Structural Covariance Network Properties Predictive of Early Adolescent Alcohol Initiation

Structural Covariance Network Properties Predictive of Early Adolescent Alcohol Initiation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Structural Covariance Network Properties Predictive of Early Adolescent Alcohol Initiation View ORCID Profile H. Byrne , R. Visontay , E. K. Devine , N. E. Wade , J. Jacobus , L. M. Squeglia , L. Mewton doi: https://doi.org/10.1101/2025.05.08.652983 H. Byrne 1 The Matilda Centre for Research in Substance Use and Mental Health, The University of Sydney Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for H. Byrne For correspondence: hollie.byrne{at}sydney.edu.au R. Visontay 1 The Matilda Centre for Research in Substance Use and Mental Health, The University of Sydney Find this author on Google Scholar Find this author on PubMed Search for this author on this site E. K. Devine 1 The Matilda Centre for Research in Substance Use and Mental Health, The University of Sydney Find this author on Google Scholar Find this author on PubMed Search for this author on this site N. E. Wade 2 Department of Psychiatry, University of California San Diego , La Jolla, CA, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site J. Jacobus 2 Department of Psychiatry, University of California San Diego , La Jolla, CA, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site L. M. Squeglia 3 Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina , Charleston Find this author on Google Scholar Find this author on PubMed Search for this author on this site L. Mewton 1 The Matilda Centre for Research in Substance Use and Mental Health, The University of Sydney Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Importance Early alcohol initiation (before age 15) is associated with adverse outcomes. Understanding mechanisms behind early alcohol initiation is essential for informing prevention efforts. Objective To examine whether structural covariance network properties at ages 9-10 years predict early alcohol initiation. Design Case-control, population-based study design. Setting Data from the Adolescent Brain Cognitive Development study were used. Baseline structural brain imaging data (ages 9-10) were used for generation and comparison of structural covariance networks. Data from baseline to 4-year follow-up (≤age 15) assessments were used to determine alcohol initiation. Participants Participants were excluded if they reported consuming a full drink of alcohol at baseline, or did not meet imaging inclusion criteria. Controls were excluded if they had not yet been assessed or were missing substance use data at 4-year follow-up. In total, 3,878 participants met study criteria, of which 182 participants initiated alcohol. Structural covariance network properties were compared between the full sample and a 1:1 propensity-matched sample based on age, sex, race, ethnicity, religion, parental education, prenatal alcohol exposure, and baseline alcohol sipping. Main Outcomes and Measures Structural covariance networks were estimated using regional cortical thickness and volume measurements. Measures of network segregation (modularity, clustering coefficient), integration (characteristic path length, global efficiency), and resilience (degree assortativity) were compared between groups. Early alcohol initiation was defined as consuming a full drink between baseline and 4-year follow-up Results Alcohol initiators ( n =182, median[IQR] age, 10.3[9.9-10.8]; 101 female[55.5%]) demonstrated lower network segregation (modularity: area-under-the-curve[AUC] difference[95%CI]=-0.017[-0.017,-0.007], p =0.030; clustering coefficient: AUC[95%CI]=-0.026[-0.027,-0.012], p =0.0495) and higher network integration (characteristic path length: AUC[95%CI]=-0.106[-0.099,-0.046], p =0.020; global efficiency: AUC[95%CI]=0.011[0.005,0.011], p=0.010), compared to non-initiators ( n =3,696, median[IQR] age, 9.9[9.4-10.4]; 1750 female[47.4%]) when controlling for age, sex, and mean cortical thickness. Within the matched sample, only differences in network integration were preserved (characteristic path length: AUC[95%CI]=-0.044[-0.032,0.035], p =0.010; global efficiency: AUC[95%CI]=0.003[-0.003,0.003], p =0.040). There were no differences between full or matched samples when comparing cortical volume structural covariance networks. Conclusions and Relevance Differences in cortical thickness structural covariance network properties at ages 9-10 predicted alcohol initiation before age 15. These findings suggest cortical thickness network topology may reflect a neuroanatomical risk marker for early alcohol initiation. Question Do structural covariance network properties at age 9-10 years predict alcohol initiation prior to age 15? Findings In this case-control study of 3,878 participants, early adolescent alcohol initiators demonstrated differences in cortical thickness network integration and segregation compared to their non-initiating peers. Meaning Alcohol-naïve adolescents who initiate alcohol use early in life demonstrate differences in structural brain network organization compared to their abstinent peers, which may reflect