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Evidence for embracing normative modeling.

Saige Rutherford1,2,3, Pieter Barkema2, Ivy F Tso3,4

  • 1Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.

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Summary
This summary is machine-generated.

This study enhances brain normative modeling for lifespan trajectories of brain structure and function. Normative modeling features significantly outperform raw data in neuroimaging benchmarks for schizophrenia and cognitive ability.

Keywords:
brain chartscomputational psychiatryfunctional neuroimagingheterogeneityhumanindividual predictionmachine learningneuroscience

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Developmental Neuroscience

Background:

  • Normative modeling provides a powerful framework for interpreting individual brain data within a developmental context.
  • Existing repositories offer valuable normative models but require expansion to cover diverse brain metrics and user accessibility.

Purpose of the Study:

  • To expand the normative model repository with lifespan trajectories of brain structural surface area and functional connectivity.
  • To develop an updated online platform for seamless transfer of normative models to new datasets.
  • To demonstrate the utility of normative modeling features against raw data in neuroimaging analysis.

Main Methods:

  • Incorporated normative models for brain structural surface area and functional connectivity using Yeo-17 and Smith-10 atlases.
  • Developed an updated online platform for model transferability.
  • Conducted head-to-head comparisons using mass univariate group difference testing, classification, and regression tasks.

Main Results:

  • Normative modeling features demonstrated superior performance across all benchmarking tasks compared to raw data.
  • Statistically significant advantages were most pronounced in group difference testing (schizophrenia vs. control) and classification tasks.
  • The updated models and platform facilitate more robust and interpretable neuroimaging analyses.

Conclusions:

  • Expanded normative models and an accessible platform enhance the application of normative modeling in neuroimaging.
  • Normative modeling offers a significant advantage for detecting group differences, classifying conditions, and predicting cognitive abilities.
  • These resources aim to promote wider adoption of normative modeling within the research community.