Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-24T10:55:50.811Z Has data issue: false hasContentIssue false
  • English
  • Français

Family history of alcohol use disorder is associated with brain structural and functional changes in healthy first-degree relatives

Published online by Cambridge University Press:  01 January 2020

Irina Filippi ,
Nicolas Hoertel ,
Eric Artiges ,
Guillaume Airagnes ,
Christophe Guérin-Langlois ,
Anne-Sophie Seigneurie ,
Pauline Frère ,
Manon Dubol ,
François Guillon  and
Hervé Lemaître
...Show all authors
Irina Filippi
Affiliation:
INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Saclay, University Paris Descartes, Digiteo-labs, Gif-sur-Yvette, France
Nicolas Hoertel
Affiliation:
Department of Psychiatry and Addictology, Hôpitaux Universitaires Paris Ouest, Assistance Publique-Hôpitaux de Paris, Paris, France Paris Descartes University, Pôles de recherche et d'enseignement supérieur Sorbonne Paris Cité, Paris, France INSERM, UMR 894, Psychiatry and Neurosciences Center, Paris, France
Eric Artiges
Affiliation:
INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Saclay, University Paris Descartes, Digiteo-labs, Gif-sur-Yvette, France GH Nord Essonne, Department of Psychiatry 91G16, Orsay Hospital, Orsay, France
Guillaume Airagnes
Affiliation:
Department of Psychiatry and Addictology, Hôpitaux Universitaires Paris Ouest, Assistance Publique-Hôpitaux de Paris, Paris, France Paris Descartes University, Pôles de recherche et d'enseignement supérieur Sorbonne Paris Cité, Paris, France INSERM UMS 011, Population-based Epidemiological Cohorts, Villejuif, France Inserm UMR 1168, VIMA, Villejuif, France
Christophe Guérin-Langlois
Affiliation:
Department of Psychiatry and Addictology, Hôpitaux Universitaires Paris Ouest, Assistance Publique-Hôpitaux de Paris, Paris, France Paris Descartes University, Pôles de recherche et d'enseignement supérieur Sorbonne Paris Cité, Paris, France
Anne-Sophie Seigneurie
Affiliation:
Department of Psychiatry and Addictology, Hôpitaux Universitaires Paris Ouest, Assistance Publique-Hôpitaux de Paris, Paris, France Paris Descartes University, Pôles de recherche et d'enseignement supérieur Sorbonne Paris Cité, Paris, France
Pauline Frère
Affiliation:
INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Saclay, University Paris Descartes, Digiteo-labs, Gif-sur-Yvette, France
Manon Dubol
Affiliation:
INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Saclay, University Paris Descartes, Digiteo-labs, Gif-sur-Yvette, France
François Guillon
Affiliation:
Department of Psychiatry and Addictology, Hôpitaux Universitaires Paris Ouest, Assistance Publique-Hôpitaux de Paris, Paris, France Paris Descartes University, Pôles de recherche et d'enseignement supérieur Sorbonne Paris Cité, Paris, France
Hervé Lemaître
Affiliation:
INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Saclay, University Paris Descartes, Digiteo-labs, Gif-sur-Yvette, France
Mehdi Rahim
Affiliation:
CEA, DSV, I2BM, Neurospin, Gif-sur-Yvette, France
Jean-Luc Martinot*
Affiliation:
INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Saclay, University Paris Descartes, Digiteo-labs, Gif-sur-Yvette, France
Frédéric Limosin
Affiliation:
Department of Psychiatry and Addictology, Hôpitaux Universitaires Paris Ouest, Assistance Publique-Hôpitaux de Paris, Paris, France Paris Descartes University, Pôles de recherche et d'enseignement supérieur Sorbonne Paris Cité, Paris, France Inserm UMR 1168, VIMA, Villejuif, France
*
*Corresponding author at: INSERM Unit 1000 Neuroimaging & Psychiatry, Digiteo-labs, bâtiment 660, rue Noetzlin, 91190, Gif-sur-Yvette, France. E-mail addressess: jean-luc.martinot@inserm.fr

Abstract

Background:

Neuroimaging studies of vulnerability to Alcohol Use Disorder (AUD) have identified structural and functional variations which might reflect inheritable features in alcohol-naïve relatives of AUD individuals (FH+) compared to controls having no such family history (FH-). However, prior research did not simultaneously account for childhood maltreatment, any clinically significant disorder and maternal AUD. Therefore, we mainly aimed to investigate the brain structure and reward-related neural activations (fMRI), using whole-brain analysis in FH+ young adults with no prevalent confounders.

Methods:

46 FH+ and 45 FH- male and female participants had no severe childhood maltreatment exposure, neither any psychiatric disorder or AUD, nor a prenatal exposure to maternal AUD. We used a 3 T MRI coupled with a whole brain voxel-based method to compare between groups the grey matter volumes and activations in response to big versus small wins during a Monetary Incentive Delay task. The Childhood Trauma Questionnaire score was used as confounding variable in the analyses to account for the remaining variance between groups.

Results:

Compared to FH- controls, FH+ participants had smaller grey matter volumes in the frontal and cingulate regions as well as in the bilateral nucleus accumbens and right insula. The FH+ participants’ fMRI datasets denoted a blunted activation in the middle cingulum with respect to FH- controls’ during the processing of reward magnitude, and a greater activation in the anterior cingulum in response to anticipation of a small win.

Conclusions:

Family history of alcohol use disorder is linked to structural and functional variations including brain regions involved in reward processes.

Keywords

Alcohol use disorderChildhood Trauma QuestionnaireMonetary Incentive Delay taskRelativesVoxel-Based MorphometryVulnerability
Type
Original article
Information
European Psychiatry , Volume 62 , October 2019 , pp. 107 - 115
Copyright
Copyright © European Psychiatric Association 2019

1. Introduction

Alcohol use disorder (AUD) is a leading cause of mortality, morbidity and disability worldwide [Reference Gowing, Ali, Allsop, Marsden, Turf and West1]. AUD has been attributed to genetic predisposition for up to 40 to 60% [Reference Bienvenu, Davydow and Kendler2, Reference Osby, Liljenberg, Kockum, Gunnar and Terenius3], hence this risk in first-degree relatives of AUD individuals is much greater than in the general population [Reference Nurnberger, Wiegand, Bucholz, O’Connor, Meyer and Reich4]. Neuroimaging studies conducted among AUD individuals discussed inherent pre-existing variations entangled with the neurotoxic effects of alcohol use per se [Reference Dupuy and Chanraud5]. Hence, investigations in adolescents from AUD families provided evidence of structural [Reference Hill, De Bellis, Keshavan, Lowers, Shen and Hall6–Reference Cservenka10] and functional [Reference Schweinsburg, Paulus, Barlett, Killeen, Caldwell and Pulido11–Reference Hardee, Weiland, Nichols, Welsh, Soules and Steinberg14] variations possibly reflecting vulnerability to AUD. In addition, there are prevalent confounders which may affect brain maturation such as childhood maltreatment, which is associated with psychopathology and risk of substance abuse [Reference Herringa, Birn, Ruttle, Burghy, Stodola and Davidson15, Reference Keyes, Eaton, Krueger, McLaughlin, Wall and Grant16]. Therefore, the previous findings need to be re-evaluated to consider the prepotent influence of the maltreatment on structural deficits (i.e. smaller grey matter volumes) and functional overactivations in limbic regions that consistently include the hippocampus or the anterior cingulate cortex [Reference Teicher, Samson, Anderson and Ohashi17]. Maltreatment, even below the threshold of reportable childhood maltreatment, can lead to significant changes in the brain’s emotion-regulating circuitry in adolescents [Reference Herringa, Birn, Ruttle, Burghy, Stodola and Davidson15]. In addition, chronic maltreatment is associated with cognitive deficits such as poorer inhibitory control [Reference Cowell, Cicchetti, Rogosch and Toth18] as described in adult first-degree relatives of AUD patients [Reference Gierski, Hubsch, Stefaniak, Benzerouk, Cuervo-Lombard and Bera-Potelle19].

