Colloquium Talk – Friday, March 21 at 2:00 PM

Dr. Wendy Meiring from UC Santa Barbara (profile), invited by Dr. Eliana Christou, will be presenting a colloquium talk on Friday, March 21, at 2:00 PM in Fretwell 116.

Dr. Meiring’s research interests include space-time processes, geophysical models, and environmental statistics and her colloquium talk will focus on Functional and Structural Brain Connectivity.

Title: Functional, and Structural Brain Connectivity: Two MRI-based Collaborations

Abstract: 

I discuss two neuroscience collaborations: the first on functional brain connectivity, and the second on structural brain connectivity.

Part I: A mixed model approach to estimate inter-regional Functional Connectivity from voxel-level BOLD signals
Resting state functional brain connectivity quantifies the similarity between neuronal firing in different brain regions. Each region consists of voxels at which dynamic signals are acquired via neuroimaging measurements, such as BOLD signals in fMRI. Pearson correlations and similar metrics are frequently adopted to estimate inter-regional connectivity, usually after averaging of signals across voxels within regions. However, dependencies between BOLD signals within each region, and the presence of noise, could contaminate such inter-regional correlation estimates. We propose a mixed-effects model with a novel covariance structure that explicitly models different sources of variability in the observed BOLD signals, including correlated regional signals, local spatiotemporal variability, and measurement error. Simulation results using a two-stage estimation algorithm demonstrate that connectivity parameter estimates from the proposed model are superior to those from the Pearson correlation of averages in the presence of spatiotemporal noise. The proposed model also is applied to a data set of BOLD signals collected from rats to estimate individual brain networks.

Part II: Analytic white matter tractography and compositional distance-based summarization of white matter brain structures (with UCSB Brain Imaging Center)
We present an analytic (simulation free) method for calculating white matter transition probabilities between neighboring brain voxels based on DSI structural MRI, including phantom evaluation, human in-vivo studies, and Voxel Graph tractography illustrations. We also present two voxel-wise univariate summary measures based on compositional data distances to describe features in white matter, including highlighting complex brain regions with splitting/fanning/kissing fibers. Our new summaries provide valuable additional insights beyond commonly used voxel-wise white matter descriptors such as fractional anisotropy (FA).