Introduction

Adolescence is a critical time for transitions from alcohol misuse and experimentation to disorder due to ongoing brain development during this time. This period is therefore considered an essential point for identification of brain circuits contributing to problems later in life, a phenomenon referred to as preaddiction. Independent of future adult drinking, high-risk alcohol use during adolescence is itself an adverse health behavior, associated with multiple negative sequelae, and represents a leading cause of disability and mortality. By identifying brain mechanisms associated with early risk, we aim to facilitate the future development of novel prevention and intervention approaches to help mitigate problematic alcohol use.

Contemporary theories of alcohol use risk emphasize the dual roles of top-down inhibitory systems involving prefrontal cortical structures and bottom-up subcortical reward systems. However, studies of the brain’s functional connectome through network neuroscience indicate that typical brain maturation involves complex functional reorganization of networks instead of simple linear maturation of individual regions. These insights have transformed understanding of basic neurodevelopment but are not well characterized within the context of alcohol use risk behaviors.

Sex differences in neurodevelopment have been widely noted, with female individuals displaying accelerated developmental trajectories relative to male individuals. Sex differences in alcohol use during adolescence are also commonly reported. Therefore, this study aimed to identify neural networks conferring vulnerability for alcohol use in adolescents with specific consideration of sex differences. We adopted a multivariate predictive modeling framework to identify and cross-validate (ie, test in novel data) brain networks associated with current and future alcohol use risk in a large sample (approximately 1500) of adolescents using neuroimaging data acquired during performance of different cognitive tasks relevant to current neurodevelopmental models: that is, a reward-processing task and an inhibitory control task. Based on contemporary theories of neurodevelopment (eAppendix in Supplement 1), we anticipated that networks identified during reward-related processes would be most relevant for predicting alcohol use behaviors in younger adolescents (approximately 14 years) vs older adolescents (approximately 19 years), and that networks identified during inhibitory-related processes would be most relevant for predicting alcohol use behaviors in older vs younger adolescents.

Methods

Overview

The IMAGEN consortium collected alcohol and neuroimaging data across 8 sites. Ethics committees at each site approved the study. A parent or guardian provided written consent, and adolescents provided verbal assent. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Figure 1 provides a schematic overview of primary data collection and analysis steps. Baseline data were acquired at age 14 years. Follow-up data were acquired at age 19 years. Analyses focused on predicting alcohol use risk at age 19 years. Data acquired at baseline (age 14 years) were used to identify networks associated with future alcohol use risk, and data acquired at follow-up (age 19 years) were used to identify networks associated with current alcohol use risk.

Neuroimaging data were processed using a validated functional connectivity pipeline (eMethods in Supplement 1) to generate individual participant functional connectivity matrices (hereafter referred to as connectomes). Connectomes provide a multivariate summary of an individual’s unique pattern of functional brain organization. While connectomes are relatively distinct across individuals (ie, able to uniquely identify individuals, a process referred to as neural fingerprinting), an individual’s connectome also varies as a function of cognitive task performance, or brain state. Moreover, recent work demonstrates that brain state manipulation (via cognitive task performance vs resting state) is optimal for identifying core individual difference factors, including predictive neuromarkers of substance use in adults. Our analyses focus on connectomes derived from neuroimaging data acquired during both reward and inhibitory tasks (Figure 1A). Connectomes were entered into connectome-based predictive models (CPMs) (Figure 1B) to identify networks associated with alcohol misuse.

Participants

Demographic characteristics are provided in the Table. An independent sample of US college students (n = 114; age 17-23 years at scan, mean [SD] age, 18.42 [0.77] years) was used as an external replication sample (in Supplement 1).

Alcohol Use

Alcohol use was assessed using the Alcohol Use Disorders Identification Test (AUDIT), a well-validated self-report measure of alcohol use risk with total scores ranging from 0 to 40. AUDIT scores at age 14 years were low with relatively little variance across the sample (mean [SD] score, 1.36 [2.42]) (eFigure 1 in Supplement 1). Variance in AUDIT scores at age 19 years was higher (mean [SD] score, 5.53 [4.47]), and scores increased significantly from age 14 years to 19 years (t₈₇₀ = 28.94; P < .001). The Table and eFigure 2 in Supplement 1 present details on other substance use.

