The investigators in this proposal study two different aspects of noise in neuroimaging data: structure of non-Gaussian random fields and their applications to neuroimaging; along with interpretable predictive modelling of fMRI. The structure of non-Gaussian random fields is expected to shed light on how useful Random Field Theory (RFT), presently used for controlling Family Wise Error Rate (FWER) in neuroimaging studies, can be expected to be for truly non-Gaussian fields. The predictive models proposed by the investigators place much emphasis on interpretability and will allow comparison with the usual approaches based on detecting correlation between experimental stimuli and neuroimaging data while controlling FWER or False Discovery Rate (FDR).
This project is motivated by the need for flexible and valid statistical procedures to interpret neuroimaging data and to confirm neuroscientific hypotheses derived from previous work. Examples of such neuroimaging data can be found in fields like social neuroscience, which seeks to understand and model human social behaviour based on the activation and interactions of various regions of the human brain; neuroeconomics, which seeks to interpret and model some of the decision-making processes of humans based on models of the human brain; and disease models such as schizophrenia in which the goal is to understand how the brains of schizophrenic patients differ in anatomy and function from those of non-schizophrenic patients. Virtually all neuroimaging data is what is known as "high-throughput" which means that huge amounts of data are recorded, typically for only a small group of individuals. Statistical tools are needed to produce consistent and reproducible results from such data. The investigators in this proposal will develop tools that can be used to confirm existing hypotheses about neuroimaging data, as well as generate hypotheses via interpretable predictive models of decision making processes.
