No. 927

Title:

Fusing EEG and fMRI based on a bottom-up model: Inferring activation and effective connectivity in neural masses

Author(s):

Riera, J.,(NICHe,Tohoku Univ.); Aubert, E.,(Cuban Neuroscience Center); Iwata, K.,(NICHe,Tohoku Univ.); Kawashima, R.,(NICHe,Tohoku Univ.); Wan, X.,(NICHe,Tohoku Univ.); Ozaki, T.,(The Institute of Statistical Mathematics)

Key words:

Effective connectivity; state space representative; bottom-up model; EEG-fMRI fusion model .

Abstract:

The elucidation of the complex machinery used by the human brain to segregate and integrate information while performing high cognitive functions is a subject of imminent future consequences. The most significant contributions in this field, known as cognitive neuroscience, have been achieved to date by using innovative neuroimaging techniques (such as EEG and fMRI), which measure variations in both the time and the space of some interpretable physical magnitudes. Extraordinary maps of cerebral activation involving function-restricted brain areas as well as graphs of the serving some spatio-temporal inverse problems, which constitutes a top-down approach. However, in many cases, a natural bridge between these maps/graphs and the causal physiological processes is lacking, leading to some misunderstandings in their interpretation. The recent advances in the comprehension of the underlying physiological mechanisms associated to different cerebral scales have provided researchers with an excellent scenario to develop sophisticated biophysical models that permit an integration of these neuroimage modalities, which must share a common etiology. This paper proposes a bottom-up approach, involving physiological parameters in a specific mesoscopic dynamic equations system. Further observation equations encapsulating the relationship between the meso-states and the EEG/fMRI data are obtained on the basis of the physical foundations of these techniques. A methodology for the estimation of parameters from fused EEG/fMRI data is also presented. In this context, the concepts of activation and effective connectivity are carefully revised. This new approach permits us to examine and discuss some future prospects for the integration of multimodal neuroimages.