Neuroimaging

Functional magnetic resonance imaging (fMRI) data were collected during a reward task (monetary incentive delay) and during an inhibitory control task (stop signal). Details on tasks, preprocessing, and connectivity analyses are in the eMethods and eFigures 3 and 4 in Supplement 1.

Connectome-Based Predictive Modeling

As shown in Figure 1B, CPM comprises the following steps: (1) feature selection, wherein regression is used to identify connectome features associated with a behavioral variable of interest (here, alcohol use severity, using a feature selection threshold of P < .05) in a training data set; (2) feature reduction, in which identified connections are summed to create a summary value for each individual in the training data set; (3) model building, wherein summary scores (independent variable) are linearly associated with the behavioral variable (dependent variable); (4) model application, in which resultant linear models are applied to novel connectomes in a testing data set to generate predictions; and (5) model evaluation, in which the predictive ability of the model is evaluated based on the correspondence between model-predicted and actual alcohol use risk scores. Unlike correlation or regression, CPM with built-in cross-validation protects against overfitting by testing the strength of the association in previously unseen data (step 5), increasing the likelihood of replication in future studies and thus applicability to other clinical samples.

Connectomes for each task and time point were entered into CPMs using a nested cross-validation scheme, wherein 1 test site was left out, and 10-fold cross-validation was used for the left-in training sites. In this approach, data from a single site is left out. The predictive model is then generated using the remaining data from the other 7 sites. Model accuracy is then tested in the left-out site. This approach is the current gold standard for multisite neuroimaging data.

Both sex-agnostic (both sexes combined) and sex-specific (female and male separately) models were conducted. Model results were Bonferroni corrected across the number of models run at each time point and considered significant at 2-tailed α < .008, as determined via permutation testing.

Network Anatomy and Virtual Lesioning

Consistent with best practice recommendations, we summarized the anatomy of identified networks associated with alcohol use risk via their spatial overlap with well-established large-scale neural networks (eFigure 5 in Supplement 1), hereafter referred to as canonical neural networks. Further, consistent with recommendations to maximize mechanistic insight, we used a post hoc virtual lesioning approach in which connections overlapping with all but a single canonical network (eg, default mode) were virtually lesioned, and the model was rerun. By doing this for each canonical network separately, we were able to determine the relative predictive accuracy, or feature importance, of connections within each of these established networks. Network masks are available for download.

Results

Cross-Validated Brain-Behavior Models

Neural Networks Associated With Future Alcohol Use Risk

Among 1359 individuals in the study, the mean (SD) age was 14.42 (0.40) years, and 729 individuals (54%) were female. Analyses of baseline data (age 14 years) found sex divergence in the predictive accuracy of CPMs assessing future alcohol use severity. Specifically, sex agnostic models (reward ρ, 0.17; total edges, 1575; P < .001; inhibition ρ, 0.22; total edges, 1640; P < .001) and female-only models (reward ρ, 0.29; total edges, 3150; P < .001; inhibition ρ, 0.26; total edges, 2119; P < .001) were successful in predicting future alcohol use severity at age 19 years, but male-only models were not (reward ρ, 0.04; total edges, 359; P = .43; inhibition ρ, 0.11; total edges, 391; P = .02). The same pattern of results emerged in post hoc analyses controlling for baseline alcohol use severity and residual motion (eResults and eTable 1 in Supplement 1). Comparisons of model accuracies using Fisher r to zconfirmed that the female-only models significantly outperformed male-only models (reward z = 4.0; P < .001; inhibition z = 2.47; P = .01).

Neural Networks Associated With Current Alcohol Use Risk

Analyses of follow-up data (age 19 years) further indicated sex divergence in the predictive accuracy of CPMs assessing current alcohol use severity. Specifically, both sex-agnostic models (reward ρ, 0.17; total edges, 2904; P < .001; inhibition ρ, 0.27; total edges, 2538; P < .001) and female-only models (reward ρ, 0.29; total edges, 2606; P < .001; inhibition ρ, 0.27; total edges, 2134; P < .001) were successful in predicting current severity across both task types. In contrast, male-only models were successful in predicting current severity using connectivity data acquired during inhibitory task performance (ρ, 0.18; total edges, 622; P < .001) but not using data acquired during reward task performance (ρ, 0.03; total edges, 906; P = 0.44), indicating task specificity of alcohol risk models in male individuals only.

