White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Functional brain connectivity analysis based on the solution of the inverse problem and on covariance analysis.

Sanchez De Lucio, Jose Alfonso (2015) Functional brain connectivity analysis based on the solution of the inverse problem and on covariance analysis. PhD thesis, University of York.

[img]
Preview
Text
Thesis Alfonso.pdf
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.

Download (22Mb) | Preview

Abstract

The Linearly Constrained Minimum Variance (LCMV) beamformer is one of the most accepted techniques used to estimate the solution of the inverse problem in functional brain dynamics studies, using magnetoencephalograms (MEG). However, since it is based on the assumption of uncorrelated brain sources, its performance decreases in the presence of correlated brain activity, compromising the accuracy of estimates of brain interactions. This problem has not stopped the use of the beamformer in techniques such as Dynamic Imaging of Coherent Sources (DICS), which estimates the functional brain dynamics in a more direct way than the LCMV, and with less computational cost. In this work it is proposed to use a modified version of the well known Minimum Norm Estimates (MNE) spatial filter to estimate the functional brain dynamics of highly correlated activity. This is achieved by using the filter to estimate the cross-spectral density matrices for the brain activity in the same way that DICS does with the LCMV beamformer. The MNE spatial filter is used as a basis because it is not affected by the presence of correlated brain activity. The results obtained from simulations shown that it is possible to estimate highly correlated brain interactions using the proposed method. However, additional methods and constraints need to be applied because of the distorted and weighted output characteristic of the MNE spatial filter. Methods such as the FOcal Undetermined System Solution (FOCUSS) and Singular Value Decomposition Truncation (SVDT) are used to reduce the distorted output, while the estimation of brain dynamics is limited to cortical surface interactions to avoid weighted solutions.

Item Type: Thesis (PhD)
Academic Units: The University of York > Electronics (York)
Identification Number/EthosID: uk.bl.ethos.647083
Depositing User: Mr Jose Alfonso Sanchez De Lucio
Date Deposited: 22 May 2015 15:02
Last Modified: 08 Sep 2016 13:32
URI: http://etheses.whiterose.ac.uk/id/eprint/8938

You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.

Actions (repository staff only: login required)