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Abstract

Background:

Alcohol-related cues are known to influence craving levels, a hallmark of alcohol misuse. Binge drinking (BD), a pattern of heavy alcohol use, has been associated with cognitive and neurofunctional alterations, including alcohol attentional bias, memory impairments, as well as disrupted activity in prefrontal- and reward-related regions. However, literature is yet to explore how memories associated with alcohol-related cues are processed by BDs, and how the recall of this information may influence their reward processing.

Aims:

The present functional magnetic resonance imaging (fMRI) study aimed to investigate the neurofunctional signatures of BD during an associative memory task.

Method:

In all, 36 university students, 20 BDs and 16 alcohol abstainers, were asked to memorize neutral objects paired with either alcohol or non-alcohol-related contexts. Subsequently, neutral stimuli were presented, and participants were asked to classify them as being previously paired with alcohol- or non-alcohol-related contexts.

Results:

While behavioral performance was similar in both groups, during the recall of alcohol-related cues, BDs showed increased brain activation in two clusters including the thalamus, globus pallidus and dorsal striatum, and cerebellum and occipital fusiform gyrus, respectively.

Conclusion:

These findings suggest that BDs display augmented brain activity in areas responsible for mental imagery and reward processing when trying to recall alcohol-related cues, which might ultimately contribute to alcohol craving, even without being directly exposed to an alcohol-related context. These results highlight the importance of considering how alcohol-related contexts may influence alcohol-seeking behavior and, consequently, the maintenance or increase in alcohol use.

Keywords

Binge drinking associative memory contextual cues attentional bias neuroimaging

Introduction

Alcohol use is the seventh leading risk factor for premature death, with hazardous consumption patterns contributing to over 5% of all global deaths (Griswold et al., 2018; World Health Organization (WHO), 2019). Particularly, among young people, alcohol use plays a significant social role, being an integral part of academic traditions and parties (Dormal et al., 2019; Kuntsche et al., 2005; Patrick et al., 2016). This social aspect of alcohol consumption is particularly concerning, as alcohol misuse is linked to more than 30% of deaths among individuals aged 15–29 years in both America and Europe (WHO, 2011).

Binge drinking (BD) is a recurrent alcohol consumption pattern characterized by alternating episodes of excessive alcohol consumption and periods of abstinence or low consumption. During BD episodes, individuals reach a blood alcohol concentration of 0.08 g/dL or higher, typically achieved by consuming five or more drinks for cisgender men and four or more drinks for cisgender women within a 2-h interval (National Institute on Alcohol Abuse and Alcoholism (NIAAA), 2004, 2022). The periods of abstinence or low consumption can last from several days to several weeks, leading to intoxication/abstinence cycles (Maurage et al., 2020). In addition, it is common to establish as a criterion for this pattern that BD episodes should occur at least once or twice per month during the last 12 months (Courtney & Polich, 2009; Maurage et al., 2020; NIAAA, 2022; Parada et al., 2011). This consumption pattern is a problematic form of alcohol misuse highly prevalent among young people, and notably widespread among European and American college students, with more than one-third engaging in recurrent BD episodes at least once a month (Kraus and Nociar, 2016; NIAAA, 2020). This highlights the concerning prevalence of BD in this population and underscores the significant impact of the college environment on alcohol consumption (Cadigan et al., 2015).

The high prevalence of BD during adolescence and youth is particularly worrying due to the critical neuromaturational processes occurring at these developmental stages (Jones et al., 2018; Sousa et al., 2018). Namely, structural and functional brain changes, such as synaptic pruning and myelination, take place in regions of late neuromaturation like the prefrontal cortex, an area crucial for the development of higher-level cognitive functions, including working memory, inhibitory control, and decision-making (Fuhrmann et al., 2015; Gogtay et al., 2004; Luna et al., 2010). These changes render the brain highly vulnerable to the neurotoxic effects of alcohol (Crews et al., 2016). Specifically, alcohol interferes with these processes by disrupting the formation of new neural connections and damaging existing neural pathways, leading to long-term cognitive deficits and impaired brain function (Jacobus and Tapert, 2013). This disruption can result not only in reduced cognitive abilities but also poor academic performance and an increased risk of developing psychiatric disorders (Castellanos-Ryan and Conrod, 2020).

BD has often been associated with enhanced impulsivity and reduced inhibitory control (Crews & Boettiger, 2009; Cservenka & Brumback, 2017; Dalley & Robbins, 2017; López-Caneda et al., 2014a). These behavioral alterations have been linked to abnormalities in the frontostriatal system (see Pérez-García et al., 2022, for a review). In particular, studies have found increased volume in the dorsolateral prefrontal cortex, nucleus accumbens, dorsal cingulate, and precuneus among individuals who engage in BD, suggesting that the structural alterations in these areas may underlie the impaired cognitive functions commonly observed in this population (Banca et al., 2016; Doallo et al., 2014; Sousa et al., 2017, 2020). In addition, binge drinkers (BDs) frequently exhibit brain hyperactivation when compared to their control peers (Cservenka & Brumback, 2017; Schacht et al., 2013; Sousa et al., 2019). Notably, increased prefrontal activity during behavioral or cognitive inhibition tasks has been interpreted as a neurocompensatory mechanism, that is, the recruitment of additional neural resources that allow BDs to perform the task at the same level as controls (Almeida-Antunes et al., 2024; Ames et al., 2014; Correas et al., 2021; López-Caneda et al., 2012; Molnar et al., 2018; Suárez-Suárez et al., 2020; Wetherill et al., 2013). Similarly, studies exploring memory processes in BDs have revealed increased frontoparietal and hippocampal activity during the novel and/or correct encoding of visual or verbal items (Dager et al., 2014; Schweinsburg et al., 2010). This suggests that the less efficient neural processing and compensatory mechanisms observed in inhibitory control are also present during working memory tasks. Moreover, stronger memory for alcohol-related associations, that is, better recall for alcohol-related associations was found to be predictive of heavier drinking in patients with alcohol use disorder (AUD) (Goldfarb et al., 2020), underscoring the importance of associative memory in understanding alcohol consumption in real-life scenarios.

Associative memory is defined as the ability to link together two or more memories/items (e.g., images, words, sounds, places) that can later be retrieved through recognition or recall of one of them (Mayes et al., 2007). In the context of alcohol use, this involves stimuli associated with alcohol-related contexts, such as bars, bottles of alcoholic beverages, people drinking and cheering, or logos of popular alcohol brands. These stimuli are often encountered in social settings and can trigger memories and associations related to drinking behavior, and alcohol-related cues can elicit craving and influence drinking behavior, a phenomenon well documented in individuals with AUD (Seo et al., 2014). By contrast, neutral cues are stimuli that have no direct connection with alcohol consumption. These might include everyday objects, like books, pens, or images of neutral environments, such as a park or a classroom.

Although no study to date has explored brain activity linked to associative memory involving both neutral and alcohol-related cues in young BDs, evidence indicates that the neurofunctional correlates of associative memory are similar to those observed in craving, the formation of consumption habits, and alcohol cue-induced reactivity in heavy alcohol users (Courtney et al., 2016; Kim, 2011; Janak & Chaudhri, 2010; Park et al., 2007; Schacht et al., 2013; Scimeca & Badre, 2012). Specifically, encoding of pairs of images involves activation of a widely spread brain network, including the fusiform gyrus, the precuneus, the hippocampus, and the prefrontal and occipital cortices (Becker et al., 2015; de Chastelaine et al., 2016; Kim, 2011). Memory retrieval, on the other hand, activates regions such as the prefrontal cortex, striatum, and hippocampus (Schott et al., 2019; Scimeca & Badre, 2012). In addition, the amygdala, the precuneus, and sensory regions including the fusiform gyrus and the occipital cortex, recruited during encoding, are often reactivated during recall, particularly if the memorized information has emotional significance or was encoded through mental imagery (Danker & Anderson, 2010). Likewise, research has shown that heavy alcohol users display higher cue-reactivity to alcohol stimuli, exhibiting increased brain activity in the prefrontal (e.g., ventromedial and dorsolateral prefrontal cortex) and visual (e.g., fusiform gyrus and precuneus) regions as well as in several structures of the limbic system, including the striatum and the thalamus (Courtney et al., 2016; Fukushima et al., 2020; Huang et al., 2018; Park et al., 2007; Schacht et al., 2013). In a related vein, the mechanisms underlying cue-induced craving, commonly observed in addiction (Heinz et al., 2009; Witteman et al., 2015), have been closely related to associative memory (Janak & Chaudhri, 2010). Both processes entail the linking of two or more stimuli—whether internal or external—that can later be recalled, either intentionally or spontaneously, upon encountering one of them (Pershin & Di Ventra, 2010; Wrase et al., 2007).

