Abstract
The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio.
| Item Type: | Paper |
|---|---|
| Keywords: | EEG, fMRI, activation probabilities, Bayesian spatial modeling, Markov Chain Monte Carlo sampling, hierarchical model |
| Faculties: | Mathematics, Computer Science and Statistics Mathematics, Computer Science and Statistics > Statistics Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
| Subjects: | 500 Science > 500 Science 500 Science > 510 Mathematics |
| URN: | urn:nbn:de:bvb:19-epub-15725-6 |
| Language: | English |
| Item ID: | 15725 |
| Date Deposited: | 26. Jun 2013 15:22 |
| Last Modified: | 13. Aug 2024 11:44 |
Actions (login required)
- View Item
