Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy.

Autor:	Chung MK; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA., Che JB; OpenAI, San Francisco, USA., Nair VA; Department of Radiology, University of Wisconsin-Madison, USA., Ramos CG; Department of Neurology, University of Wisconsin-Madison, USA., Mathis JR; Department of Neurology, Medical College of Wisconsin, USA., Prabhakaran V; Department of Radiology, University of Wisconsin-Madison, USA., Meyerand E; Departments of Medical Physics & Biomedical Engineering, University of Wisconsin-Madison, USA., Hermann BP; Department of Neurology, University of Wisconsin-Madison, USA., Binder JR; Department of Neurology, Medical College of Wisconsin, USA., Struck AF; Department of Neurology, University of Wisconsin-Madison, USA.
Jazyk:	angličtina
Zdroj:	ArXiv [ArXiv] 2024 May 13. Date of Electronic Publication: 2024 May 13.
Abstrakt:	We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.
Databáze:	MEDLINE
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