NDLI: A Probabilistic Algorithm for Meg Source Reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Zumer, J.M. Attias, H.T. Sekihara, K. Nagarajan, S.S.
Copyright Year	2006
Description	Author affiliation: Dept. of Radiol., California Univ., San Francisco, CA (Zumer, J.M.)
Abstract	We present a novel algorithm for source localization based on probabilistic modeling of stimulus-evoked MEG/EEG data. This algorithm localizes multiple dipoles with the computational complexity equivalent to a single dipole scan, and is therefore more efficient than traditional multidipole fitting procedures. The algorithm assumes that the activity of multiple dipolar sources can be characterized by a linear combination of known temporal basis functions with unknown coefficients. We model the sensor data as arising from activity in each voxel of interest, plus background activity. We estimate temporal basis functions from the data using a probabilistic algorithm called partitioned-factor analysis, previously developed in our lab. We model background activity outside the voxel of interest as an unknown linear mixture of unobserved background factors plus diagonal sensor noise. We use an expectation-maximization algorithm to calculate MAP estimates of unknown basis function coefficients, background mixing matrix, sensor noise covariance and the likelihood of a dipole in each voxel of interest. In simulations, the algorithm is able to accurately localize several simultaneously-active dipoles, at SNRs typical for averaged MEG data. The algorithm performs well even in configurations that include deep sources and highly correlated sources, and thus is superior to MUSIC and beamforming techniques which are sensitive to correlated sources. The algorithm also correctly localizes real somatosensory and auditory evoked fields to the postcentral sulcus and lower bank of the lateral sulcus, respectively
Starting Page	111
Ending Page	113
File Size	185250
Page Count	3
File Format	PDF
ISBN	1424403081
DOI	10.1109/SAM.2006.1706102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Background noise Electroencephalography Computational complexity Sensor phenomena and characterization Partitioning algorithms Algorithm design and analysis Expectation-maximization algorithms Covariance matrix Multiple signal classification
Content Type	Text
Resource Type	Article

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