Biostatistics Seminar Series: Lan Liu, PhD

Title: Efficient Inference for task fMRI studies Abstract: Functional magnetic resonance imaging (fMRI) is a popular technology that measures brain activity by detecting changes associated with blood flow. The fMRI produces a large number of voxels (cubic pixels of the brain volume), thus yielding relatively high spatial-temporal resolution. A task fMRI study collects the brain imaging data while an individual is instructed to accomplish basic tasks such as viewing images and more complex tasks such as logical inference, memorizing, and decision-making. The fMRI data from such studies provides information on behavior, brain development trajectories, and metabolic diseases. A fundamental challenge of fMRI studies has been the limited sample sizes. The main reason for such small sample size is the high scanning cost. If the sample size could be shrunk without compromising the information, a great amount of money could be saved and many additional cohorts could be explored. On the other hand, the rapid technological advances in brain imaging made it possible and routine to obtain high-resolution imaging data. While traditional methods of analysis may have produced acceptable results when the imaging data was in low resolution, the high dimensional images demand better statistical methods for more precise and efficient estimations for task fMRI studies. The goal of this project is to develop novel statistical methodologies for conducting efficient multivariate inferences for task fMRI studies. We apply our method to a task fMRI study.