AUD adults indeed displayed grey matter volume (GMV) reductions in the hippocampus [Reference Beresford, Arciniegas, Alfers, Clapp, Martin and Du20], amygdala [Reference Wrase, Makris, Braus, Mann, Smolka and Kennedy21], insula [Reference Chanraud, Martelli, Delain, Kostogianni, Douaud and Aubin22], caudate and putamen [Reference Sullivan, Deshmukh, De Rosa, Rosenbloom and Pfefferbaum23], cerebellum [Reference Sullivan, Deshmukh, Desmond, Mathalon, Rosenbloom and Lim24] as well as in the brainstem [Reference Pfefferbaum, Rosenbloom, Serventi and Sullivan25] compared to healthy controls using anatomical magnetic resonance imaging (MRI). GMV reductions were also observed in adolescents having a family history of AUD (called FH+) compared to those having no family history of AUD (called FH-) in the right amygdala [Reference Hill, De Bellis, Keshavan, Lowers, Shen and Hall6], parahippocampal gyrus, thalamus, cingulum, superior frontal gyrus [Reference Benegal, Antony, Venkatasubramanian and Jayakumar7], orbitofrontal cortex [Reference Hill, Wang, Kostelnik, Carter, Holmes and McDermott8] and cerebellum [Reference Hill, Wang, Carter, Tessner, Holmes and McDermott9]. GMV reductions have also been reported in the parahippocampal gyrus [Reference Sjoerds, M-JV, van den, der, Buchem and Aleman26] and in the amygdala [Reference Dager, McKay, Kent, Curran, Knowles and Sprooten27] in FH+ healthy adults. Interestingly, GMV reductions observed FH+ individuals may indicate brain structural changes associated with a family history of AUD.

The most acknowledged mechanisms of vulnerability to addiction include functional deregulations of the reward system [Reference Volkow and Morales28], intricately linked to deficits in inhibition [Reference Churchwell and Yurgelun-Todd29] and sensation-seeking [Reference Cservenka, Herting, Seghete, Hudson and Nagel30]. Functional magnetic resonance imaging (fMRI) showed that FH+ adolescents did not show variations of reward-related neural response [Reference Bjork, Knutson and Hommer31, Reference Müller, Gan, Banaschewski, Barker, Bokde and Büchel32] but had a blunted brain response to inhibition during Go/No-Go fMRI tasks [Reference Schweinsburg, Paulus, Barlett, Killeen, Caldwell and Pulido11, Reference Heitzeg, Nigg, W-YW, Zucker and Zubieta12, Reference Hardee, Weiland, Nichols, Welsh, Soules and Steinberg14]. However, short-term detoxified AUD individuals [Reference Beck, Schlagenhauf, Wüstenberg, Hein, Kienast and Kahnt33] and FH+ adults [Reference Andrews, Meda, Thomas, Potenza, Krystal and Worhunsky34, Reference W-YW, Zubieta, Weiland, Samudra, Zucker and Heitzeg35] had blunted activations in the ventral striatum during reward anticipation while performing a Monetary Incentive Delay (MID) task [Reference Balodis and Potenza36]. Elucidating whether reward processing in FH+ individuals is affected at the earliest stages has been deemed essential to better characterize the addictive behaviour' predisposition.

Although some previous studies controlled (as exclusion criteria or confounding variable in the analyses) for the confounding effects of any psychiatric disorder and AUD [Reference Schweinsburg, Paulus, Barlett, Killeen, Caldwell and Pulido11, Reference Cservenka and Nagel13], maternal AUD [Reference Hill, Wang, Kostelnik, Carter, Holmes and McDermott8, Reference Cservenka and Nagel13, Reference Müller, Gan, Banaschewski, Barker, Bokde and Büchel32], foetal alcohol syndrome [Reference Heitzeg, Nigg, W-YW, Zucker and Zubieta12, Reference Hardee, Weiland, Nichols, Welsh, Soules and Steinberg14, Reference Bjork, Knutson and Hommer31] or even less frequently for maltreatment exposure [Reference Sjoerds, M-JV, van den, der, Buchem and Aleman26], it is still unclear whether brain structural and functional features observed in FH+ individuals persist when controlling simultaneously for these confounding factors. Moreover, there have been calls for neuroimaging research on brain structure in healthy FH+ adults [Reference Gierski, Hubsch, Stefaniak, Benzerouk, Cuervo-Lombard and Bera-Potelle19], since developmental maturation is still ongoing in adolescents [Reference Mills and Tamnes37]. In addition, previous studies on brain structure mostly predefined regions of interest, having possibly neglected some brain variations in FH+ individuals. Finally, although deficits in differentiating a big versus small reward were reported in other addictive disorders [Reference Lole and Gonsalvez38] and impairments in the computation of expected reward value have been identified in AUD individuals [Reference Hariri, Brown, Williamson, Flory, de and Manuck39, Reference Grodin, Steckler and Momenan40], it is unknown whether they might contribute to vulnerability to AUD.

To address these limitations, the primary aim of the study was to explore the whole brain structure and processing of reward magnitude in adult first-degree relatives of AUD individuals having no prevalent confounding factors (i.e., severe maltreatment exposure, any clinically significant disorder, maternal AUD). The secondary aim was to assess inhibitory control to confirm inhibition deficits in a highly homogeneous sample. The Childhood Trauma Questionnaire (CTQ) score [Reference Bernstein, Stein, Newcomb, Walker, Pogge and Ahluvalia41] was used as confounding variable in the statistical analyses. The tertiary aim was to explore on the whole brain the effects of low-to-moderate CTQ scores on brain structure and processing of reward in the FH+ group.

Firstly, based on prior research, we hypothesized smaller GMV in FH+ individuals in regions detailed above, i.e., amygdala, parahippocampal gyrus, thalamus, cingulum, superior frontal gyrus, orbitofrontal cortex and cerebellum, and deficits in neural response to a big versus small win during a MID task. Secondly, we hypothesized poorer inhibition in cognitive tests in FH+ as compared to FH- participants. And thirdly, we hypothesized that exposure to low-to-moderate maltreatment can have a measurable impact on the brain i.e. structural deficits and reward-related overactivation in a group of FH+ individuals.

2. Material and methods

2.1 Participants’ characteristics

Participants were recruited in the Department of Psychiatry and Addictology of the Corentin-Celton Hospital (Issy-les-Moulineaux, France). Participants from the FH+ group were mainly siblings of AUD patients followed in our department or participating in Alcoholics Anonymous, while participants from the FH- group were mainly psychology and medical students. The inclusion criteria were age ranging between 18 (age of civil majority) and 35, French native language, right-handed according to the Edinburgh Handedness Inventory scale. Healthy relatives of AUD individuals (FH+) were male and female defined as having two or more first or second-degree relatives with AUD including necessarily the father since inheritability to AUD has been found to be especially paternal [Reference McGue, Iacono, Legrand and Elkins42] and excluding the mother to prevent foetal exposure to maternal AUD. Controls (FH-) were male and female defined as having no family history of AUD in neither first nor second-degree relatives.

