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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 07267naa a2201573 4500 | |
| 001 | oai:prod.swepub.kib.ki.se:238076938 | |
| 003 | SwePub | |
| 008 | 240920s2023 | |||||||||||000 ||eng| | |
| 024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:2380769382 URI |
| 024 | 7 | a https://doi.org/10.1101/2023.01.30.5235092 DOI |
| 040 | a (SwePub)ki | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a vet2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Ge, R4 aut |
| 245 | 1 0 | a Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization |
| 264 | 1 | b Cold Spring Harbor Laboratory,c 2023 |
| 520 | a Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR<0.05 indicated that the MFPR-derived sex- and region-specific models with nonlinear polynomials for age and linear effects of global measures had superior predictive accuracy; the range of the MAE of the models of regional subcortical volumes was 70-520 mm3 and the corresponding ranges for regional cortical thickness and regional cortical surface area were 0.09-0.26 mm and 24-560 mm2, respectively. The MFPR-derived models were also computationally more efficient with a CPU time below one second compared to a range of 2 seconds to 60 minutes for the other algorithms. The performance of all sex- and region-specific MFPR models plateaued at sample sizes exceeding 3,000 and showed comparable MAEs across distinct 10-year age-bins covering the human lifespan. Interpretation: These results provide an empirically benchmarked framework for normative modeling of brain morphometry that is useful for interpreting prior literature and supporting future study designs. The model and tools described here are freely available through CentileBrain (https://centilebrain.org/), a user-friendly web platform. | |
| 700 | 1 | a Yu, Y4 aut |
| 700 | 1 | a Qi, YX4 aut |
| 700 | 1 | a Fan, YV4 aut |
| 700 | 1 | a Chen, S4 aut |
| 700 | 1 | a Gao, C4 aut |
| 700 | 1 | a Haas, SS4 aut |
| 700 | 1 | a Modabbernia, A4 aut |
| 700 | 1 | a New, F4 aut |
| 700 | 1 | a Agartz, Iu Karolinska Institutet4 aut |
| 700 | 1 | a Asherson, P4 aut |
| 700 | 1 | a Ayesa-Arriola, R4 aut |
| 700 | 1 | a Banaj, N4 aut |
| 700 | 1 | a Banaschewski, T4 aut |
| 700 | 1 | a Baumeister, S4 aut |
| 700 | 1 | a Bertolino, A4 aut |
| 700 | 1 | a Boomsma, DI4 aut |
| 700 | 1 | a Borgwardt, S4 aut |
| 700 | 1 | a Bourque, J4 aut |
| 700 | 1 | a Brandeis, D4 aut |
| 700 | 1 | a Breier, A4 aut |
| 700 | 1 | a Brodaty, H4 aut |
| 700 | 1 | a Brouwer, RM4 aut |
| 700 | 1 | a Buckner, R4 aut |
| 700 | 1 | a Buitelaar, JK4 aut |
| 700 | 1 | a Cannon, DM4 aut |
| 700 | 1 | a Caseras, X4 aut |
| 700 | 1 | a Cervenka, S4 aut |
| 700 | 1 | a Conrod, PJ4 aut |
| 700 | 1 | a Crespo-Facorro, B4 aut |
| 700 | 1 | a Crivello, F4 aut |
| 700 | 1 | a Crone, EA4 aut |
| 700 | 1 | a de Haan, L4 aut |
| 700 | 1 | a de Zubicaray, GI4 aut |
| 700 | 1 | a Di Giorgio, A4 aut |
| 700 | 1 | a Erk, S4 aut |
| 700 | 1 | a Fisher, SE4 aut |
| 700 | 1 | a Franke, B4 aut |
| 700 | 1 | a Frodl, T4 aut |
| 700 | 1 | a Glahn, DC4 aut |
| 700 | 1 | a Grotegerd, D4 aut |
| 700 | 1 | a Gruber, O4 aut |
| 700 | 1 | a Gruner, P4 aut |
| 700 | 1 | a Gur, RE4 aut |
| 700 | 1 | a Gur, RC4 aut |
| 700 | 1 | a Harrison, BJ4 aut |
| 700 | 1 | a Hatton, SN4 aut |
| 700 | 1 | a Hickie, I4 aut |
| 700 | 1 | a Howells, FM4 aut |
| 700 | 1 | a Pol, HEH4 aut |