Notwithstanding, research on the relation between associative memory and the processes underlying alcohol craving (e.g., attentional bias, reward processing, habit formation) and how these interact with alcohol use is still scarce. Aiming to fill this gap, the present study aimed to explore the brain mechanisms involved in the recall of alcohol-related contexts in young BDs. Hence, we conducted a whole-brain functional magnetic resonance imaging (fMRI) analysis and compared cue-induced brain activation between BDs and alcohol abstainers when recalling alcoholic or non-alcoholic contexts in response to associated neutral stimuli. According to previous research showing augmented brain activity in the reward and the fronto occipital networks during both the processing of alcohol-related cues and encoding/recovery of visual memories (Almeida-Antunes et al., 2022; Brumback et al., 2015; Courtney et al., 2015; Dager et al., 2014), we hypothesize that BDs will display increased activation of the reward and memory brain regions when recalling alcohol-related contexts.

Method

Participants

Participants aged between 18 and 23 years were recruited via an online survey and divided into two groups. Inclusion criteria for the BD group required participants to have consumed four or more drinks for women (five or more for men) in less than 2 h at least once per month for the past 10 months, ensuring a consistent pattern of BD. The second group served as the control group, consisting of alcohol abstainers without a drinking history; that is, they had never consumed alcohol and, thus, had never experienced a BD episode in their lives. Preselected participants completed a clinical interview including different questionnaires (see Table 1), which assessed their eligibility to participate in the study. Participants were excluded from either group based on the following criteria: a score of 20 or higher on the Alcohol Use Disorders Identification Test (AUDIT), indicating alcohol abuse or dependence; left-handedness, as indicated by a score of −40 or lower on the Edinburgh Handedness Inventory; a Global Severity Index score ⩾90, or a score ⩾90 on at least two symptomatic dimensions of the SCL-90-R; uncorrected sensory deficits (e.g., participants with poor eyesight who do not use glasses or contact lenses); a personal history of traumatic brain injury or neurological disorder; regular cannabis consumption (i.e., weekly use); a personal history of regular or occasional use of other drugs, including opiates, hallucinogens, cocaine, ecstasy, amphetamines, or medically prescribed psychoactive substances; alcohol abuse or dependence as per DSM-IV-R criteria; a family history of any neurological disorder or DSM-IV axis I disorder in first-degree relatives; a family history of substance abuse, including alcohol; and any contraindications for magnetic resonance imaging (e.g., metal implants, metallic objects, pregnancy, recent surgeries, or any type of medical condition that would prevent them from entering the scanner safely).

Table 1.

Scales and questionnaires used in the clinical interview.

Portuguese version of the Alcohol Use Disorder Identification Test (AUDIT) (Babor et al., 2001; da Cunha, 2002)

Alcohol Use Questionnaire—items 10, 11, and 12 (Townshend and Duka, 2002)

Semi-structured assessment for the genetics of alcoholism, the individual assessment module, and the family history assessment module (Bucholz et al., 1994)

Portuguese version of the Symptom Checklist-90 Revised (SCL-90-R) (Derogatis and Unger, 2010; Laloni, 2001)

Portuguese version of Barratt Impulsiveness Scale 11 (BIS11) (Patton et al., 1995)

Edinburg handedness inventory (Oldfield, 1971)

Thus, the final sample included 36 college students: 20 BDs (50% female) and 16 alcohol-abstinent individuals (62.5% female). See Table 2 for complete demographic, drinking characteristics, and psychological traits.

Table 2.

Demographic, drinking characteristics, and psychological traits of the BD and control groups (mean (SD)).

	Controls mean ± SD	Binge drinkers mean ± SD	t (34)
Demographic measures
N (female)	16 (10)	20 (10)
Caucasian ethnicity (%)	100	100
Age	21.00 (1.71)	20.45 (1.60)	−0.99
Age of BD onset	—	17.45 ± 1.08
Drinking characteristics
AUDIT (total score)	0.62 ± 1.20	11.20 ± 3.25	13.43***
BD episodes per month	—	3.58 ± 1.87	8.54***
Percentage of alcohol intoxication per drinking event	—	43.25 ± 20.41	9.48***
Number of months with BD pattern	—	35.90 ± 14.03	11.44***
Quantity of alcohol consumed (gr/week)	—	151 ± 44.27	14.78***
Speed of drinking (gr/h during BD episodes)	—	34.50 ± 8.26	18.69***
Tobacco smokers (female)	—	7 (4)
Occasional users of cannabis	—	2 (2)
Psychological traits
Impulsivity (BIS11)
Total score	63.56 ± 6.05	64.80 ± 5.83	0.62
Attention	10.63 ± 2.16	10.65 ± 2.23	0.03
Cognitive instability	5.50 ± 1.51	6.10 ± 1.41	1.23
Motor	10.00 ± 2.03	11.80 ± 2.76	2.17*
Perseverance	7.31 ± 2.03	7.05 ± 1.05	–0.64
Self-control	17.38 ± 2.58	16.50 ± 2.46	–1.04
Cognitive complexity	12.75 ± 1.88	12.70 ± 1.95	–0.08
Psychopathological symptoms (SCL-90-R)
Anxiety	0.78 ± 0.61	0.58 ± 0.58	1.01
Depression	1.01 ± 0.64	0.96 ± 0.60	0.22
Global severity index	0.62 ± 0.38	0.65 ± 0.41	−0.22

AUDIT: alcohol use disorders identification test; BIS11: Barratt Impulsiveness Scale 11; BD: binge drinking; SCL-90-R: Symptom checklist-90 revised; SD: standard deviation.

All p-values reported are for two-tailed independent samples t-tests. *p ⩽ 0.05. ***p ⩽ 0.001.

Procedure

The research protocol followed the Code of Ethical Principles for Medical Research Involving Human Subjects of the World Medical Association outlined in the Declaration of Helsinki (World Medical Association, 2013), meeting the requirements for exemption and anonymity, and it was approved by the Ethics Committee for Research in Social and Human Sciences of the University of (details omitted for double-anonymized peer review).

Task

An associative memory task was specially developed to examine memory retrieval mechanisms for alcohol-related contexts (see Figure 1), building upon the task previously developed to assess associative memory with neutral stimuli by Yonelinas et al. (2001). One day prior to the scanning, during the encoding phase, participants were asked to memorize 90 pairs of images. Each pair contained an image of an object and a picture of young people in one of two social contexts, that is, drinking (alcohol context) or studying (no-alcohol context). The social context pictures were created specifically for this task and object images were obtained from the POPORO database (Kovalenko et al., 2012), and classified (1–9) as neutral for emotional valence (M = 4.97) and arousal (M = 4.63). Participants were instructed to remember which of the two (alcohol/no-alcohol) contexts was associated with each object. Subsequently, participants were randomly presented the neutral stimuli and asked to indicate whether they were previously associated with an alcohol or a no-alcohol context. To guarantee that inside the scanner participants were indeed conducting associative memory processes, as well as to ensure homogeneity between individual learning effects, participants’ performance was tested the day before the scan, as well as immediately prior to entering the scanner. Participants who did not achieve a minimum of 90% of correct recalls on either occasion were asked to repeat the encoding phase until they reached the required threshold. Participants were then scanned while making recognition memory judgments. Specifically, they were presented with a list of 135 images, consisting of 90 memorized neutral objects and 45 new objects. Each stimulus was presented sequentially followed by a fixation cross. The trial sequence and presentation time of the stimulus and fixation cross were pseudorandomized and determined using the optseq2 toolbox (https://surfer.nmr.mgh.harvard.edu/optseq/). Participants were asked to observe each of the images presented and respond by pressing one of two buttons if they thought the item was previously presented—left for pictures associated with alcoholic contexts and right for pictures associated with no-alcohol contexts—and to not respond if they thought the item was new.

Figure 1.

The overall depiction of the associative memory task for alcohol-related contexts. (a) Example of encoding phase: One day before scanning 90 pairs of images are memorized; (b) Example of recall phase: During scanning, participants had to classify each of the 135 neutral stimuli presented as being associated with either an alcohol or no-alcohol context or new stimuli.

Magnetic resonance image acquisition

Functional imaging was acquired with a Siemens Magneton TrioTrim 3T MRI scanner (Siemens Medical Solutions, Erlangen, Germany) equipped with a 32-channel receive-only coil. The blood oxygen level-dependent (BOLD) sensitive echo-planar imaging (EPI) was used with the following acquisition parameters: 39 interleaved axial slices; repetition time (TR) = 2000 ms; echo time (TE) = 29 ms; flip angle (FA) = 90º; slice thickness = 3 mm; voxel size = 3 mm × 3 mm × 3 mm; and field of view (FoV) = 222 mm. The acquisition was made in a block with a duration of 10.36 min, consisting of 311 volumes.

High-resolution 3D T1-weighted images were also acquired to coregister to the realigned mean functional image. To this end, a magnetization-prepared rapid acquisition gradient echo ascending interleaved sequence was applied using the following parameters: TR/TE = 2700/2.33 ms; inversion time = 1000 ms; delay time = 1600 ms; FA = 7°, 192 slices with 0.8 mm thickness; slab thickness = 153.6 mm; slice gap = 0 mm; in-plane resolution = 1 × 1 mm²; matrix size = 320 × 310; and 256 mm FoV.

