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Common genetic variants influence human subcortical brain structures
Derrek P. Hibar, Jason L. Stein, Miguel E. Rentería, Alejandro Arias Vásquez, Sylvane Desrivières, Neda Jahanshad, Roberto Toro, Katharina Wittfeld, Lucija Abramovic, Micael Andersson, Benjamin S. Aribisala, Nicola J. Armstrong, Manon Bernard, Marc M. Bohlken, Marco P. Boks, Janita Bralten, Andrew Brown, M. Mallar Chakravarty, Qiang Chen, Christopher R. K. Ching, Gabriel Cuéllar-Partida, Anouk den Braber, Sudheer Giddaluru, Aaron L. Goldman, O. Grimm, Tulio Guadalupe, Johanna Haß, Girma Woldehawariat, Avram J. Holmes, Martine Hoogman, Deborah Janowitz, Tianye Jia, Sungeun Kim, Marieke Klein, Bernd Kraemer, Phil H. Lee, Loes M. Olde Loohuis, Michelle Luciano, Christine Macare, Karen A. Mather, Manuel Mattheisen, Yuri Milaneschi, Kwangsik Nho, Martina Papmeyer, Adaikalavan Ramasamy, Shannon L. Risacher, Roberto Roiz‐Santiáñez, Emma J. Rose, Alireza Salami, Philipp G. Sämann, Lianne Schmaal, Andrew J. Schork, Jean Shin, Lachlan T. Strike, Alexander Teumer, Marjolein M. J. van Donkelaar, Kristel R. van Eijk, James Walters, Lars T. Westlye, Christopher D. Whelan, Anderson M. Winkler, Marcel P. Zwiers, Saud Alhusaini, Lavinia Athanasiu, Stefan Ehrlich, Marina Hakobjan, Cecilie B. Hartberg, Unn K. Haukvik, Angelien J.G.A.M. Heister, David Hoehn, Dalia Kasperavičiūtė, David C. Liewald, Lorna M. Lopez, Remco R. Makkinje, Mar Matarín, Marlies Naber, Dean McKay, Margaret Needham, Allison C. Nugent, Benno Pütz, Natalie A. Royle, Li Shen, Emma Sprooten, Daniah Trabzuni, Saskia S.L. van der Marel, K.J.E. van Hulzen, Esther Walton, Christiane Wolf, Laura Almasy, David Ames, Sampath Arepalli, Amelia A. Assareh, Mark E. Bastin, Henry Brodaty, Kazima Bulayeva, Melanie A. Carless, Sven Cichon, Aiden Corvin, Joanne E. Curran, Michael Czisch, Greig I. de Zubicaray, Allissa Dillman, Ravi Duggirala, Thomas D. Dyer, Susanne Erk, Iryna O. Fedko, Luigi Ferrucci, Tatiana Foroud, Peter T. Fox, Masaki Fukunaga, J. Raphael Gibbs, Harald H.H. Göring, Robert C. Green, Sebastian Guelfi, Narelle K. Hansell, Catharina A. Hartman, Katrin Hegenscheid, Andreas Heinz, Dena Hernández, Dirk J. Heslenfeld, Pieter J. Hoekstra, Georg Homuth, Jouke‐Jan Hottenga, Masashi Ikeda, Clifford R. Jack, Mark Jenkinson, R. E. Johnson, Ryota Kanai, Maria Keil, Jack W. Kent, Peter Kochunov, John B. Kwok, Stephen M. Lawrie, Xinmin Liu, Dan L. Longo, Katie L. McMahon, Eva Meisenzahl, Ingrid Melle, Sebastian Mohnke, Grant W. Montgomery, Jeanette C. Mostert, Thomas W. Mühleisen, Michael A. Nalls, Thomas E. Nichols, Lars Nilsson, Markus M. Nöthen, Kazutaka Ohi, Rene L. Olvera, Rocío Pérez‐Iglesias, G. Bruce Pike, Steven G. Potkin, Ivar Reinvang, Simone Reppermund, Marcella Rietschel, Nina Romanczuk‐Seiferth, Glenn D. Rosen, Dan Rujescu, Knut Schnell, Peter R. Schofield, Colin Smith, Vidar M. Steen, Jessika E. Sussmann, Anbupalam Thalamuthu, Arthur W. Toga, Bryan J. Traynor, Juan C. Troncoso, Jessica A. Turner, Maria C. Valdés Hernández, Dennis van ‘t Ent, Marcel van der Brug, Nic J.A. van der Wee, Marie‐José van Tol, Dick J. Veltman, Thomas H. Wassink, Eric Westman, Ronald H. Zielke, Alan B. Zonderman, David G. Ashbrook, Reinmar Hager, Lu Lu, Francis J. McMahon, Derek W. Morris, Robert W. Williams, Han G. Brunner, Randy L. Buckner, Jan K. Buitelaar, Wiepke Cahn, Vince D. Calhoun, Gianpiero L. Cavalleri, Benedicto Crespo‐Facorro, Anders M. Dale, Gareth E. Davies, Norman Delanty, Chantal Depondt, Srdjan Djurovic, Wayne C. Drevets, Thomas Espeseth, Randy L. Gollub, Beng‐Choon Ho, Wolfgang Hoffmann, Norbert Hosten, René S. Kahn, Stéphanie Le Hellard, Andreas Meyer‐Lindenberg, Bertram Müller‐Myhsok, Matthias Nauck, Lars Nyberg, Massimo Pandolfo, Brenda W.J.H. Penninx, Joshua L. Roffman, Sanjay M. Sisodiya, Jordan W. Smoller, Hans van Bokhoven, Neeltje E. M. van Haren, Henry Völzke, Henrik Walter, Michael W. Weiner, Wei Wen, Tonya White, Ingrid Agartz, Ole A. Andreassen, John Blangero, Dorret I. Boomsma, Rachel M. Brouwer, Dara M. Cannon, Mark Cookson, Eco J. C. de Geus, Ian J. Deary, Gary Donohoe, Guillén Fernández, Simon E. Fisher, Clyde Francks, David C. Glahn, Hans J. Grabe, Oliver Gruber, John Hardy, Ryota Hashimoto, Hilleke E. Hulshoff Pol, Erik G. Jönsson, Iwona Kłoszewska, Simon Lovestone, Venkata S. Mattay, Patrizia Mecocci, Colm McDonald, Andrew M. McIntosh, Roel A. Ophoff, Tomáš Paus, Zdenka Pausová, Mina Ryten, Perminder S. Sachdev, Andrew J. Saykin, Andy Simmons, Andrew Singleton, Hilkka Soininen, Joanna M. Wardlaw, Michael E. Weale, Daniel R. Weinberger, Hieab H.H. Adams, Lenore J. Launer, Stephan Seiler, Reinhold Schmidt, Ganesh Chauhan, Claudia L. Satizábal, James T. Becker, Lisa R. Yanek, Sven J. van der Lee, Maritza Ebling, Bruce Fischl, W.T. Longstreth, Douglas N. Greve, Helena Schmidt, Paul Nyquist, Louis Vinke, Cornelia M. van Duijn, Luting Xue, Bernard Mazoyer, Joshua C. Bis, Vilmundur Guðnason, Sudha Seshadri, M. Arfan Ikram, Nicholas G. Martin, Margaret J. Wright, Günter Schumann, Barbara Franke, Paul M. Thompson, Sarah E. Medland
Bibliographic record
VenueNature · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsToronto Rehabilitation InstituteUniversity of CalgaryMcGill UniversityDouglas Mental Health University InstituteUniversity of TorontoSickKids FoundationMontreal Neurological Institute and HospitalHospital for Sick Children
FundersBiotechnology and Biological Sciences Research CouncilNational Institute of Mental HealthMedical Research CouncilNational Institute for Health and Care ResearchLundbeckfondenWellcome Trust
KeywordsPutamenBrain sizeNeuroscienceHuman brainBiologyCaudate nucleusNeuroanatomyThalamusMagnetic resonance imagingMedicine
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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)0.000
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.013
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 head0.012
GPT teacher head0.307
Teacher spread0.296 · 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
Citations936
Published2015
Admission routes1
Has abstractno