Since we aimed to identify brain correlates of vulnerability to AUD rather than those of alcohol use per se, we considered the following exclusion criteria for the whole sample assessed using the Diagnostic Interview for Genetic Studies (DIGS [Reference Nurnberger, Blehar, Kaufmann, York-Cooler, Simpson and Harkavy-Friedman43, Reference Preisig, Fenton, Matthey, Berney and Ferrero44]): past and current psychiatric diagnoses including abuse of or dependence to alcohol or other substances except tobacco according to DSM IV-TR criteria [45]. Current psychiatric diagnoses, including posttraumatic stress disorder, were also examined during a face-to-face interview with a psychiatrist. Using the DIGS, participants were also excluded if they fulfilled at least one of the following conditions: more than three alcohol intakes per week or more than six drinks per week for women or more than nine drinks per week for men, more than one episode of binge drinking (defined as drinking on the same occasion, i.e. at the same time or within a couple of hours of each other, five or more drinks for men and four or more drinks for women [46] over the past year and/or more than ten episodes in their lifetime; cannabis use in the last three months; use of prescribed and non-prescribed psychoactive drugs in the last six months; severe exposure to at least one form of childhood maltreatment, defined as having CTQ subscale scores ≥16, ≥13, ≥13, ≥18 and ≥13 for emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect, respectively [Reference Bernstein and Fink47]. In addition to the excluded participants with maternal AUD, volunteers were not admitted to the study if they had a suspected foetal alcohol syndrome with generalized deficits in the processing and integration of information revealed by neurocognitive examination [Reference Kodituwakku48]. Exclusion criteria also involved other significant medical conditions such as medical events in perinatal history (perinatal foetal distress or prematurity of more than three weeks); any clinically significant or unstable disease; organic diseases affecting the central nervous system such as neurocognitive disorders, intellectual disabilities, history of head trauma with loss of consciousness and/or requiring hospitalization or meningoencephalitis; and MRI contra-indications.

The protocol of the study was approved by the Ile-De-France VIII ethics committee and by the National Agency of Security for French biomedical researches. All participants signed an informed consent after receiving full information on the study. All procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

2.2 Data collection

2.2.1 Family history of AUD, clinical, sociodemographic and psychometric characteristics

Assessments have been performed face-to-face by the investigators. Family history of AUD was examined using the Family Informant Schedule and Criteria (FISC). Axis I psychiatric and substance use disorders were assessed with the DIGS during a face-to-face interview with a psychiatrist. Age, sex and education (in number of years of schooling) were also collected. Maltreatment exposure was assessed with the CTQ. Sensation seeking was assessed with the Sensation-Seeking Scale-Form V of Zuckerman [Reference Loas, Verrier, Flament, Perez-Diaz, Corcos and Halfon49]. Trait impulsivity was assessed with the Barratt Impulsivity Scale (11th version) [Reference Baylé, Bourdel, Caci, Gorwood, Chignon and Adés50].

2.2.2 Magnetic resonance imaging (MRI)

Acquisitions of high-resolution images were conducted with a 3.0 T Siemens Trio scanner and a 12-channel coil at CENIR–ICM platform, Pitié-Salpêtrière Hospital (Paris, France). Pre-processing steps and statistical analyses were run with the Statistical Parametric Mapping software (SPM12, Welcome Trust Centre for Neuroimaging, University College London, UK).

2.2.2.1 Structural brain imaging

The anatomical 3D-T1-weighted sequence was carried out with the following parameters: sagittal slice plane, Repetition Time (TR) = 2.3 s, Echo Time (TE) = 2.93 ms, 256 × 256 view matrix, 160 slices, voxel size = 1.1 × 1.1 x 1.1 mm and duration = 554 s. A quality control has been conducted on T1 dataset from which images with a benign abnormality (n = 1), artifacts from excessive movements of the eyes's lens (n = 4) and head (n = 1) were excluded. The images were spatially normalized and segmented onto the MNI (Montreal Neurological Institute) tissue probability maps (TPM) into grey matter, white matter, cerebrospinal fluid, bone, soft tissue, background images – by fitting iteratively to TPM voxel's intensities [Reference Ashburner and Friston51]. Using the software R, the package "mvoutlier" detected extreme values from a normal distribution (https://cran.r-project.org/web/packages/mvoutlier/index.html) leading to the exclusion of outlier volumes (n = 4). Then, covariances were calculated with the tool "check sample homogeneity" (http://www.neuro.uni-jena.de/vbm/check-sample-homogeneity/) and volumes with covariances greater than two standard deviations from the mean were detected with the R package "extreme values" (https://cran.r-project.org/web/packages/extremevalues/index.html) thus outlier covariance volumes (n = 8) were excluded to control for errors of segmentation. Then, images were modulated and 10-mm full-width at half-maximum (FWHM) Gaussian kernel smoothed. Overall, following visual quality control and outliers' detection, 18 (19.8%) out of 91 acquisitions were excluded. The remaining sample for analysis was composed of N = 73 participants.

2.2.2.2 Functional brain imaging

An Echo-Planar Imaging sequence acquired 40 descending axial slices parallel to the anterior-posterior commissure for each n = 191 time-series, with the following parameters: thickness = 2.4millimeters (mm), TR = 2.2 s, TE = 30 ms, matrix size 64 × 64, voxel size = 3.4 × 3.4 x 2.4 mm and duration = 427 s. Excessive motion was prevented in the MRI with small wedges to fix head in place. Participants’ blood oxygen level-dependent (BOLD) signal time course was recorded while performing an event-related revised MID task in which the main difference to the original version [Reference Knutson, Westdorp, Kaiser and Hommer52] is the omission of loss trials [Reference Nees, Tzschoppe, Patrick, Vollstädt-Klein, Steiner and Poustka53]. This task displayed sequences of clue, target and feedback phases. The clue indicated the amount of the gain and the participants were instructed to respond upon appearance of the target which occurred 4 s after the clue. Response was followed by a 1.5 s visual feedback informing the participants of their trial result (Supplementary Fig. A.1). There were 42 trials (14 no wins; 14 small wins; 14 big wins).

A visual quality control has also been conducted on functional data. Images were excluded in cases of truncation (n = 1), signal abnormality (n = 2), major artefact (n = 1) and sequence error (n = 1). No excessive motion was visually detected. Per participant, pre-processing steps included timing correction of the 40 slices per volume; rigid body realignment of the 191 volumes to the mean image; nonlinear normalization to MNI standardized space, intensity bias correction, writing of 3 mmReference Osby, Liljenberg, Kockum, Gunnar and Terenius3 voxel-wrapped images which were finally 10-mm FWHM Gaussian filtered. Moreover, first-level analyses of BOLD signal changes were performed by adding anticipations and feedbacks as explanatory variables in the intra-subject model along with six motion realignment-computed parameters and derivatives. The canonical hemodynamic response function (HRF) accounted for lag between event onsets and effective BOLD signal changes. Participants not responding to all ‘no win’ conditions (n = 2) and with no or atypical BOLD signals in response to handgrip press were excluded (n = 6). Indeed, A quality control has been performed on fMRI “motor” contrasts (press left minus right, press right minus left) that usually displayed localized activations in the sensory / motor cortex and cerebellum. Therefore, any intense BOLD signal in the frontal, temporal or occipital lobe is atypical for motor contrasts (e.g. when the participant was not focusing on the task during the MID-task acquisition). Thus, out of the sample of 73 acquisitions, 13 (17.8%) were removed. Overall, the fMRI sample available for the second-level analyses was composed of N = 60 participants.

2.2.3 Cognitive assessments of inhibition

Participants performed the following cognitive tests: Go/No-Go test [Reference Kamarajan, Porjesz, Jones, Choi, Chorlian and Padmanabhapillai54] and Hayling test [Reference Burgess and Shallice55]. Further details are given in the Supplementary Appendix A.1.