Image preprocessing and analysis

The fMRI data preprocessing was performed using Statistical Parametric Mapping (SPM12; https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) implemented in Matlab (version 2021b, The Mathworks, Inc., Natick, MA, USA).

First, functional and anatomical images were reoriented to the anterior commissure. Subsequently, the intensity inhomogeneity was corrected using the field maps that were acquired during the MRI protocol. Then, functional images were corrected for slice timing and realigned and unwarped to correct movement artifacts (images with movements that were superior to voxel size were removed). The anatomical T1 images were coregistered to the realigned mean functional image and then images were transformed into standard MNI space using segmentation-based normalization parameters. Finally, the resulting functional images were spatially smoothed using an 8-mm FWHM Gaussian kernel, and low-frequency drifts were removed with a high-pass temporal filter (filter width of 128 s).

Statistical analysis

Demographic data, drinking variables, and psychological traits comparisons

Differences between groups concerning demographics, drinking, and psychological traits (impulsivity and psychopathological symptomatology) and variables were compared using multiple two-sample t-tests. In addition, given the uneven number of female participants between the two groups, a Chi-square analysis was conducted to assess the potential impact of this distribution.

Behavioral analysis

During the memory test phase, items that had been previously presented in the encoding phase and correctly associated with the alcohol- or non-alcohol-related context were considered correct responses. Thus, the percentage of correct responses was computed according to the following formula: number of correctly recalled items/number of previously recalled items × 100.

Reaction times (RTs) and percentages of correct recalls for alcohol and no-alcohol images were statistically analyzed using two-way mixed-design ANOVA with one within-subjects factor (Condition: Alcohol and No-Alcohol) and one between-subjects factor (Group: BDs and Control) with IBM Statistical Package for Social Sciences (SPSS, v.28; IBM Corp., 2021, Armonk, NY, USA). Lastly, correct inhibitions for the New condition were analyzed using two-sample t-tests.

Neuroimaging analysis

The fMRI analyses were performed using the general linear model approach with SPM12 (Wellcome Centre for Human Neuroimaging, 2014, London, UK) software. The fMRI paradigm was analyzed by creating a set of regressors at the time of each decision-making, which were convolved with the hemodynamic response function. Three different regressors were created for each participant, one for each of the three conditions: “Alcohol” (trials with correct responses for images of neutral objects associated with alcohol contexts), “No-Alcohol” (trials with correct responses for images of neutral objects associated with study contexts), and “New” (trials with no responses for the new images of neutral objects). The six motion regressors estimated at the preprocessing stage and a regressor for the baseline (fixation cross inter-stimulus) were also included in the design matrix. For each subject, three first-level statistical contrast maps were computed: the direct contrasts between the three conditions and the baseline.

Second-level analysis was also performed to analyze the main effects of the group in each contrast by two-sample t-tests. Furthermore, one-sample t-tests were conducted to analyze the differences between conditions for each group separately. Whole-brain voxel-wise threshold of p < 0.001 was used and family-wise error (FWE) correction for multiple comparisons was applied at the cluster level with a threshold of p < 0.01.

Correlation analysis

Pearson’s correlations were conducted to examine the associations between brain activation in response to neutral stimuli linked to alcohol-related context in the BD group and various alcohol consumption variables (AUDIT score, age of BD onset, BD episodes per month, percentage of alcohol intoxication per drinking event, number of months with BD pattern, quantity of alcohol consumed—grams of alcohol consumed per week, and speed of drinking—grams of alcohol consumed per hour during BD episodes), as well as impulsivity traits measured by the BIS-11. These analyses aimed to assess potential interactions between alcohol use and impulsiveness with the activation levels in brain regions where significant differences were observed.

Results

Demographic data, drinking variables, and psychological traits comparisons

Significant differences between groups for all alcohol consumption variables (AUDIT scores, BD episodes per month, percentage of alcohol intoxication per drinking event, number of months with BD pattern, quantity of alcohol consumed—grams of alcohol consumed per week, and speed of drinking—grams of alcohol consumed per hour during BD episodes) were observed. These differences were anticipated given the nature and inclusion criteria of each group. In addition, BDs scored higher than controls for motor impulsivity on the BIS-11 subscale (t = 2.17, p < 0.05), and no differences were found between groups for BIS-11 main score, its remaining subscales, nor for any of the SCL-90-R subscales (see Table 2 and, for more details, refer to the Supplemental material). Lastly, a Chi-square test was conducted to assess the difference in sex distribution between the Control and the BD group. The test indicated no significant difference in gender distribution (χ²(1) = 0.562, p = 0.453). Thus, gender was not included as a covariate in the subsequent behavioral and neurofunctional analyses.

Behavioral results

There were no significant behavioral group differences in RTs and correct recalls neither for both Alcohol and No-Alcohol conditions nor between conditions, for both the control and BD groups (see Table 3). Similarly, no differences were found between groups regarding correct inhibitions of the New stimuli.

Table 3.

Behavioral data (mean (SD)).

	Correct response (%)			Reaction times (ms)
Group	Alcohol	No-Alcohol	New	Alcohol	No-Alcohol
Binge drinkers	91.67 (12.59)	91.33 (13.79)	97.67 (0.07)	1218.19 (281.81)	1235.65 (311.03)
Controls	94.86 (4.98)	93.06 (5.84)	99.17 (0.02)	1214.42 (168.36)	1178.06 (189.44)

Neuroimaging results

Group comparisons

Whole-brain analyses concerning the alcohol condition revealed increased BOLD activation in BDs during the recall of alcohol-related stimuli when compared with the control group. Specifically, two clusters of regions were identified: the first composed of the bilateral thalamus, globus pallidus, putamen, and right caudate nucleus (t(34) > 3.36, p (FWEc) < 0.01, 804 voxel extent threshold); and the second encompassed the right cerebellum and the fusiform gyrus (t(34) > 3.36, p (FWEc) < 0.01, 754 voxel extent threshold) (see Figure 2 and Table 4). No significative differences between groups were found for the No-Alcohol and the New conditions.

Figure 2.

Images of the two clusters of regions displaying significantly increased activation in BDs when compared to controls for the alcohol condition.

Table 4.

Regions significantly activated at whole-brain analysis in the contrast BDs > Control Group for the Alcohol Condition showing significant (p (FWEc) < 0.01) BOLD signal changes, with local maxima more than 4.0 mm apart.

Region	t	Coordinates	K
Cluster 1
Right thalamus	4.25	10, −6, 6	804
Left thalamus	4.25	−14, −12, 2	804
Left pallidum	4.20	−18, −6, 2	804
Right caudate nucleus	4.17	22, 16, 14	804
Left putamen	3.67	−28, 2, 4	804
Right pallidum	3.49	18, −4, 0	804
Right putamen	3.47	24, 6, 4	804
Cluster 2
Right cerebellum Crus2	4.93	34, −66, −42	754
Right cerebellum Crus1	3.91	38, −72, −32	754
Right cerebellum_6	3.80	22, −72, −26	754
Right fusiform gyrus	3.87	40, −66, −20	754

Condition comparisons within groups

Whole-brain analyses concerning condition differences revealed that BDs exhibited increased BOLD activation when recalling Alcohol compared to No-Alcohol stimuli. Specifically, three clusters of regions were identified: the first composed of the right supramarginal, postcentral and precentral gyrus, rolandic operculum, and superior parietal lobule (t(19) > 3.58, p (FWEc) < 0.01, 3127 voxel extent threshold); the second encompassed the left fusiform gyrus and cerebellum (t(19) > 3.58, p (FWEc) < 0.01, 1124 voxel extent threshold); and the third included right thalamus and caudate nucleus (t(19) > 3.58, p (FWEc) < 0.01, 377 voxel extent threshold). In addition, BDs showed increased BOLD activation when recalling No-Alcohol compared to Alcohol stimuli in the left postcentral and precentral gyrus (t(19) > 3.58, p (FWEc) < 0.01, 2235 voxel extent threshold) (see Table 5 and Figure 3).

Table 5.

Regions significantly activated at whole-brain analysis in the contrasts Alcohol > No-Alcohol and No-Alcohol > Alcohol for the BDs showing significant (p (FWEc) < 0.01) BOLD signal changes, with local maxima more than 4.0 mm apart.