The same pattern of results emerged in post hoc analyses controlling for baseline alcohol use severity and residual motion (eTable 2 in Supplement 1). Comparisons of model accuracies indicated that the female-only model generated from reward data significantly outperformed the male-only model generated from reward data (z = 4.4; P < .001). There were no significant differences in predictive model performances between female-only and male-only models generated from inhibitory data (z = 1.67; P = .10).

Anatomy of Alcohol-Risk Networks

Figures 2 and 3 show the anatomy of the identified networks as a function of spatial overlap with canonical neural networks. Anatomy is summarized separately for positive and negative predictive connections. Positive predictive connections are those for which increased connectivity positively predicted alcohol use risk. Negative predictive connections are those for which decreased connectivity positively predicted alcohol use risk. By definition, positive and negative connections cannot overlap directly (as a single connection cannot be both a positive and negative predictor). Nonetheless, as summarized in Figures 2 and 3, positive and negative predictive connections may include connections within and between similar large-scale canonical neural networks. Figures 2 and 3 also show virtual lesioning results corresponding to the relative predictive accuracy or feature importance of connections.

Neural Networks Associated With Future Alcohol Use Risk

As shown in Figure 2, neural networks associated with future alcohol use using either reward or inhibitory control task data were similar among female adolescents, indicating the relevance of both processes in conferring vulnerability for alcohol use risk. For both tasks, positive connections included a high degree of within-network medial frontal, motor-sensory, and cerebellar connections, as well as substantial between-network connections with the cerebellar and subcortical networks. The network identified using reward data was further characterized by between-network connections within the medial frontal and motor-sensory networks. For both tasks, negative connections included a high degree of between-network motor-sensory connections.

Post hoc virtual lesioning indicated that networks with the highest individual feature weights—that is, amount of variance in future alcohol use associated with connectivity within a given canonical network—included frontoparietal, salience, motor-sensory, and cerebellar networks.

Neural Networks Associated With Current Alcohol Use Risk

As shown in Figure 3, female-only positive networks generated from both tasks were dominated by motor-sensory, cerebellar, subcortical, and salience network connections, whereas negative networks were largely characterized by motor-sensory, salience, and subcortical connections. Although the male-only networks identified using inhibitory data were sparser than the female networks, they featured connections within and between similar networks: the positive male-only network was dominated by connections between cerebellar and subcortical networks as well as by within-network cerebellar connections; the negative male-only network contained a high degree of cerebellar, subcortical, salience, and medial frontal connections as well as a high degree of within-network subcortical connections.

For female adolescents, post hoc virtual lesioning indicated that networks with the highest individual feature weights—that is, amount of variance in current alcohol use associated with connectivity within a given canonical network—included salience, motor-sensory, and cerebellar networks. For male adolescents, virtual lesioning indicated that networks associated with the highest amount of variance in current alcohol use included motor-sensory, cerebellar, and subcortical regions.

Network Change Over Time

For successful models, individual participant network summary scores were created as the sum of connectivity strengths within positive and negative networks and entered into paired t tests to characterize the development of the identified networks over time. Among female adolescents, connectivity within reward and inhibitory control networks predictive of future alcohol use significantly increased from age 14 years to age 19 years (reward t₅₃₉ = −7.09; P < .001; inhibition t₅₅₉ = −4.68; P < .001). Among both female and male adolescents, connectivity within inhibitory control networks predictive of current use also significantly increased from age 14 to 19 (female t = −5.61_{(df = 559)}, P < .001; males: t₄₆₈ = −5.87; P < .001), while changes in connectivity within the reward network predictive of current use in female adolescents from age 14 years to age 19 years did not reach significance (t₅₃₉ = −1.77; P = .08).

