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Lookup NU author(s): Dr Beth LittleORCiD, Nida Alyas, Alex Surtees, Dr David CousinsORCiD, Professor John-Paul TaylorORCiD, Professor Peter TaylorORCiD, Dr Karoline LeibergORCiD, Professor Yujiang WangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Normative models of brain structure estimate the effects of covariates such as age and sex using large samples of healthy controls. These models can then be applied to, for example, smaller clinical cohorts to distinguish disease effects from other covariates. However, these advanced statistical modelling approaches can be difficult to access, and processing large healthy cohorts is computationally demanding. Thus, accessible platforms with pre-trained normative models are needed. We present such a platform for brain morphology analysis as an open-source web application https://cnnplab.shinyapps.io/BrainMoNoCle/, with six key features: (i) user-friendly web interface, (ii) individual and group outputs, (iii) multi-site analysis, (iv) regional and whole-brain analysis, (v) integration with existing tools, and (vi) featuring multiple morphology metrics. Using a diverse sample of 3,276 healthy controls across 21 sites, we pre-trained normative models on various metrics. We validated the models with a small sample of individuals with bipolar disorder, showing outputs that aligned closely with existing literature only after applying our normative modelling. Using a cohort of people with temporal lobe epilepsy, we showed that individual-level abnormalities were in line with seizure lateralisation. Finally, with the ability to investigate multiple morphology measures in the same framework, we found that biological covariates are better explained in specific morphology measures, and for applications, only some measures are sensitive to the disease process. Our platform offers a comprehensive framework to analyse brain morphology in clinical and research settings. Validations confirm the superiority of normative models and the advantage of investigating a range of brain morphology metrics together.
Author(s): Little B, Alyas N, Surtees A, Winston GP, Duncan JS, Cousins DA, Taylor J-P, Taylor P, Leiberg K, Wang Y
Publication type: Article
Publication status: Published
Journal: Imaging Neuroscience
Year: 2025
Volume: 3
Online publication date: 02/01/2025
Acceptance date: 25/11/2024
Date deposited: 08/04/2025
ISSN (electronic): 2837-6056
Publisher: Massachusetts Institute of Technology
URL: https://doi.org/10.1162/imag_a_00438
DOI: 10.1162/imag_a_00438
Data Access Statement: Normative data may be available at the discretion of the data holders, please see the website of individual datasets for more information. The subset of the IDEAS mesial TLE dataset is freely available with the associated paper (P. N. Taylor et al., 2024).
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