  Understanding the functional architecture of the brain in terms of networks
is becoming increasingly common. In most fMRI applications functional networks
are assumed to be stationary, resulting in a single network estimated for the
entire time course. However recent results suggest that the connectivity
between brain regions is highly non-stationary even at rest. As a result, there
is a need for new brain imaging methodologies that comprehensively account for
the dynamic (i.e., non-stationary) nature of the fMRI data. In this work we
propose the Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm
which estimates dynamic brain networks from fMRI data. We apply the SINGLE
algorithm to functional MRI data from 24 healthy patients performing a
choice-response task to demonstrate the dynamic changes in network structure
that accompany a simple but attentionally demanding cognitive task. Using graph
theoretic measures we show that the Right Inferior Frontal Gyrus, frequently
reported as playing an important role in cognitive control, dynamically changes
with the task. Our results suggest that the Right Inferior Frontal Gyrus plays
a fundamental role in the attention and executive function during cognitively
demanding tasks and may play a key role in regulating the balance between other
brain regions.
