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Robust FDR Control for Neuroimaging-based Classification via Knockoffs.

Sumita Garai¹, Hongzhuo Chen¹, Frederick Xu¹

¹University of Pennsylvania, Philadelphia, PA, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|June 19, 2026

Summary

This summary is machine-generated.

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The knockoff framework offers reliable feature selection for neuroimaging analysis, providing guaranteed control over false discoveries. This robust method ensures accurate identification of brain regions in machine learning models, even with complex data structures.

Area of Science:

Neuroimaging
Machine Learning
Statistical Inference

Background:

Machine learning shows promise in medical image analysis, but reliable feature discovery in neuroimaging remains difficult.
Existing methods like the Benjamini-Hochberg procedure struggle with feature independence assumptions.

Purpose of the Study:

To demonstrate the efficacy of the knockoff framework for robust feature selection in neuroimaging.
To provide provable control over the False Discovery Rate (FDR) in complex datasets.

Main Methods:

Evaluation of various knockoff construction methods on synthetic data for binary classification.
Application of the knockoff filter to functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project (HCP).

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Main Results:

Near-perfect detection of true features and valid FDR control were observed on synthetic data.
Identified brain regions using the knockoff filter were stable and reproducible across test-retest scans.

Conclusions:

The knockoff framework offers a statistically rigorous approach to feature selection in neuroimaging.
This method enhances the reliability of machine learning models for classifying brain activity and identifying key neural regions.