Region	t	Coordinates	K
Alcohol > no-Alcohol
Cluster 1
Right precentral gyrus	6.88	38, −16, 56	3127
Right postcentral gyrus	6.73	40, −18, 48	3127
Right superior parietal lobule	5.84	26, −54, 64	3127
Right rolandic operculum	5.45	50, −18, 20	3127
Right supramarginal	4.03	58, −18, 20	3127
Cluster 2
Left cerebelum_6	7.37	−22, −52, −22	1124
Left cerebelum_4_5	7.03	−18, −52, −20	1124
Left fusiform gyrus	4.72	−22, −52, −16	1124
Cluster 3
Right thalamus	5.13	16, −18, 18	337
Right caudate nucleus	4.12	12, −6, 16	337
No-Alcohol > alcohol
Left postcentral gyrus	10.40	−48, −20, 56	2235
Left precentral gyrus	9.92	−36, −20, 60	2235

Figure 3.

The images show significant activation maps of BDs for the contrasts Alcohol > No-Alcohol and No-Alcohol > Alcohol overlaid on an anatomical MRI image.

For the control group, the whole-brain analysis revealed increased BOLD activation when recalling Alcohol compared to No-Alcohol stimuli. Specifically, two clusters of regions were identified: the first composed of the right postcentral and precentral gyrus (t(15) > 3.79, p (FWEc) < 0.01, 1732 voxel extent threshold); and the second encompassed right supramarginal gyrus and rolandic operculum (t(15) > 3.79, p (FWEc) < 0.01, 400 voxel extent threshold). In addition, like BDs, the control group showed increased BOLD activation when recalling No-Alcohol compared to Alcohol stimuli in the left postcentral and precentral gyrus (t(15) > 3.79, p (FWEc) < 0.01, 1488 voxel extent threshold) (Table 6, Figure 4).

Table 6.

Regions significantly activated at whole-brain analysis in the contrast Alcohol > No-Alcohol and No-Alcohol > Alcohol for the control group showing significant (p (FWEc) < 0.01) BOLD signal changes, with local maxima more than 4.0 mm apart.

Region	t	Coordinates	K
Alcohol > no-Alcohol
Cluster 1
Right precentral gyrus	8.52	34, −10, 60	1732
Right postcentral gyrus	13.01	46, −16, 46	1732
Cluster 2
Right rolandic operculum	5.45	46, −18, 20	400
Right supramarginal	4.51	58, −18, 20	400
No-Alcohol > alcohol
Left precentral gyrus	7.52	−32, −16, 66	1488
Left postcentral gyrus	6.35	−42, −22, 48	1488

Figure 4.

The images show significant activation maps of the Control Group for the contrasts Alcohol > No-Alcohol and No-Alcohol > Alcohol overlaid on an anatomical MRI image.

Correlations analysis

Pearson’s correlations between brain activations elicited by neutral stimuli linked to alcohol-related context in the BD group and alcohol consumption variables did not reveal any significant interactions (for more details, see Supplemental material). Regarding impulsivity, a significant correlation was found between the Self-Control subscale and activation in Cluster 3 (right Thalamus and Caudate Nucleus), when comparing Alcohol with No-Alcohol conditions for BDs (r = 0.522, p < 0.05). No further significant correlations were observed between brain activations in response to neutral stimuli associated with alcohol-related context and other impulsivity measures (for more details, see Supplemental material).

Discussion

This study aimed to examine the impact of BD on brain mechanisms linked to alcohol-related associative memory in college students. Despite the absence of significant behavioral differences, results showed a brain hyperactivity pattern in young BDs during the recall of alcohol-related stimuli. Notably, BDs exhibited increased activity in two different clusters, including reward processing regions and areas related to memory and mental imagery, when compared with abstainers.

Specifically, BDs displayed greater activation in the bilateral thalamus, globus pallidus, and dorsal striatum (DS) (putamen and right caudate nucleus), in comparison to the control group, regions known to be affected by acute alcohol consumption (Patton et al., 2023), and involved in reward processing (Diekhof et al., 2008). Augmented activity in these areas in response to alcohol/drug cues has often been reported in individuals with heavy alcohol/drug use, including BDs (Cservenka & Brumback, 2017; Delgado, 2007; George et al., 2001; Vollstädt-Klein et al., 2010). In this sense, when exposed to alcohol-related cues, heavy drinkers have shown greater activity in visual, memory-related, and incentive salience regions, including the fusiform gyrus, thalamus, and striatum nuclei (Everitt, 2014; George et al., 2001; Vollstädt-Klein et al., 2010). Therefore, the increased activation of the thalamus, globus pallidus, and DS observed in BDs might be interpreted as the ability of neutral cues, when paired with alcohol contexts, to elicit enhanced brain activation in regions associated with craving and reward processing, even in the absence of the context itself.

Importantly, preclinical and clinical research has shown that during cue-reactivity tasks, casual consumers, or those who have recently started consuming alcohol or other drugs, display greater brain activation in the ventral striatum (Courtney et al., 2018; Porrino et al., 2007). Nonetheless, individuals who have transitioned from casual drug use to habitual/compulsive consumption show increased activity—and greater dopamine release—in the DS when exposed to substance-associated cues (Everitt and Robbins, 2013; Heinz et al., 2009; Vollstädt-Klein et al., 2010). In this sense, long-lasting associations between stimulus (drug) and response (craving and substance seeking) are mediated by the DS (Everitt and Robbins, 2005; Ito et al., 2002), and higher activation in the thalamic and striatal regions has been related to increased impulsivity as well as to reward sensitivity and processing (Fede et al., 2020; Moussawi et al., 2016). Therefore, it is not surprising that neural alterations in the DS are commonly observed in individuals with addictive behaviors (Koob & Volkow, 2016; Volkow et al., 2006). Furthermore, greater cue-induced activation in the DS has been shown to predict relapse in alcohol-dependent individuals (Bach et al., 2015; Grüsser et al., 2004), while relapse has been directly associated with higher alcohol craving levels (Sliedrecht et al., 2019; Stohs et al., 2019; Witteman et al., 2015). Accordingly, the results found in the current study, showing that BDs exhibit heightened brain activation primarily in the DS during the recall of alcohol contexts when compared with the control group, are in line with the assumption that BD might potentially be a transitional consumption pattern, bridging the gap between casual consumption and alcohol abuse (Almeida-Antunes et al., 2021; Enoch, 2006; Lannoy et al., 2019; Parsons, 1998).

Moreover, our results revealed that, in comparison with controls, BDs exhibited greater activation in the cerebellum as well as in the fusiform gyrus when recalling alcohol-related contexts. These two regions have been described as relevant in visual processing (Palejwala et al., 2020) and task-driven motor processing in response to a stimulus (Guell & Schmahmann, 2020). Specifically, the fusiform gyrus is involved in the processing of visual information and semantic memory (Danker and Anderson, 2010; Khader et al., 2005; Mion et al., 2010), particularly during memory recall and imagery of content-specific or scene-related stimuli (Johnson & Rugg, 2007; Spagna et al., 2021), reinforcing thus the idea that this region is prone to activation during associative memory processing, not only during encoding but also during the retrieval of previously associated stimuli (Dijkstra et al., 2019; Park et al., 2012). Likewise, the cerebellum, although traditionally described as being purely a motor control region, has also been described as a contributor to cognitive processing and emotional control (Baumann et al., 2015; Schmahmann and Caplan, 2006; Strata, 2015). Regarding drug consumption, it has often been considered as an intermediary between the motor reward and the motivational-cognitive control systems of the striatal-cortico-limbic circuitry (Moulton et al., 2014). Notably, the cerebellum appears to be a relevant node for drug-related cue association, playing significant roles in conditioning response, goal-directed behavior, and selective attention (Brumback et al., 2015; Gil-Miravet et al., 2019; Miquel et al., 2020; Moulton et al., 2014). These functions are integral to the constructs of craving and addictive behavior, as they influence how individuals respond to drug-related cues and the subsequent behavioral outcomes (Franken, 2003). In the same vein, when exposed to alcohol cues, brain activation in the cerebellum has been positively correlated with AUD through AUDIT scores (Al-Khalil et al., 2021). Therefore, the cerebellum’s engagement in these processes underscores its role as a significant hub for drug-related cue association. By integrating motor, cognitive, and emotional responses, the cerebellum mediates the processes that occur between observing environmental cues and the behavioral outcomes associated with addiction.

Consequently, the increased activity observed in these two regions—the fusiform gyrus and the cerebellum—in the BDs relative to the control group, might suggest that, in accordance with what has been described in the literature, BDs exhibit augmented neural reactivity when exposed to alcohol-related stimuli (Almeida-Antunes et al., 2022; Blanco-Ramos et al., 2022; Pérez-García et al., 2022; Petit et al., 2014). In detail, the heightened BDs’ BOLD activation in the fusiform gyrus appears to underline the recalling and mental imagery of scene-specific alcohol-related context, while the elevated activation in the cerebellum may be related to the selective attention and increased salience toward alcohol-related stimuli, often seen in BDs (Gordon, 2007; Johnson & Rugg, 2007). In parallel, this finding might indicate that the mental representation of the drinking context could elicit craving, aligning with previous research suggesting that increased activation in the fusiform gyrus and the cerebellum is directly associated with heightened cue-induced alcohol craving (Moreno-Rius and Miquel, 2017; Olbrich et al., 2006; Park et al., 2007; Schneider et al., 2001).

