Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach
Neuropsychopharmacology, Volume 48, No. 2, Year 2023
Notification
URL copied to clipboard!
Description
Cortical thickness changes dramatically during development and is associated with adolescent drinking. However, previous findings have 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 NMF-derived 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 adolescent drinkers, an effect that is strongest among heavy drinkers. © 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
Authors & Co-Authors
Sun, Delin
United States, Durham
Brain Imaging and Analysis Center
United States
Va Medical Center
China, Hong Kong
The Education University of Hong Kong
Phillips, Rachel D.
United States, Durham
Brain Imaging and Analysis Center
United States
Va Medical Center
Baker, Fiona C.
United States, Menlo Park
Sri International
Tapert, Susan Frances
United States, La Jolla
University of California, San Diego
Brown, Sandra A.
United States, La Jolla
University of California, San Diego
Clark, Duncan B.
United States, Pittsburgh
University of Pittsburgh
Goldston, David B.
United States, Wilmington
University of North Carolina Wilmington
Nooner, Kate Brody
United States, Wilmington
University of North Carolina Wilmington
Nagel, Bonnie J.
United States, Portland
Oregon Health & Science University
Thompson, Wesley Kurt
United States, La Jolla
University of California, San Diego
de Bellis, Michael D.
United States, Durham
Brain Imaging and Analysis Center
United States, Durham
Duke University
Morey, Rajendra A.
United States, Durham
Brain Imaging and Analysis Center
United States
Va Medical Center
United States, Durham
Duke University
Statistics
Citations: 8
Authors: 12
Affiliations: 11
Identifiers
Doi:
10.1038/s41386-022-01457-4
ISSN:
0893133X
Research Areas
Substance Abuse
Study Design
Cohort Study