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The pitfalls of using Gaussian Process Regression for normative modeling.

Bohan Xu1,2, Rayus Kuplicki1, Sandip Sen2

  • 1Laureate Institute for Brain Research, Tulsa, OK, United States of America.

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Normative modeling quantifies individual deviation from expected trajectories. Gaussian Processes Regression uncertainty is irrelevant for normalizing cohort heterogeneity, despite its potential.

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Machine Learning

Background:

  • Normative modeling characterizes subject heterogeneity by quantifying deviations from expected trajectories.
  • Gaussian Processes Regression (GPR) offers uncertainty estimates, seemingly suitable for normalizing cohort heterogeneity.

Purpose of the Study:

  • To evaluate the relevance of GPR-derived uncertainty for normalizing cohort heterogeneity in normative modeling.

Main Methods:

  • Investigated the application of Gaussian Processes Regression in normative modeling.
  • Analyzed the relationship between GPR-derived uncertainty and cohort heterogeneity.

Main Results:

  • Demonstrated that uncertainty estimates directly from Gaussian Processes Regression are generally irrelevant for normalizing cohort heterogeneity.
  • Showed that GPR uncertainty does not effectively account for variations within a cohort.

Conclusions:

  • Gaussian Processes Regression uncertainty is not a suitable metric for normalizing cohort heterogeneity in normative modeling.
  • Alternative approaches may be needed to accurately characterize subject variability.