🍩 Database of Original & NonTheoretical Uses of Topology
(found 6 matches in 0.002431s)


Exact Topological Inference of the RestingState Brain Networks in Twins (2019)
Moo K. Chung, Hyekyoung Lee, Hernando Ombao, Victor Solo 
Abnormal Hole Detection in Brain Connectivity by Kernel Density of Persistence Diagram and Hodge Laplacian (2018)
Hyekyoung Lee, Moo K. Chung, Hyejin Kang, Hongyoon Choi, Yu Kyeong Kim, Dong Soo Lee 
Connectivity in fMRI: Blind Spots and Breakthroughs (2018)
Victor Solo, JeanBaptiste Poline, Martin A. Lindquist, Sean L. Simpson, F. DuBois Bowman, Moo K. Chung, Ben CassidyAbstract
In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this work we provide, firstly, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, secondly, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three address: network comparison via persistent homology, timevarying connectivity that distinguishes sample fluctuations from neural fluctuations and, network system identification that draws inferential strength from temporal autocorrelation. 
Persistent Brain Network Homology From the Perspective of Dendrogram (2012)
Hyekyoung Lee, Hyejin Kang, Moo K. Chung, BungNyun Kim, Dong Soo LeeAbstract
The brain network is usually constructed by estimating the connectivity matrix and thresholding it at an arbitrary level. The problem with this standard method is that we do not have any generally accepted criteria for determining a proper threshold. Thus, we propose a novel multiscale framework that models all brain networks generated over every possible threshold. Our approach is based on persistent homology and its various representations such as the Rips filtration, barcodes, and dendrograms. This new persistent homological framework enables us to quantify various persistent topological features at different scales in a coherent manner. The barcode is used to quantify and visualize the evolutionary changes of topological features such as the Betti numbers over different scales. By incorporating additional geometric information to the barcode, we obtain a single linkage dendrogram that shows the overall evolution of the network. The difference between the two networks is then measured by the GromovHausdorff distance over the dendrograms. As an illustration, we modeled and differentiated the FDGPET based functional brain networks of 24 attentiondeficit hyperactivity disorder children, 26 autism spectrum disorder children, and 11 pediatric control subjects. 
TopologyBased Kernels With Application to Inference Problems in Alzheimer’s Disease (2011)
Deepti Pachauri, Chris Hinrichs, Moo K. Chung, Sterling C. Johnson, Vikas SinghAbstract
Alzheimer’s disease (AD) research has recently witnessed a great deal of activity focused on developing new statistical learning tools for automated inference using imaging data. The workhorse for many of these techniques is the Support Vector Machine (SVM) framework (or more generally kernel based methods). Most of these require, as a first step, specification of a kernel matrix between input examples (i.e., images). The inner product between images Ii and Ij in a feature space can generally be written in closed form, and so it is convenient to treat as “given”. However, in certain neuroimaging applications such an assumption becomes problematic. As an example, it is rather challenging to provide a scalar measure of similarity between two instances of highly attributed data such as cortical thickness measures on cortical surfaces. Note that cortical thickness is known to be discriminative for neurological disorders, so leveraging such information in an inference framework, especially within a multimodal method, is potentially advantageous. But despite being clinically meaningful, relatively few works have successfully exploited this measure for classification or regression. Motivated by these applications, our paper presents novel techniques to compute similarity matrices for such topologicallybased attributed data. Our ideas leverage recent developments to characterize signals (e.g., cortical thickness) motivated by the persistence of their topological features, leading to a scheme for simple constructions of kernel matrices. As a proof of principle, on a dataset of 356 subjects from the ADNI study, we report good performance on several statistical inference tasks without any feature selection, dimensionality reduction, or parameter tuning.