A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data

2025-05-27

AI Summary

In voxel-wise neuroimaging multiple testing, conventional false discovery rate (FDR) methods—e.g., Benjamini–Hochberg—ignore spatial dependence, yielding high false non-discovery rates (FNR), unstable false discovery proportions (FDP) and false non-discovery proportions (FNP), and poor scalability to high-resolution data. To address this, we propose fcHMRF-LIS: a novel framework leveraging a fully connected hidden Markov random field (fcHMRF) to model complex spatial structure across brain regions; integrating local significance indexing (LIS), mean-field EM inference, CRF-RNN, and permutohedral lattice filtering for linear-time complexity. Simulation studies demonstrate substantial improvements in true positive rate and FNR reduction, alongside markedly stabilized FDP/FNP variance. Applied to ADNI FDG-PET data, fcHMRF-LIS accurately localizes Alzheimer’s disease–associated brain regions and achieves significantly higher computational efficiency than existing spatial FDR methods.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

False discovery rate (FDR) control methods are essential for voxel-wise multiple testing in neuroimaging data analysis, where hundreds of thousands or even millions of tests are conducted to detect brain regions associated with disease-related changes. Classical FDR control methods (e.g., BH, q-value, and LocalFDR) assume independence among tests and often lead to high false non-discovery rates (FNR). Although various spatial FDR control methods have been developed to improve power, they still fall short in jointly addressing three major challenges in neuroimaging applications: capturing complex spatial dependencies, maintaining low variability in both false discovery proportion (FDP) and false non-discovery proportion (FNP) across replications, and achieving computational scalability for high-resolution data. To address these challenges, we propose fcHMRF-LIS, a powerful, stable, and scalable spatial FDR control method for voxel-wise multiple testing. It integrates the local index of significance (LIS)-based testing procedure with a novel fully connected hidden Markov random field (fcHMRF) designed to model complex spatial structures using a parsimonious parameterization. We develop an efficient expectation-maximization algorithm incorporating mean-field approximation, the Conditional Random Fields as Recurrent Neural Networks (CRF-RNN) technique, and permutohedral lattice filtering, reducing the computational complexity from quadratic to linear in the number of tests. Extensive simulations demonstrate that fcHMRF-LIS achieves accurate FDR control, lower FNR, reduced variability in FDP and FNP, and a higher number of true positives compared to existing methods. Applied to an FDG-PET dataset from the Alzheimer's Disease Neuroimaging Initiative, fcHMRF-LIS identifies neurobiologically relevant brain regions and offers notable advantages in computational efficiency.

Problem

• Control false discovery rate in neuroimaging data analysis
• Address spatial dependencies and computational scalability challenges
• Improve accuracy and reduce variability in detection results

Innovation

• Uses fully connected hidden Markov random field
• Integrates LIS with efficient EM algorithm
• Achieves linear computational complexity scaling