  We present an approach to study functional segregation and integration in the
living brain based on community structure decomposition determined by maximum
modularity. We demonstrate this method with a network derived from functional
imaging data with nodes defined by individual image pixels, and edges in terms
of correlated signal changes. We found communities whose anatomical
distributions correspond to biologically meaningful structures and include
compelling functional subdivisions between anatomically equivalent brain
regions.
