Floods of data are being generated from the cognitive to the molecular levels with the aim of advancing our understanding of the human brain and human behavior. Yet, the data have hardly paved the way to new breakthroughs crossing relevant levels of biological organization. One reason for this is the absence of tools that allow researchers to collectively analyze neural, physiological and behavioral data.
The objective of this work package is to develop a data-analytic framework that will make it possible to identify common patterns in a bank of noisy biosignals (neural, physiological and behavioral). Central in this framework is Functional Data Analysis (FDA). With FDA we will represent biosignals as curves and analyze them collectively in search of modality independent patterns. As FDA transforms all data into a uniform format it also facilitates the integration of data from geographically distributed sites.
We will participate in the ID1000 experiment of Prof. Lamme and dr. Scholte (Brain and Cognition, UvA) in which 1000 subjects will be monitored while performing cognitive tasks such as, solving mathematical questions and watching movies. Monitoring includes acquisition of MRI, fMRI, heartbeat, and blood pressure. Blood samples will also be taken from each subject for DNA analysis at a later stage. We will apply FDA to these data to uncover meaningful patterns across biological levels. The tools will be deployed on top of the VL-e e-science fundament to solve the computational complexity of data analysis.