Colloquium talks will take place on Fridays, 1:00pm - 2:00pm. Some talks will be virtual, and some talks will be in-person. For in-person talks, a concurrent Microsoft Teams meeting will be run to allow virtual attendance. Please address inquiries/suggestions to Dr. Rowe at firstname.lastname@example.org.
September 1 - Daniel Adrian (Grand Valley State University)
Improved activation detection from magnitude and phase fMRI data
Functional MRI is a popular noninvasive technique for mapping brain regions activated by specific brain functions. However, as fMRI measures brain activity indirectly through blood flow, the so-called “brain or vein” problem refers to the difficulty in determining whether measured activation corresponds to (desired) brain tissue or (undesired) large veins, which may be draining blood from neighboring regions. Now, fMRI data consist of both magnitude and phase components (i.e., it is complex-valued), but in the vast majority of statistical analyses, only the magnitude data is utilized. However, while activation in the magnitude component can come from both “brain or vein”, previous work has demonstrated that activation in the phase component “discriminates” between the two: phase activation occurs in voxels with large, oriented vessels but not in voxels with small, randomly oriented vessels immediately adjacent to brain tissue. Following this motivation, we have developed a model that allows for activation in magnitude and phase, one more general than those previously proposed.
September 8 - Computational Sciences Summer Research Fellows Program presentations
Regularized Singular Spectrum Analysis
Functional time series (FTS) are constituted by dependent functions and can be used to model several applied processes. Several machine-learning approaches have been developed in the literature to gain insight into the stochastic processes that generate FTS. In this work, we present regularization techniques of the Singular spectrum analysis (SSA) method in the analysis of FTS. Regularization techniques of multivariate SSA (MSSA), functional SSA (FSSA), and Hilbert SSA (HSSA) known as regularized MSSA (reMSSA), regularized FSSA (reFSSA), and regularized HSSA (reHSSA) respectively are applied to call center data that contains the number of incoming calls to a bank's call center in Israel. We show that the proposed regularization techniques, reMSSA, reFSSA, and reHSSA, outperform MSSA, FSSA, and HSSA, respectively, by effectively smoothing the rough components generated by MSSA, FSSA, and HSSA of the MTS and FTS objects.
Numerical Implementation of the 3D Faddeev Green’s Function for the Full Nonlinear Complex Geometrical Optics Algorithm on EIT Electrode Data
The full nonlinear Complex Geometrical Optics (CGO) algorithm for Electrical Impedance Tomography (EIT) has not previously been implemented for practical EIT electrode setups in three dimensions, though approximations to the method have been utilized. In this work, we numerically implemented the Faddeev Green’s function for the full nonlinear method and successfully created image reconstructions on both simulated and experimental EIT voltage data and compared the results to those of other CGO methods. Preliminary results show valuable reconstructions and target localization.
Investigating transgenerational epigenetic inheritance in humans using IBD mapping
While transgenerational epigenetic inheritance has been observed in plants, it remains a complex biological phenomenon in humans. We hypothesize that transgenerational epigenetic inheritance occurs in regions of the genome that are identical by descent (IBD).
Comparing Traditional Iterative Reconstruction Methods with Coordinate-based Neural Networks in CT Image Reconstruction
To make X-ray computed tomography (CT) scans safer for patients, one approach is to acquire fewer X-ray measurements. However, this leads to an ill-posed inverse problem, which is traditionally solved by adding explicit regularization terms to an iterative reconstruction method. Currently, there is interest in using machine learning (ML) techniques to replace explicit regularization terms with implicit or learned forms of regularization. We focus on a recently proposed unsupervised ML technique coming from computer vision known as "coordinate-based neural networks" (CBNNs), which we compare with traditional iterative methods in their ability to reconstruct simulated CT data.
Regularized Multivariate Functional Principal Component Analysis
Functional principal component analysis (FPCA) has gained considerable attention in recent years due to its ability to handle complex data structure as functional data. Multivariate functional principal component analysis (MFPCA) is a natural extension of FPCA, where each observation consists of a vector of functions. The main contribution of my work is to introduce a regularized MFPCA (reMFPCA) method that incorporates smoothness constraints on functional principal components. In this talk, I will demonstrate the effectiveness of the proposed method through simulations and real data examples and compare the results of the proposed method with other existing regularization approaches.
