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

Abstract

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.

Methods for Simultaneous EEG-fMRI: An Introductory Review

“…The interested reader might consider a recent study by Bojak et al (2011) which elucidates the application of neurogenerative models. The authors used simulated data to investigate the plausibility of a neural population model for the analysis of simultaneous EEG-fMRI.…”

Neuroimaging, Neural Population Models for

“…A third level of modelling employs anatomically and physiologically accurate representations of the cortex, potentially of the subcortical structures, and of the surrounding head structure as needed for realistic signal expression (Bojak et al 2011;Jirsa et al 2002;Riera et al 2005Riera et al , 2013). The first step here is to obtain structural MRI data, which can be used to distinguish different tissue types.…”

“…Effectively, this leads to uncontrolled changes of the conduction velocity between vertices. On the other hand, one can directly use the extracted cortical surface and consider the mesh vertices as representing the surrounding neural tissue (Bojak et al , 2011). This does not distort the conduction velocity but is more complicated numerically since the vertices are not placed in a regular manner.…”

The sleeping brain as a complex system

“…This approach leads to so-called neural field models and is used in this issue by Bojak et al [38], Fleshner et al [39] and Robinson et al [40]. It ignores the precise firing times of neurons, which can be a reasonable approximation in neural populations for processes that are slow compared with that of the spike dynamics.…”