2.2.4 Statistical analyses

For all statistical analyses, age, sex and CTQ total score were included as confounding variables to account for residual variance between and within groups.

2.2.4.1 Voxel-Based Morphometry analyses

Voxel-Based Morphometry (VBM) analysis of grey matter images consisted of between-group voxel-to-voxel t-tests across the whole brain. The global GMV was added in the statistical model [Reference Peelle, Cusack and Henson56]. We used the xjView toolbox (http://www.alivelearn.net/xjview) to localise the regions of difference. Statistical maps had a height threshold at p < 0.001 uncorrected and an extent threshold at p < 0.05 Family-Wise Error (FWE) corrected (cluster size > 520 voxels). Because tobacco dependence [Reference McClernon57] and cannabis use [Reference Chye, Christensen and Yücel58] may have confounding effects, we performed supplementary analyses with the number of pack-years of smoking or cannabis use as confounding variable.

We performed additional analyses to examine the confounding effects of the CTQ score on the GMV on the whole-brain within the FH+ group. Significance was set at a height threshold at p < 0.05 uncorrected and an extent threshold at p < 0.05 FWE corrected (cluster size > 8000 voxels).

2.2.4.2 Functional brain imaging analyses

Between-group comparisons of BOLD signal maps were also performed as voxel-to-voxel t-tests across the whole brain for the contrast of anticipation of a “big versus small win” [Reference Nees, Tzschoppe, Patrick, Vollstädt-Klein, Steiner and Poustka53, Reference Büchel, Peters, Banaschewski, Bokde, Bromberg and Conrod59] to assess neural response to the processing of reward magnitude; as well as for the two conditions independently, i.e. anticipation of a big win and anticipation of a small win. Statistical maps had a height threshold at p < 0.001 uncorrected and an extent threshold at p < 0.05 FWE corrected (cluster size > 80 voxels) [Reference Eklund, Nichols and Knutsson60]. The BrainVISA/Anatomist software (http://brainvisa.info) was used to build the figure which displays both structural and functional results. Because tobacco dependence [Reference McClernon57] and cannabis use [Reference Chye, Christensen and Yücel58] might have confounding effects, we performed supplementary analyses with the number of pack-years of smoking or cannabis use as confounding variable.

The additional analyses to examine the confounding effects of the CTQ score on the BOLD signal within the FH+ group were significant at a height threshold at p < 0.05 uncorrected and an extent threshold at p < 0.05 FWE corrected (cluster size > 1250 voxels).

2.2.4.3 Sociodemographic, psychometric and cognitive analyses

Between-group differences regarding individual factors were tested using unequal variance t-tests or chi-square tests when the dependent variable was continuous or categorical, respectively. Between-group multivariable analyses were performed using linear or logistic regression when the dependent variable was continuous or binary, respectively. Statistical significance for cognitive data was determined using a two-sided alpha a priori set at 0.0036 (0.05 Bonferroni corrected i.e. divided by the number of tests, i.e. 14).

3. Results

3.1 Participants’ characteristics

There were 73 participants included in the psychometric, structural and cognitive analyses; 37 participants with a family history of alcohol use disorder (FH+) and 36 controls having no such history (FH-). Participants’ characteristics are given in Table 1. The total duration of education showed a non-clinically significant mean difference of 1.0 year. We found no significant between-group differences in age, sex, and substance use (all p > 0.05). However, we found a significantly greater severity of exposure to all childhood trauma subtypes in the FH+ participants compared to the FH- controls, except for sexual abuse which was not significantly different between groups.

Table 1 Comparisons of sociodemographic characteristics, childhood trauma exposure and substance use between participants with (FH+) and without (FH-) a family history of alcohol use disorder.

a Participants with a family history of alcohol use disorder had to have two or more first or second-degree relatives with alcohol use disorder (FH+) – including necessarily the father and excluding the mother. Controls had to have no family history of alcohol use disorder in neither first nor second degree relatives (FH-).

μ Unequal variance t-tests (df = 71).

β ᵪ2 tests (df = 1). Continuous variables are presented as their mean values and standard deviation (SD). Categorical variables are presented as percentages. p values in bold are statistically significant (p < 0.05).

3.2 Voxel-Based Morphometry analyses

Between-group comparison of grey matter images showed that the FH+ participants had significantly smaller GMV than the FH- controls in four clusters (Table 2; Fig. 1) comprising the right middle frontal gyrus, bilateral inferior frontal gyrus, right insula, bilateral nucleus accumbens, bilateral olfactory cortex, bilateral gyrus rectus, middle cingulate, bilateral precuneus and right pre- and post-central gyri. There was no region of larger GMV in the FH+ participants compared to the FH- controls. These results were conserved when adding the number of pack-years of smoking or cannabis use as confounding variable (Supplementary Fig. B.1; Supplementary Fig. D.1).

Fig. 1. Brain regions of differences in grey matter volumes and bold signal changes during anticipation of reward in a MID task between individuals with and without a family history of alcohol use disorder.

Participants with a positive family history of alcohol use disorder (FH+) presented smaller grey matter volumes in four clusters (blue colour) compared to participants having no family history of alcohol use disorder (FH-). FH+ individuals also had a blunted activation in response to a big versus small win (green colour) and a greater activation during anticipation of a small win (red colour) compared to FH- controls during the MID anticipation phase. Extent threshold was set at p < 0.05 Family-Wise Error (FWE) corrected; Height threshold was set at p<0.001 uncorrected. Each point on the boxplots represents the adjusted (for age, sex and Childhood Trauma Questionnaire total score) mean value for each participant. The line joining the boxplots spots the group mean. Orientation R: Right; L: Left; A: Anterior; P: Posterior; voxel resolution 1.5 × 1.5 x 1.5 mm; Montreal Neurological Institute space. Sample size: n = 37 FH+ and n = 36 FH- in the structural analyses; n = 31 FH+ and n = 29 FH- individuals in the functional analyses.

Table 2 Regions of reduction in grey matter volume in the participants having a family history of alcohol use disorder (FH+) compared to controls (FH-).

BA: Brodmann Area (if region described by Brodmann); MNI: Montreal Neurological Institute (coordinates in mm); R: Right; L: Left; Extent threshold at p < 0.05 Family-Wise Error (FWE) corrected (cluster size > 520 voxels); Height threshold at p < 0.001 uncorrected. p values in bold are statistically significant (p < 0.05 FWE corrected). Sample size: n=37 FH+ and n=36 FH-.

The confounding effects of the CTQ score in the FH+ group are shown in Supplementary Fig. C.1; Supplementary Table A.1. There were significant negative associations between the CTQ score and GMV volumes in the bilateral hippocampus, left para-hippocampal gyrus and bilateral cerebellum.

3.3 Functional brain imaging analyses

There were 60 participants included in the fMRI analyses (31 FH+ and 29 FH-). There was no significant between-group difference in their delayed final MID scores (means of 120.7 s (SD = 35.1) and 114.3 s (SD = 28.1) for FH+ and FH- participants, respectively; t=-0.12; p = 0.90). Between-group comparisons of neural response to a big versus small win indicated that the FH- controls had a significantly greater activation in the bilateral middle cingulum and right supplementary motor area (SMA) compared to the FH+ participants (Table 3; Fig. 1). As to the brain activity in response to anticipation of a small win, the FH+ participants exhibited a significant greater activity than the FH- controls in the anterior cingulum (Table 3; Fig. 1). As to the brain activity in response to anticipation of a big win, no significant difference was observed between the groups. The same results were found when adding the number of pack-years of smoking or cannabis use as confounding variable (Supplementary Fig. B.2; Supplementary Fig. D.2).

