Connecting Mean Field Models of Neural Activity to EEG and fMRI Data

• Published in: Brain topography Vol. 23; no. 2; pp. 139 - 149
• Main Authors: Bojak, Ingo, Oostendorp, Thom F, Reid, Andrew T, Kötter, Rolf
• Format: Journal Article
• Language: English
• Published: Boston Boston : Springer US 01-06-2010; Springer US; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedicine; Brain - blood supply; Brain - physiology; Brain Mapping - methods; Computer Simulation; EEG; Electroencephalography - methods; fMRI BOLD; Head - physiology; Humans; Magnetic Resonance Imaging - methods; Mean field model; Models, Biological; Models, Neurological; Multimodal imaging; Neural Pathways - blood supply; Neural Pathways - physiology; Neurology; Neurons - physiology; Neurosciences; Original Paper; Oxygen - blood; Psychiatry; Signal Processing, Computer-Assisted; Time Factors; Volume conductor model; Multimodal imaging; Volume conductor model; EEG; fMRI BOLD; Mean field model
• ISSN: 0896-0267; 1573-6792
• DOI: 10.1007/s10548-010-0140-3

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.