7 papers
Topologically Invariant Permutation Test
Sixtus Dakurah
Functional brain networks exhibit topological structures that reflect neural organization; however, statistical comparison of these networks is challenging for several reasons. Thi…
Discrete Heat Kernels on Simplicial Complexes and Its Application to Functional Brain Networks
Sixtus Dakurah
Networks constitute fundamental organizational structures across biological systems, although conventional graph-theoretic analyses capture exclusively pairwise interactions, there…
Cycles Communities from the Perspective of Dendrograms and Gradient Sampling
Sixtus Dakurah
Identifying and comparing topological features, particularly cycles, across different topological objects remains a fundamental challenge in persistent homology and topological dat…
Spanning Tree Basis for Unbiased Averaging of Network Topologies
Sixtus Dakurah
In recent years there has been a paradigm shift from the study of local task-related activation to the organization and functioning of large-scale functional and structural brain n…
Brain Networks Flow-Topology via Variance Minimization in the Wasserstein Space
Sixtus Dakurah
This work introduces a novel framework for testing topological variability in weighted networks by combining Hodge decomposition with Wasserstein variance minimization. Traditional…
MaxTDA: Robust Statistical Inference for Maximal Persistence in Topological Data Analysis
Sixtus Dakurah, Jessi Cisewski-Kehe
Persistent homology is an area within topological data analysis (TDA) that can uncover different dimensional holes (connected components, loops, voids, etc.) in data. The holes are…