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Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium
Dick Schijven, Merel C. Postema, Masaki Fukunaga, Junya Matsumoto, Kenichiro Miura, Sonja M. C. de Zwarte, Neeltje E.M. van Haren, Wiepke Cahn, Hilleke E. Hulshoff Pol, René S. Kahn, Rosa Ayesa‐Arriola, Víctor Ortiz‐García de la Foz, Diana Tordesillas‐Gutiérrez, Javier Vázquez-Bourgón, Benedicto Crespo‐Facorro, Dag Alnæs, Andreas Dahl, Lars T. Westlye, Ingrid Agartz, Ole A. Andreassen, Erik G. Jönsson, Peter Kochunov, Jason Bruggemann, Stanley V. Catts, Patricia T. Michie, Bryan Mowry, Yann Quidé, Paul E. Rasser, Ulrich Schall, Rodney J. Scott, Vaughan J. Carr, Melissa J. Green, Frans Henskens, Carmel M. Loughland, Christos Pantelis, Cynthia Shannon Weickert, Thomas W. Weickert, Lieuwe de Haan, Katharina Brosch, Julia‐Katharina Pfarr, Kai G. Ringwald, Frederike Stein, Andreas Jansen, Tilo Kircher, Igor Nenadić, Bernd Krämer, Oliver Gruber, Theodore D. Satterthwaite, Juan Bustillo, Daniel H. Mathalon, Adrian Preda, Vince D. Calhoun, Judith M. Ford, Steven G. Potkin, Yunlong Tan, Zhiren Wang, Hong Xiang, Fengmei Fan, Fabio Bernardoni, Stefan Ehrlich, Paola Fuentes‐Claramonte, María Ángeles García‐León, Amalia Guerrero‐Pedraza, Raymond Salvador, Salvador Sarró, Edith Pomarol‐Clotet, Valentina Ciullo, Fabrizio Piras, Daniela Vecchio, Nerisa Banaj, Gianfranco Spalletta, Stijn Michielse, Thérèse van Amelsvoort, Erin W. Dickie, Aristotle N. Voineskos, Kang Sim, Simone Ciufolini, Paola Dazzan, Robin Murray, Woo‐Sung Kim, Young‐Chul Chung, Christina Andreou, André Schmidt, Stefan Borgwardt, Andrew M. McIntosh, Heather C. Whalley, Stephen M. Lawrie, Stefan S. du Plessis, Hilmar Luckhoff, Freda Scheffler, Robin Emsley, Dominik Grotegerd, Rebekka Lencer, Udo Dannlowski, Jesse T. Edmond, Kelly Rootes-Murdy, Julia M. Stephen, Andrew R. Mayer, Linda A. Antonucci, Leonardo Fazio, Giulio Pergola, Alessandro Bertolino, Covadonga M. Díaz‐Caneja, Joost Janssen, Noemi G. Lois, Celso Arango, A. S. Tomyshev, И. С. Лебедева, Simon Červenka, Carl M. Sellgren, Foivos Georgiadis, Matthias Kirschner, Stefan Kaiser, Tomáš Hájek, Antonín Škoch, Filip Španiel, Minah Kim, Yoo Bin Kwak, Sanghoon Oh, Jun Soo Kwon, Anthony James, Geor Bakker, Christian Knöchel, Michael Stäblein, Viola Oertel, Anne Uhlmann, Fleur M. Howells, Dan J. Stein, Henk Temmingh, Ana M. Díaz‐Zuluaga, Julian A. Pineda‐Zapata, Carlos López‐Jaramillo, Stephanie Homan, Ellen Ji, Werner Surbeck, Philipp Homan, Simon E. Fisher, Barbara Franke, David C. Glahn, Ruben C. Gur, Ryota Hashimoto, Neda Jahanshad, Eileen Lüders, Sarah E. Medland, Paul M. Thompson, Jessica A. Turner, Theo G.M. van Erp, Clyde Francks
Bibliographic record
VenuemedRxiv · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsMcGill UniversityDalhousie UniversityMontreal Neurological Institute and HospitalUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Center for Advancing Translational SciencesFaculty of Medicine and Health, University of SydneyEuropean Social FundConsejo Superior de Investigaciones CientíficasNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchNational Institutes of HealthMacquarie Group FoundationInstituto de Física de CantabriaAmsterdam NeuroscienceAmsterdam University Medical CentersNorges ForskningsrådAustralian Schizophrenia Research BankRamsay Health CareDiakonhjemmetUniversity of Cape TownNSW Ministry of HealthUniversiteit UtrechtVetenskapsrådetInstituto de Salud Carlos IIINational Research Foundation of KoreaKorea Health Industry Development InstituteDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekMinistero della SaluteUniversidad de CantabriaQueensland Brain InstituteKarolinska InstitutetUniversity of QueenslandUniversity of TorontoU.S. Department of Veterans AffairsUniversity of New South WalesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of EdinburghNew Partnership for Africa's DevelopmentDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)National Institute for Health and Care ResearchNational Research FoundationPratt FoundationUniversitetet i OsloMinisterio de Ciencia e InnovaciónMinisterstvo Zdravotnictví Ceské RepublikyErasmus Universitair Medisch Centrum RotterdamEuropean CommissionEuropean Regional Development FundKing's College LondonMax-Planck-GesellschaftKorea Brain Research InstituteNational Institute for Physiological SciencesVrije Universiteit AmsterdamNeuroscience Research AustraliaSouth African Medical Research CouncilNational Institute of Mental HealthHelse Sør-Øst RHFHunter Medical Research InstituteCentro de Investigación Biomédica en Red de Salud MentalZonMwNational Center for Research ResourcesNational Alliance for Research on Schizophrenia and DepressionNovartis FoundationCentre for Addiction and Mental Health FoundationInstituto de Investigación Marqués de ValdecillaPfizerSylvia and Charles Viertel Charitable FoundationBrain and Behavior Research FoundationNational Science Foundation
KeywordsContext (archaeology)Schizophrenia (object-oriented programming)PsychologyLateralization of brain functionNeuroscienceAsymmetryBiologyPsychiatry
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)0.020
metaresearch head score (Gemma)0.039
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 score0.020
Threshold uncertainty score0.105
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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
Citations11
Published2022
Admission routes2
Has abstractyes