From complex to neural networks

Quantitative neuroscience is trying to exploit the increasing number of large data sharing initiatives; therefore, Big Data analytics can play a pivotal role. So far, especially for neuroimaging, two different strategies have been largely explored: voxel-based and region of interest-based approaches. A common idea is that through quantitative features extracted by brain models it is possible to learn specific patterns, pathological or physiological, especially with the use of artificial intelligence techniques borrowed by Big Data analytics expertise. However, these approaches can suffer because of several limitations. This is why a third option has gained popularity: complex networks. In this chapter we discuss how brain models can be suitably designed with complex network theory and how this approach can suitably feed learning algorithms, especially deep learning ones. Accordingly, it is possible to design quantitative evaluation frameworks for several purposes as early diagnosis support systems or fully automated age prediction models.