Table 3 Regions of BOLD signal changes in participants having a family history of alcohol use disorder (FH+) compared to controls (FH-) during a Monetary Incentive Delay (MID) task.

BA: Brodmann Area (if region described by Brodmann); MNI: Montreal Neurological Institute (coordinates in mm); R: Right; L: Left; Extent threshold at p < 0.05 Family-Wise Error (FWE) corrected (cluster size > 80 voxels); Height threshold at p < 0.001 uncorrected. p values in bold are statistically significant (p < 0.05 FWE corrected). Sample size: n=31 FH+ and n=29 FH-.

The confounding effects of the CTQ total score in the FH+ group are shown in Supplementary Fig. C.2; Supplementary Table A.2. There were significant positive associations between the CTQ score and brain activation during the processing of reward magnitude in the bilateral parahippocampal gyrus, bilateral fusiform and lingual gyri, left cerebellum and right inferior frontal gyrus.

3.4 Cognitive measures

No significant between-group differences in the sensation-seeking and trait impulsivity scores were observed (p < 0.001) (Table 4). In the Hayling test, compared to the FH- controls, the FH+ participants showed significant increases in time response per sentence and corrected time response, and a significant lower number of accurate responses. In the Go/No-Go test, the FH+ participants made significantly more errors of commission and omission than the FH- controls (p < 0.001) (Table 4).

Table 4 Comparisons of personality and cognitive measures between participants with (FH+) and without (FH-) a family history of alcohol use disorder.

a Participants with a family history of alcohol use disorder had two or more first or second-degree relatives with alcohol use disorder (FH+) – including necessarily the father and excluding the mother. Controls had no family history of alcohol use disorder in neither first nor second degree relatives (FH-). SD: standard deviation.

μ Unstandardized β coefficients and their standard errors (SE) were estimated through linear regression and adjusted for age, sex and Childhood Trauma Questionnaire total score (df = 4). p values in bold are statistically significant (alpha set a priori at 0.05/14 = 0.0036 Bonferroni corrected).

4. Discussion

This study sought to determine whether healthy adult first-degree relatives of individuals with AUD (FH+) differ from controls having no family history of AUD (FH-) in grey matter volumes (GMV), in brain response to a big versus small win during a Monetary Incentive Delay (MID) task, and in inhibition performance (Hayling and Go/No-Go tests). We ruled out the confounding effects of childhood maltreatment, and of any psychiatric disorder or AUD, and of prenatal exposure to maternal AUD. We found that family history of AUD was associated with i) smaller GMV in the frontal and cingulate regions as well as in the bilateral nucleus accumbens and right insula, ii) a blunted activation in the middle cingulum during the processing of reward magnitude and a greater activation in the anterior cingulum in response to anticipation of a small win, iii) and deficits in the accuracy of the inhibition processes and in the latency of the act of control. Overall, the present findings confirm and extend research on vulnerability to AUD.

On one hand, our findings are in line with previous studies on FH+ adolescents that showed localised GMV deficits compared to controls [Reference Hill, De Bellis, Keshavan, Lowers, Shen and Hall6–Reference Hill, Wang, Carter, Tessner, Holmes and McDermott9]. As previously identified in two distinct studies in FH+ adolescents [Reference Benegal, Antony, Venkatasubramanian and Jayakumar7, Reference Hardee, Weiland, Nichols, Welsh, Soules and Steinberg14], in the present study the middle cingulum was explicitly at the intersect of the brain structural (i.e. smaller GMV) and functional (i.e. blunted activation) differences between the FH+ and FH- participants. In healthy individuals, the volume of the middle cingulum underlies conflict monitoring [Reference Parvaz, Maloney, Moeller, Malaker, Konova and Alia-Klein61] while its activity is typically involved during cognitive processing tasks [Reference Bush, Luu and Posner62].

On the other hand, we did not find smaller hippocampus in the FH+ participants when controlling for low-to-moderate exposure to childhood maltreatment as confounding variable (i.e variable of no interest in the statistical analyses), unlike previous studies conducted in FH+ adolescents [Reference Benegal, Antony, Venkatasubramanian and Jayakumar7], FH+ adults [Reference Sjoerds, M-JV, van den, der, Buchem and Aleman26] and AUD individuals [Reference Beresford, Arciniegas, Alfers, Clapp, Martin and Du20]. It remains unknown whether previous findings of hippocampal grey matter volume reductions were due to childhood trauma. However, childhood maltreatment exposure is known to impact the developing brain in numerous brain regions, the most consistently reported being the hippocampus as well as the anterior cingulate cortex [Reference Teicher, Samson, Anderson and Ohashi17]; and our additional findings suggest that low-to-moderate maltreatment exposure may have an impact in FH+ participants on both brain structure and activity in the hippocampal and cerebellar regions.

Using the whole-brain method instead of the region-of-interest approach, additional brain regions were detected compared to earlier studies with FH+ individuals. We found that the FH+ participants had smaller GMV in the bilateral nucleus accumbens, a core region of the reward system involved in the addictive processes. Prior research conducted among AUD individuals showed GMV reductions in the caudate nucleus and putamen compared to healthy controls [Reference Sullivan, Deshmukh, De Rosa, Rosenbloom and Pfefferbaum23], supporting the hypothesis that this variation might pre-exist in the vulnerable individuals. Similarly, the insula, part of the paralimbic region, was altered in the present sample of FH+ participants on the right side, as previously described in AUD individuals [Reference Chanraud, Martelli, Delain, Kostogianni, Douaud and Aubin22] and tobacco smokers [Reference Naqvi, Rudrauf, Damasio and Bechara63, Reference Artiges, Ricalens, Berthoz, Krebs, Penttilä and Trichard64].

Despite similar MID-task performance between groups regarding brain function, the FH+ participants had a blunted activation in the middle cingulum in response to the processing of reward magnitude. Deficits in the computation of expected values have been linked to impaired reward-based decision making [Reference Galandra, Basso, Cappa and Canessa65] thus may contribute to vulnerability to AUD. However, the FH+ individuals showed an overactivity of the anterior cingulum compared to the FH- controls; a region that is thought to translate neural signals related to degree of reward expectancy [Reference Shidara and Richmond66] and is involved in the reward system [Reference Volkow and Morales28]. Among detoxified AUD individuals, self-efficacy to abstain was positively associated with the neural response in the anterior cingulum during the MID anticipation phase [Reference Becker, Gerchen, Kirsch, Hoffmann, Kiefer and Kirsch67], thus this overaction might denote a protective factor to AUD.

Deregulations of the inhibitory control have been largely reported in functional studies in FH+ adolescents [Reference Schweinsburg, Paulus, Barlett, Killeen, Caldwell and Pulido11, Reference Heitzeg, Nigg, W-YW, Zucker and Zubieta12, Reference Hardee, Weiland, Nichols, Welsh, Soules and Steinberg14]. We confirmed that the FH+ group had deficits compared to the FH- group in both cognitive tasks of inhibition, supporting the validity of the present sample. Deficient inhibitory control indicates poorer resistance to distractor or proactive interference [Reference Dick, Smith, Olausson, Mitchell, Leeman and O’Malley68], leading to a greater likelihood that a response will be executed rather than withheld [Reference Schachar, Tannock and Logan69]. Although inhibitory control has been identified as a main component of impulsivity [Reference Verdejo-García, Lawrence and Clark70], we did not find significant between-group differences neither for the Barratt Impulsivity Scale score nor for the Sensation-Seeking Scale score that are associated with substance misuse in young individuals [Reference Nees, Tzschoppe, Patrick, Vollstädt-Klein, Steiner and Poustka53]. Hence, the absence of such personality variations might denote a protective factor to AUD in the healthy FH+ individuals.

