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Record W4400642845 · doi:10.1038/s41467-024-50305-0

Investigating grey matter volumetric trajectories through the lifespan at the individual level

2024· article· en· W4400642845 on OpenAlexaff
Runye Shi, Shitong Xiang, Tianye Jia, Trevor W. Robbins, Jujiao Kang, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Sylvane Desrivières, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean‐Luc Martinot, Marie‐Laure Paillère Martinot, Éric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Sarah Hohmann, Sabina Millenet, Juliane H. Fröhner, Michael N. Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Günter Schumann, Xiaolei Lin, Barbara J. Sahakian, Jianfeng Feng, Hugh Garavan, Günter Schumann

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute on AgingMedical Research CouncilHigher Education Discipline Innovation ProjectFédération pour la Recherche sur le CerveauNational Institute of Mental HealthScience and Technology Commission of Shanghai MunicipalityFondation de FranceNational Institute for Health and Care ResearchBundesministerium für Bildung und ForschungFondation pour la Recherche MédicaleNational Natural Science Foundation of ChinaInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheEuropean CommissionDeutsche ForschungsgemeinschaftKing's College LondonNational Institute on Drug AbuseMission Interministérielle de Lutte Contre les Drogues et les Conduites AddictivesScience Foundation IrelandNational Institutes of HealthFondation de l'Avenir pour la Recherche Médicale Appliquée
KeywordsGrey matterComputer scienceBiologyArtificial intelligenceEvolutionary biologyComputational biologyMedicineWhite matterMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14-23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0040.002
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.361
Teacher spread0.265 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations10
Published2024
Admission routes1
Has abstractyes

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