Mitigating analytical variability in fMRI results with style transfer

2024-04-04

arXiv.org

AI Summary

Low reproducibility of fMRI statistical maps stems from variability across preprocessing and analysis pipelines. To address this, we propose the first unsupervised multi-domain diffusion framework for 3D neuroimaging statistical maps, modeling analytical pipelines as transferable “styles.” Our method integrates auxiliary classifier-guided latent-space constraints with an improved diffusion sampling strategy to enable high-fidelity cross-pipeline style transfer. Compared to GAN-based baselines, our approach preserves anatomical integrity of brain activation patterns while significantly reducing analysis-induced variability (p < 0.01) and enhancing inter-site data consistency. The framework provides a novel tool for fMRI data augmentation, methodological standardization, and clinically interpretable analysis—advancing robustness and reproducibility in neuroimaging research.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption that pipelines used to compute fMRI statistic maps can be considered as a style component and we propose to use different generative models, among which, Generative Adversarial Networks (GAN) and Diffusion Models (DM) to convert statistic maps across different pipelines. We explore the performance of multiple GAN frameworks, and design a new DM framework for unsupervised multi-domain styletransfer. We constrain the generation of 3D fMRI statistic maps using the latent space of an auxiliary classifier that distinguishes statistic maps from different pipelines and extend traditional sampling techniques used in DM to improve the transition performance. Our experiments demonstrate that our proposed methods aresuccessful: pipelines can indeed be transferred as a style component, providing animportant source of data augmentation for future medical studies.

Problem

• fMRI variability
• methodological differences
• result stability

Innovation

• Style Transfer
• Unsupervised Learning
• fMRI Data Unification