Despite the strengths of the study design involving participants with fewer confounding factors than previous studies, this design aiming to examine more specifically brain correlates of vulnerability to AUD, had some limitations. Firstly, there was no group of AUD individuals to compare with the FH+ participants. This would allow to disentangle the neural correlates of vulnerability to AUD from the mixed effects of alcohol intoxication observed in AUD individuals [Reference Dupuy and Chanraud5]. Secondly, although we excluded participants with severe exposure to any childhood maltreatment type, FH+ participants still had substantially higher CTQ scores than FH- participants. Thirdly, we excluded participants with AUD and maternal AUD exposure, however we cannot exclude potential brain and/or cognitive alterations due to past minimal alcohol use or minimal foetal alcohol exposure. Fourthly, psychiatric disorders were assessed following DSM-IV-TR criteria and not DSM-5 criteria. Fifthly, future studies with greater statistical power would benefit in examining whether our results hold or differ in women and men. Finally, we cannot exclude that observed differences may reflect protective factors in the FH- group instead of vulnerability factors in the FH+ group.

5. Conclusions

We showed that adult first-degree relatives of AUD individuals display GMV reductions in the frontal and cingulate regions, as well as a blunted activation in the middle cingulum in response to the processing of reward magnitude during a MID task. Moreover, our findings highlight pre-existing structural deficits in the nucleus accumbens and right insula, and an overactivation in the anterior cingulum in response to anticipation of small non-drug rewards. This study confirms and extents prior research on relatives of AUD individuals, and encourages to systematically control for the confounding effect of childhood maltreatment among other confounders, i.e. any clinically significant disorder and exposure to maternal AUD, in the study of vulnerability to AUD.

Declaration of Competing Interest

Frédéric Limosin has received speaker and consulting fees from Astra Zeneca, Euthérapie-Servier, Janssen, Lundbeck, Otsuka Pharmaceuticals and Roche. The present work is unrelated to these relationships. Other authors have no conflicts of interest or financial disclosures to make.

Acknowledgements

This research was supported by the French Ministery of Health (Programme Hospitalier de Recherche Clinique P110132, 2011), the Fondation pour la Recherche Médicale (DPA20140629802, 2014) and the Fédération pour la Recherche sur le Cerveau (C16/0932A01, 2016). The study was sponsored by Assistance Publique Hopitaux de Paris, represented by the HEGP Clinical Research Unit (Pr Gilles Chatellier). We would like to thank the team of CENIR – Centre for NeuroImaging Research, ICM - Institut du Cerveau et de la Moelle épinière, Hôpital Pitié-Salpêtrière, Paris, France. We would also like to thank the IMAGEN consortium that provided the revised version of the Monetary Incentive Delay task and Joelle Francis for her advices.

Appendix A Supplementary data

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.eurpsy.2019.08.003.

Footnotes

1.

Authors contributed equally.

2.

Authors contributed equally.

