Mapping human brain function with MEG and EEG: methods and validation

• Published in: NeuroImage (Orlando, Fla.) Vol. 23; pp. S289 - S299
• Main Authors: Darvas, F., Pantazis, D., Kucukaltun-Yildirim, E., Leahy, R.M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 2004; Elsevier Limited
• Subjects: Brain; Brain Mapping - methods; Dipole fitting; EEG; Electroencephalography; Electroencephalography - methods; Head - anatomy & histology; Head - physiology; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Imaging methods; Inverse methods; Inverse problems; Magnetoencephalography - methods; Medical imaging; MEG; Methods; Models, Anatomic; Models, Neurological; Reproducibility of Results; ROC analysis; Statistical parametric tests; Studies; Statistical parametric tests; EEG; ROC analysis; Dipole fitting; Imaging methods; Inverse methods; MEG
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2004.07.014

We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the ill-posedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the free-response receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipole-fitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.