a neuroanatomical risk marker for early alcohol use. Introduction Globally, approximately 1.34 billion people consume alcohol in harmful amounts, with alcohol use accounting for 1.78 million deaths in 2020 1 . Alcohol use often begins, and noticeably escalates, throughout adolescence 2 . Importantly, early alcohol initiation (typically defined as use before the age of 15 3 ) is a significant risk factor for alcohol use disorder later in life 4 , in addition to several other long-term adverse psychosocial and health consequences 5 , including poorer academic performance 6 , increased risk of psychiatric disorders 7 , and cancer 8 . As a result, it is essential to better understand the risk factors and mechanisms that contribute to early alcohol initiation to inform prevention and early intervention efforts. A growing body of research suggests that differences in brain structure may predict early alcohol initiation in adolescence. For example, a recent study of 9,804 children from the Adolescent Brain Cognitive Development (ABCD) study 9 identified that morphometric differences at age 9-10 years in multiple regions of the brain, including the prefrontal cortex and parahippocampal gyrus, were associated with alcohol sipping and/or consuming a full drink of alcohol prior to age 15 10 . These findings are supported by previous studies that have identified lower volume in the dorsolateral prefrontal cortex in children who are substance-naïve as predictive of early 11 and moderate-to-heavy 12 , 13 future alcohol use. However, recent work by Green et al. 15 found that neuroanatomical predictors of early substance use experimentation were distributed diffusely across the brain, rather than localized to prefrontal areas. This inconsistency between studies may be due to a focus on regional markers, despite more contemporary conceptualizations of the brain as a complex system or network 16 . Despite its importance for cognitive, emotional, and behavioral development 17 , the extent to which this brain network-level complexity in alcohol-naïve adolescents predicts early alcohol initiation is currently unknown. Structural covariance network (SCN) analysis provides a useful tool for probing the structural organization and complexity of brain networks. Based on evidence that brain regions with similar developmental trajectories are closely related in morphometry, this technique captures the covariance in cortical thickness, volume, and/or surface area between brain regions 18 . Studies have shown SCNs are sensitive to neurodevelopmental and age-related changes 19 , and importantly, may be predictive of alcohol use in later life 20 . Specifically, Ottino-González et al. 20 identified that adolescents who exhibit heavy drinking behaviors at age 19 demonstrate differences in cortical thickness SCN properties at age 14. However, only 37% of these participants were alcohol-naïve at baseline; as recent work has shown SCN properties can be affected by both recent 21 and heavy 22 substance use, it is unclear whether differences in structural brain organization exist prior to alcohol use, and importantly, whether such features are predictive of early alcohol initiation. Using data from the ABCD study, this work aimed to explore whether SCN properties in alcohol-naïve 9–10-year-olds are predictive of early initiation of alcohol use (defined as first full drink prior to age 15). We first examined whether regional cortical thickness and volume were predictive of early alcohol initiation. Next, we investigated whether whole brain SCN properties derived from cortical thickness and volume measurements at ages 9-10 differed between early initiators and abstainers. Methods Participants Data from the ABCD Study release 5.1 were used, which includes biological and behavioral data from 11,868 participants aged 9-10 years at baseline 9 . Briefly, participants were recruited from 21 sites across the United States between September 2016 and November 2018, with follow-up visits conducted at yearly intervals. Parents/caregivers provided signed informed consent and all participants gave assent. The ABCD protocol was approved by the centralised institutional review board (IRB) at the University of California, San Diego and by the IRBs at the 21 sites. Release 5.1 includes complete data up to the 3-year follow-up (N=11,868, ages 12-13) and a subset of data for the 4-year follow-up (N=4,754, ages 13-14), due to ongoing data collection during this period. Given the small number of participants who initiated alcohol use at follow-up 3 23 , the current study included data from the subset of participants at follow-up 4. Participants were excluded if they reported consuming a full drink at baseline (n=21), had missing structural MRI data at baseline (n=140), and/or did not meet neuroimaging quality control inclusion criteria (n=458). Participants were excluded if they did not have complete alcohol use data at follow-up 4 or had not yet participated at this timepoint (n=6,708). Finally, because the ABCD dataset is oversampled for siblings and twins, siblings were excluded at random in the final dataset to reduce potential genetic and/or environmental biases (n=663). Of the 11,868 youths enrolled in the study at baseline, 3,878 met our inclusion criteria ( Figure 1 ). Download figure Open in new tab Fig. 1. Flowchart for participant inclusion/exclusion criteria. Figure created in Biorender.com . Measures Alcohol Initiation Early alcohol use was defined as consuming a full drink of alcohol at any timepoint between baseline and 4-year follow-up (“initiator” group). This was assessed using binary ‘Yes’/’No’ participant responses to the question: “Have you ever tried alcohol at any time in your life? A full drink of beer, wine or liquor (rum, vodka, gin, whiskey),” as self-reported during the Substance Use Interview 24 . Imaging Acquisition Baseline preprocessed imaging data acquired at ages 9-10 years were used in the current study 25 . Cortical reconstruction and volumetric segmentation of the structural T1-weighted data were performed using FreeSurfer (v7.1.1; https://surfer.nmr.mgh.harvard.edu/ ). Only cortical surface reconstruction data that met recommended quality control criteria were included 25 . Postprocessed cortical thickness and volume measurements mapped to the Desikan-Killiany atlas included 34 cortical parcellations per hemisphere, for a total of 68 regions of interest (ROIs), respectively 26 . SCN generation All processing and statistical analyses were performed using R Statistical Software (v4.3.3) 27 , conducted in RStudio (v2024.04.2) 28 . First, to minimize scanner effects, imaging data were harmonized prior to analysis using the RELIEF package (v0.1.0) 29 . SCNs were generated ( Figure 2 ) using the brainGraph package (v3.1.0) 30 , 31 . ROIs for cortical thickness and volume were used to define the ‘nodes’ of the networks. ‘Edges’ were defined as the similarity between pairs of ROIs, calculated using Pearson’s correlations between studentized residuals for each ROI within a given modality. Analyses examining cortical thickness and cortical volume SCNs were conducted separately (generating 68x68 adjacency matrices, respectively). Download figure Open in new tab Fig. 2. SCN generation pipeline, partially generated using BioRender.com . Network measures Global (whole brain) SCN properties were summarized using graph theory metrics to assess differences in network segregation (modularity and clustering coefficient), integration (characteristic path length and global efficiency), and resilience (degree assortativity) between groups across densities (i.e. the proportion of non-zero edges retained after thresholding). Briefly, 32 network segregation describes functional specialization of the brain into modules, enabling efficient processing of specific types of information (such as visual input); integration measures the brain’s ability to combine information across regions, enabling coordinated functions such as cognitive processing; resilience refers to the brain’s capacity to maintain function/integrity despite disruptions to the network. These measures have been described in further detail previously 32 and are summarized in eTable 1 . Covariates Current recommendations for neuroimaging analysis in population-based datasets suggest adjusting for age, sex, and global brain measures prior to the inclusion of additional (i.e. sociodemographic) covariates 33 – 35 . However, unlike the established relationship between regional cortical volume and whole brain volume 36 , there is ongoing debate concerning whether adjusting for mean cortical thickness in regional cortical thickness measurements can obscure meaningful individual heterogeneity 33 , 37 . Therefore, preliminary testing was conducted to explore the impact of mean cortical thickness on model variance ( Supplementary Materials ; eTable 3 ). This testing confirmed that adjusting for average cortical thickness alongside age and sex in our cortical thickness models was appropriate for our study, with volumetric comparisons adjusted for age, sex, and brain volume (estimated total intracranial volume [eTIV]) as recommended. Next, to capture the sociodemographic variability in the ABCD sample and to balance sample sizes between groups, the analysis was repeated on a sample matched by first-(age and sex only) and second-level covariates. The second-level covariates chosen for this analysis were identified based on potential relationships with both brain structure and earlier initiation of alcohol use in the ABCD sample. This included race 38 , ethnicity 39 , parental education (highest obtained) 40 , religion 15 , maternal alcohol use during pregnancy 41 and participant-reported sipping at baseline 42 . Participants were matched at a 1:1 ratio (see Supplementary Materials ; eFigure 1 ) using the MatchIt package (v4.5.5) 43 . Participants were not matched by global brain measures (i.e. mean cortical thickness or eTIV) as the relevance of these measures depend on the measurement being analyzed; therefore, these were adjusted for as a covariate in their respective models in brainGraph . Statistical Analysis Differences in baseline structural brain measurements between groups To determine whether baseline cortical thickness and volume measurements were associated with initiation of alcohol use within