A TWO-WAY REGULARIZATION METHOD FOR MEG SOURCE RECONSTRUCTION

被引：13

作者：

Tian, Tian Siva ^{[1
]}

Huang, Jianhua Z. ^{[2
]}

Shen, Haipeng ^{[3
]}

Li, Zhimin ^{[4
]}

机构：

[1] Univ Houston, Dept Psychol, Houston, TX 77204 USA

[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA

[3] Univ N Carolina, Dept Stat & Operat Res, Chapel Hill, NC 27599 USA

[4] Med Coll Wisconsin, Dept Neurol, Milwaukee, WI 53226 USA

来源：

ANNALS OF APPLIED STATISTICS | 2012年 / 6卷 / 03期

基金：

美国国家科学基金会;

关键词：

Inverse problem; MEG; two-way regularization; spatio-temporal; ELECTROMAGNETIC TOMOGRAPHY; PRINCIPAL-COMPONENTS; ELECTRICAL-ACTIVITY; SOURCE LOCALIZATION; INVERSE; EEG; MAGNETOENCEPHALOGRAPHY; POTENTIALS; PRIORS;

D O I：

10.1214/11-AOAS531

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：1021 / 1046

页数：26

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