Linked Life Data

18455439

Source:http://linkedlifedata.com/resource/pubmed/id/18455439

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-6-2
pubmed:abstractText
We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886-3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1053-8119
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
41
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
924-40
pubmed:dateRevised
2011-5-23
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data.
pubmed:affiliation
Biomagnetic Imaging Lab, Department of Radiology, University of California, San Francisco, San Francisco, CA 94143-0628, USA.
pubmed:publicationType
Journal Article