A probabilistic solution to the MEG inverse problem via MCMC methods: The reversible jump and parallel tempering algorithms

被引:26
作者
Bertrand, C
Ohmi, M
Suzuki, R
Kado, H
机构
[1] Kanazawa Inst Technol, Appl Elect Labs, Minato Ku, Tokyo 1070052, Japan
[2] Kanazawa Inst Technol, Matto Labs Human Informat Syst, Matto City, Ishikawa 9240838, Japan
基金
日本学术振兴会;
关键词
Bayesian model; inverse problem; magnetoencephalography (MEG); Markov chain Monte Carlo methods (MCMC);
D O I
10.1109/10.918592
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
We investigated the usefulness of probabilistic Markov chain Monte Carlo (MCMC) methods for solving the magnetoencephalography (MEG) inverse problem, by using an algorithm composed of the combination of two MCMC samplers: Reversible Jump (RJ) and Parallel Tempering (PT), The MEG inverse problem was formulated in a probabilistic Bayesian approach, and we describe how the RJ and PT algorithms are fitted to our application. This approach offers better resolution of the MEG inverse problem even when the number of source dipoles is unknown (RJ), and significant reduction of the probability of erroneous convergence to local modes (PT), First estimates of the accuracy and resolution of our composite algorithm are given from results of simulation studies obtained with an unknown number of sources, and with white and neuromagnetic noise. In contrast to other approaches, MCMC methods do not just give an estimation of a "single best" solution, but they provide confidence interval for the source localization, probability distribution for the number of fitted dipoles, and estimation of other almost equally likely solutions.
引用
收藏
页码:533 / 542
页数:10
相关论文
共 22 条
[1]  
AINE CJ, 1995, CRIT REV NEUROBIOL, V9, P229
[2]   A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem [J].
Baillet, S ;
Garnero, L .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1997, 44 (05) :374-385
[3]   IMPROVED LOCALIZATION OF CORTICAL ACTIVITY BY COMBINING EEG AND MEG WITH MRI CORTICAL SURFACE RECONSTRUCTION - A LINEAR-APPROACH [J].
DALE, AM ;
SERENO, MI .
JOURNAL OF COGNITIVE NEUROSCIENCE, 1993, 5 (02) :162-176
[4]  
FOX C, P LEEDS ANN STAT RES
[5]   EQUIVALENT DIPOLE PARAMETER-ESTIMATION USING SIMULATED ANNEALING [J].
GERSON, J ;
CARDENAS, VA ;
FEIN, G .
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1994, 92 (02) :161-168
[6]   ANNEALING MARKOV-CHAIN MONTE-CARLO WITH APPLICATIONS TO ANCESTRAL INFERENCE [J].
GEYER, CJ ;
THOMPSON, EA .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1995, 90 (431) :909-920
[7]  
GEYER CJ, 1991, COMPUTING SCIENCE AND STATISTICS, P156
[8]  
Green PJ, 1995, BIOMETRIKA, V82, P711, DOI 10.2307/2337340
[9]   MULTIPLE CURRENT DIPOLE ESTIMATION USING SIMULATED ANNEALING [J].
HANEISHI, H ;
OHYAMA, N ;
SEKIHARA, K ;
HONDA, T .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1994, 41 (11) :1004-1009
[10]   MONTE-CARLO SAMPLING METHODS USING MARKOV CHAINS AND THEIR APPLICATIONS [J].
HASTINGS, WK .
BIOMETRIKA, 1970, 57 (01) :97-&