Content area
Adolescence marks a critical window wherein individual differences in brain structure may influence the emergence of alcohol use behaviors. The orbitofrontal cortex (OFC), a region involved in reward processing and behavioral regulation, may play a key role in shaping early responses to alcohol. This study examined whether smaller OFC volume at ages 9–10 predicted likelihood of experiencing subjective effects of alcohol by ages 13–14. Participants (N = 206; 57 % female) were drawn from the Adolescent Brain Cognitive Development Study. Baseline medial and lateral OFC volumes were used. Subjective response to alcohol was measured during follow-up using a binary outcome (1 = any effect, 0 = no effects). Mixed-effects logistic regression models tested the association between OFC and alcohol response, adjusting for sex, parental education, race/ethnicity, intracranial volume, and site. Smaller left medial OFC at Baseline was significantly associated with greater odds of reporting subjective effects (OR = 1.70, p = .026). Youth who reported subjective effects also consumed more alcohol in the past year (p < .001), but did not differ in their alcohol expectancies. Among those reporting subjective effects, OFC volume was not significantly associated with the amount or frequency of alcohol use. These findings suggest that smaller OFC volume may not reflect pharmacological sensitivity per se, but instead relate to early drinking behavior sufficient to elicit noticeable effects. This may reflect underlying impulsivity-related traits or altered neurodevelopmental trajectories that predispose youth to early and potentially riskier patterns of alcohol use. Results underscore the potential value of identifying structural brain markers that contribute to individual vulnerability for alcohol use during adolescence.
1 Introduction
Alcohol use during adolescence is prevalent and poses a significant public health issue with potential long-term consequences. The 2023 national Monitoring the Future survey in the United States reported past-year use of any alcohol rates of 15.1 % among 8th graders with a substantial increase to 30.6 % among 10th graders and 45.7 % among 12th graders (Miech et al., 2024). Moreover, early alcohol use during adolescence is associated with increased risk of future alcohol use, alcohol-related problems, substance use disorders, as well as reduced neuropsychological functioning ( Buu et al., 2012; DeWit et al., 2000; Duncan et al., 1997; Halladay et al., 2020; Moss et al., 2014; Nguyen-Louie et al., 2015, 2017).
Adolescence is a critical period of neurodevelopment marked by heightened reward sensitivity and underdeveloped cognitive control, which may contribute to increased risk-taking behaviors, such as substance use ( Geier et al., 2013; Meredith & Squeglia, 2020). One brain region that undergoes extensive development during adolescence is the orbitofrontal cortex (OFC), which plays a crucial role in decision-making, reward processing, and emotional regulation ( Rolls et al., 2020). The OFC is involved in processing sensory and emotional information to assign motivational value to potential rewards, and its structural characteristics may influence how adolescents experience and respond to rewarding substances like alcohol ( Costumero et al., 2013; Dom et al., 2005). Impairments or atypical development in this region are associated with problems in decision-making, increased craving, and heightened expectancy, particularly among individuals with substance use disorders ( London et al., 2000).
Previous research shows that alcohol consumption during this developmental stage is associated with atypical patterns of brain structure and function, as well as impaired neurocognition ( Luciana et al., 2013; Pfefferbaum et al., 2017). For example, animal models of adolescent alcohol use show enduring changes in OFC mediated reward processing, such as blunted electrophysiological activity in reward-related neuronal populations and increased risk preference in adulthood ( McMurray et al., 2016). In addition, research in rodent models shows that alcohol exposure during adolescence disrupts how the OFC encodes decision-related elements, leading to a diminished ability to differentiate uncertain rewards and long-term changes in decision-making into adulthood ( Corwin & Roitman, 2021). Similarly, human studies find that adolescents who engage in heavy alcohol use exhibit structural and functional aberrations in the OFC ( Lees et al., 2021; Squeglia et al., 2014; Xiao et al., 2013).
In addition, sex may moderate how alcohol use impacts prefrontal neurodevelopment ( Medina et al., 2008) as the OFC in females exhibits volume growth earlier in adolescence than in males and is larger in adult females than adult males ( Giedd et al., 2012; Lenroot & Giedd, 2010). Given that adolescents and females may exhibit greater susceptibility to the neurotoxic effects of alcohol, understanding neuroanatomical relationships and alcohol use in adolescents, by sex, is important ( Lees et al., 2021; Verplaetse et al., 2021).
