Source-space ICA for MEG source imaging

• Published in: Journal of neural engineering Vol. 13; no. 1; p. 16005
• Main Authors: Jonmohamadi, Yaqub, Jones, Richard D
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.02.2016
• Subjects: Algorithms; beamformer; Beamforming; Brain - physiology; Brain Mapping - methods; Computer Simulation; Data Interpretation, Statistical; Electroencephalography; Humans; Imaging; Imaging, Three-Dimensional - methods; independent component analysis; Inverse; localization; Magnetoencephalography - methods; MEG; Models, Neurological; Models, Statistical; Principal Component Analysis; Reconstruction; Reproducibility of Results; Sensitivity and Specificity; Simulation; Spatial resolution; Spatio-Temporal Analysis; time-course reconstruction
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2560/13/1/016005

Objective. One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and localize multiple concurrent sources. Among inverse techniques, the minimum-variance beamformers offer a high spatial resolution. However, in order to have both high spatial resolution of beamformer and be able to take on multiple concurrent sources, sensor-space ICA + beamformer is not an ideal combination. Approach. We propose source-space ICA for MEG as a powerful alternative approach which can provide the high spatial resolution of the beamformer and handle multiple concurrent sources. The concept of source-space ICA for MEG is to apply the beamformer first and then singular value decomposition + ICA. In this paper we have compared source-space ICA with sensor-space ICA both in simulation and real MEG. The simulations included two challenging scenarios of correlated/concurrent cluster sources. Main Results. Source-space ICA provided superior performance in spatial reconstruction of source maps, even though both techniques performed equally from a temporal perspective. Real MEG from two healthy subjects with visual stimuli were also used to compare performance of sensor-space ICA and source-space ICA. We have also proposed a new variant of minimum-variance beamformer called weight-normalized linearly-constrained minimum-variance with orthonormal lead-field. Significance. As sensor-space ICA-based source reconstruction is popular in EEG and MEG imaging, and given that source-space ICA has superior spatial performance, it is expected that source-space ICA will supersede its predecessor in many applications.