Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI
Citations Over TimeTop 10% of 2021 papers
Abstract
Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.
Related Papers
- → Simultaneous electroencephalography and functional magnetic resonance imaging of primary and secondary somatosensory cortex in humans after electrical stimulation(2002)42 cited
- → Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging of General Anesthesia(2009)5 cited
- → Combining Electroencephalography and Functional Magnetic Resonance Imaging in Pain Research(2022)1 cited
- 신경계 혈류역학반응을 이용한 대뇌피질의 미니칼럼에서의 fMRI BOLD 신호 시뮬레이션(2011)