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Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years
Sophia Frangou, Amirhossein Modabbernia, Steven Williams, Efstathios Papachristou, Gaëlle E. Doucet, Ingrid Agartz, Moji Aghajani, Theophilus N. Akudjedu, Anton Albajes‐Eizagirre, Dag Alnæs, Kathryn Alpert, Micael Andersson, Nancy C. Andreasen, Ole A. Andreassen, Philip Asherson, Tobias Banaschewski, Núria Bargalló, Sarah Baumeister, Ramona Baur‐Streubel, Alessandro Bertolino, Aurora Bonvino, Dorret I. Boomsma, Stefan Borgwardt, Josiane Bourque, Daniel Brandeis, Alan Breier, Henry Brodaty, Rachel M. Brouwer, Jan K. Buitelaar, Geraldo F. Busatto, Randy L. Buckner, Vince D. Calhoun, Erick J. Canales‐Rodríguez, Dara M. Cannon, Xavier Caseras, F. Xavier Castellanos, Simon Červenka, Tiffany Chaim-Avancini, Christopher R. K. Ching, Viktoria Chubar, Vincent P. Clark, Patricia Conrod, Annette Conzelmann, Benedicto Crespo‐Facorro, Fabrice Crivello, Eveline A. Crone, Anders M. Dale, Udo Dannlowski, Christopher G. Davey, Eco J. C. de Geus, Lieuwe de Haan, Greig I. de Zubicaray, Anouk den Braber, Erin W. Dickie, Annabella Di Giorgio, Nhat Trung Doan, Erlend S. Dørum, Stefan Ehrlich, Susanne Erk, Thomas Espeseth, Helena Fatouros‐Bergman, Simon E. Fisher, Jean‐Paul Fouché, Barbara Franke, Thomas Frodl, Paola Fuentes‐Claramonte, David C. Glahn, Ian H. Gotlib, Hans J. Grabe, O. Grimm, Nynke A. Groenewold, Dominik Grotegerd, Oliver Gruber, Patricia Gruner, Rachel E. Gur, Ruben C. Gur, Tim Hahn, Ben J. Harrison, Catharine A Hartman, Sean N. Hatton, Andreas Heinz, Dirk J. Heslenfeld, Derrek P. Hibar, Ian B. Hickie, Beng‐Choon Ho, Pieter J. Hoekstra, Sarah Hohmann, Avram J. Holmes, Martine Hoogman, Norbert Hosten, Fleur M. Howells, Hilleke E. Hulshoff Pol, Chaim Huyser, Neda Jahanshad, Anthony James, Terry L. Jernigan, Jiyang Jiang, Erik G. Jönsson, John A. Joska, René S. Kahn, Andrew Kalnin, Ryota Kanai, Marieke Klein, T. P. Klyushnik, Laura Koenders, Sanne Koops, Bernd Krämer, Jonna Kuntsi, Jim Lagopoulos, Luisa Lázaro, И. С. Лебедева, Won Hee Lee, Klaus‐Peter Lesch, Christine Löchner, Marise W. J. Machielsen, Sophie Maingault, Nicholas G. Martin, Ignacio Martínez‐Zalacaín, David Mataix‐Cols, Bernard Mazoyer, Colm McDonald, Brenna C. McDonald, Andrew M. McIntosh, Katie L. McMahon, Genevieve McPhilemy, Susanne Meinert, José M. Menchón, Sarah E. Medland, Andreas Meyer‐Lindenberg, Jilly Naaijen, Pablo Najt, Tomohiro Nakao, Jan Egil Nordvik, Lars Nyberg, Jaap Oosterlaan, Víctor Ortiz‐García de la Foz, Yannis Paloyelis, Paul Pauli, Giulio Pergola, Edith Pomarol‐Clotet, Marı́a J. Portella, Steven G. Potkin, Joaquim Raduà, Andreas Reif, Daniel A. Rinker, Joshua L. Roffman, Pedro G. P. Rosa, Matthew D. Sacchet, Perminder S. Sachdev, Raymond Salvador, Pascual Sánchez‐Juan, Salvador Sarró, Theodore D. Satterthwaite, Andrew J. Saykin, Maurício H. Serpa, Lianne Schmaal, Knut Schnell, Günter Schumann, Kang Sim, Jordan W. Smoller, Iris E. Sommer, Carles Soriano‐Mas, Dan J. Stein, Lachlan T. Strike, Suzanne C. Swagerman, Christian K. Tamnes, Henk Temmingh, Sophia I. Thomopoulos, A. S. Tomyshev, Diana Tordesillas‐Gutiérrez, Julian N. Trollor, Jessica A. Turner, Anne Uhlmann, Odile A. van den Heuvel, Dennis van den Meer, Nic J.A. van der Wee, Neeltje E. M. van Haren, Dennis van ‘t Ent, Theo G.M. van Erp, Ilya M. Veer, Dick J. Veltman, Aristotle N. Voineskos, Henry Völzke, Henrik Walter, Esther Walton, Lei Wang, Yang Wang, Thomas H. Wassink, Bernd Weber, Wei Wen, John D. West, Lars T. Westlye, Heather C. Whalley, Lara M. Wierenga, Katharina Wittfeld, Daniel H. Wolf, Amanda Worker, Margaret J. Wright, Kun Yang, Yuliya Yoncheva, Marcus V. Zanetti, Georg Ziegler, Paul M. Thompson, Danai Dima
Bibliographic record
VenueHuman Brain Mapping · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaUniversité de MontréalVancouver Coastal Health
FundersNational Center for Research ResourcesNational Cancer InstituteNational Institute on Drug AbuseNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchSeventh Framework ProgrammeSchool of Medicine, Indiana UniversityNational Institutes of HealthNational Center for Advancing Translational SciencesMedical Research CouncilMedical Research Council CanadaRheinische Friedrich-Wilhelms-Universität BonnUniversity of Cape TownInstituto de Salud Carlos IIIUniversitat de BarcelonaUniversidade de São PauloUniversity of GalwayCardiff UniversityKing's College LondonUniversity of EdinburghNational Institute on AgingNational Institute for Health and Care ResearchNorthwestern UniversityYork UniversityIcahn School of Medicine at Mount SinaiEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentZonMwNational Alliance for Research on Schizophrenia and DepressionNederlandse Organisatie voor Wetenschappelijk OnderzoekWellcome TrustUniversiteit LeidenNational University of IrelandInstituto de Investigación Marqués de ValdecillaNorges ForskningsrådUniversity of OxfordSouth London and Maudsley NHS Foundation TrustJohns Hopkins UniversityManitoba Health Research CouncilFP7 Ideas: European Research CouncilPsychiatry Research TrustNational Institute of Mental HealthUniversität BaselVetenskapsrådet
KeywordsPsychologyNeuroscienceHealthy agingDevelopmental psychologyAudiologyGerontologyMedicine
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.101
GPT teacher head0.382
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations294
Published2021
Admission routes2
Has abstractyes