A growing body of research explores OFC structural characteristics as predictors of future substance use ( Lees et al., 2021). For instance, Cheetham et al. (2012) found that smaller OFC volume at age 12 predicted cannabis use initiation by age 16; however, OFC volume was not predictive of alcohol-related problems in adolescence ( Cheetham et al., 2014). Follow-up analyses within that same longitudinal sample indicated smaller regions of the left and right OFC measured at age 12 along with lower effortful control prospectively predicted substance use disorder by age 18 ( Cheetham et al., 2017), suggesting OFC volume may contribute to phenotypic drivers of substance use risk. By comparison, Wade et al. (2019) reported that OFC volumes in early adolescence (ages 12 to 15) did not independently predict future alcohol use over a nine-year follow-up period, though larger left lateral OFC was predictive of cannabis use and alcohol co-use. Further, they found that OFC volume was positively correlated with baseline reward responsiveness, which suggests that OFC characteristics may be a better metric for reward-based learning. Though directionality of OFC volume size (i.e., larger vs smaller) differed between these two longitudinal cohort studies ( Cheetham et al., 2012; Wade et al., 2019), this may be an artifact of age at initial MRI scan and length of follow-up period. Consistent with established addiction theories, these results suggest that the OFC is particularly vulnerable to early substance use onset and heightened reward sensitivity, which together may increase the risk for developing substance use disorders ( Jordan & Andersen, 2017; Koob & Volkow, 2010; Volkow & Fowler, 2000). Overall, these studies highlight the potential role of OFC characteristics in relation to reward sensitivity, which could influence substance use patterns in adolescence. As summarized by Lees et al. (2021), the OFC is a significant predictor of alcohol and substance use. Further, it may be that OFC characteristics may be more predictive of reward response to substances, as suggested by Wade et al. (2019), rather than directly predicting substance use itself.
An individual's subjective response to alcohol is a critical factor in understanding idiographic differences in alcohol use risk ( King et al., 2014; Quinn & Fromme, 2011; Schuckit, 1984; Schuckit, Tipp, et al., 1997). Subjective alcohol response represents how sensitive an individual is to the physiological effects of alcohol, including stimulation and sedation ( Morean & Corbin, 2010; Morean et al., 2013). Studies show that the self-rating of the effects of alcohol is a reliable and valid tool for measuring subjective alcohol responses during the ascending limb of the breath alcohol concentration (BrAC) and prospectively predicts alcohol problems in individuals with heightened responses to the effects of alcohol ( Ray et al., 2011; Schuckit, Tipp, et al., 1997). Specifically, individuals with a low level of response, defined as requiring more drinks to achieve intoxication, during early alcohol use is associated with an increased risk for later problematic drinking ( Morean & Corbin, 2010; Trim et al., 2009; Schuckit, 1994).
Given that alcohol activates mesocorticolimbic pathways, including dopaminergic projections from the nucleus accumbens to prefrontal regions such as the OFC, subjective response to alcohol in adults may be linked to mechanisms of reward valuation and craving ( Koob & Volkow, 2010). In support of this, greater sensitivity to alcohol-related stimulation has been associated with individual differences in reward sensitivity, suggesting that subjective response may reflect underlying variation in neural reward processing ( Morris et al., 2016). Additionally, previous work in adults with bipolar disorder and controls has shown that orbitofrontal cortex volume is associated with subjective response to alcohol ( Tretyak et al., 2021), though the direction of this association differed by group. The relationship between OFC volume and subjective response to alcohol has yet to be investigated in youth or in non-clinical samples. Further, while early subjective response (around age 12) is shown to predict later alcohol outcomes ( Morean & Corbin, 2010), some youth may consume alcohol at such low doses that they do not experience its effects. For example, only 12 % of youth who sipped alcohol in 6th grade reported consuming a full standard drink three years later, with an even smaller proportion (4 %) reporting intoxication ( Jackson et al., 2015), highlighting the importance of first determining whether an adolescent has experienced a subjective response to alcohol at all.
In light of conflicting prior findings on OFC volume and alcohol use and given prior literature supporting a role for an individual's first subjective experiences to alcohol, examining the link between OFC volume and subjective response to alcohol may offer a novel approach to identifying those at heightened risk for early alcohol consumption. Here we investigate whether OFC volume in early adolescence predicts the likelihood of perceiving subjective alcohol effects during early drinking occasions in adolescence. Specifically, we examine whether bilateral medial and lateral OFC volumes at ages 9–10 prospectively predict having any subjective response to alcohol during their first five drinking episodes by ages 11–15. We hypothesize that smaller OFC volumes will be associated with increased odds of reporting subjective alcohol effects (e.g., stimulation and sedation). Additionally, we expect these associations to be moderated by biological sex, providing further insight into the interplay between brain structure, reward processing, and adolescent substance use.
