Statistical Models for Group Comparison of Functional MRI Data
Recently, many large-scale neuroimaging datasets have been collected and analyzed in an attempt to elucidate brain activities including but not limited to the pathology of psychiatric disorders and cognitive brain functions. However, only a few approaches have been developed for simultaneously analyzing multi-subject neuroimaging data. In this project, we will propose statistical models for integrating functional connectivity pattern across subjects. We will consider two types of data collected in different ways: 1) multi-subject functional MRI data obtained from one or more populations, and 2) multi-subject repeated-measures fMRI data obtained from one or more populations. In summary, we will study statistical models to analyze multi-subject fMRI data collected in various ways, and also consider spatial-temporal correlations as well as high-dimensionality of the data for proposing new statistical procedures such as model selection criteria. The proposed research is important because it addresses the essential steps for analyzing highly correlated fMRI data for multi-subject and multi-group conditions. By applying the proposed models, we will be able to detect group differences with increased power. Moreover, the statistical models we will develop will help us to address research questions effectively in multi-subject fMRI studies.