Year: 2023
Type: article
Abstract: The closed loops or cycles in a brain network embeds higher order signal transmission paths, which provide fundamental insights into the functioning of the brain. In this work, we propose an efficient algorithm for systematic identification and modeling of cycles using persistent homology and the Hodge Laplacian. Various statistical inference procedures on cycles are developed. We validate the our methods on simulations and apply to brain networks obtained through the resting state functional magnetic resonance imaging. The computer codes for the Hodge Laplacian are given in https://github.com/laplcebeltrami/hodge.
Authors: D. Vijay Anand, Moo K. Chung
Institution: University of Wisconsin–Madison
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Dynamic manifold-valued time series model in functional brain imaging, G266842233, BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain, Novel Continuous Structural and Functional Networks and Prediction of Individual Cognition, Dynamic embedding time series models in functional brain imaging
OpenAlex ID: W4313524911