Dynamic Functional Connectivity Using Heat Kernel

Huang, Shih-Gu; Chung, Moo K.; Carroll, Ian C.; Goldsmith, H. Hill

doi:10.1109/dsw.2019.8755550

Cited by 5 publications

(7 citation statements)

References 19 publications

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“…This approach extends the traditional cosine Fourier transform by incorporating an additional exponential weight. This modification effectively smooths out highfrequency noise and diminishes the Gibbs phenomenon [84,86]. Crucially, WFS eliminates the need for sliding windows (SW) when computing time-correlated data.…”

Section: Weighted Fourier Series Representationmentioning

confidence: 99%

“…The diffusion time s is usually referred to as the kernel bandwidth and controls the amount of smoothing. Heat kernel satisfies R 1 0 K s ðt; t 0 Þ dt ¼ 1 for any t 0 and s. To reduce unwanted boundary effects near the data boundary t = 0 and t = 1 [77,86], we project the data onto the circle C with circumference 2 by the mirror reflection:…”

Section: Plos Computational Biologymentioning

confidence: 99%

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Topological state-space estimation of functional human brain networks

Chung,

Huang,

Carroll

et al. 2024

PLoS Comput Biol

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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. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.

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Section: Weighted Fourier Series Representationmentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

Topological state-space estimation of functional human brain networks

Chung,

Huang,

Carroll

et al. 2024

PLoS Comput Biol

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“…our early neuroimaging and epigenetic studies use the MZ difference design (Adluru et al, 2017;Alisch et al, 2017;Burghy et al, 2016). The twin neuroimaging data were also used to illustrate novel computational MRI methods (Chung, Luo, Adluru et al, 2018;Chung, Luo, Leow et al, 2018;Huang et al, 2019). A majority of our neuroimaging work incorporates Research Domain Criteria (RDoC) as a framework for the behavioral assessments.…”

Section: Pubertymentioning

confidence: 99%

Wisconsin Twin Project Overview: Temperament and Affective Neuroscience

2019

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The Wisconsin Twin Project encompasses nearly 30 years of longitudinal research that spans infancy to early adulthood. The twin sample was recruited from statewide birth records for birth cohorts 1989–2004. We summarize early recruitment, assessment, retention and recently completed twin neuroimaging studies. In addition to the focal twins, longitudinal data were also collected from two parents and nontwin siblings. Our adolescent and young adult neuroimaging sample (N = 600) completed several previous behavioral and environmental assessments, beginning shortly after birth. The extensive phenotyping is meant to support a range of empirical investigations with potentially differing theoretical perspectives.

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“…It is unclear how to integrate such discrete topological representation over multiple time points in a continuous fashion. Dynamic-TDA as introduced in Songdechakraiwut and Chung (2020a) encodes rsfMRI as a time-ordered sequence of Rips complexes and their corresponding barcodes in Figure 20: Top: Dynamically changing correlation matrices computed from rs-fMRI using the sliding window of size 60 for a subject (Huang et al, 2019a). Bottom: The constructed correlation matrices are superimposed on top of the white matter fibers, which are colored based on correlations between parcellations.…”

Section: Conclusion and Disucssionmentioning

confidence: 99%

“…We would like to thank Robin Henderson of Newcastle University, U.K. for providing the coordinates and connectivity information of nodes of the binary tree used in Garside et al (2020). We would like to thank Hill Goldsmith of University of Wisconsin for providing one subject rsfMRI data used in the figure, which came from the study Huang et al (2019a). We thank Taniguchi Masanobu of Waseda University for discussion on canonical correlations.…”

Section: Acknowledgementsmentioning

confidence: 99%

Topological Distances Between Brain Networks

Chung

Lee

Solo

et al. 2017

Lecture Notes in Computer Science

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Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.

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Dynamic Functional Connectivity Using Heat Kernel

Cited by 5 publications

References 19 publications

Topological state-space estimation of functional human brain networks

Topological state-space estimation of functional human brain networks

Wisconsin Twin Project Overview: Temperament and Affective Neuroscience

Topological Distances Between Brain Networks

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