A Joint Analysis of Brain Signal, Genetics, and Behavior

• Activity Number: 147 - High-Dimensional Time Series Analysis and Its Applications
• Type: Invited
• Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Zhaoxia Yu* and Hernando Ombao and Dustin Pluta and Tong Shen
• Companies: UCI and King Abdullah University of Science and Technology and University of California, Irvine and University of California, Irvine
• Keywords: brain signal; high dimensional; genetics; integrated analysis; behavior

Abstract

Brain signal data are inherently "big" and challenging: massive in amount, complex in structure, high in dimensions, and low in signal to noise ratio. As a result, the integration of multiple data modalities to efficiently analyze multi-modal brain data is of prime importance. In this talk we present a joint analysis of brain connectivity with other data modalities, where the main focus is brain connectivity estimated by electroencephalogram (EEG) coherence at two conditions: rest and a reinforcement learning experiment. Our initial assessment of brain connectivity suggests that coherence (EEG) and function connectivity (fMRI) produce correlated results, which provides a solid justification of data integration. Integrating with genetic, behavioral, and fMRI data, we then find that different frequency bands of coherence show varying degrees of heritability. Finally, we conduct a systematic investigation on how genetic and brain signal jointly affect human behavior.