2 Methods
2.1 Participants & procedures
Participants were part of the Adolescent Brain Cognitive Development (ABCD) Study, a longitudinal, multi-site study of a demographically diverse group of children between 9 and 10 years old (N = 11,880) from 21 study sites across the United States ( Volkow et al., 2018). Recruitment for the ABCD study aimed to reflect the sociodemographic diversity of the U.S. population as accurately as possible by using a multi-stage probability sampling method ( Garavan et al., 2018).
At baseline, families interested in participation completed a telephone screening to determine whether their child(ren) were eligible to participate. After the initial screening, eligible youth were invited for an in-person study session at their local study site to enroll in the study and completed subsequent annual in-person visits. Exclusion criteria included being younger than 9 or older than 10 at study entry; MRI contraindications; residing outside the recruitment area; being born before 28 weeks of gestation or with a low birth weight (<1,200g); experiencing birth complications that resulted in hospitalization for more than a month; having a major neurological disorder (e.g., epilepsy or history of seizures); a diagnosis of schizophrenia; moderate or severe autism spectrum disorder; intellectual disability; or alcohol or substance use disorder. Parental consent and participant assent were obtained prior to participation. For the present analyses, participants who consumed a full drink of alcohol after baseline and had complete data with subjective response scores and MRI data were included (N = 206); participants who reported consuming at least a full drink at baseline were excluded from the analysis (N = 21).
2.2 Sociodemographics
Parents and participating youth provided sociodemographic information during the baseline visit, including youth sex assigned at birth, race, ethnicity, and parental education ( Barch et al., 2018).
2.3 Substance use
Alcohol use was assessed through a semi-structured interview conducted by a trained research assistant. At Baseline – Year 2 Follow-Up, participants were asked if they had heard of alcohol to determine familiarity. If they were familiar with alcohol, they were further asked whether they had ever sipped alcohol and, if so, whether they had consumed a full alcoholic drink. Starting at Year 3, participants were not asked if they had heard of a substance and just asked if they had had a sip of alcohol since their last visit. Following the Baseline visit, participants were asked at each annual visit and mid-year phone interview if they had consumed alcohol since their last visit in order to monitor for alcohol use over time ( Lisdahl et al., 2018). If participants endorsed >2 episodes of alcohol use, they also completed the Timeline Followback ( Sobell et al., 1992) to measure total days and drinks of alcohol use in the past year. Alcohol use days were defined as the number of days within the past year that an individual reported consuming any amount of alcohol, while total drinks referred to the cumulative number of alcoholic drinks consumed over the past year. Participants were also queried on other substance use by drug class (e.g., nicotine and tobacco products; cannabis products). We include descriptives regarding percent of participants reporting consumption of a full standard unit of any other use of substances (more than experimenting) at any point in their lives.
2.4 Subjective response to the effects of alcohol
The 4-item Acute Subjective Responses to Alcohol measure from the PhenX Toolkit (
For this study, analyses focused on acute subjective responses during the first five drinking occasions. The First Five SRE score reflects the number of drinks needed to experience subjective alcohol effects (e.g., feeling different, dizziness, impaired coordination) across an individual's first five drinking occasions. The First Five SRE score is calculated by dividing the total number of drinks reported for these occasions by the number of questions endorsed. Higher scores indicate a lower subjective response to alcohol, as more drinks were needed to experience the specified effects. In contrast to other measures of subjective response to alcohol such as the Biphasic Alcohol Effects Scale (BAES;
Martin et al., 1993), it does not provide an opportunity to distinguish between sedating and stimulating effects of alcohol (see
Table 1
Participants were instructed to enter “0” for any item if they did not experience that particular effect during their first five drinking occasions. Subjective response was binarized: reporting any effects of alcohol was coded as 1, while reporting no effects, regardless of the amount of self-reported alcohol consumed, was coded as 0. This approach was intended to capture whether individuals experienced any effects of alcohol, rather than the quantity required to experience them. However, it may be that an absence of reported effects may reflect either a lack of perceived effects or very low levels of consumption insufficient to experience subjective alcohol effects.
