Bayesian Methods for Source Separation in Magnetoencephalography

Magnetoencephalography (MEG) is a non-invasive brain imaging modality which localizes the sources of electrical activity within the brain based on measurements of the induced magnetic field outside the head. It has a variety of clinical applications; in particular, when a patient suffering from focal epilepsy is not responsive to drug treatment, the next step is often surgical intervention to remove the part of the brain where the seizures originate. MEG can potentially be used to localize the foci of the onset of seizures in order to assist with surgery planning.A great challenge in the MEG inverse problem is that the data are severely corrupted by noise generated by both independent external sources and biological noise sources within the brain itself. Therefore, it is of paramount interest in MEG to develop methods to distinguish between the signal generated by the sources of interest from that which arises from noise sources.We address the source separation problem within the Bayesian framework for both single time slice data and time series data. For single time slice data, we propose a mixture prior which incorporates the different statistical characteristics of the sources of interest and the noise sources. In addition, we propose a novel depth-scanning algorithm to identify and localize deep focal sources, overcoming the tendency of MEG inverse methods to explain all data with cortical sources. When considering source separation for time series data, we specifically address the problem of separating the signal of interest from the noise signal generated by spontaneous brain activity. It is well-known that this brain noise is correlated in both space and time. We take the novel approach of solving the MEG inverse problem using a Krylov subspace iterative method combined with statistically inspired left and right preconditioners. In particular, the left preconditioner is related to the covariance structure of the brain noise, while the right preconditioner is used to convey our prior beliefs about the statistical behavior of the unknown sources of interest.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Applied Mathematics

Grantor dc:publisher

Case Western Reserve University School of Graduate Studies

Year dc:date

2013

Author dc:creator

• Homa, Laura A.

Contributors dc:contributor

• Calvetti, Daniela
• Somersalo, Erkki

• Mathematics

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=case1365175207

OAI identifier oai:identifier

oai:etd.ohiolink.edu:case1365175207