September 15 - Computational Sciences Summer Research Fellows Program presentations
A more representative simulation of complex-valued fMRI data
Current functional magnetic resonance imaging (fMRI) simulation techniques disregard vast amounts of information regarding the nuclear magnetic resonance (NMR) process and the complex-valued data that is output by the machine. In this talk we present work towards a package for simulating fMRI data that is more representative of the machine used in practice through implementation of the gradient echo MR signal equation. This work is supplemented by an application of reconstructing non-Cartesian sampled data at accelerated rates.
A CAIPI Approach for mSPECS with Through-Plane and In-Plane Acceleration
In order to accelerate the number of images per unit of time to create each volume and decrease the total scan time, efforts have been paid into fMRI studies. Techniques such as SENSE and GRAPPA measure fewer data in an image slice but are able to reconstruct an image. The simultaneous multi-slice (SMS) techniques provide an alternative reconstruction method that multiple slices are acquired and aliased concurrently. Controlled Aliasing in Parallel Imaging (CAIPI) is a technique where the field-of-view is shifted for decreasing the influence of the geometry factor. In this project, a novel SMS technique called A CAIPI approach for mSPECS with Through-Plane and In-Plane Acceleration will be presented. With this approach, an improvement of SNR ratio is accomplished, meanwhile a decrease in geometry factor is achieved.
September 29 - Todd Ogden (Columbia University)
Functional data analysis of a compartment modeling framework with applications in dynamic PET imaging
Compartment modeling describes the movement of substances or individuals among different states and has application in epidemiology, pharmacokinetics, ecology, and many other areas. Fitting such a model to data typically involves solving a system of linear differential equations and estimating the parameters upon which the functions depend. In order for this approach to be valid, it is necessary that a number of fairly strong assumptions hold, assumptions involving various aspects of the kinetic behavior under investigation. In many situations, such models are understood to be simplifications of the "true" kinetic process. While in some circumstances such a simplified model may be a useful (and close) approximation to the truth, in other cases, important aspects of the kinetic behavior cannot be represented. We present a nonparametric approach, based on principles of functional data analysis, to modeling of pharmacokinetic data. We illustrate its use through application to data from a dynamic PET imaging study of the human brain.
October 6 - Michelle Guindani (UCLA)
Bayesian methods for studying heterogeneity in the brain
An improved understanding of the heterogeneity of brain mechanisms is considered critical for developing interventions based on observed neuroimaging features. in this talk, we will discuss two examples where models able to capture such heterogeneity appear necessary. First, we will focus on the reorganization of functional connections between brain areas throughout a neuroscience experiment. The transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive efforts. We will propose a multi-subject Bayesian framework for estimating dynamic functional networks as a function of time-varying exogenous physiological covariates that are simultaneously recorded in each subject during an fMRI experiment. Another general problem in neuroscience is detecting regional patterns of brain activation associated with a subject’s activity or condition, preferably at cellular resolution. Most existing statistical methods solve this problem by partitioning the brain regions into two classes: significantly and non-significantly activated areas. However, we will show that, for highly-noised data like those recorded in novel thin-section microscopy experiments, such binary grouping may provide overly simplistic discoveries by filtering out weak but essential signals. To overcome this limitation, we will propose a new Bayesian approach that allows classifying the brain regions into several tiers with varying degrees of statistical relevance. We will then conclude by outlining extensions and further research directions to study brain connectivity in animal and human experiments.
October 27 - Applied Statistics Practicum presentations
Data Visualization at Kohler Company
Using Publicly Available Data to Model Six Species of Lepidoptera in Kruger National Park
Decoding Customer Insights: A Data-Driven Dive into Chicago's E-commerce and Smart Products Landscape
Data Science Deployment in the Credit Risk Industry
November 3 - Jorg Polzehl (Weierstrass Institute)
Smoothing techniques for quantitave MR
Unlike conventional weighted MRI, leading to T1-, T2-, T2*-, or proton density (PD) weighted images in arbitrary units, quantitative MRI (qMRI) aims to estimate absolute physical metrics. qMRI is of increasing interest in neuroscience and clinical research for its greater specificity and its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. Furthermore, the measurement of quantitative data allows for comparison across sites, time points and participants, and enables longitudinal studies and multi-center trials. Examples are DIffusion Weighted MRI, Multi-Parameter Mapping, and Inversion Recovery MRI. Within this talk I'll discuss statistical issues in modeling of qMRI experiments. Special emphasis will be on adaptive, edge preserving smoothing techniques for parameter maps obtained in qMRI.
