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dc.contributor.authorTian, Tian Siva
dc.contributor.authorHuang, Jianhua Z.
dc.contributor.authorShen, Haipeng
dc.contributor.authorLi, Zhimin
dc.date.accessioned2016-02-25T12:33:08Z
dc.date.available2016-02-25T12:33:08Z
dc.date.issued2012-09
dc.identifier.citationTian TS, Huang JZ, Shen H, Li Z (2012) A two-way regularization method for MEG source reconstruction. The Annals of Applied Statistics 6: 1021–1046. Available: http://dx.doi.org/10.1214/11-aoas531.
dc.identifier.issn1932-6157
dc.identifier.doi10.1214/11-aoas531
dc.identifier.urihttp://hdl.handle.net/10754/597432
dc.description.abstractThe MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples. © Institute of Mathematical Statistics, 2012.
dc.description.sponsorshipSupported in part by the University of Houston New Faculty Research Program.Supported in part by NCI (CA57030), NSF (DMS-09-07170, DMS-10-07618) and King AbdullahUniversity of Science and Technology (KUS-CI-016-04).Supported in part by NIDA (1 RC1 DA029425-01) and NSF (CMMI-0800575, DMS-11-06912).
dc.publisherInstitute of Mathematical Statistics
dc.subjectInverse problem
dc.subjectMeg
dc.subjectSpatio-temporal
dc.subjectTwo-way regularization
dc.titleA two-way regularization method for MEG source reconstruction
dc.typeArticle
dc.identifier.journalThe Annals of Applied Statistics
dc.contributor.institutionUniversity of Houston, Houston, United States
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionThe University of North Carolina at Chapel Hill, Chapel Hill, United States
dc.contributor.institutionMedical College of Wisconsin, Milwaukee, United States
kaust.grant.numberKUS-CI-016-04


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