Abstract
Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.
Original language | English |
---|---|
Article number | e72904 |
Journal | eLife |
Volume | 11 |
DOIs | |
Publication status | Published - 1 Feb 2022 |
Keywords
- big data
- brain chart
- growth chart
- human
- individual prediction
- lifespan
- neuroscience
- normative model
Access to Document
Other files and links
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver
}
In: eLife, Vol. 11, e72904, 01.02.2022.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Charting brain growth and aging at high spatial precision
AU - Rutherford, Saige
AU - Fraza, Charlotte
AU - Dinga, Richard
AU - Kia, Seyed Mostafa
AU - Wolfers, Thomas
AU - Zabihi, Mariam
AU - Berthet, Pierre
AU - Worker, Amanda
AU - Verdi, Serena
AU - Andrews, Derek
AU - Han, Laura K.M.
AU - Bayer, Johanna M.M.
AU - Dazzan, Paola
AU - McGuire, Phillip
AU - Mocking, Roel T.
AU - Schene, Aart
AU - Sripada, Chandra
AU - Tso, Ivy F.
AU - Duval, Elizabeth R.
AU - Chang, Soo Eun
AU - Penninx, Brenda W.J.H.
AU - Heitzeg, Mary M.
AU - Burt, S. Alexandra
AU - Hyde, Luke W.
AU - Amaral, David
AU - Nordahl, Christine Wu
AU - Andreasssen, Ole A.
AU - Westlye, Lars T.
AU - Zahn, Roland
AU - Ruhe, Henricus G.
AU - Beckmann, Christian
AU - Marquand, Andre F.
N1 - Funding Information: This research was supported by grants from the European Research Council (ERC, grant ‘MENTALPRE-CISION’ 10100118 and ‘BRAINMINT’ 802998), the Wellcome Trust under an Innovator award (‘BRAIN-CHART,’ 215698/Z/19/Z) and a Strategic Award (098369/Z/12/Z), the Dutch Organisation for Scientific Research (VIDI grant 016.156.415) the Research Council of Norway (223273, 249795, 298646, 300768, and 276082), the South-Eastern Norway Regional Health Authority (2014097, 2015073, 2016083, and 2019101), the KG Jebsen Stiftelsen, an Autism Center of Excellence grant awarded by the National Institute of Child Health and Development (NICHD) (P50 HD093079) as well as the National Institute of Mental Health (R01MH104438 and R01MH103371). TW also gratefully acknowledges the Niels Stensen Fellowship as well as the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement no. 895011. RZ was funded by Medical Research Council grant (G0902304). IFT was funded by National Institute of Mental Health K23MH108823. SC was funded by National Institute on Deafness and other Communication Disorders (NIDCD/NIH) grant R01DC011277. CS was funded by the National Institute of Mental Health R01MH107741. LD was funded by Michigan Institute for Clinical Health Research (MICHR) Pilot Grant Program (UL1TR002240) through an NIH Clinical and Translational Science Award (CTSA). MTwiNS was supported by the National Institute of Mental Health and the Office of the Director National Institute of Health, under Award Number UG3MH114249 and the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award number R01HD093334 to SAB and LWH. RJTM was funded by an ABC Talent Grant. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under Award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. Funding Information: This research was supported by grants from the European Research Council (ERC, grant ?MENTALPRECISION? 10100118 and ?BRAINMINT? 802998), the Wellcome Trust under an Innovator award (?BRAINCHART,? 215698/Z/19/Z) and a Strategic Award (098369/Z/12/Z), the Dutch Organisation for Scientific Research (VIDI grant 016.156.415) the Research Council of Norway (223273, 249795, 298646, 300768, and 276082), the South-Eastern Norway Regional Health Authority (2014097, 2015073, 2016083, and 2019101), the KG Jebsen Stiftelsen, an Autism Center of Excellence grant awarded by the National Institute of Child Health and Development (NICHD) (P50 HD093079) as well as the National Institute of Mental Health (R01MH104438 and R01MH103371). TW also gratefully acknowledges the Niels Stensen Fellowship as well as the European Union?s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement no. 895011. RZ was funded by Medical Research Council grant (G0902304). IFT was funded by National Institute of Mental Health K23MH108823. SC was funded by National Institute on Deafness and other Communication Disorders (NIDCD/NIH) grant R01DC011277. CS was funded by the National Institute of Mental Health R01MH107741. LD was funded by Michigan Institute for Clinical Health Research (MICHR) Pilot Grant Program (UL1TR002240) through an NIH Clinical and Translational Science Award (CTSA). MTwiNS was supported by the National Institute of Mental Health and the Office of the Director National Institute of Health, under Award Number UG3MH114249 and the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award number R01HD093334 to SAB and LWH. RJTM was funded by an ABC Talent Grant. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under Award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147.Funder Grant reference number Author H2020 European Research Council H2020 European Research Council 10100118 Andre F Marquand 802998 Lars T Westlye Wellcome Trust 215698/Z/19/Z Andre F Marquand Wellcome Trust 098369/Z/12/Z Christian Beckmann Nederlandse Organisatie voor Wetenschappelijk Onderzoek VIDI grant 016.156.415 Andre F Marquand National Institute of Mental Health R01MH104438 David Amaral Christine Wu NordahlNational Institute of Mental Health R01MH103371 David Amaral Christine Wu Nordahl Eunice Kennedy Shriver National Institute of Child Health and Human Development P50 HD093079 David Amaral Christine Wu Nordahl H2020 Marie Sk?odowskaCurie Actions 895011 Thomas Wolfers Medical Research Council G0902304 Roland Zahn National Institute of Mental Health K23MH108823 Ivy F Tso National Institute on Deafness and Other Communication Disorders R01DC011277 Soo-Eun Chang National Institute of Mental Health R01MH107741 Chandra Sripada Michigan Institute for Clinical and Health Research UL1TR002240 Elizabeth R Duval National Institute of Mental Health UG3MH114249 S Alexandra Burt Luke Hyde Eunice Kennedy Shriver National Institute of Child Health and Human Development R01HD093334 S Alexandra Burt Luke Hyde The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Publisher Copyright: © Rutherford et al.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.
AB - Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.
KW - big data
KW - brain chart
KW - growth chart
KW - human
KW - individual prediction
KW - lifespan
KW - neuroscience
KW - normative model
UR - http://www.scopus.com/inward/record.url?scp=85124444210&partnerID=8YFLogxK
U2 - https://doi.org/10.7554/ELIFE.72904
DO - https://doi.org/10.7554/ELIFE.72904
M3 - Article
C2 - 35101172
SN - 2050-084X
VL - 11
JO - eLife
JF - eLife
M1 - e72904
ER -