Using ICA and realistic BOLD models to obtain joint EEG/fMRI solutions to the problem of source localization.
We develop two techniques to solve for the spatio-temporal neural activity patterns using Electroencephalogram (EEG) and Functional Magnetic Resonance Imaging (fMRI) data. EEG-only source localization is an inherently underconstrained problem, whereas fMRI by itself suffers from poor temporal resolution. Combining the two modalities transforms source localization into an overconstrained problem, and produces a solution with the high temporal resolution of EEG and the high spatial resolution of fMRI. Our first method uses fMRI to regularize the EEG solution, while our second method uses Independent Components Analysis (ICA) and realistic models of Blood Oxygen-Level Dependent (BOLD) signal to relate the EEG and fMRI data. The second method allows us to treat the fMRI and EEG data on equal footing by fitting simultaneously a solution to both data types. Both techniques avoid the need for ad hoc assumptions about the distribution of neural activity, although ultimately the second method provides more accurate inverse solutions.
Brookings
Ted
T
Department of Biology, Brandeis University, Waltham, MA 02454, USA. brookings@brandeis.edu
Ortigue
Stephanie
S
Grafton
Scott
S
Carlson
Jean
J
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity