Fig. 5.

Distributed coherent networks of MEG generators. A: topographical maps of the factor loadings for the first PCA factor for each subject (S1, S2 … S8). Factor loadings represent the contribution of each sensor to the PCA component. Separate maps are shown for the orthogonal planar gradiometers: grad1 (G1) and grad2 (G1). PCA factors were derived using grad1 and grad2 recordings after concatenating all spindles in a given subject (i.e., factors were chosen to account for the variance across all spindles simultaneously). Because gradiometer signals are maximal over their generating cortex, these maps may be interpreted as indicating the likely lobe and hemisphere of the variance underlying each indicated PCA factor. B: topographical maps of the factor loadings for the 1st 7 PCA factors (C1, C2 … C7) for subject 8. C: coherence maps in subject 8. The 7 sensors with the peak factor loadings in the 1st principle component were chosen as seeds (marked in the top row of B), and the coherence map was calculated to each seed as indicated by the * on each plot.