| Improving source detection and separation in a spatiotemporal Bayesian inference dipole analysis. | |
| | |
MedLine Citation:
|
PMID: 16675860 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
Most existing spatiotemporal multi-dipole approaches for MEG/EEG source localization assume that the dipoles are active for the full time range being analysed. If the actual time range of activity of sources is significantly shorter than the time range being analysed, the detectability, localization and time-course determination of such sources may be adversely affected, especially for weak sources. In order to improve detectability and reconstruction of such sources, it is natural to add active time range information (starting time point and ending time point of source activation) for each candidate source as unknown parameters in the analysis. However, this adds additional nonlinear free parameters that could burden the analysis and could be unfeasible for some methods. Recently, we described a spatiotemporal Bayesian inference multi-dipole analysis for the MEG/EEG inverse problem. This approach treated the number of dipoles as a free parameter, produced realistic uncertainty estimates using a Markov chain Monte Carlo numerical sampling of the posterior distribution and included a method to reduce the unwanted effects of local minima. In this paper, our spatiotemporal Bayesian inference multi-dipole analysis is extended to incorporate active time range parameters of starting and stopping time points. The properties of this analysis in comparison to the previous one without active time range parameters are demonstrated through extensive studies using both simulated and empirical MEG data. |
| | |
Authors:
|
Sung C Jun; John S George; Sergey M Plis; Doug M Ranken; David M Schmidt; C C Wood |
Publication Detail:
|
Type: Comparative Study; Evaluation Studies; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't Date: 2006-04-26 |
Journal Detail:
|
Title: Physics in medicine and biology Volume: 51 ISSN: 0031-9155 ISO Abbreviation: Phys Med Biol Publication Date: 2006 May |
Date Detail:
|
Created Date: 2006-05-05 Completed Date: 2006-07-27 Revised Date: 2007-11-14 |
Medline Journal Info:
|
Nlm Unique ID: 0401220 Medline TA: Phys Med Biol Country: England |
Other Details:
|
Languages: eng Pagination: 2395-414 Citation Subset: IM |
Affiliation:
|
MS-D454, Biological & Quantum Physics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. jschan@lanl.gov |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Action Potentials
/
physiology* Bayes Theorem Brain / physiology* Brain Mapping / methods* Diagnosis, Computer-Assisted / methods* Electroencephalography / methods* Humans Magnetoencephalography / methods* Models, Neurological* Models, Statistical Monte Carlo Method Reproducibility of Results Sensitivity and Specificity |
| Grant Support | |
ID/Acronym/Agency:
|
2 R01 EB000310-05/EB/NIBIB NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Development of pregnant female, hybrid voxel-mathematical models and their application to the dosime...
Next Document: Resolution enhancement in digital x-ray imaging.