Network Overlap Across Sexes

Despite significant differences in predictive accuracies, networks identified as associated with alcohol use behaviors were relatively consistent across male and female adolescents. Consistent with this, cross-sex analyses demonstrated that models developed in female adolescents at age 19 years generalized to predict alcohol use in male adolescents at age 19 years (reward: ρ, 0.14; P = .001; inhibition: ρ, 0.15; P < .001), and that models developed in male adolescents at age 19 years generalized to predict alcohol use in female adolescents at age 19 years for inhibitory, but not reward, task data (reward: ρ, 0.07; P = .08 inhibition: ρ, 0.26; P < .001).

Specificity and Generalizability

Specificity Analyses

To determine the specificity of the identified anatomical connections to alcohol use, we repeated CPMs while controlling for variation in impulsivity and neuroticism. For all models, predictive accuracies were robust to these factors, indicating that the identified connections were specific to alcohol use and that model accuracies were not driven by a more general latent trait personality factor (eTables 3 and 4 Supplement 1).

As shown in the Table, substance use rates were low at age 14 years but were higher at age 19 years (eg, approximately 64% lifetime cigarette use). Post hoc analyses indicated significant associations between substance use and connectivity within the male-only inhibition model such that connectivity was greater among males with nonalcohol substance use. Follow-up analyses indicated that associations between alcohol use and connectivity remained significant within each group (substance-use positive and substance-use negative) separately (eTables 3-5 in Supplement 1).

Generalizability

The ultimate test of statistical reliability is whether a model produces consistent results in novel individuals and settings. For this reason, in our analyses above, we adopted a rigorous leave-one-site-out cross-validation scheme, in which models were tested in previously unseen testing sites. As a further test of the robustness of the effects, we sought to determine whether the identified neural networks would also be associated with alcohol use risk in an entirely separate sample of 114 adolescents from the Brain and Alcohol Research in College Students (BARCS) study who were recruited independently from the IMAGEN consortium (eResults in Supplement 1).

BARCS differed from IMAGEN in multiple ways, including a different within-scanner inhibitory control task (go/no-go task), different imaging acquisition parameters, a smaller sample size, recruitment from the US (vs Europe), and different alcohol use risk assessments. Despite these differences, connections identified in IMAGEN as associated with current alcohol use risk replicated to predict risky alcohol use (in this case, defined as the largest number of alcoholic drinks consumed in 24 hours) in this external test sample (ρ, 0.20; total edges, 113; P = .03) (Figure 4). Additional details and analyses related to BARCS are in the eResults in Supplement 1.

Discussion

This study leveraged data from the IMAGEN consortium to identify neural networks associated with alcohol use risk using an advanced connectome-based approach. Using data from 2 cognitive tasks with relevance to adolescent risk behaviors—a reward task and an inhibitory control task—we identified both common and distinct neurobiological substrates of early alcohol use risk among female and male adolescents, as well as sex divergence in model accuracies. For female adolescents, models generated using neuroimaging data from both task types were successful in identifying reliable neural signatures of future and current alcohol use risk. In contrast, for male adolescents, only the model generated using inhibitory control task data was successful in predicting current alcohol use risk, and no reliable signature of future risk was identified (ie, brain features identified in training data did not predict alcohol use in previously unseen testing data). Despite these differences, connections identified as commonly associated with current alcohol use risk across male and female adolescents not only generalized to held-out testing sites within IMAGEN, but also predicted alcohol behaviors in an entirely independent cohort of adolescents. Thus, the identified alcohol use risk network may be considered as a robust neuromarker that may be targeted in future prevention and intervention efforts.