2.5 Alcohol use expectancies
Youth's beliefs about the anticipated effects of alcohol use were assessed using the Alcohol Expectancy Questionnaire – Adolescent, Brief (AEQ-AB; Stein et al., 2007). This 7-item self-report questionnaire was administered annually in the ABCD Study (with the exception of Year 4) and asked youth to rate how likely various outcomes would occur if they were to drink alcohol ( Lisdahl et al., 2018). Items reflect both positive (e.g., “Alcohol helps a person relax, feel happy, feel less tense, and can keep a person's mind off of mistakes at school or work”) and negative (e.g., “Alcohol hurts how people think, and it hurts their coordination”) expectancies. Items were categorized into positive (items 1, 2, 4, and 6) and negative (items 3, 5, and 7) expectancy domains, and mean scores were calculated for each expectancy type.
2.6 MRI data acquisition
T-1 weighted anatomical magnetic resonance images (MRI) were collected locally and harmonized across 21 sites ( Casey et al., 2018). Structural scan parameters were as follows: voxel size = 1.0 x 1.0 × 1.0 mm3, matrix = 256 x 256, slices = 176 to 225, field of view phase = 93.75 %–100 %, TR = 6.31–2500 ms, TE = 2–2.9 ms, flip angle = 8°, and acquisition time = 5:38 to 7:12. Structural MRI scans were processed using a standardized pipeline ( Hagler et al., 2019), yielding segmented cortical volumes using the Desikan-Killany atlas ( Desikan et al., 2006). Baseline estimates of left and right medial and lateral orbitofrontal volumes were measured in cubic millimeters (mm 3).
2.7 Statistical analysis
Data analysis was performed using R version 4.4.1 in RStudio (Posit team, 2024). The data used in this study were obtained from ABCD Study data release 5.1 (
Volumetric data for each brain region was mean centered before analysis in order to standardize the variables and allow for clearer interpretation of the associations between brain volumes and outcome variables. Individuals with values three standard deviations above or below mean OFC volume subregion were excluded (n = 3). The final analysis included a total of 206 participants. Reference groups were reversed to ensure the odds ratio was directly interpretable (i.e., the OR was not below 1; McHugh, 2009).
2.8 Missingness
Multiple imputations were utilized for missing sociodemographic covariate data (e.g., 1 % parental education, 1 % race and ethnicity) using the mice R package ( van Buuren & Groothuis-Oudshoorn, 2011).
2.9 Primary analysis
The lme4 package in R ( Bates et al., 2015) was used to run a mixed-effects logistic regression model evaluating whether baseline OFC volume (at ages 9–10) predicts subjective response to alcohol at follow-up (mean age = 13.79, range = 10.00–15.75 years). The model included fixed effects for OFC volume, biological sex, parental education, age at baseline, race and ethnicity, and intracranial volume. Study site was modeled as a random intercept to account for clustering within sites. Given evidence of sex-specific neurodevelopmental trajectories and in the influence of alcohol on neurodevelopment ( Geidd et al., 2012; Medina et al., 2008), the model also included a sex by OFC interaction term to examine whether biological sex moderates the relationship between baseline OFC volume and subjective response to alcohol. Separate analyses were conducted on the volumes of the left and right medial and lateral OFC. All variables were standardized using the scale function from the base package in R, transforming values to a mean of zero and a standard deviation of one to increase interpretability ( R Core Team, 2024). Significance was set at p < .05.
2.10 Post-hoc analyses
To further examine differences between individuals who perceived a subjective alcohol response and those who did not, independent-samples t-tests were conducted on past-year alcohol use patterns, including total alcohol consumption and total alcohol use days, and on alcohol expectancies. Among individuals who endorsed a subjective response, Pearson's correlations were also conducted to assess the association between medial OFC volume and average subjective response scores, and medial OFC volume was associated with past-year alcohol use patterns (total alcohol drinks and total alcohol use days). Due to the timing of alcohol expectancies questionnaire administration, these data were only available for a subset of participants (n = 96).
