EEG-fMRI information fusion: Biophysics and data analysis

Abstract

Cerebral activity has many attributes: bioelectrical, metabolic, haemodynamic, hormonal, endogenous, exogenous, specialised, integrated, pathological, stable, dynamic, to mention but a few. The diverse nature of biological processes has been recognised for centuries. It seems obvious that moving from unimodal recordings to multimodal measurements will allow neuroscientists to better understand the nature and structure of cerebral activity. This means that fusing electrophysiological data and BOLD-related measurements represents an important methodological challenge. The realisation of any cognitive, motor or sensory process rests on cerebral dynamics and creates order in the bioelectric and haemodynamic signals measured with EEG and fMRI, respectively. To detect and interpret the relevant features of these signals, one typically describes processes at their own temporal and spatial scales. The main sources of scalp EEG signals are postsynaptic cortical currents associated with large pyramidal neurons, which are oriented perpendicular to the cortical surface (Nunez 1981). However, the scalp topology of measured electrical potentials does not, without additional (prior) information, uniquely specify the location of underlying bioelectric activity. This issue is referred to as the ill-posed nature of the EEG/MEG inverse problem. Conversely, even though fMRI discloses complementary features of neuronal activity (Nunez and Silberstein 2000; Mukamel et al. 2005), it is only an indirect measure, through metabolism, oxygenation and blood flow, where these slow mechanisms provide temporally smoothed correlates of neuronal activity. © Springer-Verlag Berlin Heidelberg 2010.