Among female adolescents, identified connections included subcomponents of multiple well-established resting state networks, including medial frontal, frontoparietal, salience, motor-sensory, and cerebellar networks. Connectivity within these networks generally increased from age years 14 to age 19 years, concurrent with increases in alcohol use. In addition, despite similar features overall, the relative predictive weights of network subcomponents associated with future (age 14 years) vs current (age 19 years) alcohol use risk varied over time, such that medial frontal and salience networks were primarily associated with future alcohol use risk, whereas cerebellar and motor-sensory networks emerged as primarily associated with current alcohol use risk. These findings are consistent with recent data demonstrating altered cerebellar growth trajectories among adolescents with heavy alcohol consumption as well as with much broader literature implicating cerebellar and motor systems in substance-use pathophysiology (eDiscussion in Supplement 1). Together, these data suggest that targeting prefrontal cortical regions may be particularly relevant to prevention efforts in younger adolescents and that targeting cerebellar and motor-sensory regions may be more relevant for intervention efforts in older adolescents who have already initiated drinking. Consistent with this, network connectivity at age 19 years generalized to predict alcohol use in our external sample of older young adults.

Inhibitory control (vs reward) brain states were more relevant for predicting alcohol use in older adolescents; however, this effect was specific to male adolescents. This finding is consistent with work demonstrating that the accuracy of generalizable brain-behavior models of core individual difference factors (eg, intelligence) differs as a function of both sex and brain state, but for the first time to our knowledge demonstrates similar sex specificity within the context of alcohol use risk. Together, these data suggest that interventions focusing on inhibitory control processes may be particularly effective in combating current alcohol use risk in male adolescents but that both inhibitory and reward-related processes are likely of relevance to current alcohol use behaviors in female adolescents. Sex-specific tailoring of interventions has been proposed previously but is seldom—if ever—grounded in neurobiology. By identifying robust markers of risky alcohol use during development, we hope to support the development of such neurobiologically informed prevention and intervention efforts.

Strengths and Limitations

This study has several strengths, including its unique longitudinal sample and the use of a wholly data-driven approach with the incorporation of rigorous leave-one-site-out internal validation and external validation using an independent sample. This study also has several limitations, including the absence of a consistent measure of alcohol use severity across internal and external validation samples and the absence of more frequently acquired neuroimaging data that could be used to track trajectories. Further, our external replication sample was primarily White European, so it remains to be seen whether these predictive networks will generalize to more diverse populations.

Conclusion

These data provide a connectome-wide assessment of neural networks subserving alcohol use risk and identify a dimensional neural signature of alcohol use risk in adolescents. External validation supported these findings.

Summary

This study investigated neural networks associated with adolescent alcohol use risk, considering sex differences. Utilizing a large sample (approximately 1500) of adolescents from the IMAGEN consortium, researchers employed a multivariate predictive modeling framework and neuroimaging data acquired during reward and inhibitory control tasks. The study aimed to identify and cross-validate brain networks predicting current and future alcohol use risk, anticipating differences based on age and sex.

Methods

This research employed data from the IMAGEN consortium, adhering to rigorous ethical guidelines and standardized reporting procedures. Functional magnetic resonance imaging (fMRI) data were collected during reward and inhibitory control tasks. A validated functional connectivity pipeline generated individual functional connectivity matrices (connectomes), which were then used in connectome-based predictive models (CPMs). These CPMs involved feature selection, reduction, model building, application, and evaluation, incorporating a nested cross-validation scheme to ensure robustness and minimize overfitting. An independent external replication sample was also utilized. Alcohol use was assessed using the Alcohol Use Disorders Identification Test (AUDIT).

Participants

The study comprised a large adolescent sample from the IMAGEN consortium (approximately 1500) across multiple sites, with approximately 54% identifying as female. An independent sample of US college students served as an external validation group. Demographic characteristics are detailed in the accompanying tables.

Alcohol Use, Neuroimaging, and Connectome-Based Predictive Modeling

Alcohol use was assessed at baseline (age 14) and follow-up (age 19). Neuroimaging data included fMRI during reward and inhibitory control tasks. CPMs were used to identify and test brain networks associated with alcohol use, incorporating both sex-agnostic and sex-specific models. A post-hoc virtual lesioning analysis examined the relative predictive accuracy of connections within established large-scale neural networks.

Results

Sex-specific differences in predictive model accuracy emerged. For female adolescents, models using both reward and inhibitory task data successfully predicted future and current alcohol use risk. Male adolescents showed successful prediction only for current alcohol use risk using inhibitory control task data. Identified networks involved various canonical networks, including medial frontal, frontoparietal, salience, motor-sensory, and cerebellar networks. Connectivity within these networks generally increased from age 14 to 19. Virtual lesioning analyses highlighted the relative contributions of various network components. Finally, cross-sex analyses indicated some generalizability of models across sexes.