3 Results
3.1 Participant characteristics
The study sample comprised 206 participants with a mean baseline age of 10.28 years (SD = 0.55, range = 8.92–11.00 years). There was a modestly higher proportion of female participants (57.3 %) compared to male participants. Regarding race and ethnicity, the majority of the participants identified as White (65.5 %). Participants reported high rates of lifetime co-substance use (94.66 %). Those who experienced a subjective response to alcohol reported more standard drinks of alcohol use in the past-year than those who did not experience a subjective response (t(124.27) = −4.63, p < .001). Full sociodemographic and substance use characteristics are presented in
Table 2
3.2 Subjective response to alcohol
Among the 206 participants that reported drinking at least a full drink of alcohol since baseline, 123 (59.7 %) endorsed experiencing at least one subjective alcohol effect, reflecting those who met the threshold to report effects across any of the four subjective alcohol response questions. Additional descriptive statistics for these participants, including the mean number of drinks required to experience various effects, are presented in Table 1. The remaining participants (n = 83) did not experience any of these effects and were not included in the table.
3.3 Primary analyses
In mixed-effects logistic regression analyses, smaller baseline left medial OFC volume was significantly associated with increased odds of reporting experiencing a subjective response to alcohol during early drinking occasions, after controlling for biological sex, parental education, age at baseline, race/ethnicity, intracranial volume, and study site (OR = 1.70, SE = 0.24,
p = .026, CI [1.07–2.71]; see
Fig. 1
3.4 Post-hoc analyses
Alcohol Use and Subjective Response. Participants who reported perceiving a subjective response to alcohol endorsed significantly higher total alcohol use days (p < .001) and total alcohol consumption (p < .001) in the past year compared to those who did not.
OFC Volume and Subjective Response Scores. Additionally, Pearson's correlations were conducted to examine the associations between medial OFC volume and subjective response. Analyses revealed no significant correlation between left medial OFC volume (r(121) = 0.08, p = .39) and average subjective response score.
OFC Volume and Drinking Behaviors. Among individuals who reported subjective effects, Pearson's correlations were conducted to examine the relationship between medial OFC volume and past-year alcohol use behaviors. Left medial OFC volume was not significantly correlated with total alcohol consumption (r(116) = 0.06, p = .50) or total alcohol use days (r(116) = 0.09, p = .34).
Alcohol Expectancies and Subjective Response. Independent-samples t-tests revealed no significant differences in alcohol expectancies between individuals who endorsed subjective alcohol effects and those who did not for positive (t(93.79) = −0.95, p = .34) and negative (t(93.97) = −0.78, p = .44) expectancies. See
Table 5
4 Discussion
Understanding how neurodevelopmental differences contribute to early alcohol experiences is critical for identifying adolescents at risk for later substance misuse. The orbitofrontal cortex (OFC), which supports decision making and reward processing, may play a role in shaping whether youth perceive alcohol's effects early in their drinking trajectories. This study investigated whether orbitofrontal cortex (OFC) volume, a brain region key for decision making and reward response, predicts the likelihood of reporting subjective alcohol effects during early adolescent drinking occasions. Results demonstrated that smaller baseline left medial OFC volume was significantly associated with increased odds of reporting a subjective response to alcohol. While an interaction between OFC∗biological sex was considered, the interaction was not significant. Furthermore, individuals who had experienced a subjective response had more past year alcohol use days and total drinks, though did not differ in their alcohol expectancies. Taken together, these findings suggest that OFC volume may not directly reflect alcohol sensitivity, but rather that OFC volume is related to drinking behavior and, in particular, consuming enough alcohol to produce noticeable effect during early use.
The current study builds upon literature emphasizing that the OFC plays a key role in adolescent alcohol use ( Koob & Volkow, 2010). However, our findings suggest that OFC volume may not directly reflect sensitivity to alcohol's pharmacological effects, instead reflecting early drinking behavior. The association between smaller left medial OFC volume at ages 9–10 and increased likelihood of reporting alcohol effects at ages 10–15 may reflect neurodevelopmental variation that contributes to risk for earlier or heavier drinking. The OFC follows a trajectory of expansion in childhood followed by cortical thinning, with adolescence marking a critical period of synaptic pruning and structural refinement ( Foulkes & Blakemore, 2018). Thus, smaller OFC volume at this stage could reflect either an accelerated pruning process leading to earlier maturation or a delayed trajectory of cortical development. These structural differences may underlie traits such as impulsivity or reward sensitivity, which increase the likelihood of early drinking to a sufficient extent to experience alcohol's effects. Individuals with smaller OFC volume may exhibit behavioral tendencies associated with reduced behavioral regulation, including impulsivity, which has been consistently linked to OFC dysfunction ( Berlin et al., 2004). These impulsivity-related traits may, in turn, contribute to earlier engagement in risk-taking behaviors such as alcohol use ( Herman & Duka, 2019). These data suggesting the OFC predicts the amount of alcohol use support prior indications that the OFC is critical to decision-making and reward valuation ( Rolls et al., 2020) and that the OFC may relate more to heavier substance use and reward responsiveness ( Wade et al., 2019).
