Understanding of normal and pathological brain function requires the
identification and localization of functional connections between specialized
regions. The availability of high time resolution signals of electric neuronal
activity at several regions offers information for quantifying the connections
in terms of information flow. When the signals cover the whole cortex, the
number of connections is very large, making visualization and interpretation
very difficult. We introduce here the singular value decomposition of
time-lagged multiple signals, which localizes the senders, hubs, and receivers
(SHR) of information transmission. Unlike methods that operate on large
connectivity matrices, such as correlation thresholding and graph-theoretic
analyses, this method operates on the multiple time series directly, providing
3D brain images that assign a score to each location in terms of its sending,
relaying, and receiving capacity. The scope of the method is general and
encompasses other applications outside the field of brain connectivity.