Fall 2020 Colloquia
- 09/11/20: RJ Liu (Florida State University); Time: 11:00 AM - 12:30 PM via Zoom
- 09/25/20: Rui Duan (Biostatistics, Harvard T.H. Chan School of Public Health); Time: 11:00 AM - 12:30 PM via Zoom
- 10/02/20: Bogdan Pasaniuc (Department of Pathology & Laboratory Medicine, Geffen School of Medicine at UCLA); Time: 11:00 AM - 12:30 PM via Zoom
- 10/09/20: Aiyi Liu (Biostatistics & Bioinformatics Branch. NICHD/ DIPHR); Time: 11:00 AM - 12:30 PM via Zoom
- 10/16/20: Will Fithian (Statistics, UC Berkeley); Time: 11:00 AM - 12:30 PM via Zoom
- 10/30/20: Nancy Reid (Statistics, University of Toronto): Hollander Lecture; Time: 11:00 AM - 12:30 PM via Zoom - Click here to learn more
- 11/06/20: Hongyu Zhao (Statistics and Data Science, Biostatistics, and Genetics, Yale University); Time: 11:00 AM - 12:30 PM via Zoom
- 11/13/20: Xihong Lin (Biostatistics, Harvard T.H. Chan School of Public Health); Time: 10 - 11:30 AM via Zoom
- 11/20/20: Daniel Schaid (Biostatistics, Mayo Clinic); Time: 10 - 11:30 AM via Zoom
Title: PPA: Principal Parcellation Analysis for Human Brain Connectomes of Multiple Human Traits
Abstract: Human brain parcellation plays a fundamental role in neuroimaging. Standard practice parcellates the brain into Regions Of Interest (ROIs) based roughly on anatomical function. However, many different schemes are available involving different numbers and locations of ROIs, and choosing which scheme to use in practice is challenging. We propose a novel tractography-based Principal Parcellation Analysis (PPA), which conducts the clustering analysis on the fibers' ending points to redefine parcellation and eventually predict human traits. Specifically, our PPA eliminates the need to choose ROIs manually, reduces subjectivity and leads to a substantially different representation of the connectome. We illustrate the proposed approach through applications to HCP data and show that PPA connectomes are able to improve power in predicting a variety of human traits, while dramatically improving parsimony, compared to anatomical parcellation based connectomes.
Title: Scalable and Consistent Estimation of Random Graph Models With Dependent Edge Variables and Parameter Vectors of Increasing Dimension Using the Pseudolikelihood
Abstract: An important question in statistical network analysis is how to construct models of dependent network data without sacrificing computational scalability and statistical guarantees. In this talk, we demonstrate that scalable estimation of random graph models with dependent edges and parameter vectors of increasing dimension is possible, using maximum pseudolikelihood estimators. On the statistical side, we establish the first consistency results and convergence rates for maximum pseudolikelihood estimators in scenarios where a single observation of dependent random variables is available and the number of parameters increases without bound. The main results make weak assumptions and may be of independent interest. These results help establish the first consistency results and convergence rates for maximum pseudolikelihood estimators of random graph models with dependent edges and parameter vectors of increasing dimension, under weak dependence and smoothness conditions. We showcase consistency results and convergence rates by using generalized β-models with dependent edges and parameter vectors of increasing dimension, in dense- and sparse-graph settings. The talk concludes with a discussion of potential future work and extensions. The primary results presented in this talk assume a complete observation of the random graph is observed. We will discuss how the theoretical developments presented in this talk offer avenues to advance the challenging topic of subgraph-to-graph estimation and inference, which considers estimating a random graph model based only on an observed subgraph.
Title: Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model
Abstract: Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activities. Growing evidence now suggests that the brain connectivity network experiences alterations with the presence of numerous neurological disorders, thus differential brain network analysis may provide new insights into disease pathologies. The data from neurophysiological measurement are often multi-dimensional and in a matrix form, posing a challenge in brain connectivity analysis. Existing graphical model estimation methods either assume a vector normal distribution that in essence requires the columns of the matrix data to be independent, or fail to address the estimation of differential networks across different populations. To tackle these issues, we propose an innovative Matrix-Variate Differential Network (MVDN) model. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix, and use an ADMM algorithm for the optimization problem. Theoretical and simulation studies demonstrate that MVDN significantly outperforms other state-of-the-art methods in dynamic differential network analysis. We illustrate with a functional connectivity analysis of an Attention Deficit Hyperactivity Disorder (ADHD) dataset. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies.