Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic

2026-03-25

AI Summary

This study addresses the high cost barrier limiting widespread adoption of high-resolution fMRI by proposing a novel EEG-conditioned generative framework that enables continuous reconstruction of high-resolution, vertex-level dynamic fMRI from low-cost, high-temporal-resolution EEG. The method introduces a null-space intermediate frame mechanism to effectively handle irregular real-world sampling, ensuring both measurement consistency and temporal continuity. Evaluated on the CineBrain dataset, the approach demonstrates superior performance in voxel-wise reconstruction fidelity and temporal consistency across the whole brain and functionally specific regions. Critically, the reconstructed fMRI signals preserve essential functional information and significantly enhance downstream visual decoding tasks, highlighting the framework’s potential for scalable, high-quality neuroimaging.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (fMRI) provides high-resolution cortical representations that form a strong basis for characterizing fine-grained brain activity patterns. The high acquisition cost of fMRI limits large-scale applications, therefore making high-quality fMRI reconstruction a crucial task. Electroencephalography (EEG) offers millisecond-level temporal cues that complement fMRI. Leveraging this complementarity, we present an EEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and strong temporal coherence at the cortical-vertex level. To address sampling irregularities common in real fMRI acquisitions, we incorporate a null-space intermediate-frame reconstruction, enabling measurement-consistent completion of arbitrary intermediate frames and improving sequence continuity and practical applicability. Experiments on the CineBrain dataset demonstrate superior voxel-wise reconstruction quality and robust temporal consistency across whole-brain and functionally specific regions. The reconstructed fMRI also preserves essential functional information, supporting downstream visual decoding tasks. This work provides a new pathway for estimating high-resolution fMRI dynamics from EEG and advances multimodal neuroimaging toward more dynamic brain activity modeling.

Problem

• fMRI reconstruction
• spatiotemporal neural activity
• EEG-fMRI fusion
• dynamic brain imaging
• high-resolution neuroimaging

Innovation

• EEG-conditioned fMRI reconstruction
• spatiotemporal neural modeling
• null-space intermediate-frame completion
• cortical-vertex resolution
• multimodal neuroimaging