no code implementations • 11 Mar 2024 • Jinghan Huang, Qiufeng Chen, Yijun Bian, Pengli Zhu, Nanguang Chen, Moo K. Chung, Anqi Qiu
Additionally, we propose a pooling operator to coarsen $k$-simplices, combining features through simplicial attention mechanisms of self-attention and cross-attention via transformers and SP operators, capturing topological interconnections across multiple dimensions of simplices.
no code implementations • 1 Dec 2023 • Anass B. El-Yaagoubi, Shuhao Jiao, Moo K. Chung, Hernando Ombao
Our approach, the spectral TDA (STDA), has the ability to capture more nuanced and detailed information about the underlying brain networks.
1 code implementation • 1 Jul 2023 • Zijian Chen, Soumya Das, Moo K. Chung
We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images.
1 code implementation • 12 Apr 2023 • Moo K. Chung, Tahmineh Azizi, Jamie L. Hanson, Andrew L. Alexander, Richard J. Davidson, Seth D. Pollak
Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood.
no code implementations • 18 Feb 2023 • Jinghan Huang, Moo K. Chung, Anqi Qiu
We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing the $k-th$ Hodge-Laplacian (HL) operator.
2 code implementations • 13 Feb 2023 • Moo K. Chung, Camille Garcia Ramos, Felipe Branco De Paiva, Jedidiah Mathis, Vivek Prabharakaren, Veena A. Nair, Elizabeth Meyerand, Bruce P. Hermann, Jeffrey R. Binder, Aaron F. Struck
Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks.
no code implementations • 19 Nov 2022 • D. Vijay Anand, Moo K. Chung
We analyze brain networks by decomposing them into three orthogonal components: gradient, curl, and harmonic flows, through the Hodge decomposition, a technique advantageous for capturing complex topological features.
1 code implementation • 17 Oct 2022 • Moo K. Chung, Soumya Das, Hernando Ombao
We propose a novel dynamic-TDA framework that builds persistent homology over a time series of brain networks.
1 code implementation • 7 Apr 2022 • Moo K. Chung, Zijian Chen
Human brain activity is often measured using the blood-oxygen-level dependent (BOLD) signals obtained through functional magnetic resonance imaging (fMRI).
1 code implementation • 6 Apr 2022 • Soumya Das, D. Vijay Anand, Moo K. Chung
Understanding the common topological characteristics of the human brain network across a population is central to understanding brain functions.
no code implementations • 13 Mar 2022 • Moo K. Chung, Jamie L. Hanson, Seth D. Pollak
In this paper, we review widely used statistical analysis frameworks for data defined along cortical and subcortical surfaces that have been developed in last two decades.
1 code implementation • 1 Jan 2022 • Moo K. Chung, Shih-Gu Huang, Ian C. Carroll, Vince D. Calhoun, H. Hill Goldsmith
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest.
1 code implementation • 15 Oct 2021 • D. Vijay Anand, Moo K. Chung
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.
no code implementations • 1 May 2021 • Moo K. Chung, Hernando Ombao
We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations.
no code implementations • 9 Mar 2021 • Moo K. Chung
Recent developments in graph theoretic analysis of complex networks have led to deeper understanding of brain networks.
no code implementations • 25 Nov 2020 • Tananun Songdechakraiwut, Moo K. Chung
This paper proposes a novel topological learning framework that integrates networks of different sizes and topology through persistent homology.
no code implementations • 26 Oct 2020 • Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative
This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator.
no code implementations • 6 Oct 2020 • Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative
Even though graph convolutional neural network (graph-CNN) has been widely used in deep learning, there is a lack of augmentation methods to generate data on graphs or surfaces.