{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T10:04:00Z","timestamp":1779876240181,"version":"3.53.1"},"posted":{"date-parts":[[2026,4,12]]},"group-title":"Neuroscience","reference-count":36,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:00:00Z","timestamp":1775952000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health (NIH) \u2013 National Institute on Drug Abuse","award":["DP1DA054373"],"award-info":[{"award-number":["DP1DA054373"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Adolescent substance use initiation is shaped by multiple genetic and neurobiological factors. Externalizing liability, a transdiagnostic genetic dimension capturing shared predisposition to impulsivity, disinhibition, and related traits, is among the strongest polygenic predictors of early substance initiation. Yet how this genetic risk relates to brain structure and function, and whether baseline brain phenotypes statistically account for or instead act in parallel with genetic liability, remains unresolved.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Using the ABCD Study, we analyzed an analytic cohort of 10,608 participants with genotype data, baseline multimodal neuroimaging-derived phenotypes (IDPs), and longitudinal substance initiation assessments. Outcome-specific models included up to 10,599 participants after complete-case filtering for survival variables and covariates. We implemented a multistage framework linking an externalizing polygenic risk score (extPRS) to baseline IDPs and longitudinal substance initiation outcomes, including alcohol, nicotine, cannabis, and any substance. Stage 1 screened extPRS\u2013IDP associations using covariate-adjusted linear models with false discovery rate (FDR) control. Stage 2 estimated extPRS effects on time-to-initiation using Cox proportional hazards models. Stage 3 fit joint extPRS + IDP Cox models to identify IDPs that predicted initiation beyond extPRS. Stage 4 conducted bootstrap-based mediation analyses to quantify average causal mediation effects (ACME), average direct effects (ADE), and the proportion of the extPRS\u2013initiation association statistically accounted for by individual IDPs.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    Higher extPRS was robustly associated with earlier initiation across all substances: alcohol, hazard ratio (HR) = 1.13; nicotine, HR = 1.63; cannabis, HR = 1.67; and any substance, HR = 1.15. Thousands of extPRS-associated IDPs were identified at baseline, with highly concordant effect profiles across robustness specifications. In joint models, numerous IDPs independently predicted initiation timing above and beyond extPRS: 31 for alcohol, 32 for any substance, 137 for cannabis, and 459 for nicotine, with a replicated core set across specifications. Cannabis and nicotine initiation were jointly predicted by superficial white matter (SWM) microstructural integrity in sensorimotor cortex as a protective factor, and by irregular activity in a right-hemisphere region as a risk factor. Alcohol initiation was predicted by a largely distinct, strongly left-lateralized frontolimbic SWM intensity axis. Nicotine initiation additionally and uniquely involved restricted gray matter diffusion in the anterior cingulate cortex and subcallosal cortex. Despite these robust independent IDP associations, mediation analyses showed that indirect effects through individual baseline IDPs were very small in magnitude (ACME \u2248 10\n                    <jats:sup>\u22124<\/jats:sup>\n                    ), accounting for less than 2% of the total extPRS effect, with FDR-significant mediation surviving only for alcohol and any-substance initiation.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Within the scope of externalizing polygenic risk and baseline neuroimaging, the predominant pattern is one of largely parallel, additive contributions to adolescent substance initiation rather than a dominant genetic \u2192 brain \u2192 behavior pathway. Baseline brain features, particularly prefrontal functional variability and frontolimbic and sensorimotor white matter integrity, predict initiation risk beyond extPRS, indicating neurobiological vulnerabilities not captured by this genetic dimension. However, these baseline IDPs explain only a small fraction of the extPRS\u2013initiation association, suggesting that externalizing genetic liability may operate through pathways not fully represented by cross-sectional baseline imaging. Whether other genetic risk dimensions, such as substance-specific PRS, or dynamic longitudinal brain measures show stronger mediation patterns remains an important open question.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.04.08.717299","type":"posted-content","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:20:14Z","timestamp":1776054014000},"source":"Crossref","is-referenced-by-count":0,"title":["Parallel Contributions of Externalizing Polygenic Liability and Brain Imaging Phenotypes to Adolescent Substance Use Initiation Timing: A Multistage Analysis in the ABCD Study"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0458-7140","authenticated-orcid":false,"given":"Mengman","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Neuroscience, The Scripps Research Institute, 10550 N Torrey Pines Rd, La Jolla, 92037, CA, U.S."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2662-3412","authenticated-orcid":false,"given":"Qian","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, The Scripps Research Institute, 10550 N Torrey Pines Rd, La Jolla, 92037, CA, U.S."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026052702251070000_2026.04.08.717299v2.1","doi-asserted-by":"publisher","DOI":"10.1037\/0021-843X.116.4.645"},{"key":"2026052702251070000_2026.04.08.717299v2.2","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.20230882"},{"issue":"1","key":"2026052702251070000_2026.04.08.717299v2.3","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s13073-020-00742-5","article-title":"Polygenic risk scores: from research tools to clinical instruments","volume":"12","year":"2020","journal-title":"Genome medicine"},{"key":"2026052702251070000_2026.04.08.717299v2.4","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-021-01348-y"},{"key":"2026052702251070000_2026.04.08.717299v2.5","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1146\/annurev-clinpsy-021815-093253","article-title":"Trait impulsivity and the externalizing spectrum","volume":"13","year":"2017","journal-title":"Annual review of clinical psychology"},{"key":"2026052702251070000_2026.04.08.717299v2.6","doi-asserted-by":"publisher","DOI":"10.1016\/S0899-3289(97)90009-2"},{"key":"2026052702251070000_2026.04.08.717299v2.7","doi-asserted-by":"publisher","DOI":"10.1001\/archpedi.160.7.739"},{"key":"2026052702251070000_2026.04.08.717299v2.8","doi-asserted-by":"crossref","unstructured":"Lloyd Johnston . Monitoring the future: National survey results on drug use, 1975-2006. 