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Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle.

Bethany Little, Nida Alyas, Alexander Surtees

    Arxiv
    |June 17, 2024
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    Summary

    We developed an open-source web platform for brain morphology analysis, providing accessible normative models. This tool helps distinguish disease effects from normal variations in clinical and research settings.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Biostatistics

    Background:

    • Normative models of brain structure are crucial for identifying disease-specific changes by accounting for covariates like age and sex.
    • Current advanced statistical modeling approaches for normative analysis are often inaccessible and computationally intensive, limiting their widespread application.
    • There is a need for accessible platforms that provide pre-trained normative models for brain morphology analysis.

    Purpose of the Study:

    • To present an open-source web application for brain morphology analysis featuring pre-trained normative models.
    • To offer a user-friendly platform that facilitates the application of advanced statistical modeling to clinical and research cohorts.
    • To enable the investigation of multiple brain morphology metrics within a unified framework.

    Main Methods:

    • Development of an open-source web application with a user-friendly interface for brain morphology analysis.
    • Training of normative models using a diverse sample of 3,276 healthy controls across 21 sites, covering various morphology metrics.
    • Validation of the normative models using clinical cohorts, including individuals with bipolar disorder and temporal lobe epilepsy.

    Main Results:

    • The platform provides individual and group outputs, supports multi-site analysis, and integrates with existing tools.
    • Validation demonstrated that normative modeling is superior to traditional methods for distinguishing disease effects.
    • Analysis revealed that biological covariates are better explained by specific morphology measures, and disease sensitivity varies across metrics.

    Conclusions:

    • The developed web platform offers a comprehensive and accessible framework for brain morphology analysis in clinical and research settings.
    • The study confirms the superiority of normative models and highlights the advantage of analyzing multiple brain morphology metrics concurrently.
    • The findings underscore the utility of the platform for accurate disease-specific abnormality detection and covariate analysis.