A two-way regularization method for MEG source reconstruction

Tian Siva Tian, Jianhua Z. Huang, Haipeng Shen, Zhimin Li

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1021-1046
Number of pages26
JournalThe Annals of Applied Statistics
Issue number3
StatePublished - Sep 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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