Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

• Published in: Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 369; no. 1952; pp. 3785 - 3801
• Main Authors: Bojak, I., Oostendorp, Thom F., Reid, Andrew T., Kötter, Rolf
• Format: Journal Article
• Language: English
• Published: England The Royal Society Publishing 13-10-2011; The Royal Society
• Subjects: Brain - anatomy & histology; Brain - physiology; Brain Dynamics; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Cognitive models; Computer Graphics; Connectivity; Cortical Connectivity; Electroencephalography; Electroencephalography - methods; Humans; Image Processing, Computer-Assisted; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mean-Field Model; Models, Neurological; Multimodal Integration; Neural Population Model; Neuroimaging; Neurons; Oxygen - blood; Scalp; Signal Processing, Computer-Assisted; Simultaneous Eeg And Fmri Bold; Skull; Sleep; Systems Integration; Vertices
• ISSN: 1364-503X; 1471-2962
• DOI: 10.1098/rsta.2011.0080

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.