a univariate ROI approach, separate linear regression models were performed with each of the 68 brain regions as outcomes using the lme4 package (v1.1.35.4) 44 . Models including cortical thickness predictors were adjusted for age, sex, and mean cortical thickness, while models including cortical volume predictors were adjusted for age, sex and eTIV. False Discovery Rate (FDR) correction was used to adjust for multiple comparisons 45 , with statistical significance set to p FDR <0.05. Differences in network measures between groups To assess differences in structural covariance, permutation testing was used to generate a null distribution of t -values for graph measures of network segregation, integration, and resilience between groups for the cortical thickness and volume models. To ensure comparisons were independent of density thresholds, area-under-the-curve (AUC) tests were conducted across 16 densities (0.05–0.2), where each curve represents changes in a specific network measure for each group as a function of network density. To determine the significance of between-group differences in AUC, the observed AUC difference was compared to the corresponding permutation distribution, with p -values derived from its percentile position 30 . The groups were shuffled 1000 times, and AUC tests were performed at a two-tailed α of 0.05. AUC results are reported as the primary findings. Sensitivity analyses Two sensitivity analyses were performed. First, to investigate the impact of the second-level covariates in the full sample, a sensitivity analysis was performed repeating the cortical thickness SCN analysis in the full sample using first- and second-level covariates. Second, as individuals who initiated use of other substances during follow-up may present different network properties at baseline compared to total abstainers, a sensitivity analysis was performed excluding participants in the non-initiator group who initiated use of other substances (defined here as cigarette, e-cigarette, or cannabis use) for cortical thickness SCN comparisons (n=142). These analyses are reported in the Supplementary Materials . Results Study sample Participants who initiated alcohol use (n=182) at any point during follow-up were, on average, older at baseline than those who did not (n=3,696; Table 1 ). A higher proportion of initiators were female, had been exposed to alcohol in utero , and reported sipping alcohol at baseline. While differences in race were observed between the groups, no significant differences were found in ethnicity, religion, or parental education. At follow-up, those who initiated alcohol use also reported higher rates of other substances compared to non-initiators. Demographics for the matched sample are reported in eTable 2 . View this table: View inline View popup Download powerpoint Table 1. Participant characteristics between early adolescent alcohol initiators and non-initiators. Group differences in regional structural brain measurements After adjusting for age, sex, and mean global cortical thickness, comparisons of cortical thickness between groups revealed differences in the left medial orbitofrontal gyrus and right supramarginal gyrus. However, these differences did not survive FDR correction ( eTable 4 ). Similarly, when comparing volumetric measurements, we observed between-group differences in the bilateral superior temporal gyrus and right frontal pole, but these also did not survive FDR correction. No group differences were found in any other ROIs. Group differences in cortical thickness structural covariance When examining group differences in cortical thickness SCN measures adjusting for first-level covariates, the initiator group exhibited lower modularity, clustering coefficient, characteristic path length, and degree assortativity, in addition to higher global efficiency, compared to non-initiators ( Table 2 ). When comparing between the matched samples, initiators exhibited lower characteristic path length and degree assortativity, in addition to higher global efficiency, compared to non-initiators. No other differences between groups were observed. These findings were replicated when adjusting for second-level covariates in the full (unmatched) sample, as well as when excluding participants who had used other substances from the alcohol non-initiator group (see Supplementary materials ). Given the small number of participants initiating alcohol use, further robustness checks were conducted and reported in Supplementary Materials (eFigures 2 and 3) . View this table: View inline View popup Download powerpoint Table 2. Results from the cortical thickness SCN graph-level AUC network comparisons in the full and matched samples. Group differences in cortical volume structural covariance When controlling for first-level covariates and when comparing against the matched sample, no statistically significant differences in cortical volume SCN measures between groups were identified ( Table 3 ). However, similar trends of lower integration and higher segregation in the initiator group were observed. These findings were replicated when adjusting for second-level covariates in the full (unmatched) sample ( Supplementary materials ). View this table: View inline View popup Download powerpoint Table 3. Results from the cortical volume SCN graph-level AUC network comparisons in the full and matched samples. Discussion The present study investigated whether whole brain network-level properties in alcohol-naïve children aged 9-10 are predictive of alcohol initiation prior to age 15. Our findings suggest early adolescent alcohol initiators exhibit distinct cortical thickness SCN characteristics, including lower segregation (lower modularity and clustering coefficient) and higher integration (lower characteristic path length and higher global efficiency) compared to non-initiators. These differences were not observed at the regional cortical level, and differences in network integration remained robust after controlling for confounders using a matched sample. However, no similar differences were found in the cortical volume network comparisons, possibly related to the reduced sensitivity of composite measurements 46 . Collectively, these findings provide novel evidence to suggest global brain network organization may play a role in early alcohol use vulnerability, highlighting the need to investigate early life mechanisms driving these differences in brain network development. The observations made in this study replicate a SCN profile previously identified in adults with alcohol use disorder 20 , 22 , and in adolescents who later engaged in hazardous drinking 20 . Specifically, Ottino-Gonzalez et al. 20 identified that lower network segregation and higher integration aged 14 predicted hazardous drinking behaviors at age 19, attributed to delayed maturation of cortico-cortical networks involved in reward- and risk-taking behaviours. However, as network differences between groups were eradicated once excluding alcohol-naïve participants, it is difficult to eliminate the possibility of reverse causation. Our findings are the first to identify that these replicable network differences precede the onset of alcohol use independent of prior alcohol consumption, representing a potential early neuroanatomical marker of future alcohol involvement. While the functional implications of these findings require further investigation, they highlight the potential neurobiological involvement in alcohol use behaviors. Lower segregation and higher integration, particularly during development, is suggestive of a more flexible and less specialized brain network 17 . This more “randomized”, less mature network topology in SCNs is often observed during typical neurodevelopment between the ages of 4 and 11, preceding the network specialization processes that occur later in adolescence 17 , 48 . While the long-term implications of the network differences observed in the current study remain unclear, it is worth noting that both protracted 49 and accelerated 12 , 50 markers of cortical development have been identified in early alcohol initiation. The network topology identified in our initiator group may therefore reflect a less mature network structure at this age (protracted) or an earlier shift towards the typical network changes that occur between ages 4 to 11 (accelerated). While the functional implications of these findings require further exploration, these results suggest atypical network properties in pre-adolescence precede early alcohol initiation, underscoring the importance of investigating the long-term whole-brain neuroanatomical mechanisms associated with early alcohol use. Unlike previous studies using ABCD data 10 , 15 , the current study found no statistically significant regional predictors of early initiation of alcohol use. Importantly, the current study differed from these previous studies in terms of the definition of “initiation” of alcohol use. These previous studies defined alcohol initiation as either “sipping” or consuming a “full drink”. However, research has shown that early initiation of sipping may be more reflective of parenting practices and other external environmental influences, rather than a propensity to engage in problem behaviors that may be more reliably predicted by underlying biology 50 . As a result, our more stringent definition of initiation focused on consuming a full drink only. The lack of relationships between regional measures of brain structure and consuming a full drink of alcohol highlights the importance of modelling the brain’s network-level complexity when predicting more stringent conceptualizations of alcohol use initiation. While these findings indicate novel neuroanatomical mechanisms associated with early alcohol use, these were only present in cortical thickness networks and not cortical volume networks. While cortical thickness is considered to capture meaningful biological variation associated with cognitive, behavioral, and neurodevelopmental changes 51 , these estimates are prone to relative measurement errors, and there have been concerns that these may yield unreliable estimates for SCN analyses 52 . To address this issue, cortical volume SCNs were also calculated to test for robustness. Despite a lack of statistical significance for the cortical volume