CRCNS: BrainPack: A Suite of Advanced Statistical Techniques for Mmulti-Subject, Multi-Group Neuroimaging Data Analysis

Understanding the neural bases of psychiatric disorders is a critical component in the development of targeted behavioral or drug therapies. Given the rapid advancements in availability of, and access to brain imaging equipment, there now exists a large literature reporting on functional neuroimaging results differentiating people with psychotic disorders (such as schizophrenia) from healthy people. The literature is littered, however, with failures to replicate. The popularity of imaging as a research tool in psychiatric disorders, and the lack of consistency in results, provide a compelling demonstration of why resources would be well invested on the development of more reliable, accurate and sensitive tools for analyzing data. These tools must be based on sound statistical theory, yet accommodate the actual, practical challenges caused by the realities of the data. The project develops a suite of robust, sensitive and effective statistical methods which will help neuroscientists better understand the etiology of psychiatric disorders. The enhanced sensitivity of these tools also creates a better means for evaluating new treatments, as it provides improved assessment of changes across time that are currently difficult to capture due to their subtle nature.

The suite of methods developed in the project (BrainPack) is a comprehensive system for the analysis of group-level imaging data that makes minimal assumptions on the distributional behavior of the measured signal. It does not require an a priori model of the expected activation across sessions, can effectively reduce the size of large data sets containing mostly irrelevant information, account for spatial and temporal correlations, and quantify the discrepancy between groups and assess the statistical significance of these discrepancies. As such BrainPack will provide robust identification of subtle group differences that are common across many types of neuroimaging studies. These advancements are generalizable and readily adapted across a wide range of neuroimaging studies and beyond.