In one paragraphArticle in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
20 authors.
Edvard O S GrødemCenter for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Norway.ORCID 0000-0003-1915-1872 Didac Vidal-PineiroCenter for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Norway.ORCID 0000-0001-9997-9156 Øystein SørensenCenter for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Norway.ORCID 0000-0003-0724-3542 David Bartrés-FazDepartment of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.ORCID 0000-0001-6020-4118 Andreas M BrandmaierCenter for Lifespan Psychology, Max Planck Institute for Human Development, Germany.ORCID 0000-0001-8765-6982 Gabriele CattaneoInstitut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Spain.ORCID 0000-0002-7411-6829 Pablo F GarridoCenter for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Norway.ORCID 0000-0002-6005-7178 Richard N HensonMRC Cognition and Brain Sciences Unit, Department of Psychiatry, University of Cambridge, United Kingdom.ORCID 0000-0002-0712-2639 Simone KühnMax Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Germany.ORCID 0000-0001-6823-7969 Ulman LindenbergerCenter for Lifespan Psychology, Max Planck Institute for Human Development, Germany.ORCID 0000-0001-8428-6453 Bradley J MacIntoshComputational Radiology and Artificial Intelligence (CRAI), Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway.ORCID 0000-0001-7300-2355 Lars NybergCenter for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Norway.ORCID 0000-0002-3367-1746 Alvaro Pascual-LeoneHinda and Arthur Marcus Institute for Aging Research, Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, MA, USA.ORCID 0000-0001-8975-0382 Stephen M SmithOxford Centre for Functional MRI of the Brain (FMRIB/WIN), Oxford University, United Kingdom.
Cristina Solé-PadullésDepartment of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.
Javier Solana-SánchezInstitut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Spain.ORCID 0000-0003-0880-7856 Leiv Otto WatneOslo Delirium Research Group, Institute of Clinical Medicine, Campus Ahus, University of Oslo, Norway.ORCID 0000-0003-0441-6700 Kristine B WalhovdCenter for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Norway.ORCID 0000-0003-1918-1123 Atle BjørnerudComputational Radiology and Artificial Intelligence (CRAI), Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway.ORCID 0000-0002-3486-3141 Anders M FjellCenter for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Norway.ORCID 0000-0003-2502-8774 Funding
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0MSmartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI Tammie Lee Smith Benzinger · 1985 to 2026
$69.5MWASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1MVascular factors, physical activity, and inflammation as modulators of neurodegenerative and cognitive trajectories (Project 2)P01AG036694 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Rebecca E Amariglio · 2010 to 2026
$50.2MThe natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI SCHINDLER, SUZANNE ELIZABETH · 2005 to 2025
$49.5MWashington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4MResearch Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI DAVID M. HOLTZMAN · 2020 to 2026
$28.7MThe VETSA Longitudinal Twin Study of Cognition and Aging (VETSA 4)R01AG050595 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELMAN, JEREMY A, FRANZ, CAROL ELAINE · 2015 to 2024
$28.0MMORPHOMETRY BIOMEDICAL INFORMATICS RESEARCH NETWORKU24RR021382 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2004 to 2008
$24.1MThe VETSA Longitudinal MRI Twin Study of AgingR01AG022381 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KREMEN, WILLIAM S. · 2003 to 2019
$22.3MSTRUCTURE, FUNCTION AND COGNITION IN SCHIZOPHRENIAP50MH071616 · NIMH · WASHINGTON UNIVERSITY · PI BARCH, DEANNA · 2004 to 2008
$11.2MThe VETSA Longitudinal Twin Study of Cognition and AgingR01AG018384 · NIA · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI LYONS, MICHAEL J. · 2002 to 2012
$9.8MNCATS NIH HHS UL1 TR000448NCRR NIH HHS U24 RR021382NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P01 AG036694NIA NIH HHS P30 AG066444NIA NIH HHS P50 AG005681NIA NIH HHS R01 AG018384NIA NIH HHS R01 AG018386NIA NIH HHS R01 AG021910NIA NIH HHS R01 AG022381NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG050595NIA NIH HHS R01 AG076838NIA NIH HHS U01 AG024904NIBIB NIH HHS R01 EB009352NIMH NIH HHS P50 MH071616Wellcome Trust
6 · The paper itselfAbstract
Differences in the volumes of brain structures between individuals are often linked to various conditions, including Alzheimer's disease, schizophrenia, and overall brain health. However, it remains unclear to what extent these differences reflect individual levels present at young adulthood or diverging aging trajectories at later ages. In this study, we analyze the aging dynamics of the volume of six brain structures based on MRI scans from a large cross-cohort longitudinal sample of cognitively healthy adults (n = 8,311 with 18,520 MRIs, ages from 18 to 97 years). From general assumptions about structural brain dynamics and measurement noise, a stochastic dynamical model was fit to the data to estimate both the variability and persistence of structural changes across adulthood. Using this model, we calculated how much of the variance in individual volumetric differences can be attributed to stable levels from young adulthood versus systematic changes at older ages, as well as the theoretical sensitivity of longitudinal studies to detect individual differences in changes. The findings were as follows: 1) Before age 60 years, inter-individual differences in neuroanatomical volumes almost exclusively reflect stable differences between individuals, while the influence from systematic differences in rate-of-change increases thereafter; up to 40 % of the variation being due to differences in change at 80 years. In contrast, ventricular volume reflects differences in change from early adulthood. 2) Current brain-age models are unlikely to be sensitive to detect differences in aging trajectories. 3) Imaging studies have a low reliability to detect inter-individual brain change before age 60. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, it is critical to distinguish between stable levels from early adulthood and systematic differences in change when studying adult brain aging.
Indexed as
brain agingmagnetic resonance imagingstochastic dynamical system
Identifiers
PMID40501598
PMCPMC12154645
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