In one paragraphArticle in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors.
Diliana PechevaCenter for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla, CA.ORCID 0000-0002-7100-3191 Pravesh ParekhNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.ORCID 0000-0002-1930-1167 Ashley BeckerCenter for Human Development, University of California, San Diego, La Jolla, CA.
Robert LoughnanPopulation Neuroscience and Genetics Lab, University of California, San Diego, La Jolla, CA.ORCID 0000-0003-4870-4501 Thomas E NicholsBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID 0000-0002-4516-5103 Anders M DaleCenter for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla, CA.ORCID 0000-0002-6126-2966 Funding
ABCD-USA Consortium: Coordinating CenterU24DA041147 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SANDRA A BROWN, TERRY L. JERNIGAN · 2015 to 2026
$54.7MABCD-USA Consortium: Data Analysis, Informatics and Resource CenterU24DA041123 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANDERS M DALE · 2015 to 2026
$51.5MAdolescent Substance Use Initiation: Disentangling neurocognitive risks from consequences using longitudinal and genetically-informed methodsU01DA041120 · NIDA · UNIVERSITY OF MINNESOTA · PI Monica Luciana, Sylia Wilson · 2015 to 2026
$34.5MABCD-USA Consortium: Research ProjectU01DA041089 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Joanna Jacobus, Susan F. Tapert · 2015 to 2026
$31.7MProspective Research Studies of Maturation (PRISM)- Research ProjectU01DA041134 · NIDA · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI ERIN MCGLADE, PERRY FRANKLIN RENSHAW · 2015 to 2026
$29.2MABCD-USA CONSORTIUM: RESEARCH PROJECTU01DA041048 · NIDA · CHILDREN'S HOSPITAL OF LOS ANGELES · PI Megan Marie Herting, ELIZABETH R SOWELL · 2015 to 2026
$28.7MABCD-USA Consortium: Research ProjectU01DA041106 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mary M Heitzeg, Chandra Sekhar Sripada · 2015 to 2026
$24.9MFIU-ABCD: Pathways and Mechanisms to Addiction in the Latino Youth of South FloridaU01DA041156 · NIDA · FLORIDA INTERNATIONAL UNIVERSITY · PI Raul Gonzalez, Angela R Laird · 2015 to 2026
$22.8MABCD-USA Consortium: Research ProjectU01DA041148 · NIDA · OREGON HEALTH & SCIENCE UNIVERSITY · PI Damien A Fair, Rebekah S Huber · 2015 to 2026
$22.3MABCD-USA: NYC Research ProjectU01DA041174 · NIDA · YALE UNIVERSITY · PI Arielle Ryan Baskin-Sommers, Betty J Casey · 2015 to 2026
$19.7MAdolescent Brain Cognitive Development (ABCD) Prospective Research in Studies of Maturation (PRISM) ConsortiumU01DA041117 · NIDA · UNIVERSITY OF MARYLAND BALTIMORE · PI LINDA CHANG, THOMAS M ERNST · 2015 to 2026
$19.5M15/21 ABCD-USA Consortium: Research Project Site at LIBRU01DA050989 · NIDA · LAUREATE INSTITUTE FOR BRAIN RESEARCH · PI ROBIN L AUPPERLE, MARTIN P. PAULUS · 2020 to 2026
$14.7MNIDA NIH HHS F30 DA057078NIDA NIH HHS U01 DA041022NIDA NIH HHS U01 DA041025NIDA NIH HHS U01 DA041028NIDA NIH HHS U01 DA041048NIDA NIH HHS U01 DA041089NIDA NIH HHS U01 DA041093NIDA NIH HHS U01 DA041106NIDA NIH HHS U01 DA041117NIDA NIH HHS U01 DA041120NIDA NIH HHS U01 DA041134NIDA NIH HHS U01 DA041148NIDA NIH HHS U01 DA041156NIDA NIH HHS U01 DA041174NIDA NIH HHS U01 DA050987NIDA NIH HHS U01 DA050988NIDA NIH HHS U01 DA050989NIDA NIH HHS U01 DA051016NIDA NIH HHS U01 DA051018NIDA NIH HHS U01 DA051037NIDA NIH HHS U01 DA051038NIDA NIH HHS U01 DA051039NIDA NIH HHS U24 DA041123NIDA NIH HHS U24 DA041147
6 · The paper itselfAbstract
Introduction: Adolescence is a pivotal period in brain structural development and maturation. However, investigation of cortical and subcortical brain changes during this time have been limited by small sample size and have generally examined the brain at the level of predetermined regions of interest. The recently developed Fast Efficient Mixed-Effects Algorithm (FEMA) allows for increased computational speed using mixed-effects models applied at the voxel or vertex level, as well as across multiple regions of interest. Methods: We extended the existing FEMA framework to represent predictors using natural spline basis functions, enabling us to model nonlinear trajectories of brain structure as a function of age. We then applied this model to the The Adolescent Brain Cognitive Development Results: Global volume variables, including total subcortical gray matter volume, peaked near 13 years in females and 15 years in males. Vertexwise cortical surface area followed an inverted U-shaped curve, whereas vertexwise cortical thickness followed a monotonic decrease during the age range studied. Voxelwise imaging analysis revealed regional differences in age trajectories at the subregional level. Discussion: The results of this work replicate and extend prior findings related to adolescent brain development, and illustrate distinct spatiotemporal patterns of structural changes in subcortical regions. The updated FEMA framework is publicly available for use in similar large datasets.
Identifiers
PMID41000698
PMCPMC12458219
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