Diagnosis and Exploration of Massively Univariate Neuroimaging Data

Thomas E Nichols, PhDDept. Biostatistics,
University of Michigan, Ann ArborHost: Prof. Richard LeahyAbstract:For either fMRI or EEG/MEG, the massively univariate approach a linear model at each of, say, 100,000 spatial elements in the brain. The p-values computed at each voxel depend on assumptions on the data, and inferences can not be trusted unless these assumptions are checked. However, distributional assumptions are rarely checked in neuroimaging due to the sheer scale of the data. In lieu of examining 100,000 diagnostic plots, we propose a combination of statistical and graphical techniques to efficiently diagnose model fit. We create images of diagnostic statistics sensitive to typical model-violations, and time series of summaries that detect problem scans. Together with an interactive spatiotemporal viewer, we demonstrate how summaries can be used to swiftly find rare anomalies in millions of data elements We demonstrate the method on single-subject fMRI data as well as group-level fMRI data. One specific finding is that, while the popular SPM software assumes the temporal autocorrelation tis spatially homogeneous, we find dramatic variation of the autocorrelation strength over the brain, suggesting that fMRI data requires spatially-varying autocorrelation modeling.Biography:Thomas Nichols is an Assistant Professor of Biostatistics at the University of Michigan. He received his Ph.D. in statistics from Carnegie Mellon University in 2001 where he also trained in cognitive neuroscience at the Center for the Neural Basis of Cognition. He has been active in the field of functional neuroimaging since 1992 when he joined the University of Pittsburgh's Positron Emission Tomography (PET) Center as a programmer and statistician. Dr. Nichols' research focuses on modeling and inference of functional neuroimaging data, including PET and Functional Magnetic Resonance Imaging (fMRI). He has developed methods and software for: Nonparametric analysis of PET fMRI data, inference methods which account for the multiplicity of searching the brain for changes in activity (SnPM); diagnosis and exploration of massively univariate models fit on imaging data (SPMd); and high temporal resolution reconstruction methods for PET.

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

Audiences: Everyone Is Invited

Contact: Regina Morton