Nonlinear local electrovascular coupling. I: A theoretical model.
Here we present a detailed biophysical model of how brain electrical and vascular dynamics are generated within a basic cortical unit. The model was obtained from coupling a canonical neuronal mass and an expandable vasculature. In this proposal, we address several aspects related to electroencephalographic and functional magnetic resonance imaging data fusion: (1) the impact of the cerebral architecture (at different physical levels) on the observations; (2) the physiology involved in electrovascular coupling; and (3) energetic considerations to gain a better understanding of how the glucose budget is used during neuronal activity. The model has three components. The first is the canonical neural mass model of three subpopulations of neurons that respond to incoming excitatory synaptic inputs. The generation of the membrane potentials in the somas of these neurons and the electric currents flowing in the neuropil are modeled by this component. The second and third components model the electrovascular coupling and the dynamics of vascular states in an extended balloon approach, respectively. In the first part we describe, in some detail, the biophysical model and establish its face validity using simulations of visually evoked responses under different flickering frequencies and luminous contrasts. In a second part, a recursive optimization algorithm is developed and used to make statistical inferences about this forward/generative model from actual data.
Riera
Jorge J
JJ
Advanced Science and Technology of Materials, NICHe, Tohoku University, Sendai, Japan. riera@idac.tohoku.ac.jp
Wan
Xiaohong
X
Jimenez
Juan Carlos
JC
Kawashima
Ryuta
R
D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D003198:Computer Simulation; D004594:Electrophysiology; D006801:Humans; D008564:Membrane Potentials / Q000502:physiology; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net / Q000166:cytology / Q000502:physiology; D017711:Nonlinear Dynamics; D013997:Time Factors