  In this paper, we propose a novel method for mild cognitive impairment
detection based on jointly exploiting the complex network and the neural
network paradigm. In particular, the method is based on ensembling different
brain structural "perspectives" with artificial neural networks. On one hand,
these perspectives are obtained with complex network measures tailored to
describe the altered brain connectivity. In turn, the brain reconstruction is
obtained by combining diffusion-weighted imaging (DWI) data to tractography
algorithms. On the other hand, artificial neural networks provide a means to
learn a mapping from topological properties of the brain to the presence or
absence of cognitive decline. The effectiveness of the method is studied on a
well-known benchmark data set in order to evaluate if it can provide an
automatic tool to support the early disease diagnosis. Also, the effects of
balancing issues are investigated to further assess the reliability of the
complex network approach to DWI data.
