Understanding the functioning of brain network is one of the grand challenges of Science: many methods have been applied to analyze and study its structure and function. Interest is clearly motivated by the fact that the some general principles of brain functioning seem to govern also other complex networks, including social, biological and communications networks.
In principle, a brain network can be modelled as graph representing activities in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. Functional magnetic resonance imaging (fMRI) has been used to extract data about how networks of neurons are performing tasks. In these experiments the activity of the brain is measured, in time steps of a few seconds, in data associated to a number of “voxels” of dimension of about 27 mm3. Data are organised in correlation matrices (also with Multidimensional Scaling techniques) to see where there is a correlation among the variables. A correlation matrix can describe correlation among M variables: it is a square symmetrical MxM matrix with the (ij)th element equal to the correlation coefficient r_ij between the (i)th and the (j)th variable.
Over the last two decades, said data analysis techniques have transformed neuroscience: some scientists are even claiming they can identify the brain regions responsible for musical ability, food preference, fairness and even skills by analysing and correlating voxels data.
Also the recent explosion in gene expression experiments has generated a large amount of data to be correlated: think about The Cancer Genome Anatomy Project (CGAP), a program of the National Cancer Institute (NCI) that is researching the molecular patterns changes occurring when a normal cell is transformed into a cancer cell or the relationships between the gene expression profile of a tissue and the pattern of its neighbours.
These are just two example where the capabilities of correlating data have an incredibly high impact value. Analysing interconnectivity is so fundamental to understanding the behaviour of any complex networks.
Also Telecom Operators have huge amount of data, still to be leveraged: in this sense, methods and techniques adopted by neuroscience and genomics can provide valuable lessons learnt.