These findings also align with prior research examining subjective response to alcohol in early adolescence. For instance, prior work Schuckit et al. (2008) demonstrated an average subjective response score of 2.4 (SD = 1.50) in 12-year-olds when reflecting on their first five drinking episodes. Similarly, the present analyses demonstrated that youth ages 11–14 who endorsed experiencing a subjective effect reported an average subjective response score of 2.46 (SD = 2.33) during their first five drinking episodes. These parallels suggest that the subjective responses observed in our sample reflect a perhaps normative range of alcohol exposure across early adolescence. On balance, nearly half our participants did not report experiencing a subjective response to alcohol during their first five drinking experiences. While this could reflect alcohol insensitivity, it may also be that the lack of subjective response was due to insufficient quantity of alcohol consumption. Thus, while subjective response to alcohol is a promising predictor of individual risk for problematic alcohol use, tracking individual variability in early alcohol exposure and response over time is needed. This distinction is particularly important in early adolescence, when drinking episodes often involve low or variable quantities of alcohol.
Although medial OFC volume was associated with whether youth reported early subjective effects of alcohol, follow-up analyses within individuals who reported a subjective response to alcohol revealed no significant associations between OFC volume and total past-year alcohol consumption or drinking days. However, youth who experienced a subjective response to alcohol endorsed higher levels of past-year alcohol use. These findings suggest that medial OFC volume may instead relate more broadly to the likelihood of consuming enough alcohol to perceive such effects rather than track variation in quantity of alcohol consumption. In addition, while the current study examined alcohol expectancies as a potential explanatory factor, no significant differences emerged between groups. A limitation of this data is that expectancy data were only available for a subset of participants. Future research should include more comprehensive assessments of alcohol expectancies, subjective response, and other factors (e.g., family factors) to better disentangle pharmacological sensitivity from learned expectations or environmental factors during early stages of alcohol use.
Despite prior literature suggesting that sex differences influence alcohol sensitivity and neurodevelopmental trajectories ( Giedd et al., 2012; Lenroot & Giedd, 2010; Medina et al., 2008),we did not find significant sex effects in the relationship between OFC volume and subjective response to alcohol. While prior neuroimaging studies suggest that adult females may be more vulnerable to the neurotoxic effects of alcohol compared to males ( Verplaetse et al., 2021), this potential sex-based difference did not then relate to different brain-behavior relationships in early adolescence. Given that neurostructural differences between sexes are often modest and change over time, we may have been underpowered to fully detect subtle sex differences in the interaction. Assessing sex at other ages, in larger samples, and across the brain is still needed. Moreover, the relative predictive utility of sex differences in alcohol use behaviors may become clearer later in adolescence as drinking behaviors escalate, highlighting the importance of future longitudinal studies. Investigating potential interactions between OFC structure, sex, and other individual differences (e.g., genetic predisposition, social environment) may help clarify the role of neurodevelopmental factors in shaping subjective alcohol response over time.
This study benefited from a prospective design of a diverse cohort. Even so, several limitations should be noted. Alcohol and other substance use was measured via self-report, which may be prone to intentional or unintentional misreporting ( Wade et al., 2022). Our sample included high rates of lifetime substance co-use. Though every effort was made in recruitment of the ABCD cohort to include a diverse sample, only a select subset of participants endorsed alcohol use. Findings may not generalize to all adolescent populations.
The current study utilized early subjective responses to alcohol during an individual's first five drinking episodes. While this measure captures impairing or sedative-like effects of alcohol (e.g., dizziness, stumbling, passing out), it does not assess positively reinforcing effects such as increased stimulation or arousal. This is a key limitation, as prior research demonstrates that heightened sensitivity to the stimulating effects of alcohol, particularly during the ascending limb of intoxication, is a stronger predictor of future alcohol use disorder than sensitivity to the sedating effects of alcohol ( King et al., 2021). Other validated instruments, such as the Biphasic Alcohol Effects Scale (BAES), are designed to differentiate between stimulant and sedative effects and may offer more nuanced insight into early alcohol response profiles. The integration of additional measures in future studies could enhance risk identification by distinguishing between youth who experience reinforcing versus impairing effects during initial drinking episodes.