November 29 - Pei Wang (Miami University)
Sufficient Dimension Reduction and Variable Selection by Feature Filter
The minimum discrepancy approach proves useful in sufficient dimension reduction (SDR). In this study, we propose two novel SDR estimators based on a feature filter technique derived from the characteristic function, employing the minimum discrepancy function. In an ultra-high dimension setting with sparse assumptions, we introduce a regularization method aiming to achieve SDR and SVS (Sufficient Variable Selection) simultaneously. We establish asymptotic results and provide an estimation method for determining the structural dimension. To showcase the efficacy of our method, we conduct extensive simulations and present a real data example.
December 1 - Jarek Harezlak (Indiana University)
Novel penalized regression method applied to study the association of brain functional connectivity and alcohol drinking
The intricate associations between brain functional connectivity and clinical outcomes are difficult to estimate. Common approaches used do not account for the interrelated connectivity patterns in the functional connectivity (FC) matrix, which can jointly and/or synergistically affect the outcomes. In our application of a novel penalized regression approach called SpINNEr (Sparsity Inducing Nuclear Norm Estimator), we identify brain FC patterns that predict drinking outcomes. Results dynamically summarized in the R shiny app indicate that this scalar-on-matrix regression framework via the SpINNEr approach uncovers numerous reproducible FC associations with alcohol consumption.
December 8 - Rene Gutierrez (Texas A&M University)
Multi-object Data Integration in the Study of Primary Progressive Aphasia
This talk focuses on a multi-modal imaging data application where structural/anatomical information from grey matter (GM) and brain connectivity information in the form of a brain connectome network from functional magnetic resonance imaging (fMRI) are available for a number of subjects with different degrees of primary progressive aphasia (PPA), a neurodegenerative disorder (ND) measured through a speech rate measure on motor speech loss. The clinical/scientific goal in this study becomes the identification of brain regions of interest significantly related to the speech rate measure to gain insight into ND pathways. Viewing the brain connectome network and GM images as objects, we develop a flexible joint object response regression framework of network and GM images on the speech rate measure. A novel joint prior formulation is proposed on network and structural image coefficients in order to exploit network information of the brain connectome while leveraging the topological linkages among connectome network and anatomical information from GM to do inference on brain regions significantly related to the speech rate measure. The principled Bayesian framework allows precise characterization of the uncertainty in ascertaining a region being actively related to the speech rate measure. Empirical results with simulated data illustrate substantial inferential gains of the proposed framework over its popular competitors. Our framework yields new insights into the relationship of brain regions with PPA, offering a deeper understanding of neuro-degeneration pathways for PPA.
December 11 - Jaihee Choi (Rice University)
Inference for Set-Based Effects in Genome-Wide Association Studies with Multiple Interval-Censored Outcomes
Massive genetic compendiums such as the UK Biobank have become an invaluable resource for identifying genetic variants that are associated with complex diseases. Due to the difficulties of massive data collection, a common practice of these compendiums is to collect interval-censored data. One challenge in analyzing such data is the lack of methodology available for genetic association studies with interval-censored data. Genetic effects are difficult to detect because of their rare and weak nature, and often the time-to-event outcomes are transformed to binary phenotypes for access to more powerful signal detection approaches. However transforming the data to binary outcomes can result in loss of valuable information. To alleviate such challenges, this work develops methodology to associate genetic variant sets with multiple interval-censored outcomes. Testing sets of variants such as genes or pathways is a common approach in genetic association settings to lower the multiple testing burden, aggregate small effects, and improve interpretations of results. Instead of performing inference with only a single outcome, utilizing multiple outcomes can increase statistical power by aggregating information across multiple correlated phenotypes. Simulations show that the proposed strategy can offer significant power gains over a single outcome approach. We apply the proposed test to the investigation that motivated this study, a search for the genes that perturb risks of bone fractures and falls in the UK Biobank.