network properties, the same direction of relationships were observed in the cortical volume SCNs, suggesting our results may reflect loss of sensitivity when using cortical volume 46 . These findings are also consistent with previous studies of regional neuroanatomical predictors of early alcohol use initiation, where regional cortical thickness was more likely to be related to early alcohol use initiation when compared with regional cortical volume 10 , 15 . This study has several strengths, including the use of a large, community-based sample that broadly reflects the diversity of the US population, an alcohol-naïve sample to eliminate reverse causation, and a matched sample to reduce confounding bias. However, the stringent definition of alcohol initiation limited the sample size of initiators, potentially affecting the reliability of relationships between brain network organization and early alcohol use (reflected in effect sizes and variability in the network resilience measure). Finally, the small number of alcohol initiators reduced our capacity to examine other, more nuanced contributing factors linked to early alcohol initiation, such as peer influence 53 , family history of alcohol use 54 , and childhood trauma 55 , which is central to understanding the broader picture of early alcohol initiation. In conclusion, we provide novel evidence to suggest that differences in structural brain network organization in alcohol-naïve adolescents precede early alcohol initiation prior to age 15, independent of regional differences in brain structure. Further research in a larger, longitudinal sample tracking potential psychosocial, behavioral, and environmental factors associated with these relationships would be beneficial. Data sharing statement The data used in this study are from the Adolescent Brain Cognitive Development (ABCD) Study, an open-source dataset available to researchers. The ABCD Study data can be accessed via the National Institute of Mental Health Data Archive (NDA) at https://nda.nih.gov/abcd . Data sharing is subject to the terms and conditions outlined by the NDA, including registration and approval for access to the study’s datasets. Acknowledgements Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development SM (ABCD) Study ( https://abcdstudy.org ), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html . A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/ . ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88 , 01AA030575 , K24AA031052 Footnotes ↵ * Joint senior authors Disclosures/Funding: 01AA030575 (Mewton/Squeglia); K24AA031052 (Squeglia). Table numbers updated; Supplemental files updated. References 1. ↵ Bryazka D , Reitsma MB , Griswold MG , et al. Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020 . The Lancet . 2022 ; 400 ( 10347 ): 185 – 235 . doi: 10.1016/S0140-6736(22)00847-9 OpenUrl CrossRef PubMed 2. ↵ Miech RA , Johnston LD , Patrick ME , O’Malley PM , Bachman JG . Monitoring the Future National Survey Results on Drug Use, 1975-2023: Overview and Detailed Results for Secondary School Students . Inst Soc Res . Published online 2024. 3. ↵ Gardner LA , Stockings E , Champion KE , Mather M , Newton NC . Alcohol initiation before age 15 predicts earlier hazardous drinking: A survival analysis of a 7-year prospective longitudinal cohort of Australian adolescents . Addiction . 2024 ; 119 ( 3 ): 518 – 529 . doi: 10.1111/add.16376 OpenUrl CrossRef PubMed 4. ↵ Brown SA , Tapert SF . Adolescence and the Trajectory of Alcohol Use: Basic to Clinical Studies . Ann N Y Acad Sci . 2004 ; 1021 ( 1 ): 234 – 244 . doi: 10.1196/annals.1308.028 OpenUrl CrossRef PubMed Web of Science 5. ↵ Lees B , Meredith LR , Kirkland AE , Bryant BE , Squeglia LM . Effect of alcohol use on the adolescent brain and behavior . Pharmacol Biochem Behav . 2020 ; 192 : 172906 . doi: 10.1016/j.pbb.2020.172906 OpenUrl CrossRef PubMed 6. ↵ Hjarnaa L , Møller SP , Curtis AB , et al. Alcohol Intake and Academic Performance and Dropout in High School: A Prospective Cohort Study in 65,233 Adolescents . J Adolesc Health . 2023 ; 73 ( 6 ): 1083 – 1092 . doi: 10.1016/j.jadohealth.2023.07.008 OpenUrl CrossRef PubMed 7. ↵ Mustonen A , Alakokkare AE , Salom C , et al. Age of first alcohol intoxication and psychiatric disorders in young adulthood - A prospective birth cohort study . Addict Behav . 2021 ; 118 : 106910 . doi: 10.1016/j.addbeh.2021.106910 OpenUrl CrossRef PubMed 8. ↵ Dydjow-Bendek DA , Zagozdzon P. Early Alcohol Use Initiation, Obesity, Not Breastfeeding, and Residence in a Rural Area as Risk Factors for Breast Cancer: A Case-Control Study . Cancers . 2021 ; 13 ( 16 ). doi: 10.3390/cancers13163925 OpenUrl CrossRef 9. ↵ Casey BJ , Cannonier T , Conley MI , et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites . Adolesc Brain Cogn Dev ABCD Consort Ration Aims Assess Strategy . 2018 ; 32 : 43 – 54 . doi: 10.1016/j.dcn.2018.03.001 OpenUrl CrossRef PubMed 10. ↵ Miller AP , Baranger DAA , Paul SE , et al. Neuroanatomical variability associated with early substance use initiation: Results from the ABCD Study . MedRxiv Prepr Serv Health Sci . Published online March 8, 2024:2024.03.06.24303876. doi: 10.1101/2024.03.06.24303876 OpenUrl Abstract / FREE Full Text 11. ↵ Baranger DAA , Demers CH , Elsayed NM , et al. Convergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption . Biol Psychiatry . 