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Brain morphology normative modelling platform for abnormality and centile estimation: Brain MoNoCle.

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We developed an accessible web platform with pre-trained brain morphology normative models. This tool helps researchers distinguish disease effects from normal variations in brain structure, aiding clinical research.

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bipolar disorderbrain structuremorphologynormative modelstructural abnormalitytemporal lobe epilepsy

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

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

Purpose of the Study:

  • To present an open-source web application providing pre-trained normative models for brain morphology analysis.
  • To offer a user-friendly platform for researchers to analyze brain structure in clinical and research settings.
  • To validate the utility of normative modeling in distinguishing disease effects from covariates and to explore multiple morphology metrics.

Main Methods:

  • Developed an open-source web application with a user-friendly interface for brain morphology analysis.
  • Trained normative models on a diverse dataset of 3,276 healthy controls across 21 sites, using various morphology metrics.
  • Validated the platform using cohorts with bipolar disorder and temporal lobe epilepsy, comparing results with existing literature and clinical data.

Main Results:

  • The platform provides individual and group outputs, supports multi-site analysis, and integrates regional/whole-brain analyses with multiple morphology metrics.
  • Validation with bipolar disorder and temporal lobe epilepsy cohorts demonstrated the platform's ability to accurately identify disease-specific abnormalities.
  • 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 using normative models.
  • The findings underscore the superiority of normative modeling and the advantage of analyzing multiple brain morphology metrics concurrently.
  • The tool facilitates the distinction between disease-related brain changes and normal variations, supporting clinical neuroscience research.