[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127684-en":3,"doc-seo-127684-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127684,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning - an unsupervised machine learning approach","Cortical thickness changes markedly during development and relates to adolescent drinking, yet prior results have been inconsistent and constrained by region-of-interest analyses that cannot capture spatially heterogeneous alcohol effects. Using unsupervised machine learning, adolescents (n=657; 12–22 years) from NCANDA with little to no baseline alcohol use were assessed with structural MRI and followed across four yearly intervals. Seven vertex-level cortical thickness patterns were derived via non-negative matrix factorization, and longitudinal mixed-effects models tested alcohol-consumption effects. Six patterns showed age-dependent thinning rates that differed by drinking severity.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nAdolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach  \nPermalink  \n[https://escholarship.org/uc/item/50q8974j](https://escholarship.org/uc/item/50q8974j)  \nJournal  \nNeuropsychopharmacology, 48(2)  \nISSN  \n0893-133X  \nAuthors  \nSun, Delin  \nAdduru, Viraj R Phillips, Rachel Det al.  \nPublication Date  \n2023  \nDOI  \n10.1038/s41386-022-01457-4 Peer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n[www.nature.com/npp](www.nature.com/npp)  \nARTICLE   \nAdolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach  \nDelin Sun 1,2,3, Viraj R. Adduru 1,2,4, Rachel D. Phillips1,2, Heather C. Bouchard1,2, Aristeidis Sotiras 5, Andrew M. Michael1,4, Fiona C. Baker6, Susan F. Tapert7, Sandra A. Brown 7, Duncan B. Clark8, David Goldston9, Kate B. Nooner9, Bonnie J. Nagel 10, Wesley K. Thompson7, Michael D. De Bellis 1,11 and Rajendra A. Morey 1,2,11,12 ✉  \nThis is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022  \n\n|  | Cortical thickness changes dramatically during development and is associated with adolescent drinking. However, previous findingshave been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying spatially heterogeneous effects of alcohol. In this study, adolescents (n = 657; 12–22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study who endorsed little to no alcohol use at baseline were assessed with structural magnetic resonance imaging and followed longitudinally at four yearly intervals. Seven unique spatial patterns of covarying cortical thickness were obtained from the baseline scans by applying an unsupervised machine learning method called non-negative matrix factorization (NMF) . The cortical thickness maps of all participants’ longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. We found in six NMFderived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts. Among moderate drinkers the decline was faster in the younger adolescent cohort and slower in the older cohort. Among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort. The findings suggested that unsupervised machine learning successfully delineated spatially coordinated patterns of vertex-level cortical thickness variation that are unconstrained by neuroanatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| adolescent drinkers, an effect that is strongest among heavy drinkers. |  |  |\n|  | Neuropsychopharmacology (2023) 48:317–326; [https://doi.org/10.1038/s41386-022-01457-4](https://doi.org/10.1038/s41386-022-01457-4) |  |\n|  |  |  |\n\nINTRODUCTION  \nNeuromaturation during childhood and adolescence undergoes a dramatic transformation of cortical gray-matter thickness and volume. Gray matter volume peaks before the teen years and then declines into adulthood as underutilized connections between neurons are pruned [1–3]. Widespread differences in brain morphometry are observed in adolescent drinkers [4, 5]. Heavy adolescent alcohol use [6] is associated with faster cortical grey matter decline, possibly related to vulnerability during adolescent development [4]. Howev","cbCaitf93o9gtJnk","https://ap.wps.com/l/cbCaitf93o9gtJnk","pdf",1544854,1,11,"English","en",105,"# Introduction\n## Neuromaturation and cortical thickness changes\n## Alcohol misuse prevalence among adolescents\n## Limitations of region-of-interest approaches and study rationale","[{\"question\":\"How many participants and what age range were included in the study?\",\"answer\":\"The study analyzed 657 adolescents aged 12–22 years at baseline from the NCANDA cohort.\"},{\"question\":\"What method was used to derive cortical thickness patterns?\",\"answer\":\"Seven unique spatial patterns of covarying cortical thickness were obtained using an unsupervised non-negative matrix factorization (NMF) approach.\"},{\"question\":\"What was the main finding regarding age-appropriate cortical thinning across drinking levels?\",\"answer\":\"No/low drinkers showed similar longitudinal thinning across age cohorts, while moderate and heavy drinkers exhibited faster thinning in younger adolescents and slower thinning in older adolescents, with the effect strongest among heavy drinkers.\"}]","Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning - an unsupervised machine learning approach | PDF",1785940822,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"adolescent-alcohol-use-is-linked-to-disruptions-in-age-appropriate-cortical-thinning-an-unsupervised-machine-learning-approach","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/adolescent-alcohol-use-is-linked-to-disruptions-in-age-appropriate-cortical-thinning-an-unsupervised-machine-learning-approach/127684/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How many participants and what age range were included in the study?","Question",{"text":76,"@type":77},"The study analyzed 657 adolescents aged 12–22 years at baseline from the NCANDA cohort.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What method was used to derive cortical thickness patterns?",{"text":81,"@type":77},"Seven unique spatial patterns of covarying cortical thickness were obtained using an unsupervised non-negative matrix factorization (NMF) approach.",{"name":83,"@type":74,"acceptedAnswer":84},"What was the main finding regarding age-appropriate cortical thinning across drinking levels?",{"text":85,"@type":77},"No/low drinkers showed similar longitudinal thinning across age cohorts, while moderate and heavy drinkers exhibited faster thinning in younger adolescents and slower thinning in older adolescents, with the effect strongest among heavy drinkers.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]