2020 ; 87 ( 7 ): 645 – 655 . doi: 10.1016/j.biopsych.2019.08.029 OpenUrl CrossRef 12. ↵ Squeglia LM , Ball TM , Jacobus J , et al. Neural Predictors of Initiating Alcohol Use During Adolescence . Am J Psychiatry . 2017 ; 174 ( 2 ): 172 – 185 . doi: 10.1176/appi.ajp.2016.15121587 OpenUrl CrossRef PubMed 13. ↵ Brumback TY , Worley M , Nguyen-Louie TT , Squeglia LM , Jacobus J , Tapert SF . Neural predictors of alcohol use and psychopathology symptoms in adolescents . Dev Psychopathol . 2016 ; 28 ( 4pt1 ): 1209 – 1216 . doi: 10.1017/S0954579416000766 OpenUrl CrossRef PubMed 14. Nixon SJ . Executive functioning among young people in relation to alcohol use . Curr Opin Psychiatry . 2013 ; 26 ( 4 ): 305 – 309 . doi: 10.1097/YCO.0b013e328361ea3c OpenUrl CrossRef PubMed 15. ↵ Green R , Wolf BJ , Chen A , et al. Predictors of Substance Use Initiation by Early Adolescence . Am J Psychiatry . 2024 ; 181 ( 5 ): 423 – 433 . doi: 10.1176/appi.ajp.20230882 OpenUrl CrossRef PubMed 16. ↵ Pessoa L. Understanding brain networks and brain organization . Phys Life Rev . 2014 ; 11 ( 3 ): 400 – 435 . doi: 10.1016/j.plrev.2014.03.005 OpenUrl CrossRef PubMed 17. ↵ Khundrakpam BS , Reid A , Brauer J , et al. Developmental Changes in Organization of Structural Brain Networks . Cereb Cortex . 2013 ; 23 ( 9 ): 2072 – 2085 . doi: 10.1093/cercor/bhs187 OpenUrl CrossRef PubMed Web of Science 18. ↵ Alexander-Bloch A , Giedd JN , Bullmore E. Imaging structural co-variance between human brain regions . Nat Rev Neurosci . 2013 ; 14 ( 5 ): 322 – 336 . doi: 10.1038/nrn3465 OpenUrl CrossRef PubMed 19. ↵ Fan Y , Shi F , Smith JK , Lin W , Gilmore JH , Shen D. Brain anatomical networks in early human brain development . NeuroImage . 2011 ; 54 ( 3 ): 1862 – 1871 . doi: 10.1016/j.neuroimage.2010.07.025 OpenUrl CrossRef PubMed Web of Science 20. ↵ Ottino-González J , Garavan H. Brain structural covariance network differences in adults with alcohol dependence and heavy-drinking adolescents . Addict Abingdon Engl . 2022 ; 117 ( 5 ): 1312 – 1325 . doi: 10.1111/add.15772 OpenUrl CrossRef 21. ↵ Li J , Xu H. Abnormal structural covariance networks in young adults with recent cannabis use . Addict Behav . 2024 ; 155 : 108029 . doi: 10.1016/j.addbeh.2024.108029 OpenUrl CrossRef PubMed 22. ↵ Ottino-González J , Cupertino RB , Cao Z , et al. Brain structural covariance network features are robust markers of early heavy alcohol use . Addict Abingdon Engl . 2024 ; 119 ( 1 ): 113 – 124 . doi: 10.1111/add.16330 OpenUrl CrossRef 23. ↵ Sullivan RM , Wade NE , Wallace AL , et al. Substance use patterns in 9 to 13-year-olds: Longitudinal findings from the Adolescent Brain Cognitive Development (ABCD) study . Drug Alcohol Depend Rep . 2022 ; 5 : 100120 . doi: 10.1016/j.dadr.2022.100120 OpenUrl CrossRef 24. ↵ Lisdahl KM , Sher KJ , Conway KP , et al. Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods . Adolesc Brain Cogn Dev ABCD Consort Ration Aims Assess Strategy . 2018 ; 32 : 80 – 96 . doi: 10.1016/j.dcn.2018.02.007 OpenUrl CrossRef 25. ↵ Hagler DJJ , Hatton S , Cornejo MD , et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study . NeuroImage . 2019 ; 202 : 116091 . doi: 10.1016/j.neuroimage.2019.116091 OpenUrl CrossRef PubMed 26. ↵ Desikan RS , Ségonne F , Fischl B , et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest . NeuroImage . 2006 ; 31 ( 3 ): 968 – 980 . doi: 10.1016/j.neuroimage.2006.01.021 OpenUrl CrossRef PubMed Web of Science 27. ↵ R Core Team . R: A Language and Environment for Statistical Computing . Published online 2024. https://www.R-project.org/ 28. ↵ RStudio Team . RStudio: Integrated Development for R . Published online 2020. http://www.rstudio.com/ 29. ↵ Zhang R , Oliver LD , Voineskos AN , Park JY . RELIEF: A structured multivariate approach for removal of latent inter-scanner effects . Imaging Neurosci . 2023 ; 1 : 1 – 16 . doi: 10.1162/imag_a_00011 OpenUrl CrossRef 30. ↵ Watson , C. brainGraph: Graph Theory Analysis of Brain MRI Data . 2024 ;R package version 3.1.0. https://CRAN.R-project.org/package=brainGraph 31. ↵ Csardi G , Nepusz T , Traag V , et al. igraph: Network Analysis and Visualization in R . R Package Version . 2023 ; 1 ( 1 ): 10 – 5281 . OpenUrl CrossRef 32. ↵ Rubinov M , Sporns O. Complex network measures of brain connectivity: uses and interpretations . NeuroImage . 2010 ; 52 ( 3 ): 1059 – 1069 . doi: 10.1016/j.neuroimage.2009.10.003 OpenUrl CrossRef PubMed Web of Science 33. ↵ Backhausen LL , Herting MM , Tamnes CK , Vetter NC . Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders . Neuropsychol Rev . 2022 ; 32 ( 2 ): 400 – 418 . doi: 10.1007/s11065-021-09496-2 OpenUrl CrossRef 34. Hyatt CS , Owens MM , Crowe ML , Carter NT , Lynam DR , Miller JD . The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables . NeuroImage . 2020 ; 205 : 116225 . doi: 10.1016/j.neuroimage.2019.116225 OpenUrl CrossRef PubMed 35. ↵ Saragosa-Harris NM , Chaku N , MacSweeney N , et al. A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets . Dev Cogn Neurosci . 