A two-way regularization method for MEG source reconstruction

Handle URI:
http://hdl.handle.net/10754/597432
Title:
A two-way regularization method for MEG source reconstruction
Authors:
Tian, Tian Siva; Huang, Jianhua Z.; Shen, Haipeng; Li, Zhimin
Abstract:
The 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.
Citation:
Tian 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.
Publisher:
Institute of Mathematical Statistics
Journal:
The Annals of Applied Statistics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Sep-2012
DOI:
10.1214/11-aoas531
Type:
Article
ISSN:
1932-6157
Sponsors:
Supported 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).
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorTian, Tian Sivaen
dc.contributor.authorHuang, Jianhua Z.en
dc.contributor.authorShen, Haipengen
dc.contributor.authorLi, Zhiminen
dc.date.accessioned2016-02-25T12:33:08Zen
dc.date.available2016-02-25T12:33:08Zen
dc.date.issued2012-09en
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.en
dc.identifier.issn1932-6157en
dc.identifier.doi10.1214/11-aoas531en
dc.identifier.urihttp://hdl.handle.net/10754/597432en
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.en
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).en
dc.publisherInstitute of Mathematical Statisticsen
dc.subjectInverse problemen
dc.subjectMegen
dc.subjectSpatio-temporalen
dc.subjectTwo-way regularizationen
dc.titleA two-way regularization method for MEG source reconstructionen
dc.typeArticleen
dc.identifier.journalThe Annals of Applied Statisticsen
dc.contributor.institutionUniversity of Houston, Houston, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionThe University of North Carolina at Chapel Hill, Chapel Hill, United Statesen
dc.contributor.institutionMedical College of Wisconsin, Milwaukee, United Statesen
kaust.grant.numberKUS-CI-016-04en
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