Comparison of the Alcohol and No-Alcohol conditions revealed increased activation in the primary sensorimotor cortex, contralateral to the hand used by participants to classify the stimuli, in both groups. Accordingly, when participants were presented with alcohol-related stimuli—identified by pressing a button with the left hand—increased activation was observed in the right precentral and postcentral gyri. Conversely, when they were presented with No-Alcohol stimuli—identified by pressing a button with the right hand—greater activation was seen in the left precentral and postcentral gyri reflecting thus the well-known contralateral motor effect (Allison et al., 2000). Moreover, BDs displayed increased BOLD activation in an additional cluster of regions when presented with alcohol-related stimuli compared to No-Alcohol stimuli. Namely, this heightened activation was observed in the thalamus, caudate nucleus, cerebellum, and fusiform gyrus—regions involved in the visual and reward networks (Guell & Schmahmann, 2020; Palejwala et al., 2020) that show increased cue reactivity to alcohol-related stimuli in heavy drinkers and BDs, as described above (Cservenka & Brumback, 2017; Schacht et al., 2013). This suggests that BD experiences intensified alcohol craving in response to neutral stimuli associated with drinking-related contexts, as indicated by increased brain activation in these areas, a response not observed in control participants.

Furthermore, a significant positive correlation was observed between the self-control subscale of the BIS-11 and brain activation in the thalamus and caudate nucleus when BDs were exposed to alcohol-related cues. Specifically, higher scores on this subscale, indicating lower self-control (Patton et al., 1995), were associated with increased activity in these brain regions. Given the thalamus’s role in processing sensory information (Shine, 2023) and the caudate nucleus’ involvement in reward-related processes (Delgado et al., 2004), these findings suggest that BDs with lower self-control may experience intensified cue-induced craving for alcohol, reflected by heightened brain activation in these areas. This relationship aligns with previous research concerning substance use disorders (Li et al., 2010), highlighting the potential link between diminished self-control and increased alcohol craving (Bernard et al., 2021).

At the behavioral level, both groups exhibited comparable performance in both the Alcohol and No-Alcohol conditions, displaying similar accuracy, that is, a number of correct recalls for both conditions. Since the primary objective of our study was to investigate the brain mechanisms involved in associative memory, we ensured a correct recall rate of at least 90% before conducting the task within the MRI scanner. Therefore, a ceiling effect may have influenced the results. In addition, previous studies suggest that through neural compensation, experimental groups (i.e., those with alcohol consumption) can exhibit similar behavioral performance as controls while showing higher brain activation (Almeida-Antunes et al., 2021; Bagga et al., 2014; Hatchard et al., 2017; Suárez-Suárez et al., 2020). Regarding task performance speed, while group comparisons revealed higher motor impulsivity—as measured by the BIS-11—for BDs compared to controls, there were no differences in reaction times between groups for either the Alcohol or No-Alcohol conditions. The tendency to act impulsively with little regard for negative consequences has been widely explored in BDs (Bø et al., 2016; Maurage et al., 2019), highlighting the pronounced reward-seeking and impulsive behaviors characteristic of this population (Lees et al., 2019). Literature has shown that high consumption patterns, such as BD, are associated with higher impulsivity (Balodis et al., 2009). Nevertheless, neuropsychological assessments as well as behavioral results derived from neuroimaging/EEG studies often lack the sensitivity to detect potential brain alterations caused by BD, even in the presence of neurofunctional differences between BDs and controls (Crego et al. 2009, 2012; Carbia et al., 2018).

The present study has some limitations that need to be addressed in future research. First, prior to, or during data collection, no measurement of subjective craving was assessed. Such a measure would have provided important information for understanding how brain activation in the clusters of interest could be related to craving, and how it may influence and/or be influenced by such associative memory processes. In addition, our sample comprised two polarized groups, including individuals at the two non-clinical polar ends of alcohol consumption, BDs and abstainers. Future investigation, including a group of light or social drinkers (non-harmful and casual consumption), might provide a more comprehensive understanding of the development of BD as a harmful consumption pattern and elucidate how and when craving begins to influence/be influenced by alcohol-related memories. Likewise, longitudinal research on the developmental trajectory of BD and its impact on the brain would be valuable for understanding potential causal effects. This research could further explore how these brain alterations evolve with continued alcohol use and whether they might revert after the cessation of BD episodes (Brumback et al., 2015; López-Caneda et al., 2014b). These insights would enhance our understanding of the continuum hypothesis between BD and AUD—the idea that BD and alcohol dependence may constitute successive stages of the same phenomenon (Enoch, 2006; Parsons, 1998). This hypothesis is supported by observed impairments in BDs similar to those seen in individuals with AUD (e.g., Crego et al., 2010; López-Caneda et al., 2017; Maurage et al., 2009; Petit et al., 2014; Sanhueza et al., 2011), suggesting that BD during adolescence might be a starting point for the development of alcohol dependence in adulthood (Bonomo et al., 2004; McCambridge et al., 2011; McCarty et al., 2004). Lastly, no data were collected regarding the menstrual cycle phase of female participants on the day of the fMRI scan. Recent literature indicates that menstrual cycle phases, particularly fluctuations in progesterone and estradiol levels, can influence brain dynamics (Avila-Varela et al., 2024), so future research should consider these hormonal variations to ensure more robust and reliable results.

In conclusion, despite the absence of significant behavioral differences between groups, neutral cues linked to alcohol-related contexts may trigger heightened brain activity in the BD population. Namely, our findings revealed augmented activation in two clusters comprising the bilateral thalamus and the striatum, as well as by the cerebellum and the fusiform gyrus. These regions have been associated with memory and mental imagery (Spagna et al., 2021; Mion et al., 2010), the processing of visual cues (Tsapkini, et al. 2011), and reward and craving modulation (Braus et al., 2001; Moulton et al., 2014; Paulus, 2007). Results are in line with existing literature indicating that prior consumption contexts significantly influence craving and relapse in alcohol-dependent individuals (Janak & Chaudhri, 2010). Moreover, our findings suggest that real-life contexts involving alcohol consumption (Kuerbis et al., 2020; Nees et al., 2012) may become linked or associated with otherwise neutral stimuli. Consequently, these newly associated stimuli could elicit brain responses similar to those triggered by direct exposure to alcohol-related contexts. This association might lead to an increase in craving and, subsequently, substance-seeking and taking behaviors (Papachristou et al., 2012; Weiss, 2005). Overall, while evidence remains limited, this study sheds light on the importance of alcohol-associated memories and their potential impact on craving and alcohol use in BDs. Future research on alcohol abuse and BD should consider the context of consumption and explore how those environmental cues might influence alcohol-seeking behavior and the maintenance or escalation of alcohol consumption.

Supplemental Material

sj-docx-1-jop-10.1177_02698811241282624 – Supplemental material for Associative memory in alcohol-related contexts: An fMRI study with young binge drinkers

Supplemental material, sj-docx-1-jop-10.1177_02698811241282624 for Associative memory in alcohol-related contexts: An fMRI study with young binge drinkers by Rui Pedro Serafim Rodrigues, Sónia Silva Sousa, Eduardo López-Caneda, Natália Almeida-Antunes, Alberto Jacobo González‑Villar, Adriana Sampaio and Alberto Crego in Journal of Psychopharmacology

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was conducted at the Psychology Research Center (PSI/01662), School of Psychology, University of Minho, supported by the Foundation for Science and Technology (FCT) through the Portuguese State Budget (Ref.: UIDB/PSI/01662/2020). This study was also supported by the project PTDC/PSI-ESP/1243/2021. Eduardo López Caneda, Alberto J. González-Villar and Alberto Crego were supported by the FCT and the Portuguese Ministry of Science, Technology and Higher Education, within the scope of the Individual Call to Scientific Employment Stimulus (CEECIND/07751/2022; CEECIND/02639/2017), and the Transitory Disposition of the Decree No. 57/2016, of 29 August, amended by Law No. 57/2017 of 19 July, respectively. Natália Almeida-Antunes (SFRH/BD/146194/2019) and Rui Rodrigues (2021.05276.BD) were supported by the FCT, MCTES, and European Union through the European Social Fund.

ORCID iD

Rui Pedro Serafim Rodrigues

Supplemental material

Supplemental material for this article is available online.

References

Al-Khalil

Vakamudi

Witkiewitz

, et al. (2021) Neural correlates of alcohol use disorder severity among nontreatment-seeking heavy drinkers: An examination of the incentive salience and negative emotionality domains of the alcohol and addiction research domain criteria. Alcohol Clin Exp Res 45: 1200–1214.

Allison

Meador

Loring

, et al. (2000) Functional MRI cerebral activation and deactivation during finger movement. Neurology 54: 135–135.