The interpretation of a binary subjective response to alcohol relative to no response score requires careful consideration. While a score of 0 may reflect reduced sensitivity to the effects of alcohol, it could also result from drinking a quantity too low to elicit noticeable effects in an individual. This ambiguity limits the ability to fully distinguish pharmacological sensitivity from drinking behavior. This limitation highlights the need to account for both subjective experience and drinking behavior in future studies examining early alcohol use risk.
Identifying neurobiological factors, such as OFC structure, that contribute to variability in early alcohol exposure may help identify youth at elevated risk for alcohol misuse. From a clinical perspective, these findings highlight the potential utility of integrating neuroimaging measures with behavioral assessments to identify adolescents who may be more likely to engage in heavier early drinking and to experience alcohol's effects. Such efforts could inform prevention strategies aimed at reducing substance misuse risk and understanding individual differences in risk during this critical developmental period.
CRediT authorship contribution statement
L.S. Aguilar: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization. A.L. Wallace: Writing – review & editing, Conceptualization. K.E. Courtney: Writing – review & editing, Conceptualization. N.E. Wade: Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Conceptualization.
Acknowledgements
N.E. Wade was supported by
Table 1
| Effect of drinking alcohol (For the 5 times you ever drank) | M (SD), range |
| 1. How many drinks did it take for you to begin to feel different? (Where you could feel an effect)
Do no count sips taken as a child. |
2.48 (1.87), 0-10 |
| 2. How many drinks did it take for you to feel a bit dizzy or to begin to slur your speech? | 2.99 (2.75), 0-12 |
| 3. How many drinks did it take you to begin stumbling, or walking in an uncoordinated manner? | 2.87 (3.28), 0-12 |
| 4. How many drinks did it take you to pass out, or fall asleep when you did not want to? | 1.51 (3.28), 0-12 |
Table 2
| Sociodemographics | n (%) |
| n | 206 |
| Baseline Age M (SD), range | 9.7 (0.45), 8-10 |
| Sex % | |
| Male | 42.7 % |
| Female | 57.3 % |
| Race/Ethnicity | |
| White | 135 (65.5 %) |
| Hispanic | 40 (19.4 %) |
| Asian or Black | <10 (<5 %) |
| Other | <25 (<12 %) |
| Parental Education | |
| Less than high school diploma OR diploma or GED | 25 (12.1 %) |
| Some college | 70 (34.0 %) |
| Bachelor's degree | 50 (24.3 %) |
| Post-graduate degree | 61 (29.6 %) |
| Substance Use Characteristics | |
| % reporting SRE >0 | 123 (59.7 %) |
| Age of first subjective response to alcohol
M (SD), range |
13.79 (1.13), [10–15.75] |
| Past-year total drinks consumed for full sample M (SD), range | 7.02 (15.22), [0–145] |
| Past-year total drinks consumed (Experienced Subjective Response) | 10.14 (18.67), [0–145] |
| Past-year total drinks consumed (No Subjective Response) | 2.06 (2.62), [0–16] |
| % with lifetime use of any other substance | 195 (94.66 %) |
Table 3
| Mean | Standard Deviation | Range | |
| Left Medial | 5953.29 | 713.04 | [4098, 8008] |
| Right Medial | 6754.01 | 720.87 | [5242, 8836] |
| Left Lateral | 9469.17 | 944.40 | [7389, 11920] |
| Right Lateral | 9056.56 | 972.48 | [6337, 11626] |
| Intracranial Volume | 1481791 | 132154.30 | [1113540, 1783964] |
Table 4
| Model 1: Primary Analysis of Main Effects of OFC Predicting SRE | ||||||||||||
| L Lateral | L Medial | R Lateral | R Medial | |||||||||
| β | OR | p | β | OR | p | β | OR | p | β | OR | p | |
| OFC | −0.83 | 0.92 | 0.71 | 0.53 | 1.70 | 0.03 | 0.54 | 1.05 | 0.81 | 0.34 | 1.41 | 0.13 |
Table 5
| Group | Positive Expectancies (M, SD) | Negative Expectancies (M, SD) |
| No Subjective Response | 3.23 (0.50) | 3.21 (0.78) |
| Experienced Subjective Response | 3.33 (0.50) | 3.34 (0.82) |
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