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Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain: Algorithm Benchmarking and Model Optimization
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Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain: Algorithm Benchmarking and Model Optimization

Ruiyang Ge, Yuetong Yu, Yi Qi, Yunan Vera Fan, Shiyu Chen, Chuntong Gao, Shalaila Haas, Amirhossein Modabbernia, Faye New, Ingrid Agartz, …
bioRxiv, Vol.19 March 2023
Cold Spring Harbor Laboratory Press
2023
DOI: https://doi.org/10.1101/2023.01.30.523509
PMCID: PMC10705253
PMID: 38076938
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Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain Algorithm Benchmarking and Model Optimization1.10 MBDownloadView
Preprint Version Open Access CC BY-NC-ND V4.0
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https://doi.org/10.1101/2023.01.30.523509View
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Abstract

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 regionspecific 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.

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