Specificity and Generalizability

Specificity analyses confirmed that the identified connections were specific to alcohol use and not driven by broader personality factors. Generalizability was demonstrated through leave-one-site-out cross-validation and replication in an independent external sample.

Discussion

The study revealed sex-specific neural networks associated with alcohol use risk. These findings suggest that interventions should consider these sex differences, targeting prefrontal cortical regions for younger adolescents and cerebellar/motor-sensory regions for older adolescents. The generalizability of findings across samples underscores the robustness of the identified alcohol use risk network as a potential target for prevention and intervention.

Strengths and Limitations

Strengths included the large longitudinal sample, data-driven approach, and rigorous validation procedures. Limitations included inconsistencies in alcohol use severity measures and a lack of more frequent neuroimaging data. The external validation sample’s limited diversity may also restrict generalizability to other populations.

Conclusion

This study offers a comprehensive connectome-wide assessment of neural networks underlying alcohol use risk in adolescents, emphasizing sex-specific differences and providing evidence for a robust, generalizable neural signature of alcohol use risk.

Summary

This study investigated neural networks associated with adolescent alcohol use risk, considering sex differences. Utilizing data from the IMAGEN consortium, a large-scale longitudinal study, researchers employed connectome-based predictive modeling (CPM) to analyze functional connectivity data acquired during reward and inhibitory control tasks. The goal was to identify brain networks predicting both future (age 19) and current (age 19) alcohol use severity, acknowledging the dynamic nature of brain development during adolescence and sex-related differences in neurodevelopment and alcohol use patterns.

Methods

The study leveraged neuroimaging data and alcohol use assessments from approximately 1500 adolescents within the IMAGEN consortium, with a subset used for external validation. Functional connectivity matrices (connectomes) were generated from fMRI data during reward and inhibitory control tasks. CPMs were used to identify and cross-validate brain networks associated with alcohol use, employing a rigorous leave-one-site-out cross-validation strategy to ensure model robustness. Both sex-specific and sex-agnostic models were created and compared. Post-hoc analyses included virtual lesioning to assess the relative contribution of different brain networks to the predictive models.

Results

Significant sex differences were observed in the predictive accuracy of the CPMs. For female adolescents, models using both task types effectively predicted future and current alcohol use. In male adolescents, only inhibitory control task data predicted current alcohol use; no reliable prediction of future risk was found. Despite these differences, networks associated with current alcohol use showed some overlap across sexes and generalized to an independent sample, suggesting a robust neuromarker for alcohol use risk. Networks identified in females consistently involved medial frontal, frontoparietal, salience, motor-sensory, and cerebellar regions. Over time, connectivity within these networks increased concurrently with alcohol use. For males, inhibitory control networks were primarily associated with alcohol risk.

Discussion

This study's findings highlight sex-specific neural substrates of alcohol use risk during adolescence. While females showed predictive models for both task types, males only showed predictive models for inhibitory tasks. The identified networks involve crucial regions implicated in reward processing, inhibitory control, and motor function. The results suggest that interventions targeting specific brain networks might improve the efficacy of prevention and intervention efforts, tailored to sex and developmental stage.

Strengths and Limitations

The study's strengths include its large longitudinal sample size and rigorous validation methods. Limitations include inconsistencies in alcohol use measures across samples and the limited diversity of the external validation sample.

Conclusion

The study identified a robust neural signature of alcohol use risk, validated across multiple datasets, providing insights into sex-specific neurobiological mechanisms underlying adolescent alcohol use. These findings have implications for developing targeted prevention and intervention strategies.

Summary

This study investigated brain networks linked to alcohol use risk in adolescents, considering sex differences. Researchers used neuroimaging data from about 1500 adolescents, analyzing brain activity during reward and inhibitory control tasks. The goal was to identify brain patterns that predict current and future alcohol misuse and to see if these patterns differed between boys and girls.