Almeida-Antunes

Antón-Toro

Crego

, et al. (2024) Trying to forget alcohol: Brain mechanisms underlying memory suppression in young binge drinkers. Prog Neuropsychopharmacol Biol Psychiatry 134: 111053.

Almeida-Antunes

Antón-Toro

Crego

, et al. (2022) “It’s a beer!”: Brain functional hyperconnectivity during processing of alcohol-related images in young binge drinkers. Addict Biol 27: e13152.

Almeida-Antunes

Crego

Carbia

, et al. (2021) Electroencephalographic signatures of the binge drinking pattern during adolescence and young adulthood: A PRISMA-driven systematic review. NeuroImage Clin 29: 102537.

Ames

Wong

Bechara

, et al. (2014) Neural correlates of a Go/NoGo task with alcohol stimuli in light and heavy young drinkers. Behav Brain Res 274: 382–389.

Avila-Varela

Hidalgo-Lopez

Dagnino

, et al. (2024) Whole-brain dynamics across the menstrual cycle: The role of hormonal fluctuations and age in healthy women. NPJ Womens Health 2: 8.

Bach

Vollsta Dt-Klein

Kirsch

, et al. (2015) Increased mesolimbic cue-reactivity in carriers of the mu-opioid-receptor gene OPRM1 A118G polymorphism predicts drinking outcome: A functional imaging study in alcohol dependent subjects. Eur Neuropsychopharmacol 25: 1128–1135.

Bagga

Singh

, et al. (2014) Assessment of abstract reasoning abilities in alcohol-dependent subjects: An fMRI study. Neuroradiology 56: 69–77.

10.

Balodis

Potenza

Olmstead

(2009) Binge drinking in undergraduates: relationships with sex, drinking behaviors, impulsivity, and the perceived effects of alcohol. Behavioural Pharmacology 20: 518–526.

11.

Banca

Lange

Worbe

, et al. (2016) Reflection impulsivity in binge drinking: Behavioural and volumetric correlates. Addict Biol 21: 504–515.

12.

Babor

Higgins-Biddle

Saunders

, et al. and World Health Organization (2001) AUDIT: the alcohol use disorders identification test: Guidelines for use in primary health care (No. WHO/MSD/MSB/01.6 a). World Health Organization.

13.

Baumann

Borra

Bower

, et al. (2015) Consensus paper: The role of the cerebellum in perceptual processes. Cerebellum 14: 197–220.

14.

Becker

Laukka

Kalpouzos

, et al. (2015) Structural brain correlates of associative memory in older adults. NeuroImage 118: 146–153.

15.

Bernard

Cyr

Bonnet-Suard

, et al. (2021) Drawing alcohol craving process: A systematic review of its association with thought suppression, inhibition and impulsivity. Heliyon 7(1).

16.

Blanco-Ramos

Antón-Toro

Cadaveira

, et al. (2022) Alcohol-related stimuli modulate functional connectivity during response inhibition in young binge drinkers. Addict Biol 27: e13141.

17.

Bø

Aker

Billieux

, et al. (2016) Binge drinkers are fast, able to stop—but they fail to adjust. J Int Neuropsychol Soc 22: 38–46.

18.

Bonomo

Bowes

Coffey

, et al. (2004) Teenage drinking and the onset of alcohol dependence: A cohort study over seven years. Addiction 99: 1520–1528.

19.

Braus

Wrase

Grüsser

, et al. (2001) Alcohol-associated stimuli activate the ventral striatum in abstinent alcoholics. J Neural Trans 108: 887–894.

20.

Brumback

Squeglia

Jacobus

, et al. (2015) Adolescent heavy drinkers’ amplified brain responses to alcohol cues decrease over one month of abstinence. Addict Behav 46: 45–52.

21.

Bucholz

Henry

Henley

(1994) Fixation with bioabsorbable screws for the treatment of fractures of the ankle. JBJS 76: 319–324.

22.

Cadigan

Martens

Herman

(2015) A latent profile analysis of drinking motives among heavy drinking college students. Addict behav 51: 100–105.

23.

Carbia

Cadaveira

López-Caneda

, et al. (2017) Working memory over a six-year period in young binge drinkers. Alcohol 61: 17–23.

24.

Carbia

López-Caneda

Corral

, et al. (2018) A systematic review of neuropsychological studies involving young binge drinkers. Neurosci Biobehav Rev 90: 332–349.

25.

Correas

Cuesta

Rosen

, et al. (2021) Compensatory neuroadaptation to binge drinking: Human evidence for allostasis. Addict Biol 26: e12960.

26.

Courtney

Polich

(2009) Binge drinking in young adults: Data, definitions and determinants. Psychol Bull 135: 142–156.

27.

Castellanos-Ryan

Conrod

(2020) Cognitive risk factors for alcohol and substance addictions. Cognit Addict 91–102.

28.

Courtney

Rapuano

Sargent

, et al. (2018) Reward system activation in response to alcohol advertisements predicts college drinking. J Stud Alcohol Drugs 79: 29–38.

29.

Courtney

Ghahremani

Ray

(2015) The effect of alcohol priming on neural markers of alcohol cue-reactivity. Am J Drug Alcohol Abuse 41: 300–308.

30.

Courtney

Schacht

Hutchison

, et al. (2016) Neural substrates of cue reactivity: Association with treatment outcomes and relapse. Addict Biol 21: 3–22.

31.

Crego

Rodriguez-Holguín

Parada

, et al. (2010) Reduced anterior prefrontal cortex activation in young binge drinkers during a visual working memory task. Drug Alcohol Depend 109: 45–56.

32.

Crews

Boettiger

(2009) Impulsivity, frontal lobes and risk for addiction. Pharmacol Biochem Behav 93: 237–247.

33.

Crews

Vetreno

Broadwater

, et al. (2016) Adolescent alcohol exposure persistently impacts adult neurobiology and behavior. Pharmacol Rev 68: 1074–1109.

34.

Cservenka

Brumback

(2017) The burden of binge and heavy drinking on the brain: Effects on adolescent and young adult neural structure and function. Front Psychol 8: 1111.

35.

da Cunha

(2002) Validação da versão portuguesa dos questionários AUDIT e Five-shot para identificação de consumo excessivo de álcool. Lisboa: Internato Complementar de Clínica Geral da Zona Sul.

36.

Dager

Jamadar

Stevens

, et al. (2014) fMRI response during figural memory task performance in college drinkers. Psychopharmacology 231: 167–179.

37.

Dalley

Robbins

(2017) Fractionating impulsivity: Neuropsychiatric implications. Nat Rev Neurosci 18: 3.

38.

Danker

Anderson

(2010) The ghosts of brain states past: Remembering reactivates the brain regions engaged during encoding. Psychol Bull 136: 87–102.

39.

de Chastelaine

Mattson

Wang

, et al. (2016) The relationships between age, associative memory performance, and the neural correlates of successful associative memory encoding. Neurobiol Aging 42: 163–176.

40.

Delgado

(2007) Reward-related responses in the human striatum. Ann N Y Acad Sci 1104: 70–88.

41.

Delgado

Stenger

Fiez

(2004) Motivation-dependent responses in the human caudate nucleus. Cerebral Cortex 14: 1022–1030.

42.

Derogatis

Unger

(2010) Symptom checklist-90-revised. The Corsini Encyclopedia of Psychology 1–2.

43.

Diekhof

Falkai

Gruber

(2008) Functional neuroimaging of reward processing and decision-making: A review of aberrant motivational and affective processing in addiction and mood disorders. Brain Res Rev 59: 164–184.

44.

Dijkstra

Bosch

van Gerven

MAJ

(2019) Shared neural mechanisms of visual perception and imagery. Trends Cogn Sci 23: 423–434.

45.

Doallo

Cadaveira

Corral

, et al. (2014) Larger mid-dorsolateral prefrontal gray matter volume in young binge drinkers revealed by voxel-based morphometry. PLoS One 9: e96380.

46.

Dormal

Lannoy

Maurage

(2019) Impact of exchange stay on alcohol consumption: longitudinal exploration in a large sample of European students. Alcoholism: Clinical and Experimental Research 43: 1220–1224.

47.

Enoch

M-A

(2006) Genetic and environmental influences on the development of alcoholism. Ann N Y Acad Sci 1094: 193–201.

48.

Everitt

(2014) Neural and psychological mechanisms underlying compulsive drug seeking habits and drug memories—indications for novel treatments of addiction. Eur J Neurosci 40: 2163–2182.

49.

Everitt

Robbins

(2013) From the ventral to the dorsal striatum: Devolving views of their roles in drug addiction. Neurosci Biobehav Rev 37: 1946–1954.

50.

Everitt

Robbins

(2005) Neural systems of reinforcement for drug addiction: From actions to habits to compulsion. Nat Neurosci 8: 1481–1489.

51.

Fede

Abrahao

Cortes

, et al. (2020). Alcohol effects on globus pallidus connectivity: Role of impulsivity and binge drinking. PLoS One 15: e0224906.

