MEG and fMRI for nonlinear estimation of neural activity

Sergey M. Plis, Terran Lane, Michael P. Weisend, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work we demonstrate improvement of the analysis of functional neuroimaging by combining electromagnetic measurements and functional MRI. We show that magnetoencephalography and functional MRI can complement each other improving estimation of neural activity and BOLD response. Tracking hidden neural activity is performed as inference of latent variables in a dynamic Bayesian network with continuous parameters. Inference is performed using a particle filter. We demonstrate that MEG and fMRI fusion improves estimation of the hidden neural activity and smoothes tracking of the BOLD response. We demonstrate that joint analysis stabilizes the differential system and reduces computational requirements.

Original languageEnglish (US)
Title of host publicationConference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages1598-1602
Number of pages5
DOIs
StatePublished - Dec 1 2009
Externally publishedYes
Event43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 1 2009Nov 4 2009

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other43rd Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/1/0911/4/09

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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