TR&D 3: Advanced Statistical Methods for Functional MRI

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TR&D 3. Advanced Statistical Methods for Functional MRI. Principle Investigators: James J. Pekar, PhD., Associate Professor of Radiology Brian S. Caffo, Ph.D., Professor of Biostatistics SUMMARY The biological description of the brain as an evolved ensemble of distributed neural networks underlies the significance of applying imaging measures of functional connectivity to clinical research. Our collaborative projects use blood oxygenation level dependent functional MRI (BOLD fMRI) to assess changes in brain networks in autism, ADHD, Alzheimer's disease, multiple sclerosis, schizophrenia, primary progressive aphasia, and Huntington's disease, seeking to develop noninvasive imaging-based biomarkers in order to reveal disease mechanisms, improve diagnosis and prognosis, and assess therapeutic interventions. Their studies are limited by the sensitivity and specificity of BOLD fMRI acquisitions. The overarching goal of this TR&D is to work with our collaborators to enhance the sensitivity and specificity of their functional connectivity measures by developing novel empirical Bayesian analysis approaches that exploit two ongoing transformations that are dramatically improving the acquisition and availability of fMRI data, namely simultaneous multi-slice (SMS) MRI, and the availability of large public datasets. Accordingly, we have developed three specific aims: 1. To develop time-invariant approaches to autoregressive modeling, and optimize them for SMS fMRI data. 2. To develop time-invariant approaches to nuisance regression, and optimize them for SMS fMRI data. 3. To design, implement, and assess empirical Bayesian methods for combining information from large public databases with data obtained from single subject/small sample studies.