52.

Franken

IHA

(2003) Drug craving and addiction: Integrating psychological and neuropsychopharmacological approaches. Prog Neuropsychopharmacol Biol Psychiatry 27: 563–579.

53.

Fuhrmann

Knoll

Blakemore

S-J

(2015) Adolescence as a sensitive period of brain development. Trends Cogn Sci 19: 558–566.

54.

Fukushima

Kuga

Oribe

, et al. (2020) Behavioural cue reactivity to alcohol-related and non-alcohol-related stimuli among individuals with alcohol use disorder: An fMRI study with a visual task. PLoS One 15: e0229187.

55.

George

Anton

Bloomer

, et al. (2001) Activation of prefrontal cortex and anterior thalamus in alcoholic subjects on exposure to alcohol-specific cues. Arch Gen Psychiatry 58: 345–352.

56.

Gil-Miravet

Guarque-Chabrera

Carbo-Gas

, et al. (2019) The role of the cerebellum in drug-cue associative memory: Functional interactions with the medial prefrontal cortex. Eur J Neurosci 50: 2613–2622.

57.

Gogtay

Giedd

Lusk

, et al. (2004) Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci 101: 8174–8179.

58.

Goldfarb

Fogelman

Sinha

(2020) Memory biases in alcohol use disorder: Enhanced memory for contexts associated with alcohol prospectively predicts alcohol use outcomes. Neuropsychopharmacology 45: 8.

59.

Gordon

(2007) The cerebellum and cognition. Eur J Paediatr Neurol 11: 232–234.

60.

Griswold

Fullman

Hawley

, et al. (2018) Alcohol use and burden for 195 countries and territories, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet 392: 1015–1035.

61.

Grüsser

Wrase

Klein

, et al. (2004) Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychopharmacology 175: 296–302.

62.

Guell

Schmahmann

(2020) Cerebellar functional anatomy: A didactic summary based on human fMRI evidence. Cerebellum 19: 1–5.

63.

Hatchard

Mioduszewski

Fall

, et al. (2017) Neural impact of low-level alcohol use on response inhibition: An fMRI investigation in young adults. Behav Brain Res 329: 12–19.

64.

Heinz

Beck

Grüsser

, et al. (2009) Identifying the neural circuitry of alcohol craving and relapse vulnerability. Addict Biol 14: 108–118.

65.

Howell

Worbe

Lange

, et al. (2013) Increased ventral striatal volume in college-aged binge drinkers. PLoS One 8: e74164.

66.

Huang

Mohan

De Ridder

, et al. (2018) The neural correlates of the unified percept of alcohol-related craving: A fMRI and EEG study. Scient Rep 8: 1.

67.

IBM Corp (2021) IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp.

68.

Ito

Dalley

Robbins

, et al. (2002) Dopamine release in the dorsal striatum during cocaine-seeking behavior under the control of a drug-associated cue. J Neurosci 22: 6247–6253.

69.

Jacobus

Tapert

(2013) Neurotoxic effects of alcohol in adolescence. Ann Rev Clin Psychol 9: 703–721.

70.

Janak

Chaudhri

(2010) The potent effect of environmental context on relapse to alcohol-seeking after extinction. Open Addict J 3: 76.

71.

Johnson

Rugg

(2007) Recollection and the reinstatement of encoding-related cortical activity. Cerebral Cortex 17: 2507–2515.

72.

Jones

Lueras

Nagel

(2018) Effects of binge drinking on the developing brain. Alcohol Res Curr Rev 39: 87–96.

73.

Khader

Burke

Bien

, et al. (2005) Content-specific activation during associative long-term memory retrieval. NeuroImage 27: 805–816.

74.

Kim

(2011) Neural activity that predicts subsequent memory and forgetting: A meta-analysis of 74 fMRI studies. NeuroImage 54: 2446–2461.

75.

Koob

Volkow

(2016) Neurobiology of addiction: A neurocircuitry analysis. Lancet Psychiatry 3: 760–773.

76.

Kovalenko

Chaumon

Busch

(2012) A Pool of Pairs of Related Objects (POPORO) for investigating visual semantic integration: behavioral and electrophysiological validation. Brain Topog 25: 272–284.

77.

Kraus

Nociar

(2016). ESPAD Report 2015: Results from the European School Survey Project on Alcohol and Other Drugs. Lisbon, Portugal: European Monitoring Centre for Drugs and Drug Addiction.

78.

Kuerbis

Shao

Padovano

, et al. (2020) Context and craving among individuals with alcohol use disorder attempting to moderate their drinking. Exp Clin Psychopharmacol 28: 677–687.

79.

Kuntsche

Knibbe

Gmel

, et al. (2005) Why do young people drink? A review of drinking motives. Clin Psychol Rev 25: 841–861.

80.

Laloni

(2001) Symptoms Assessment Scale− 90-R-SCL− 90-R: adaptation, reliability and validity (Doctoral dissertation). Catholic University of Campinas.

81.

Lannoy

Billieux

Dormal

, et al. (2019) Behavioral and cerebral impairments associated with binge drinking in youth: A critical review. Psychol Belg 59: 116–155.

82.

Lees

Mewton

Stapinski

, et al. (2019) Neurobiological and cognitive profile of young binge drinkers: A systematic review and meta-analysis. Neuropsychol Rev 29: 357–385.

83.

Sangthong

Chongsuvivatwong

, et al. (2010) Lifetime multiple substance use pattern among heroin users before entering methadone maintenance treatment clinic in Yunnan, China. Drug and Alcohol Review 29: 420–425.

84.

López-Caneda

Cadaveira

Correas

, et al. (2017) The brain of binge drinkers at rest: Alterations in theta and beta oscillations in first-year college students with a binge drinking pattern. Front Behav Neurosci 11: 274601.

85.

López-Caneda

Cadaveira

Crego

, et al. (2012) Hyperactivation of right inferior frontal cortex in young binge drinkers during response inhibition: A follow-up study. Addiction 107: 1796–1808.

86.

López-Caneda

Rodríguez Holguín

Cadaveira

, et al. (2014a) Impact of alcohol use on inhibitory control (and vice versa) during adolescence and young adulthood: A review. Alcohol Alcohol 49: 173–181.

87.

López-Caneda

Holguín

Corral

, et al. (2014b) Evolution of the binge drinking pattern in college students: Neurophysiological correlates. Alcohol 48: 407–418.

88.

Luna

Padmanabhan

O’Hearn

(2010) What has fMRI told us about the development of cognitive control through adolescence? Brain Cogn 72: 101–113.

89.

Maurage

Lannoy

Naassila

, et al. (2019) Impulsivity and binge drinking: A neurocognitive perspective. Neurosci Alcohol 335–343.

90.

Maurage

Lannoy

Mange

, et al. (2020) What we talk about when we talk about binge drinking: Towards an integrated conceptualization and evaluation. Alcohol Alcohol 55: 468–479.

91.

Maurage

Pesenti

Philippot

, et al. (2009) Latent deleterious effects of binge drinking over a short period of time revealed only by electrophysiological measures. J Psychiatry Neurosci 34: 111–118.

92.

Mayes

Montaldi

Migo

(2007) Associative memory and the medial temporal lobes. Trends Cogn Sci 11: 126–135.

93.

McCambridge

McAlaney

Rowe

(2011) Adult consequences of late adolescent alcohol consumption: A systematic review of cohort studies. PLoS Med 8: e1000413.

94.

McCarty

Ebel

Garrison

, et al. (2004) Continuity of binge and harmful drinking from late adolescence to early adulthood. Pediatrics 114: 714–719.

95.

Mion

Patterson

Acosta-Cabronero

, et al. (2010) What the left and right anterior fusiform gyri tell us about semantic memory. Brain 133: 3256–3268.

96.

Miquel

Gil-Miravet

Guarque-Chabrera

(2020) The cerebellum on cocaine. Front Syst Neurosci 14: 586574.

97.

Molnar

Beaton

Happer

, et al. (2018) Behavioral and brain activity indices of cognitive control deficits in binge drinkers. Brain Sci 8: 9.

98.

Moreno-Rius

Miquel

(2017) The cerebellum in drug craving. Drug Alcohol Depend 173: 151–158.

99.

Moulton

Elman

Becerra

, et al. (2014) The cerebellum and addiction: Insights gained from neuroimaging research. Addict Biol 19: 317–331.

100.

Moussawi

Kalivas

Lee

(2016) Abstinence from drug dependence after bilateral globus pallidus hypoxic-ischemic injury. Biol Psychiatry 80: e79–e80.

101.

National Institute of Alcohol Abuse and Alcoholism (2004) NIAAA Council Approves Definition of Binge Drinking. NIAAA Newsletter, 3. Available at: https://www.niaaa.nih.gov/sites/default/files/newsletters/Newsletter_Number3.pdf

102.