Methods

The study used data from the IMAGEN consortium, which collected brain scans and alcohol use information from adolescents at ages 14 and 19. Brain scans measured functional connectivity, essentially how different brain regions communicate. This information was used to create "connectomes," individual maps of brain organization. These connectomes were then used in predictive models to see how well they could forecast alcohol use at age 19. The study included separate analyses for boys and girls and used rigorous statistical methods to avoid errors and ensure the results were reliable. An additional sample of college students was used to check the findings in a different group.

Participants

The main study included roughly 1500 adolescents (ages 14 and 19), with about half being female. A separate group of about 114 college students (ages 17-23) served as an independent test group. Alcohol use was measured using the AUDIT questionnaire.

Results

For girls, brain networks identified at age 14 predicted future alcohol use, and networks identified at age 19 predicted current alcohol use. This was true for both reward and inhibitory control tasks. For boys, only inhibitory control networks at age 19 predicted current alcohol use. Brain regions involved included parts of the frontal, sensory-motor, and cerebellar areas, amongst others. Interestingly, the strength of connections within these networks increased from age 14 to 19 in girls, correlating with increased alcohol use. The findings were generally consistent across both sexes and replicated in the independent sample.

Discussion

The study found that distinct brain networks are related to alcohol use risk, with some differences between boys and girls. For girls, both reward and control processes seem important in predicting alcohol use at different ages. For boys, inhibitory control appears more crucial. This suggests that prevention and treatment approaches might need to be tailored to the sex of the adolescent. The findings were reliable and consistent across different groups and settings.

Strengths and Limitations

The study’s strengths include its large, longitudinal sample, rigorous statistical methods, and successful replication in a separate group. Limitations include the lack of consistent alcohol use measures across all samples and limited diversity in the replication sample.

Conclusion

This research offers valuable insights into the brain mechanisms involved in adolescent alcohol misuse and the importance of sex-specific approaches. The identified brain networks represent potential targets for preventing and treating problematic alcohol use.

Summary

This study looked at how the brain's connections relate to alcohol use in teens. Scientists used brain scans and questionnaires to see how brain activity during certain tasks predicted future and current alcohol use. They found differences between girls and boys. For girls, brain scans were good at predicting both future and current alcohol use. For boys, scans only predicted current alcohol use. The study also showed some brain areas that were important for alcohol use in both girls and boys.

Methods

Scientists studied a large group of teenagers at ages 14 and 19. They used brain scans to see how different parts of the brain talked to each other. They also asked teens about their alcohol use. They used special computer programs to find the brain connections linked to how much alcohol the teens used.

Participants

The study included about 1500 teens, roughly half girls and half boys, from different places. Another smaller group of college students was used to check the results. They measured how much alcohol the teens drank using a special test.

Alcohol Use

The scientists measured alcohol use at ages 14 and 19. At age 14, most teens drank very little alcohol. By age 19, they drank more.

Neuroimaging

The scientists used brain scans (fMRI) while teens did tasks that tested their ability to control themselves and how they responded to rewards.

Connectome-Based Predictive Modeling

Scientists used special computer models to figure out which brain connections were linked to alcohol use. These models helped them see what connections were important for predicting how much alcohol someone would drink in the future.

Results

Brain scans were better at predicting future alcohol use in girls than boys. For girls, both types of tasks (rewards and controlling impulses) helped predict alcohol use. For boys, only impulse-control tasks helped predict alcohol use.

Anatomy of Alcohol-Risk Networks

The scientists found that certain parts of the brain were connected in specific ways for teens who drank more alcohol. These areas included parts involved in movement, feelings, and decision-making.

Network Change Over Time

In girls, the connections in the brain that predicted alcohol use got stronger between ages 14 and 19.

Network Overlap Across Sexes

Even though the results were different for girls and boys, some similar brain connections were linked to alcohol use in both.

Specificity and Generalizability

The results showed that the brain connections found were specifically related to alcohol use, and not just general personality traits. The study also showed that the findings were accurate in a different group of teens.

Conclusion

This study showed that brain scans can help predict alcohol use in teens, especially in girls. Different brain areas were important for girls and boys, suggesting that we might need different ways to help teens avoid problems with alcohol.