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
What 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
5 · Who and what moneyAuthors and funding
29 authors.
Leon Qi Rong OoiIntegrative Sciences and Engineering Programme (ISEP), National University of Singapore.ORCID 0000-0002-3546-4580 Csaba OrbanCentre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0000-0001-9133-3561 Shaoshi ZhangIntegrative Sciences and Engineering Programme (ISEP), National University of Singapore.ORCID 0000-0002-6352-9150 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 Trevor Wei Kiat TanIntegrative Sciences and Engineering Programme (ISEP), National University of Singapore.
Ru KongCentre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0000-0001-7842-0329 Nico U F DosenbachMallinckrodt Institute of Radiology, Washington University, School of Medicine, USA.ORCID 0000-0002-6876-7078 Fang JiCentre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0000-0002-7471-0115 Joanna Su Xian ChongCentre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0009-0008-5077-5206 Qingyu HuDivision of Brain Sciences, Changping Laboratory, Beijing, China.
Juan Helen ZhouIntegrative Sciences and Engineering Programme (ISEP), National University of Singapore.ORCID 0000-0002-0180-8648 Danilo BzdokDepartment of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Canada.ORCID 0000-0003-3466-6620 Simon B EickhoffInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.ORCID 0000-0001-6363-2759 Avram J HolmesDepartment of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA.ORCID 0000-0001-6583-803X B T Thomas YeoIntegrative Sciences and Engineering Programme (ISEP), National University of Singapore.ORCID 0000-0002-0119-3276 Alzheimer’s Disease Neuroimaging Initiative
Funding
Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI MONICA G. RIVERA-MINDT · 2016 to 2026
$226.7MABCD-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
$51.5MMapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7MAdolescent 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
$19.7MNIA NIH HHS U19 AG024904NIDA 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 DA041147NIMH NIH HHS R00 MH129616NIMH NIH HHS R01 MH120080NIMH NIH HHS R01 MH133334NIMH NIH HHS U54 MH091657
6 · The paper itselfAbstract
A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies extremely well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R
Identifiers
PMID38405815
PMCPMC10889017
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