.H.
      • et al.
      The effects of age on reward magnitude processing in the monetary incentive delay task.
      ). However, considering that older FH+ subjects who did not develop alcohol dependence are likely particularly resilient, we did not find any group differences in mesolimbic activation when comparing age-matched subsamples of FH+ and AD or FH and AD subjects, respectively. In addition, since ROI analyses including age as a covariate did not reveal any significant group differences during gain or loss anticipation, we deem it unlikely that age differences between groups could have obscured group differences in neural responses—they rather appear to have amplified them. In light of this, we consider a comparable neuronal activation in mesocorticolimbic regions and the AI during gain and loss anticipation in all groups the most likely scenario.
      Moreover, it remains unclear whether functional differences exist between FH+ and FH subjects with high- and low-risk drinking behaviors or between those prospectively developing and not developing alcohol dependence: even though findings are ambiguous (
      • Joseph J.E.
      • Zhu X.
      • Corbly C.R.
      • DeSantis S.
      • Lee D.C.
      • Baik G.
      • et al.
      Influence of neurobehavioral incentive valence and magnitude on alcohol drinking behavior.
      ,
      • Martz M.E.
      • Zucker R.A.
      • Schulenberg J.E.
      • Heitzeg M.M.
      Psychosocial and neural indicators of resilience among youth with a family history of substance use disorder.
      ,
      • Ivanov I.
      • Parvaz M.A.
      • Velthorst E.
      • Shaik R.B.
      • Sandin S.
      • Gan G.
      • et al.
      Substance use initiation, particularly alcohol, in drug-naive adolescents: Possible predictors and consequences from a large cohort naturalistic study.
      ), one study (
      • Yau W.Y.
      • Zubieta J.K.
      • Weiland B.J.
      • Samudra P.G.
      • Zucker R.A.
      • Heitzeg M.M.
      Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use.
      ) reported decreased anticipatory activity in gain and loss trials in the VS only among relatives with low-risk drinking behavior. Moreover, recent studies suggest that neural activation in the VS, among others, during reward anticipation predicts future alcohol use in association with certain personality traits and other risk factors (
      • Swartz J.R.
      • Weissman D.G.
      • Ferrer E.
      • Beard S.J.
      • Fassbender C.
      • Robins R.W.
      • et al.
      Reward-related brain activity prospectively predicts increases in alcohol use in adolescents.
      ,
      • Cope L.M.
      • Martz M.E.
      • Hardee J.E.
      • Zucker R.A.
      • Heitzeg M.M.
      Reward activation in childhood predicts adolescent substance use initiation in a high-risk sample.
      ,
      • Büchel C.
      • Peters J.
      • Banaschewski T.
      • Bokde A.L.W.
      • Bromberg U.
      • Conrod P.J.
      • et al.
      Blunted ventral striatal responses to anticipated rewards foreshadow problematic drug use in novelty-seeking adolescents.
      ). Differences between such subgroups may have masked overall group differences and should be considered in future longitudinal studies.
      Finally, the MIDT version used included only comparatively low reward and loss magnitudes, which might have limited possible group differences in — and reliability of — mesolimbic activation (
      • Wu C.C.
      • Samanez-Larkin G.R.
      • Katovich K.
      • Knutson B.
      Affective traits link to reliable neural markers of incentive anticipation.
      ). However, we observed robust whole-brain FWE-corrected VS activity in all groups. Moreover, 1€ likely elicited a stronger mesolimbic response than similar magnitudes in MIDT versions using additional larger reward or loss magnitudes within the same task (
      • Nieuwenhuis S.
      • Heslenfeld D.J.
      • von Geusau N.J.
      • Mars R.B.
      • Holroyd C.B.
      • Yeung N.
      Activity in human reward-sensitive brain areas is strongly context dependent.
      ). For a detailed discussion of this aspect and of further limitations, see Supplemental Discussion.

      Conclusions

      Conclusively, neither the reward deficiency syndrome (
      • Blum K.
      • Braverman E.R.
      • Holder J.M.
      • Lubar J.F.
      • Monastra V.J.
      • Miller D.
      • et al.
      Reward deficiency syndrome: A biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors.
      ) nor the allostatic hypothesis (
      • Koob G.F.
      • Ahmed S.H.
      • Boutrel B.
      • Chen S.A.
      • Kenny P.J.
      • Markou A.
      • et al.
      Neurobiological mechanisms in the transition from drug use to drug dependence.
      ,
      • Koob G.F.
      • Le Moal M.
      Plasticity of reward neurocircuitry and the “dark side” of drug addiction.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug abuse: Hedonic homeostatic dysregulation.
      ,
      • Koob G.F.
      • Le Moal M.
      Drug addiction, dysregulation of reward, and allostasis.
      ) is supported by the present findings, at least not insofar as the latter posits persisting hyposensitivity of the reward system to non-drug reward-indicating cues. Concomitantly, our data do not support the hypothesis that functional abnormalities in mesocorticolimbic regions are a predisposing factor for the development of alcohol dependence. The reduced activation in the left AI in AD subjects during loss anticipation compared with FH subjects is likely due to group age differences and was no longer significant after controlling for age. Future research should investigate drug and non-drug reward cue processing over the course of abstinence and control for individual differences including sign-tracking behavior as a potential confound (
      • Robinson T.E.
      • Yager L.M.
      • Cogan E.S.
      • Saunders B.T.
      On the motivational properties of reward cues: Individual differences.
      ,
      • Schad D.J.
      • Rapp M.A.
      • Garbusow M.
      • Nebe S.
      • Sebold M.
      • Obst E.
      • et al.
      Dissociating neural learning signals in human sign- and goal-trackers.
      ). This research could assess 1) whether the incentive salience hypothesis—compared with the reward deficiency syndrome and allostatic hypotheses—allows for more accurate predictions regarding mesocorticolimbic brain activity of AD and FH subjects during reward and loss anticipation, and 2) which neural substrates underlie a predisposition to dependence.

      Acknowledgments and Disclosures

      The e:Med project was funded by the Bundesministerium für Bildung und Forschung ( BMBF , German Federal Ministry of Education and Research; Grant No. 01ZX1611E [to FK, HW, AH]). This work was partly supported by the SFB/TRR 265 Losing and Regaining Control over Drug Intake, funded by the Deutsche Forschungsgemeinschaft (German Research Foundation; Project ID 402170461 [to AH, AB, FK, SV-K, HW]), and by the Cluster of Excellence EXC 2049 Neurocure, funded by the BMBF (Project No. 390688087 [to AH]).
      The Family History Assessment Module has been provided by the Collaborative Study on the Genetics of Alcoholism, supported by NIH Grant No. U10AA08401 from the National Institute on Alcohol Abuse and Alcoholism.
      We thank all study participants for supporting our research.
      Data were collected between May 2016 and March 2019 at the Charité - Universitätsmedizin Berlin, Germany, and the Central Institute of Mental Health in Mannheim, Germany, as part of subproject 10 within the consortium SysMedAlcoholism of the e:Med project.
      The authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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