References

Gowing, L.R., Ali, R.L., Allsop, S., Marsden, J., Turf, E.E., West, R., et al., Global statistics on addictive behaviours: 2014 status report. Addiction. 2015 June 110; 6: 904–919.CrossRefGoogle ScholarPubMed
Bienvenu, O.J., Davydow, D.S., Kendler, K.S., Psychiatric ‘diseases’ versus behavioral disorders and degree of genetic influence. Psychol Med. 2011 January 41; 1: 33–40.CrossRefGoogle ScholarPubMed
Osby, U., Liljenberg, J., Kockum, I., Gunnar, A., Terenius, L., Genes and alcohol. Eur Psychiatry. 2010 June 25; 5: 281–283.CrossRefGoogle ScholarPubMed
Nurnberger, J.I., Wiegand, R., Bucholz, K., O’Connor, S., Meyer, E.T., Reich, T., et al., A family study of alcohol dependence: coaggregation of multiple disorders in relatives of alcohol-dependent probands. Arch Gen Psychiatry. 2004 December 61; 12: 1246–1256.CrossRefGoogle ScholarPubMed
Dupuy, M., Chanraud, S., Imaging the addicted brain: alcohol. Int Rev Neurobiol, 2016; 129: 1–31.CrossRefGoogle ScholarPubMed
Hill, S.Y., De Bellis, M.D., Keshavan, M.S., Lowers, L., Shen, S., Hall, J., et al., Right amygdala volume in adolescent and young adult offspring from families at high risk for developing alcoholism. Biol Psychiatry. 2001 June 49; 11: 894–905.CrossRefGoogle Scholar
Benegal, V., Antony, G., Venkatasubramanian, G., Jayakumar, P.N., Gray matter volume abnormalities and externalizing symptoms in subjects at high risk for alcohol dependence. Addict Biol. 2007 March 12; 1: 122–132.CrossRefGoogle ScholarPubMed
Hill, S.Y., Wang, S., Kostelnik, B., Carter, H., Holmes, B., McDermott, M., et al., Disruption of orbitofrontal cortex laterality in offspring from multiplex alcohol dependence families. Biol Psychiatry. 2009 January 65; 2: 129–136.CrossRefGoogle ScholarPubMed
Hill, S.Y., Wang, S., Carter, H., Tessner, K., Holmes, B., McDermott, M., et al., Cerebellum volume in high-risk offspring from multiplex alcohol dependence families: association with allelic variation in GABRA2 and BDNF. Psychiatry Res. 2011 December 194; 3: 304–313.CrossRefGoogle ScholarPubMed
Cservenka, A., Neurobiological phenotypes associated with a family history of alcoholism. Drug Alcohol Depend. 2016 January 1; 158: 8–21.CrossRefGoogle Scholar
Schweinsburg, A.D., Paulus, M.P., Barlett, V.C., Killeen, L.A., Caldwell, L.C., Pulido, C., et al., An FMRI study of response inhibition in youths with a family history of alcoholism. Ann N Y Acad Sci, 1021June2004 391–394.CrossRefGoogle ScholarPubMed
Heitzeg, M.M., Nigg, J.T., W-YW, Yau, Zucker, R.A., Zubieta, J.-K., Striatal dysfunction marks preexisting risk and medial prefrontal dysfunction is related to problem drinking in children of alcoholics. Biol Psychiatry. 2010 August 68; 3: 287–295.CrossRefGoogle ScholarPubMed
Cservenka, A., Nagel, B.J., Risky decision-making: an FMRI study of youth at high risk for alcoholism. Alcohol Clin Exp Res. 2012 April 36; 4: 604–615.CrossRefGoogle ScholarPubMed
Hardee, J.E., Weiland, B.J., Nichols, T.E., Welsh, R.C., Soules, M.E., Steinberg, D.B., et al., Development of impulse control circuitry in children of alcoholics. Biol Psychiatry. 2014 November 76; 9: 708–716.CrossRefGoogle ScholarPubMed
Herringa, R.J., Birn, R.M., Ruttle, P.L., Burghy, C.A., Stodola, D.E., Davidson, R.J., et al., Childhood maltreatment is associated with altered fear circuitry and increased internalizing symptoms by late adolescence. Proc Natl Acad Sci USA. 2013 November 110; 47: 19119–19124.CrossRefGoogle ScholarPubMed
Keyes, K.M., Eaton, N.R., Krueger, R.F., McLaughlin, K.A., Wall, M.M., Grant, B.F., et al., Childhood maltreatment and the structure of common psychiatric disorders. Br J Psychiatry. 2012 February 200; 2: 107–115.CrossRefGoogle ScholarPubMed
Teicher, M.H., Samson, J.A., Anderson, C.M., Ohashi, K., The effects of childhood maltreatment on brain structure, function and connectivity. Nat Rev Neurosci, 2016; 17(10): 652–666.CrossRefGoogle ScholarPubMed
Cowell, R.A., Cicchetti, D., Rogosch, F.A., Toth, S.L., Childhood maltreatment and its effect on neurocognitive functioning: timing and chronicity matter. Dev Psychopathol. 2015 May 27; 2: 521–533.CrossRefGoogle ScholarPubMed
Gierski, F., Hubsch, B., Stefaniak, N., Benzerouk, F., Cuervo-Lombard, C., Bera-Potelle, C., et al., Executive functions in adult offspring of alcohol-dependent probands: toward a cognitive endophenotype?. Alcohol Clin Exp Res, 37January Suppl. 12013 E356–363.CrossRefGoogle Scholar
Beresford, T.P., Arciniegas, D.B., Alfers, J., Clapp, L., Martin, B., Du, Y., et al., Hippocampus volume loss due to chronic heavy drinking. Alcohol Clin Exp Res. 2006 November 30; 11: 1866–1870.CrossRefGoogle ScholarPubMed
Wrase, J., Makris, N., Braus, D.F., Mann, K., Smolka, M.N., Kennedy, D.N., et al., Amygdala volume associated with alcohol abuse relapse and craving. Am J Psychiatry. 2008 September 165; 9: 1179–1184.CrossRefGoogle ScholarPubMed
Chanraud, S., Martelli, C., Delain, F., Kostogianni, N., Douaud, G., Aubin, H.-J., et al., Brain morphometry and cognitive performance in detoxified alcohol-dependents with preserved psychosocial functioning. Neuropsychopharmacology. 2007 February 32; 2: 429–438.CrossRefGoogle ScholarPubMed
Sullivan, E.V., Deshmukh, A., De Rosa, E., Rosenbloom, M.J., Pfefferbaum, A., Striatal and forebrain nuclei volumes: contribution to motor function and working memory deficits in alcoholism. Biol Psychiatry. 2005 April 57; 7: 768–776.CrossRefGoogle ScholarPubMed
Sullivan, E.V., Deshmukh, A., Desmond, J.E., Mathalon, D.H., Rosenbloom, M.J., Lim, K.O., et al., Contribution of alcohol abuse to cerebellar volume deficits in men with schizophrenia. Arch Gen Psychiatry. 2000 September 57; 9: 894–902.CrossRefGoogle ScholarPubMed
Pfefferbaum, A., Rosenbloom, M., Serventi, K.L., Sullivan, E.V., Corpus callosum, pons, and cortical white matter in alcoholic women. Alcohol Clin Exp Res. 2002 March 26; 3: 400–406.CrossRefGoogle ScholarPubMed
Sjoerds, Z., M-JV, Tol, van den, Brink W., der, Wee N.J.A.V., Buchem, M.A.V., Aleman, A., et al., Family history of alcohol dependence and gray matter abnormalities in non-alcoholic adults. World J Biol Psychiatry. 2013 December 14; 8: 565–573.CrossRefGoogle ScholarPubMed
Dager, A.D., McKay, D.R., Kent, J.W., Curran, J.E., Knowles, E., Sprooten, E., et al., Shared genetic factors influence amygdala volumes and risk for alcoholism. Neuropsychopharmacology. 2015 January 40; 2: 412–420.CrossRefGoogle ScholarPubMed
Volkow, N.D., Morales, M., The brain on drugs: from reward to addiction. Cell. 2015 August 162; 4: 712–725.CrossRefGoogle ScholarPubMed
Churchwell, J.C., Yurgelun-Todd, D.A., Neuroimaging, adolescence, and risky behavior. Inhibitory control and drug abuse prevention, New York, NY: Springer, 2011 101–122.CrossRefGoogle Scholar
Cservenka, A., Herting, M.M., Seghete, K.L.M., Hudson, K.A., Nagel, B.J., High and low sensation seeking adolescents show distinct patterns of brain activity during reward processing. Neuroimage. 2013 February 1; 66: 184–193.CrossRefGoogle Scholar
Bjork, J.M., Knutson, B., Hommer, D.W., Incentive‐elicited striatal activation in adolescent children of alcoholics. Addiction. 2008 August 103; 8: 1308–1319.CrossRefGoogle ScholarPubMed
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. Addict Biol. 2015 May 20; 3: 534–545.CrossRefGoogle ScholarPubMed
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. Biol Psychiatry. 2009 October 66; 8: 734–742.CrossRefGoogle ScholarPubMed
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. Biol Psychiatry. 2011 April 69; 7: 675–683.CrossRefGoogle ScholarPubMed
W-YW, Yau, 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. J Neurosci. 2012 February 32; 7: 2544–2551.Google Scholar
Balodis, I.M., Potenza, M.N., Anticipatory reward processing in addicted populations: a focus on the monetary incentive delay task. Biol Psychiatry. 2015 March 77; 5: 434–444.CrossRefGoogle ScholarPubMed
Mills, K.L., Tamnes, C.K., Methods and considerations for longitudinal structural brain imaging analysis across development. Dev Cogn Neurosci, 2014 July; 9: 172–190.CrossRefGoogle ScholarPubMed
Lole, L., Gonsalvez, C.J., Does size matter? An examination of problem gamblers’ skin conductance responses to large and small magnitude rewards. Psychophysiology. 2017 October 54; 10: 1541–1548.CrossRefGoogle ScholarPubMed
Hariri, A.R., Brown, S.M., Williamson, D.E., Flory, J.D., de, Wit H., Manuck, S.B., Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. J Neurosci. 2006 December 26; 51: 13213–13217.CrossRefGoogle ScholarPubMed
Grodin, E.N., Steckler, L.E., Momenan, R., Altered striatal response during effort-based valuation and motivation in alcohol-dependent individuals. Alcohol Alcohol. 2016 November 51; 6: 638–646.CrossRefGoogle ScholarPubMed
Bernstein, D.P., Stein, J.A., Newcomb, M.D., Walker, E., Pogge, D., Ahluvalia, T., et al., Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse Negl. 2003 February 27; 2: 169–190.CrossRefGoogle ScholarPubMed
McGue, M., Iacono, W.G., Legrand, L.N., Elkins, I., Origins and consequences of age at first drink. II. Familial risk and heritability. Alcohol Clin Exp Res. 2001 August 25; 8: 1166–1173.CrossRefGoogle ScholarPubMed
Nurnberger, J.I., Blehar, M.C., Kaufmann, C.A., York-Cooler, C., Simpson, S.G., Harkavy-Friedman, J., et al., Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative. Arch Gen Psychiatry, 51November (111994 849–859, discussion 863-864.CrossRefGoogle ScholarPubMed
Preisig, M., Fenton, B.T., Matthey, M.L., Berney, A., Ferrero, F., Diagnostic interview for genetic studies (DIGS): inter-rater and test-retest reliability of the French version. Eur Arch Psychiatry Clin Neurosci, 1999; 249(4): 174–179.CrossRefGoogle ScholarPubMed
American Psychiatric Association, Diagnostic and statistical manual of mental disorders: DSM-IV, 4th ed revised, Washington, DC: American Psychiatric Publishing, 2000.Google Scholar
National Institute on Alcohol Abuse and Alcoholism, Winter). NIAAA council approves definition of binge drinking. NIAAA newsletter, 2004; Vol. 3: 3, Retrieved from https://pubs.niaaa.nih.gov/publications/Newsletter/winter2004/Newsletter_Number3.pdf.Google Scholar
Bernstein, D., Fink, L., Manual for the childhood trauma questionnaire, New York: The Psychological Corporation, 1998.Google Scholar
Kodituwakku, P.W., Neurocognitive profile in children with fetal alcohol spectrum disorders. Dev Disabil Res Rev, 2009; 15(3): 218–224.CrossRefGoogle ScholarPubMed
Loas, G., Verrier, A., Flament, M.F., Perez-Diaz, F., Corcos, M., Halfon, O., et al., Factorial structure of the Sensation-Seeking Scale-Form V: confirmatory factorial analyses in nonclinical and clinical samples. Can J Psychiatry. 2001 November 46; 9: 850–855.CrossRefGoogle ScholarPubMed
Baylé, F.J., Bourdel, M.C., Caci, H., Gorwood, P., Chignon, J.M., Adés, J., et al., Factor analysis of french translation of the Barratt impulsivity scale (BIS-10). Can J Psychiatry. 2000 March 45; 2: 156–165.CrossRefGoogle Scholar
Ashburner, J., Friston, K.J., Voxel-based morphometry--the methods. Neuroimage, 2000 (June (6 Pt 1)); 11: 805–821.CrossRefGoogle Scholar
Knutson, B., Westdorp, A., Kaiser, E., Hommer, D., FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage. 2000 July 12; 1: 20–27.CrossRefGoogle ScholarPubMed
Nees, F., Tzschoppe, J., Patrick, C.J., Vollstädt-Klein, S., Steiner, S., Poustka, L., et al., Determinants of early alcohol use in healthy adolescents: the differential contribution of neuroimaging and psychological factors. Neuropsychopharmacology. 2012 March 37; 4: 986–995.CrossRefGoogle ScholarPubMed
Kamarajan, C., Porjesz, B., Jones, K.A., Choi, K., Chorlian, D.B., Padmanabhapillai, A., et al., Alcoholism is a disinhibitory disorder: neurophysiological evidence from a Go/No-Go task. Biol Psychol. 2005 July 69; 3: 353–373.CrossRefGoogle ScholarPubMed
Burgess, P.W., Shallice, T., Response suppression, initiation and strategy use following frontal lobe lesions. Neuropsychologia. 1996 April 34; 4: 263–272.CrossRefGoogle ScholarPubMed
Peelle, J.E., Cusack, R., Henson, R.N.A., Adjusting for global effects in voxel-based morphometry: gray matter decline in normal aging. Neuroimage. 2012 April 60; 2: 1503–1516.CrossRefGoogle ScholarPubMed
McClernon, F.J., Neuroimaging of nicotine dependence: key findings and application to the study of smoking-mental illness comorbidity. J Dual Diagn. 2009 April 5; 2: 168–178.CrossRefGoogle Scholar
Chye, Y., Christensen, E., Yücel, M., Cannabis use in adolescence: a review of neuroimaging findings. J Dual Diagn, 2019 July; 16: 1–23.CrossRefGoogle Scholar
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. Nat Commun, 21February2017 8.Google Scholar
Eklund, A., Nichols, T.E., Knutsson, H., Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci U S A.. 2016 July 113; 28: 7900–7905.CrossRefGoogle ScholarPubMed
Parvaz, M.A., Maloney, T., Moeller, S.J., Malaker, P., Konova, A.B., Alia-Klein, N., et al., Multimodal evidence of regional midcingulate gray matter volume underlying conflict monitoring. Neuroimage Clin, 2014; 5: 10–18.CrossRefGoogle ScholarPubMed
Bush, G., Luu, P., Posner, M.I., Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000 June 4; 6: 215–222.CrossRefGoogle ScholarPubMed
Naqvi, N.H., Rudrauf, D., Damasio, H., Bechara, A., Damage to the insula disrupts addiction to cigarette smoking. Science. 2007 January 315; 5811: 531–534.CrossRefGoogle ScholarPubMed
Artiges, E., Ricalens, E., Berthoz, S., Krebs, M.-O., Penttilä, J., Trichard, C., et al., IMAGING STUDY: Exposure to smoking cues during an emotion recognition task can modulate limbic fMRI activation in cigarette smokers. Addict Biol, 2009; 14(4): 469–477.CrossRefGoogle ScholarPubMed
Galandra, C., Basso, G., Cappa, S., Canessa, N., The alcoholic brain: neural bases of impaired reward-based decision-making in alcohol use disorders. Neurol Sci. 2018 March 39; 3: 423–435.CrossRefGoogle ScholarPubMed
Shidara, M., Richmond, B.J., Anterior cingulate: single neuronal signals related to degree of reward expectancy. Science. 2002 May 296; 5573: 1709–1711.CrossRefGoogle ScholarPubMed
Becker, A., Gerchen, M.F., Kirsch, M., Hoffmann, S., Kiefer, F., Kirsch, P., Striatal reward sensitivity predicts therapy-related neural changes in alcohol addiction. Eur Arch Psychiatry Clin Neurosci. 2018 April 268; 3: 231–242.CrossRefGoogle ScholarPubMed
Dick, D.M., Smith, G., Olausson, P., Mitchell, S.H., Leeman, R.F., O’Malley, S.S., et al., Understanding the construct of impulsivity and its relationship to alcohol use disorders. Addict Biol. 2010 April 15; 2: 217–226.CrossRefGoogle ScholarPubMed
Schachar, R.J., Tannock, R., Logan, G., Inhibitory control, impulsiveness, and attention deficit hyperactivity disorder. Clin Psychol Rev. 1993 January 13; 8: 721–739.CrossRefGoogle Scholar
Verdejo-García, A., Lawrence, A.J., Clark, L., Impulsivity as a vulnerability marker for substance-use disorders: review of findings from high-risk research, problem gamblers and genetic association studies. Neurosci Biobehav Rev, 2008; 32(4): 777–810.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Comparisons of sociodemographic characteristics, childhood trauma exposure and substance use between participants with (FH+) and without (FH-) a family history of alcohol use disorder.