| 700 | 1 | a Huyser, C4 aut |
| 700 | 1 | a Jernigan, TL4 aut |
| 700 | 1 | a Jiang, J4 aut |
| 700 | 1 | a Joska, JA4 aut |
| 700 | 1 | a Kahn, RS4 aut |
| 700 | 1 | a Kalnin, AJ4 aut |
| 700 | 1 | a Kochan, NA4 aut |
| 700 | 1 | a Koops, S4 aut |
| 700 | 1 | a Kuntsi, J4 aut |
| 700 | 1 | a Lagopoulos, J4 aut |
| 700 | 1 | a Lazaro, L4 aut |
| 700 | 1 | a Lebedeva, IS4 aut |
| 700 | 1 | a Lochner, C4 aut |
| 700 | 1 | a Martin, NG4 aut |
| 700 | 1 | a Mazoyer, B4 aut |
| 700 | 1 | a McDonald, BC4 aut |
| 700 | 1 | a McDonald, C4 aut |
| 700 | 1 | a McMahon, KL4 aut |
| 700 | 1 | a Nakao, T4 aut |
| 700 | 1 | a Nyberg, L4 aut |
| 700 | 1 | a Piras, F4 aut |
| 700 | 1 | a Portella, MJ4 aut |
| 700 | 1 | a Qiu, J4 aut |
| 700 | 1 | a Roffman, JL4 aut |
| 700 | 1 | a Sachdev, PS4 aut |
| 700 | 1 | a Sanford, N4 aut |
| 700 | 1 | a Satterthwaite, TD4 aut |
| 700 | 1 | a Saykin, AJ4 aut |
| 700 | 1 | a Schumann, G4 aut |
| 700 | 1 | a Sellgren, CMu Karolinska Institutet4 aut |
| 700 | 1 | a Sim, K4 aut |
| 700 | 1 | a Smoller, JW4 aut |
| 700 | 1 | a Soares, J4 aut |
| 700 | 1 | a Sommer, IE4 aut |
| 700 | 1 | a Spalletta, G4 aut |
| 700 | 1 | a Stein, DJ4 aut |
| 700 | 1 | a Tamnes, CK4 aut |
| 700 | 1 | a Thomopolous, SI4 aut |
| 700 | 1 | a Tomyshev, AS4 aut |
| 700 | 1 | a Tordesillas-Gutiérrez, D4 aut |
| 700 | 1 | a Trollor, JN4 aut |
| 700 | 1 | a van 't Ent, D4 aut |
| 700 | 1 | a van den Heuvel, OA4 aut |
| 700 | 1 | a van Erp, TG4 aut |
| 700 | 1 | a van Haren, NE4 aut |
| 700 | 1 | a Vecchio, D4 aut |
| 700 | 1 | a Veltman, DJ4 aut |
| 700 | 1 | a Walter, H4 aut |
| 700 | 1 | a Wang, Y4 aut |
| 700 | 1 | a Weber, B4 aut |
| 700 | 1 | a Wei, D4 aut |
| 700 | 1 | a Wen, W4 aut |
| 700 | 1 | a Westlye, LT4 aut |
| 700 | 1 | a Wierenga, LM4 aut |
| 700 | 1 | a Williams, SC4 aut |
| 700 | 1 | a Wright, MJ4 aut |
| 700 | 1 | a Medland, S4 aut |
| 700 | 1 | a Wu, MJ4 aut |
| 700 | 1 | a Yu, K4 aut |
| 700 | 1 | a Jahanshad, N4 aut |
| 700 | 1 | a Thompson, PM4 aut |
| 700 | 1 | a Frangou, S4 aut |
| 710 | 2 | a Karolinska Institutet4 org |
| 773 | 0 | t bioRxiv : the preprint server for biologyd : Cold Spring Harbor Laboratory |
| 856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:238076938 |
| 856 | 4 8 | u https://doi.org/10.1101/2023.01.30.523509 |
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- Baumeister, S
- Bertolino, A
- Boomsma, DI
- Borgwardt, S
- Bourque, J
- Brandeis, D
- Breier, A
- Brodaty, H
- Brouwer, RM
- Buckner, R
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- Cannon, DM
- Caseras, X
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- Crivello, F
- Crone, EA
- de Haan, L
- de Zubicaray, GI
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- Fisher, SE
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- Gruber, O
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- Kalnin, AJ
- Kochan, NA
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- Kuntsi, J
- Lagopoulos, J
- Lazaro, L
- Lebedeva, IS
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- Martin, NG
- Mazoyer, B
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- McDonald, C
- McMahon, KL
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- Schumann, G
- Sellgren, CM
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- Weber, B
- Wei, D
- Wen, W
- Westlye, LT
- Wierenga, LM
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