Challenges in the analysis of high dimensional brain signals.
Hernando Ombao (King Abdullah University of Science and Technology, Saudi Arabia), terrà un workshop dal titolo: Challenges in the analysis of high dimensional brain signals.
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.
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 confronting in this interface between neuroscience and the data science (computational science, statistical learning and modeling). We will also present some of the work developed by members of the KAUST Biostatistics Group for visualizing and characterizing the dynamics brain connectivity and research opportunities in the Statistics program.
Hernando Ombao is Professor of Statistics at KAUST. He is the PI of the Biostatistics Research Group [https://biostats.kaust.edu.sa] within the Statistics Program. He develops statistical models for time series with dynamic and complex structures. Prior to coming to KAUST, he was a faculty member at UC Irvine, Brown University, University of Illinois and the University of Pittsburgh. He is Co-Editor of the Handbook of Statistical Methods for Neuroimaging. He was PI of several US NSF awards. He is a Fellow of the American Statistical Association (ASA). He serves the community as AE of Statistics journals (Metron, JASA, JRSS-B) and is currently chair of the Statistics in Imaging Section of the ASA.