Efficient electromagnetic source imaging with adaptive standardized LORETA/FOCUSS

Functional brain imaging and source localization based on the scalp's potential field require a solution to an ill-posed inverse problem with many solutions. This makes it necessary to incorporate a priori knowledge in order to select a particular solution. A computational challenge for some subject-specific head models is that many inverse algorithms require a comprehensive sampling of the candidate source space at the desired resolution. In this study, we present an algorithm that can accurately reconstruct details of localized source activity from a sparse sampling of the candidate source space. Forward computations are minimized through an adaptive procedure that increases source resolution as the spatial extent is reduced. With this algorithm, we were able to compute inverses using only 6% to 11% of the full resolution lead-field, with a localization accuracy that was not significantly different than an exhaustive search through a fully-sampled source space. The technique is, therefore, applicable for use with anatomically-realistic, subject-specific forward models for applications with spatially concentrated source activity.

[1]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[2]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[3]  K. Sekihara,et al.  Multiple current dipole estimation using simulated annealing , 1994, IEEE Transactions on Biomedical Engineering.

[4]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[5]  G. Fein,et al.  Equivalent dipole parameter estimation using simulated annealing. , 1994 .

[6]  I F Gorodnitsky,et al.  Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. , 1995, Electroencephalography and clinical neurophysiology.

[7]  R. Srebro.  Iterative refinement of the minimum norm solution of the bioelectric inverse problem , 1996, IEEE Transactions on Biomedical Engineering.

[8]  Per Christian Hansen,et al.  Rank-Deficient and Discrete Ill-Posed Problems , 1996 .

[9]  Yongmin Kim,et al.  An efficient tissue classifier for building patient-specific finite element models from X-ray CT images , 1996, IEEE Transactions on Biomedical Engineering.

[10]  M Wagner,et al.  Inverse localization of electric dipole current sources in finite element models of the human head. , 1997, Electroencephalography and clinical neurophysiology.

[11]  Jens Haueisen,et al.  Realistic Computer Modelling of Electric and Magnetic Fields of Human Head and Torso , 1998, Parallel Comput..

[12]  R. Salmelin,et al.  Global optimization in the localization of neuromagnetic sources , 1998, IEEE Transactions on Biomedical Engineering.

[13]  M. Fuchs,et al.  Linear and nonlinear current density reconstructions. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[14]  R. Pascual-Marqui.  Review of methods for solving the EEG inverse problem , 1999 .

[15]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[16]  Anders M. Dale,et al.  Improved Localization of Cortical Activity By Combining EEG and MEG with MRI Cortical Surface Reconstruction , 2002 .

[17]  Jens Haueisen,et al.  Dipole models for the EEG and MEG , 2002, IEEE Transactions on Biomedical Engineering.

[18]  Barak A. Pearlmutter,et al.  Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise. , 2002, Physics in medicine and biology.

[19]  W. Hackbusch,et al.  Efficient Computation of Lead Field Bases and Influence Matrix for the FEM-based EEG and MEG Inverse Problem. Part I: Complexity Considerations , 2003 .

[20]  Dezhong Yao,et al.  A Self-Coherence Enhancement Algorithm and its Application to Enhancing Three-Dimensional Source Estimation from EEGs , 2001, Annals of Biomedical Engineering.

[21]  Leonid Zhukov,et al.  Lead-field Bases for Electroencephalography Source Imaging , 2000, Annals of Biomedical Engineering.

[22]  Fusheng Yang,et al.  A recursive algorithm for the three-dimensional imaging of brain electric activity: shrinking LORETA-FOCUSS , 2004, IEEE Transactions on Biomedical Engineering.

[23]  L. Geddes,et al.  The specific resistance of biological material—A compendium of data for the biomedical engineer and physiologist , 1967, Medical and biological engineering.