Figure 1

Fig. 1. Brain regions of differences in grey matter volumes and bold signal changes during anticipation of reward in a MID task between individuals with and without a family history of alcohol use disorder.Participants with a positive family history of alcohol use disorder (FH+) presented smaller grey matter volumes in four clusters (blue colour) compared to participants having no family history of alcohol use disorder (FH-). FH+ individuals also had a blunted activation in response to a big versus small win (green colour) and a greater activation during anticipation of a small win (red colour) compared to FH- controls during the MID anticipation phase. Extent threshold was set at p < 0.05 Family-Wise Error (FWE) corrected; Height threshold was set at p<0.001 uncorrected. Each point on the boxplots represents the adjusted (for age, sex and Childhood Trauma Questionnaire total score) mean value for each participant. The line joining the boxplots spots the group mean. Orientation R: Right; L: Left; A: Anterior; P: Posterior; voxel resolution 1.5 × 1.5 x 1.5 mm; Montreal Neurological Institute space. Sample size: n = 37 FH+ and n = 36 FH- in the structural analyses; n = 31 FH+ and n = 29 FH- individuals in the functional analyses.

Figure 2

Table 2 Regions of reduction in grey matter volume in the participants having a family history of alcohol use disorder (FH+) compared to controls (FH-).

Figure 3

Table 3 Regions of BOLD signal changes in participants having a family history of alcohol use disorder (FH+) compared to controls (FH-) during a Monetary Incentive Delay (MID) task.

Figure 4

Table 4 Comparisons of personality and cognitive measures between participants with (FH+) and without (FH-) a family history of alcohol use disorder.

Submit a response

Comments

No Comments have been published for this article.