2022 ; 55 : 101115 . doi: 10.1016/j.dcn.2022.101115 OpenUrl CrossRef PubMed 36. ↵ Mills KL , Goddings AL , Herting MM , et al. Structural brain development between childhood and adulthood: Convergence across four longitudinal samples . NeuroImage . 2016 ; 141 : 273 – 281 . doi: 10.1016/j.neuroimage.2016.07.044 OpenUrl CrossRef PubMed 37. ↵ Vijayakumar N , Mills KL , Alexander-Bloch A , Tamnes CK , Whittle S. Structural brain development: A review of methodological approaches and best practices . Methodol Chall Dev Neuroimaging Contemp Approaches Solut . 2018 ; 33 : 129 – 148 . doi: 10.1016/j.dcn.2017.11.008 OpenUrl CrossRef PubMed 38. ↵ Atilano-Barbosa D , Barrios FA . Brain morphological variability between whites and African Americans: the importance of racial identity in brain imaging research . Front Integr Neurosci . 2023 ; 17 . https://www.frontiersin.org/journals/integrative-neuroscience/articles/10.3389/fnint.2023.1027382 39. ↵ Chartier KG , Hesselbrock MN , Hesselbrock VM . Ethnicity and Adolescent Pathways to Alcohol Use . J Stud Alcohol Drugs . 2009 ; 70 ( 3 ): 337 – 345 . doi: 10.15288/jsad.2009.70.337 OpenUrl CrossRef PubMed 40. ↵ Norbom LB , Rokicki J , Eilertsen EM , et al. Parental education and income are linked to offspring cortical brain structure and psychopathology at 9–11 years . JCPP Adv . 2024 ; 4 ( 1 ): e12220 . doi: 10.1002/jcv2.12220 OpenUrl CrossRef 41. ↵ Lees B , Mewton L , Stapinski LA , Teesson M , Squeglia LM . Association of prenatal alcohol exposure with preadolescent alcohol sipping in the ABCD study® . Drug Alcohol Depend . 2020 ; 214 : 108187 . doi: 10.1016/j.drugalcdep.2020.108187 OpenUrl CrossRef PubMed 42. ↵ Murphy MA , Dufour SC , Gray JC . The association between child alcohol sipping and alcohol expectancies in the ABCD study . Drug Alcohol Depend . 2021 ; 221 : 108624 . doi: 10.1016/j.drugalcdep.2021.108624 OpenUrl CrossRef PubMed 43. ↵ Ho D , Imai K , King G , Stuart EA . MatchIt: Nonparametric Preprocessing for Parametric Causal Inference . J Stat Softw . 2011 ; 42 ( 8 ): 1 – 28 . doi: 10.18637/jss.v042.i08 OpenUrl CrossRef 44. ↵ Bates D , Mächler M , Bolker B , Walker S. Fitting Linear Mixed-Effects Models Using lme4 . J Stat Softw . 2015 ; 67 ( 1 ): 1 – 48 . doi: 10.18637/jss.v067.i01 OpenUrl CrossRef PubMed 45. ↵ Benjamini Y , Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing . J R Stat Soc Ser B Methodol . 1995 ; 57 ( 1 ): 289 – 300 . OpenUrl CrossRef 46. ↵ Winkler AM , Kochunov P , Blangero J , et al. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies . NeuroImage . 2010 ; 53 ( 3 ): 1135 – 1146 . doi: 10.1016/j.neuroimage.2009.12.028 OpenUrl CrossRef PubMed Web of Science 47. Isaksson J , Sjöblom , Sebastian Schwab-Stone , Mary , Stickley Andrew , and Ruchkin V. Risk Factors Associated with Alcohol Use in Early Adolescence among American Inner-City Youth: A Longitudinal Study . Subst Use Misuse . 2020 ; 55 ( 3 ): 358 – 366 . doi: 10.1080/10826084.2019.1671867 OpenUrl CrossRef PubMed 48. ↵ Cao M , Huang H , Peng Y , Dong Q , He Y. Toward Developmental Connectomics of the Human Brain . Front Neuroanat . 2016 ;Volume 10-2016 . https://www.frontiersin.org/journals/neuroanatomy/articles/10.3389/fnana.2016.00025 49. ↵ Bava S , Tapert SF . Adolescent brain development and the risk for alcohol and other drug problems . Neuropsychol Rev . 2010 ; 20 ( 4 ): 398 – 413 . doi: 10.1007/s11065-010-9146-6 OpenUrl CrossRef PubMed 50. ↵ Donovan JE , Molina BSG . Antecedent predictors of children’s initiation of sipping/tasting alcohol . Alcohol Clin Exp Res . 2014 ; 38 ( 9 ): 2488 – 2495 . doi: 10.1111/acer.12517 OpenUrl CrossRef PubMed 51. ↵ Fischl B , Dale AM . Measuring the thickness of the human cerebral cortex from magnetic resonance images . Proc Natl Acad Sci U S A . 2000 ; 97 ( 20 ): 11050 – 11055 . doi: 10.1073/pnas.200033797 OpenUrl Abstract / FREE Full Text 52. ↵ Carmon J , Heege J , Necus JH , et al. Reliability and comparability of human brain structural covariance networks . NeuroImage . 2020 ; 220 : 117104 . doi: 10.1016/j.neuroimage.2020.117104 OpenUrl CrossRef PubMed 53. ↵ Moore A , Lewis B , Elton A , Squeglia LM , Nixon SJ . An investigation of multimodal predictors of adolescent alcohol initiation . Drug Alcohol Depend . 2024 ; 265 : 112491 . doi: 10.1016/j.drugalcdep.2024.112491 OpenUrl CrossRef PubMed 54. ↵ Maggs JL , Staff J , Patrick ME , Wray-Lake L. Very early drinking: Event history models predicting alcohol use initiation from age 4 to 11 years . Addict Behav . 2019 ; 89 : 121 – 127 . doi: 10.1016/j.addbeh.2018.09.030 OpenUrl CrossRef PubMed 55. ↵ Oberleitner LMS , Smith PH , Weinberger AH , Mazure CM , McKee SA . Impact of Exposure to Childhood Maltreatment on Transitions to Alcohol Dependence in Women and Men . Child Maltreat . 2015 ; 20 ( 4 ): 301 – 308 . doi: 10.1177/1077559515591270 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted May 11, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Structural Covariance Network Properties Predictive of Early Adolescent Alcohol Initiation Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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