EEG-informed fMRI analysis during a hand grip task: estimating the relationship between EEG rhythms and the BOLD signal

• Published in: Frontiers in human neuroscience Vol. 8; p. 186
• Main Authors: Sclocco, Roberta, Tana, Maria G., Visani, Elisa, Gilioli, Isabella, Panzica, Ferruccio, Franceschetti, Silvana, Cerutti, Sergio, Bianchi, Anna M.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 01.04.2014; Frontiers Media S.A
• Subjects: Alpha Rhythm; Beta Rhythm; BOLD; Brain; Brain mapping; Dimensional analysis; EEG; EEG-fMRI; Electroencephalography; Functional magnetic resonance imaging; GLM; Grasping; Heuristic; neural correlates; Neuroscience; NMR; Nuclear magnetic resonance; Rhythm; Italy; EEG-fMRI; alpha rhythm; GLM; BOLD; beta rhythm; motor task; neural correlates
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2014.00186

In the last decade, an increasing interest has arisen in investigating the relationship between the electrophysiological and hemodynamic measurements of brain activity, such as EEG and (BOLD) fMRI. In particular, changes in BOLD have been shown to be associated with changes in the spectral profile of neural activity, rather than with absolute power. Concurrently, recent findings showed that different EEG rhythms are independently related to changes in the BOLD signal: therefore, it would be also important to distinguish between the contributions of the different EEG rhythms to BOLD fluctuations when modeling the relationship between the two signals. Here we propose a method to perform EEG-informed fMRI analysis where the changes in the spectral profile are modeled, and, at the same time, the distinction between rhythms is preserved. We compared our model with two other frequency-dependent regressors modeling using simultaneous EEG-fMRI data from healthy subjects performing a motor task. Our results showed that the proposed method better captures the correlations between BOLD signal and EEG rhythms modulations, identifying task-related, well localized activated volumes. Furthermore, we showed that including among the regressors also EEG rhythms not primarily involved in the task enhances the performance of the analysis, even when only correlations with BOLD signal and specific EEG rhythms are explored.