Poster Presentation

Poster A in Poster Session A: Tuesday, August 4, 9:30 – 11:15 am, Kimmel Center, Shorin & Rosenthal Rooms

Predicting EEG Spectrograms from fMRI to Identify Brainwide Activity Underlying Neural Rhythms

Arnav Aggarwal¹, Leandro P. L. Jacob¹, Laura D. Lewis¹; ¹Massachusetts Institute of Technology

Presenter: Arnav Aggarwal

Simultaneous EEG-fMRI combines EEG’s high temporal resolution with fMRI’s high spatial resolution, allowing investigation of the spatiotemporal dynamics of electrophysiological neural rhythms that underlie brain states. However, the complex relationship between fMRI and EEG signals and the substantial inter-subject variability in how these signals are related make jointly analyzing the two challenging. As a novel approach for analyzing EEG-fMRI, we developed a machine learning framework to predict EEG spectrograms (0–17.1 Hz) from brainwide fMRI data. By predicting spectrograms, our model leverages the covarying structure of neural oscillations. Our model consists of an RNN encoder and a nonlinear fully-connected decoder. We created a Subject Generalization model that predicts EEG spectrograms for unseen subjects, and a Subject Specific model that learns individualized EEG features. We applied this approach to an EEG-fMRI dataset of 27 subjects transitioning through wakefulness and non-REM sleep stages, brain states which are defined by neural rhythms. We found that the Subject Generalization model accurately predicted EEG spectrograms. The Subject Specific model significantly improved prediction for all frequencies above 6 Hz, demonstrating that inter-subject variability in the EEG and fMRI relationship is smaller in slower frequencies. To determine which brain areas contain predictive information, we trained models on each gray matter region. We found that certain networks, such as arousal systems, broadly predicted all frequencies, whereas others selectively predicted specific rhythms, such as the visual network predicting alpha (8.5–12 Hz) power. Our results show that fMRI contains rich information about neural rhythms that allow prediction of EEG dynamics across vigilance states, and that this information is distributed across large-scale brain networks. Additionally, this approach enables investigation of brainwide dynamics underlying oscillatory activity in a variety of experimental paradigms.

Topic Area: Methods, Tools, Theory & Neural Coding