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.
| Original language | English |
|---|---|
| Pages (from-to) | e211-e221 |
| Number of pages | 11 |
| Journal | The Lancet. Digital Health |
| Volume | 6 |
| Issue number | 3 |
| Early online date | 21 Feb 2024 |
| DOIs | |
| Publication status | Published - Mar 2024 |
Bibliographical note
Publisher Copyright:© 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Funding
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).
| Funders | Funder number |
|---|---|
| Child Health and Human Development, Drug Abuse | |
| Knut och Alice Wallenbergs Stiftelse | |
| University of British Columbia | |
| National Institutes of Health | |
| European Commission | |
| National Center for Advancing Translational Sciences | UL1 TR000153, R01CA101318, R01MH090553, R01MH116147, P30AG10133, U54EB020403, R01MH117014, R01AG19771, T32MH122394, U24RR025736, R01MH104284, U24RR021992, RC2DA029475, U24RR025761, R01HD050735, R01MH113619, R01MH042191 |
| Biobanking and Biomolecular Resources Research Infrastructure | 84.021.00, 184–033–111 |
| Helse Sør-Øst RHF | 2013–054, 2017–112 |
| Russian Foundation for Basic Research | 20–013–00748 |
| Vetenskapsrådet | K2008–62P–20597–01–3, 521–2014–3487, K2012–61X–15078–09–3, K2010–62X–15078–07–2, 523–2014–3467, 2017–00949, K2007–62X–15077–04–1 |
| Icahn School of Medicine at Mount Sinai | UL1TR004419 |
| Not added | 51–02–061, MagW 480–04–004, 51–02–062, NWO 433–09–220, 433–09–229, 400-05-717, NW0-SP 56–464–14192 |
| Organization for Health Research and Development | 400–07–080, 481–08–011, 451–04–034, 463–06–001, 912–10–020, 31160008, 904–61–193, 480–15–001/674, 911–09–032, 016–115–035, 056–32–010, 480–04–004, 024–001–003, 985–10–002, 904–61–090 |
| Seventh Framework Programme | 602805, 602450, 278948, 603016 |
| Horizon 2020 Framework Programme | 667302, 643051 |
| Norges forskningsråd | 223273 |
| Bundesministerium für Bildung und Forschung | 01ZZ0403, 016–130–669, 91619115, 01ZZ9603, 01ZZ0103 |
| National Health and Medical Research Council | 496682, 1009064 |
| Instituto de Investigación Marqués de Valdecilla | NCT0235832, NCT02534363, API07/011 |
| Instituto de Salud Carlos III | PI060507, PI050427, PI14/00639, PI020499, PI14/00918 |
| Medical Research Council Canada | G0500092 |
| European Research Council | ERC–230374, ED1615 |
| Dutch Health Research Council | 10–000–1001 |