National Institute of Alcohol Abuse and Alcoholism (2020) Alcohol facts and statistics. Available at: https://www.niaaa.nih.gov/sites/default/files/AlcoholFactsAndStats.pdf

103.

National Institute of Alcohol Abuse and Alcoholism (2022) Understanding Binge Drinking. Available at: https://www.niaaa.nih.gov/sites/default/files/publications/NIAAA_Binge_Drinking_3.pdf

104.

Nees

Diener

Smolka

, et al. (2012) The role of context in the processing of alcohol-relevant cues. Addict Biol 17: 441–451.

105.

Olbrich

Valerius

Paris

, et al. (2006) Brain activation during craving for alcohol measured by positron emission Tomography. Austr N Zeal J Psychiatry 40: 171–178.

106.

Oldfield

(1971) The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia 9: 97–113.

107.

Palejwala

O’Connor

Milton

, et al. (2020) Anatomy and white matter connections of the fusiform gyrus. Scient Rep 10: 1.

108.

Papachristou

Nederkoorn

Havermans

, et al. (2012) Can’t stop the craving: The effect of impulsivity on cue-elicited craving for alcohol in heavy and light social drinkers. Psychopharmacology 219: 511–518.

109.

Parada

Corral

Caamaño-Isorna

, et al. (2011) Definición del concepto de consumo intensivo de alcohol adolescente (binge drinking). Adicciones 23: 53–63.

110.

Park

Shannon

Biggan

, et al. (2012) Neural activity supporting the formation of associative memory versus source memory. Brain Res 1471: 81–92.

111.

Park

M-S

Sohn

J-H

Suk

J-A

, et al. (2007) Brain substrates of craving to alcohol cues in subjects with alcohol use disorder. Alcohol Alcohol 42: 417–422.

112.

Parsons

(1998) Neurocognitive deficits in alcoholics and social drinkers: A ccontinuum? Alcohol Clin Exp Res 22: 954–961.

113.

Patrick

Terry-McElrath

Kloska

, et al. (2016) High-intensity drinking among young adults in the United States: Prevalence, frequency, and developmental change. Alcoholism: Clinical and E xperimental Research 40: 1905–1912.

114.

Patton

Stanford

Barratt

(1995) Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology 51: 768–774.

115.

Patton

Sheats

Siclair

, et al. (2023) Alcohol potentiates multiple GABAergic inputs to dorsal striatum fast-spiking interneurons. Neuropharmacology 232: 109527.

116.

Paulus

(2007) Neural basis of reward and craving—A homeostatic point of view. Dialog Clin Neurosci 9: 379–387.

117.

Pérez-García

Suárez-Suárez

Doallo

, et al. (2022) Effects of binge drinking during adolescence and emerging adulthood on the brain: A systematic review of neuroimaging studies. Neurosci Biobehav Rev 137: 104637.

118.

Pershin

Di Ventra

(2010) Experimental demonstration of associative memory with memristive neural networks. Neural Networks 23: 881–886.

119.

Petit

Kornreich

Dan

, et al. (2014) Electrophysiological correlates of alcohol- and non-alcohol-related stimuli processing in binge drinkers: A follow-up study. J Psychopharmacol 28: 1041–1052.

120.

Petit

Maurage

Kornreich

Verbanck

, et al. (2014) Binge drinking in adolescents: A review of neurophysiological and neuroimaging research. Alcohol Alcohol 49: 198–206.

121.

Porrino

Smith

Nader

, et al. (2007) The effects of cocaine: A shifting target over the course of addiction. Progr Neuropsychopharmacol Biol Psychiatry 31: 1593–1600.

122.

Sanhueza

García-Moreno

Expósito

(2011) Weekend alcoholism in youth and neurocognitive aging. Psicothema 23: 209–214.

123.

Schacht

Anton

Myrick

(2013) Functional neuroimaging studies of alcohol cue reactivity: A quantitative meta-analysis and systematic review. Addict Biol 18: 121–133.

124.

Schmahmann

Caplan

(2006) Cognition, emotion and the cerebellum. Brain 129: 290–292.

125.

Schneider

Habel

Wagner

, et al. (2001) Subcortical correlates of craving in recently abstinent alcoholic patients. Am J Psychiatry 158: 1075–1083.

126.

Schott

Wüstenberg

Lücke

, et al. (2019). Gradual acquisition of visuospatial associative memory representations via the dorsal precuneus. Human Brain Map 40: 1554–1570.

127.

Schweinsburg

McQueeny

Nagel

, et al. (2010) A preliminary study of functional magnetic resonance imaging response during verbal encoding among adolescent binge drinkers. Alcohol 44: 111–117.

128.

Scimeca

Badre

(2012) Striatal contributions to declarative memory retrieval. Neuron 75: 380–392.

129.

Seo

Sinha

(2014) The neurobiology of alcohol craving and relapse. Handbook Clin Neurol 125: 355–368.

130.

Shine

(2023) Neuromodulatory control of complex adaptive dynamics in the brain. Interface Focus 13: 20220079.

131.

Sliedrecht

de Waart

Witkiewitz

, et al. (2019) Alcohol use disorder relapse factors: A systematic review. Psychiatry Res 278: 97–115.

132.

Sousa

Amaro

Crego

, et al. (2018) Developmental trajectory of the prefrontal cortex: A systematic review of diffusion tensor imaging studies. Brain Imaging Behav 12: 1197–1210.

133.

Sousa

Sampaio

López-Caneda

, et al. (2020) Increased nucleus accumbens volume in college binge drinkers—preliminary evidence from manually segmented MRI analysis. Front Psychiatry 10: 1005.

134.

Sousa

Sampaio

Marques

, et al. (2017) Gray matter abnormalities in the inhibitory circuitry of young binge drinkers: A voxel-based morphometry study. Front Psychol 8: 1567.

135.

Sousa

Sampaio

Marques

, et al. (2019) Functional and structural connectivity of the executive control network in college binge drinkers. Addict Behav 99: 106009.

136.

Spagna

Hajhajate

Liu

, et al. (2021) Visual mental imagery engages the left fusiform gyrus, but not the early visual cortex: A meta-analysis of neuroimaging evidence. Neurosci Biobehav Rev 122: 201–217.

137.

Strata

(2015) The emotional cerebellum. Cerebellum 14: 570–577.

138.

Stohs

Schneekloth

Geske

, et al. (2019) Alcohol craving predicts relapse after residential addiction treatment. Alcohol Alcohol 54: 167–172.

139.

Suárez-Suárez

Doallo

Pérez-García

, et al. (2020) Response inhibition and binge drinking during transition to university: An fMRI study. Front Psychiatry 11: 535.

140.

Townshend

Duka

(2002) Patterns of alcohol drinking in a population of young social drinkers: a comparison of questionnaire and diary measures. Alcohol and Alcoholism 37: 187–192.

141.

Tsapkini

Vindiola

Rapp

(2011) Patterns of brain reorganization subsequent to left fusiform damage: fMRI evidence from visual processing of words and pseudowords, faces and objects. Neuroimage 55: 1357–1372.

142.

Volkow

Wang

G-J

Telang

, et al. (2006) Cocaine cues and dopamine in dorsal striatum: Mechanism of craving in cocaine addiction. J Neurosci 26: 6583–6588.

143.

Vollstädt-Klein

Wichert

Rabinstein

, et al. (2010) Initial, habitual and compulsive alcohol use is characterized by a shift of cue processing from ventral to dorsal striatum. Addiction 105: 1741–1749.

144.

Weiss

(2005) Neurobiology of craving, conditioned reward and relapse. Curr Opin Pharmacol 5: 9–19.

145.

Wellcome Centre for Human Neuroimaging (2014) SPM12. London, UK: University College London.

146.

Wetherill

Squeglia

Yang

, et al. (2013) A longitudinal examination of adolescent response inhibition: Neural differences before and after the initiation of heavy drinking. Psychopharmacology 230: 663–671.

147.

Witteman

Post

Tarvainen

, et al. (2015) Cue reactivity and its relation to craving and relapse in alcohol dependence: A combined laboratory and field study. Psychopharmacology 232: 3685–3696.

148.

World Health Organization (2011) Global status report on alcohol and health. World Health Organization. https://iris.who.int/bitstream/handle/10665/44499/9789241564151_eng.pdf?sequence=1

149.

World Health Organization (2019) Status Report on Alcohol Consumption, Harm and Policy Responses in 30 European Countries 2019 (No. WHO/EURO: 2019-3545-43304-60696). Geneva: World Health Organization. Regional Office for Europe.

150.

World Medical Association (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310: 2191–2194.

151.

Wrase

Schlagenhauf

Kienast

, et al. (2007) Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics. NeuroImage 35: 787–794.

152.

Yonelinas

Hopfinger

Buonocore

, et al. (2001) Hippocampal, parahippocampal and occipital-temporal contributions to associative and item recognition memory: An fMRI study. Neuroreport 12: 359–363.

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