<div>Advances in imaging technology has given unprecedented access for neuroscientists to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). It is also multi-faceted and cannot be fully characterized by a single data modality. To fully appreciate brain processes, one must integrate various data that probe into both the anatomical structure and specific functionality such as electrical, metabolic and hemodynamic. 

<br>

<br>There are many challenges to analyzing brain data. First, brain data is massive - these are recordings across many location and over long recording times. Second, it has a complex structure with non-stationary properties that evolve over space and time. Third, brain data is often dominated by noise. Thus, this environment has provided big opportunities for data scientists to develop new tools and models for addressing the current research in the neuroscience community. This talk will highlight these challenges and the different research expertise at KAUST covering the neurosciences and the data sciences (computational science, statistical learning and modeling). We will also present some of the work developed by members of the KAUST Biostatistics Group and the UC Irvine Space-Time Modeling Group for visualizing and characterizing the dynamics brain connectivity. </div>