RESUMO
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 37â407 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.
Assuntos
Benchmarking , Longevidade , Humanos , Masculino , Feminino , Encéfalo/diagnóstico por imagem , Modelos Estatísticos , AlgoritmosRESUMO
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) 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 Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear 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 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).
RESUMO
High-resolution transmission electron microscopy (HRTEM), X-ray diffractometry (XRD), energy dispersive spectroscopy (EDS), and hardness testing are used to investigate the evolution of the long strip-shaped S' phase of spray-formed fine-grained Al-Cu-Mg alloy during rapid cold stamping deformation. The elongated S' phase of the extruded Al-Cu-Mg alloy is subjected to twisting, brittle fracturing, redissolution, and necking during rapid cold stamping deformation. As a result, the morphology, size, distribution, and orientation relationship with the matrix of the long strip-shaped S' phase changed significantly. The regularly distributed long strip-shaped nanoscale precipitates evolved into irregularly distributed short rod-shaped S' phases and diffusely distributed granular reprecipitates. The twist and brittle fracture of the long strip-shaped S' phase significantly increased the contact surface between the precipitated phase and the aluminum matrix, improved the interfacial distortion energy of the precipitated phase and the aluminum matrix, and promoted the redissolution of the S' phase. The supersaturation state is reached, thus resulting in reprecipitation, which then lowered the matrix free energy. The hardness of the extruded Al-Cu-Mg alloy increased from 54.2 HB to 128.1 HB during the rapid cold stamping process.