报告题目：Challenges and Progress in Imaging Genetic – Big Data Squared Studies
报告摘要：Neuroimaging has been an essential tool for collecting data on the functioning of brain. High throughput technologies have provided ultra-dense genetic markers to enable us in identifying genetic variants for complex diseases. Only until recently, datasets of reasonably large scale become available that contain both imaging and genetic data. Due to the complexities and high dimensionality in such data, most of the existing datasets are still relatively small in sample sizes but larger datasets are in the horizon. Thus, it is timely and important to develop statistical methods and analytic tools to analyze imaging genetic data – the so-called big data squared. In this talk, I will present the basic technologies, concepts, challenges, and methods related to imaging genetic data. I will use specific data on learning disorders illustrate how to quantify neurobiological risk for learning problems with neuroimaging biomarkers and how to integrate imaging and genetic data in our understanding of cognition and genetic etiologies.