A fast iterative greedy algorithm for MEG source localization

Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy, and robustness.

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“… ☆ This work has been presented in part at the 2012 IEEE Engineering in Medicine and Biology Conference (Obregon-Henao et al, 2012). …”

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confidence: 99%

A Subspace Pursuit-based Iterative Greedy Hierarchical solution to the neuromagnetic inverse problem

et al. 2014

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“…All of the above-mentioned heuristic algorithms are population-based except Simulated Annealing, and some of them are classified as swarm intelligence. These population-based algorithms, when applied to complex problems, frequently outperform classical methods such as linear programming [ 13 ], gradient methods [ 14 ], and greedy algorithms [ 15 ]. Although the heuristic algorithms can solve some problems, sometimes they converge on local optima or find solutions near but not at the global optimum.…”

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confidence: 99%

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

Cao

Goodman

2016

Computational Intelligence and Neuroscience

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A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

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Simultaneous spatio-temporal matching pursuit decomposition of evoked brain responses in MEG

et al. 2017

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We present a novel approach to the spatio-temporal decomposition of evoked brain responses in magnetoencephalography (MEG) aiming at a sparse representation of the underlying brain activity in terms of spatio-temporal atoms. Our approach is characterized by three attributes which constitute significant improvements with respect to existing approaches: (1) the spatial and temporal decomposition is addressed simultaneously rather than sequentially, with the benefit that source loci and corresponding waveforms can be unequivocally allocated to each other, and, hence, allow a plausible physiological interpretation of the parametrized data; (2) it is free from severe a priori assumptions about the solution space; (3) it comprises an optimization technique for the use of very large spatial and temporal subdirectories to greatly reduce the otherwise enormous computational cost by making use of the Cauchy–Schwarz inequality. We demonstrate the efficiency of the approach with simulations and real MEG data obtained from a subject exposed to a simple auditory stimulus.Electronic supplementary materialThe online version of this article (doi:10.1007/s00422-016-0707-5) contains supplementary material, which is available to authorized users.

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A fast iterative greedy algorithm for MEG source localization

Cited by 3 publications

References 14 publications

A Subspace Pursuit-based Iterative Greedy Hierarchical solution to the neuromagnetic inverse problem

A Subspace Pursuit-based Iterative Greedy Hierarchical solution to the neuromagnetic inverse problem

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

Simultaneous spatio-temporal matching pursuit decomposition of evoked brain responses in MEG

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