2007.","DOI":"10.1037\/e567272009-001"},{"key":"2026052702251070000_2026.04.08.717299v2.9","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.65.6.674"},{"key":"2026052702251070000_2026.04.08.717299v2.10","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291714002165"},{"key":"2026052702251070000_2026.04.08.717299v2.11","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05477-4"},{"key":"2026052702251070000_2026.04.08.717299v2.12","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-018-0307-5"},{"key":"2026052702251070000_2026.04.08.717299v2.13","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-018-0206-1"},{"key":"2026052702251070000_2026.04.08.717299v2.14","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291719004045"},{"key":"2026052702251070000_2026.04.08.717299v2.15","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-021-00908-3"},{"key":"2026052702251070000_2026.04.08.717299v2.16","doi-asserted-by":"crossref","unstructured":"Laurence Steinberg . A social neuroscience perspective on adolescent risk-taking. In Biosocial theories of crime, pages 435\u2013463. Routledge, 2017.","DOI":"10.4324\/9781315096278-19"},{"key":"2026052702251070000_2026.04.08.717299v2.17","doi-asserted-by":"publisher","DOI":"10.1016\/j.dr.2007.08.003"},{"key":"2026052702251070000_2026.04.08.717299v2.18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.dcn.2015.12.010","article-title":"The dual systems model: Review, reappraisal, and reaffirmation","volume":"17","year":"2016","journal-title":"Developmental cognitive neuroscience"},{"key":"2026052702251070000_2026.04.08.717299v2.19","doi-asserted-by":"publisher","DOI":"10.1038\/nrn3119"},{"key":"2026052702251070000_2026.04.08.717299v2.20","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.dcn.2018.02.006","article-title":"Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (abcd) baseline neurocognition battery","volume":"32","year":"2018","journal-title":"Developmental cognitive neuroscience"},{"key":"2026052702251070000_2026.04.08.717299v2.21","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-021-00867-9"},{"issue":"1","key":"2026052702251070000_2026.04.08.717299v2.22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1177\/155005940904000110","article-title":"The influence of substance use on adolescent brain development","volume":"40","year":"2009","journal-title":"Clinical EEG and neuroscience"},{"key":"2026052702251070000_2026.04.08.717299v2.23","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.dcn.2017.10.002","article-title":"The conception of the abcd study: From substance use to a broad nih collaboration","volume":"32","year":"2018","journal-title":"Developmental cognitive neuroscience"},{"key":"2026052702251070000_2026.04.08.717299v2.24","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.dcn.2018.03.001","article-title":"The adolescent brain cognitive development (abcd) study: imaging acquisition across 21 sites","volume":"32","year":"2018","journal-title":"Developmental cognitive neuroscience"},{"key":"2026052702251070000_2026.04.08.717299v2.25","doi-asserted-by":"publisher","DOI":"10.1038\/s41386-020-0736-6"},{"key":"2026052702251070000_2026.04.08.717299v2.26","unstructured":"National Institutes of Health (NIH). Adolescent brain cognitive development (abcd) study.org\/, n.d. https:\/\/abcdstudy."},{"key":"2026052702251070000_2026.04.08.717299v2.27","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.dcn.2018.04.004","article-title":"Recruiting the abcd sample: Design considerations and procedures","volume":"32","year":"2018","journal-title":"Developmental cognitive neuroscience"},{"issue":"12","key":"2026052702251070000_2026.04.08.717299v2.28","doi-asserted-by":"crossref","first-page":"e2452027","DOI":"10.1001\/jamanetworkopen.2024.52027","article-title":"Neuroanatomical variability and substance use initiation in late childhood and early adolescence","volume":"7","year":"2024","journal-title":"JAMA network open"},{"issue":"12","key":"2026052702251070000_2026.04.08.717299v2.29","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1093\/jamia\/ocaf153","article-title":"Hillclimb-causal inference: a data-driven approach to identify causal pathways among parental behaviors, genetic risk, and externalizing behaviors in children","volume":"32","year":"2025","journal-title":"Journal of the American Medical Informatics Association"},{"key":"2026052702251070000_2026.04.08.717299v2.30","unstructured":"Mengman Wei and Qian Peng . Time-varying environmental and polygenic predictors of substance use initiation in youth: A survival and causal modeling study in the abcd cohort. Manuscript in preparation, 2026."},{"key":"2026052702251070000_2026.04.08.717299v2.31","doi-asserted-by":"publisher","DOI":"10.1007\/s10519-023-10152-z"},{"key":"2026052702251070000_2026.04.08.717299v2.32","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giz082"},{"key":"2026052702251070000_2026.04.08.717299v2.33","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu848"},{"key":"2026052702251070000_2026.04.08.717299v2.34","doi-asserted-by":"publisher","DOI":"10.2307\/2346101"},{"key":"2026052702251070000_2026.04.08.717299v2.35","doi-asserted-by":"publisher","DOI":"10.2307\/2282330"},{"key":"2026052702251070000_2026.04.08.717299v2.36","doi-asserted-by":"publisher","DOI":"10.1038\/sj.hdy.6800717"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.04.08.717299","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T09:25:19Z","timestamp":1779873919000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.04.08.717299"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":36,"URL":"https:\/\/doi.org\/10.64898\/2026.04.08.717299","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"subtype":"preprint"}}