Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance

Chung, Moo K.; Ramos, Camille Garcia; De Paiva, Felipe Branco; Mathis, Jedidiah; Prabhakaran, Vivek; Nair, Veena A.; Meyerand, Mary E.; Hermann, Bruce P.; Binder, Jeffrey R.; Struck, Aaron F.

doi:10.1016/j.neuroimage.2023.120436

Cited by 2 publications

(5 citation statements)

References 99 publications

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“…The direct algebraic proof is fairly involving and given in [ 17 ]. Here we provide a more intuitive explanation.…”

Section: Methodsmentioning

confidence: 99%

“…[ 15 , 16 ] showed how persistent homology could be used in the analysis of functional brain connectivity using EEG. [ 17 ] utilized persistent homology to analyze brain networks for studying abnormal white matter in maltreated children. These studies collectively emphasize the potential of persistent homology in providing a robust framework for multiscale network analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 24 ], center persistency was shown to outperform the network-based statistic and element-wise multiple corrections. In [ 17 ], persistent homology based clustering is shown to outperform k -means clustering and hierarchical clustering. However, the method has been mainly used on static networks or a static summary of time-varying networks.…”

Section: Introductionmentioning

confidence: 99%

“…However, the method has been mainly used on static networks or a static summary of time-varying networks. The dynamic pattern of persistent homology for time-varying brain networks was rarely investigated, with a few exceptions [9,17,[25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the connection to the optimal mass transport, which enjoys various optimal properties, the Wasserstein distance has been applied to various imaging applications. Nonetheless, its application in network data analysis remains relatively limited [17,37]. [38] used the Wasserstein distance in resampling brain surface meshes.…”

Section: Introductionmentioning

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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“…The direct algebraic proof is fairly involving and given in [ 17 ]. Here we provide a more intuitive explanation.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Topological state-space estimation of functional human brain networks

Chung,

Huang,

Carroll

et al. 2024

PLoS Comput Biol

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Altered topological structure of the brain white matter in maltreated children through topological data analysis

Chung,

Azizi,

Hanson

et al. 2024

Network Neuroscience

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Childhood maltreatment may adversely affect brain development and consequently behavioral, emotional, and psychological patterns during adulthood.In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter structure in maltreated and typically developing children.We perform topological data analysis (TDA) to assess the alteration in global topology of the brain white-matter structural covariance network in children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing controls, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly demonstrate that TDA can be used as a baseline framework to model altered topological structures of the brain. The MATLAB codes and processed data used to perform this study can be found at https://github.com/laplcebeltrami/maltreated.

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Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance

Cited by 2 publications

References 99 publications

Topological state-space estimation of functional human brain networks

Topological state-space estimation of functional human brain networks

Altered topological structure of the brain white matter in maltreated children through topological data analysis

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