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Abstract

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3–90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

OriginalspracheEnglisch
ZeitschriftThe Lancet Digital Health
Jahrgang6
Ausgabenummer3
Seiten (von - bis)e211-e221
DOIs
PublikationsstatusVeröffentlicht - 03.2024

Fördermittel

We thank the following organisations for funding: EU Seventh Framework Programme (278948, 602450, 603016, 602805, and 602450); EU Horizon 2020 Programme (667302 and 643051); European Research Council (ERC–230374); EU Joint Programme-Neurodegenerative Disease Research (FKZ:01ED1615); Australian National Health and Medical Research Council (496682 and 1009064); German Federal Ministry of Education and Research (01ZZ9603, 01ZZ0103, and 01ZZ0403); Vici Innovation Program (91619115 and 016–130–669); Nederlandse Organisatie voor Wetenschappelijk Onderzoek: Cognition Excellence Program (433–09–229, NW0-SP 56–464–14192, NWO–MagW 480–04–004, NWO 433–09–220, NWO 51–02–062, and NWO 51–02–061); Organization for Health Research and Development (480–15–001/674, 024–001–003, 911–09–032, 056–32–010, 481–08–011, 016–115–035, 31160008, 400–07–080, 400–05–717, 451–04–034, 463–06–001, 480–04–004, 904–61–193, 912–10–020, 985–10–002, 904–61–090, 912–10–020, 451–04–034, 481–08–011, 056–32–010, and 911–09–032); Dutch Health Research Council (10–000–1001); Biobanking and Biomolecular Resources Research Infrastructure (184–033–111 and 84.021.00); Research Council of Norway (223273); South and Eastern Norway Regional Health Authority (2017–112, 2019–107, 2014–097, and 2013–054); Russian Foundation for Basic Research (20–013–00748); Fundación Instituto de Investigación Marqués de Valdecilla (API07/011, NCT02534363 , and NCT0235832 ); Instituto de Salud Carlos III (PI14/00918, PI14/00639, PI060507, PI050427, and PI020499); Swedish Research Council (523–2014–3467, 2017–00949, 521–2014–3487, K2007–62X–15077–04–1, K2008–62P–20597–01–3, K2010–62X–15078–07–2, and K2012–61X–15078–09–3); Knut and Alice Wallenberg Foundation; UK Medical Research Council (G0500092); and US National Institutes of Health—Mental Health, Aging, Child Health and Human Development, Drug Abuse, and National Center for Advancing Translational Sciences (UL1 TR000153, U24RR025736–01, U24RR021992, U54EB020403, U24RR025736, U24RR025761, P30AG10133, R01AG19771, R01MH117014, R01MH042191, R01HD050735, 1009064, 496682, R01MH104284, R01MH113619, R01MH116147, R01MH116147, R01MH113619, R01MH104284, R01MH090553, R01MH090553, R01CA101318, RC2DA029475, and T32MH122394). We thank Dr Andre F Marquand and Dr Seyed Mostafa Kia (Radboud University, Netherlands) for their guidance with the HBR models. This work was supported by the computational resources and staff expertise provided by the Advanced Research Computing at the University of British Columbia and by the Scientific Computing at the Icahn School of Medicine at Mount Sinai (supported by the Clinical and Translational Science Awards grant UL1TR004419 from the National Center for Advancing Translational Sciences).

TrägerTrägernummer
Biobanking and Biomolecular Resources Research Infrastructure84.021.00, 184–033–111
Child Health and Human Development, Drug Abuse
Geestkracht Programme of the Dutch Health Research Council10–000–1001
Netherlands Organization for Health Research and Development400–07–080, 481–08–011, 451–04–034, 463–06–001, 912–10–020, 31160008, 400–05–717, 904–61–193, 480–15–001/674, 911–09–032, 016–115–035, 056–32–010, 480–04–004, 024–001–003, 904–61–090, 985–10–002
National Center for Advancing Translational Sciences (NCATS)UL1 TR000153, R01CA101318, R01MH090553, R01MH116147, P30AG10133, U54EB020403, R01MH117014, R01AG19771, T32MH122394, U24RR025736, R01MH104284, U24RR021992, RC2DA029475, U24RR025761, R01HD050735, R01MH113619, R01MH042191
National Center for Advancing Translational Sciences (NCATS)
Mount Sinai School of MedicineUL1TR004419
Horizon 2020 Framework Programme667302, 643051
European Research CouncilERC–230374, ED1615
National Health and Medical Research Council496682, 1009064
Russian Foundation for Basic Research20–013–00748
Bundesministerium für Bildung und Forschung01ZZ0403, 016–130–669, 91619115, 01ZZ0103, 01ZZ9603
Nederlandse Organisatie voor Wetenschappelijk Onderzoek51–02–061, MagW 480–04–004, 51–02–062, NWO 433–09–220, 433–09–229, NW0-SP 56–464–14192
Knut och Alice Wallenbergs Stiftelse
VetenskapsrådetK2008–62P–20597–01–3, 521–2014–3487, K2012–61X–15078–09–3, 2017–00949, 523–2014–3467, K2010–62X–15078–07–2, K2007–62X–15077–04–1
Instituto de Salud Carlos IIIPI060507, PI050427, PI14/00639, PI020499, PI14/00918
Seventh Framework Programme602805, 602450, 278948, 603016
University of British Columbia
Norges Forskningsråd223273
Helse Sør-Øst RHF2013–054, 2017–112
Helse Sør-Øst RHF
Medical Research Council CanadaG0500092
Instituto de Investigación Marqués de ValdecillaNCT0235832, API07/011, NCT02534363

    UN SDGs

    Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

    1. SDG 3 – Gesundheit und Wohlergehen
      SDG 3 – Gesundheit und Wohlergehen

    Strategische Forschungsbereiche und Zentren

    • Forschungsschwerpunkt: Gehirn, Hormone, Verhalten - Center for Brain, Behavior and Metabolism (CBBM)

    DFG-Fachsystematik

    • 2.23-10 Klinische Psychiatrie, Psychotherapie und Kinder- und Jugendpsychiatrie

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