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Record W3128375503 · doi:10.1002/hbm.25320

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years

2021· article· en· W3128375503 on OpenAlexafffund
Danai Dima, Amirhossein Modabbernia, 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, Udo Dannlowski, Anders M. Dale, 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, Steven Williams, Katharina Wittfeld, Daniel H. Wolf, Amanda Worker, Margaret J. Wright, Kun Yang, Yuliya Yoncheva, Marcus V. Zanetti, Georg Ziegler, Paul M. Thompson, Sophia Frangou

Bibliographic record

VenueHuman Brain Mapping · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoCentre for Addiction and Mental HealthVancouver Coastal HealthUniversité de Montréal
FundersNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Institute on Drug AbuseHelse Sør-Øst RHFNational Health and Medical Research CouncilNational Institutes of HealthEpilepsy SocietyInstituto de Salud Carlos IIISiemens HealthineersNational Center for Advancing Translational SciencesMedical Research CouncilHersenstichtingMedical Research Council CanadaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Alliance for Research on Schizophrenia and DepressionEpilepsy Foundation of VictoriaKarolinska InstitutetThe Research CouncilVetenskapsrådetUniversity of QueenslandKing's College LondonInstituto de Investigación Marqués de ValdecillaNorges ForskningsrådFP7 Ideas: European Research CouncilParents Against Childhood EpilepsyPsychiatry Research TrustNational Institute on AgingNational Institute for Health and Care ResearchNational Institute of Child Health and Human DevelopmentSouth London and Maudsley NHS Foundation TrustNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchIcahn School of Medicine at Mount SinaiIndiana State Department of HealthNational Institute of Mental HealthAmerican Epilepsy SocietyUniversiteit UtrechtStockholms Läns LandstingNational Cancer InstituteKnut och Alice Wallenbergs StiftelseDeutsches Zentrum für Herz-KreislaufforschungNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsPsychologyNeuroscienceHealthy agingGerontologyMedicine

Abstract

fetched live from OpenAlex

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.388
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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".

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Citations163
Published2021
Admission routes2
Has abstractyes

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