Joint EEG/fMRI state space model for the detection of directed interactions in human brains—a simulation study

Abstract

An often addressed challenge in neuroscience research is the assignment of different tasks to specific brain regions. In many cases several brain regions are activated during a single task. Therefore, one is also interested in the temporal evolution of brain activity to infer causal relations between activated brain regions. These causal relations may be described by a directed, task specific network which consists of activated brain regions as vertices and directed edges. The edges describe the causal relations. Inference of the task specific brain network from measurements like electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) is challenging, due to the low spatial resolution of the former and the low temporal resolution of the latter. Here, we present a simulation study investigating a possible combined analysis of simultaneously measured EEG and fMRI data to address the challenge specified above. A nonlinear state space model is used to distinguish between the underlying brain states and the (simulated) EEG/fMRI measurements. We make use of a modified unscented Kalman filter and a corresponding unscented smoother for the estimation of the underlying neural activity. Model parameters are estimated using an expectation-maximization algorithm, which exploits the partial linearity of our model. Inference of the brain network structure is then achieved using directed partial correlation, a measure for Granger causality. The results indicate that the convolution effect of the fMRI forward model imposes a big challenge for the parameter estimation and reduces the influence of the fMRI in combined EEG-fMRI models. It remains to be investigated whether other models or similar combinations of other modalities such as, e.g., EEG and magnetoencephalography can increase the profit of the promising idea of combining various modalities.

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

“…The classification task was achieved with CNN, where the generated signals were formatted into image dimension. Estimation of brain activation with CNN was addressed in [133], where the temporal region was modified with unscented Kalman filter and a corresponding unscented smoother to observe inference relation of task-specific brain network. The CNN model parameters were estimated using expectation-maximization algorithm to exploit the partial linearity of the model.…”

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

“…Modeldriven symmetric fusion seeks to model the shared but unobserved neural states that give rise to EEG and fMRI recordings. For example, several studies have employed state-space models to link the temporal evolution of latent neural dynamics with joint EEG and fMRI observations via their own biophysical generative processes (Daunizeau et al 2007, Deneux & Faugeras 2010, Jun et al 2008, Lenz et al 2011, Purdon et al 2010, Riera et al 2006, Rosa et al 2010a, Valdes-Sosa et al 2009. This type of model aims at making inferences directly on the shared latent brain states given EEG and fMRI observations.…”

Overarching framework for data-based modelling

“…In the following we assume, however, that Δt = δt. The more general case in which nΔt = δt with n ∈ N being a positive integer can be coped with by modifying the observation equation accordingly; an observation is only made every nΔt integration steps [11]. Thus, we obtain the following model: 6)…”