Symmetric Fusion of fMRI and EEG for Spectrally Resolved Functional Neuroimaging
Kim, J.-H.; Liu, Z.
Show abstract
Simultaneous electroencephalography (EEG) and functional MRI (fMRI) offers complementary sensitivity to fast electrophysiological dynamics of EEG and spatially resolved hemodynamics of fMRI, yet previous joint-analysis approaches are confined to fixed task paradigms and struggle with continuous or naturalistic brain states. We FSINC (Fusing Source Imaging based on a Neurovascular Coupling) model, a unified EEG-fMRI source imaging framework that reconstructs cortical activity to simultaneously explain both modalities. FSINC integrates frequency-resolved EEG source activity with fMRI via a data-driven neurovascular coupling model that estimates band-specific coupling coefficients ({beta}) and accommodates a tunable spatial-temporal trade-off through hyperparameters ({lambda}2,{lambda} 3). In realistic simulations, FSINC outperformed conventional methods (wMNE, LORETA) in both spatial and temporal accuracy across EEG SNRs (-10 to 10dB) and numbers of concurrent sources (up to five), with optimal performance at{lambda} 2 = 102 and{lambda} 3=1 (e.g., LE: 0.51{+/-}0.24mm; SDI: 0.03{+/-}0.37mm; temporal accuracy: 0.95 {+/-} 0.05). Applied to simultaneous EEG-fMRI during contrast-reversing visual stimulation (=5.95Hz), FSINC revealed stimulus-locked responses localized to early visual cortex and stimulus-induced modulation of intrinsic alpha oscillations extending into visual and attention networks, patterns that conventional methods failed to capture. Estimated {beta}-weights were broadly consistent with prior reports of negative (theta/alpha) and positive (gamma) BOLD-electrophysiology associations. These findings demonstrate that FSINC enables high-spatiotemporal-resolution source imaging from EEG-fMRI recordings via data-driven hemodynamic modelling, and is expected to be well-suited for continuous and naturalistic brain states (e.g., resting state, natural moving-watching, and narrative listening) that are difficult to interrogate with either modality alone.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging 97%
- NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis 97%
- Neuro-Current Response Functions: A Unified Approach to MEG Source Analysis under the Continuous Stimuli Paradigm 97%
Similar papers in this journal
- Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI 96%
- Simultaneous Confidence Regions for Image Excursion Sets: a Validation Study with Applications in fMRI 96%
- Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations 96%
Similar papers in this journal
Similar papers in this journal
- Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain source-networks at high-spatial resolution 97%
- Estimating functional EEG sources using topographical templates 96%
- Mapping brain lesions to conduction delays: the next step for personalized brain models in Multiple Sclerosis 95%
Similar papers in this journal
- Source Localization Using Recursively Applied and Projected MUSIC with Flexible Extent Estimation 96%
- Spatial (Mis)match Between EEG and fMRI Signal Patterns Revealed by Spatio-Spectral Source-Space EEG Decomposition 95%
- A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.