Ruiyang Ge, Yuetong Yu, Yi Xuan Qi, Yu-Nan Fan, Shiyu Chen, Chuntong Gao, Shalaila S Haas, Faye New, Dorret I Boomsma, Henry Brodaty, Rachel M Brouwer, Randy Buckner, Xavier Caseras, Fabrice Crivello, Eveline A Crone, Susanne Erk, Simon E Fisher, Barbara Franke, David C Glahn, Udo Dannlowski, Dominik Grotegerd, Oliver Gruber, Hilleke E Hulshoff Pol, Gunter Schumann, Christian K Tamnes, Henrik Walter, Lara M Wierenga, Neda Jahanshad, Paul M Thompson, Sophia Frangou
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...
March 2024: The Lancet. Digital health