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Entropy in Brain Networks [PDF]
Entropy, 2021 A thorough and comprehensive understanding of the human brain ultimately depends on knowledge of large-scale brain organization[...]
Jesús Poza +2 more
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Multilayer Brain Networks [PDF]
Journal of Nonlinear Science, 2018 The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and
Michael Vaiana, Sarah Feldt Muldoon
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Network Embedding For Brain Connectivity [PDF]
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021 In Neurosciences, networks are currently used for representing the brain connections system with the purpose of determining the specific characteristics of the brain itself. However, discriminating between a healthy human brain network and a pathological one using common network descriptors could be misleading.
Carboni, Lucrezia +2 more
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Annual Review of Psychology, 2016 The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools.
Olaf, Sporns, Richard F, Betzel
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Navigation of brain networks [PDF]
Proceedings of the National Academy of Sciences, 2018 Significance We show that the combination of topology and geometry in mammalian cortical networks allows for near-optimal decentralized communication under navigation routing. Following a simple propagation rule based on local knowledge of the distance between cortical regions, we demonstrate that brain networks can be successfully navigated ...
Seguin, C +2 more
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Advances in Neural Information Processing Systems 35, 2022 Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for
Xuan Kan +5 more
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Topological learning for brain networks [PDF]
The Annals of Applied Statistics, 2020 Abstract This paper proposes a novel topological learning framework that can integrate networks of different sizes and topology through persistent homology. This is possible through the introduction of a new topological loss function that enables such challenging task.
Tananun Songdechakraiwut, Moo K. Chung
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‘Hierarchy’ in the organization of brain networks [PDF]
Philosophical Transactions of the Royal Society B: Biological Sciences, 2020 Concepts shape the interpretation of facts. One of the most popular concepts in systems neuroscience is that of ‘hierarchy’. However, this concept has been interpreted in many different ways, which are not well aligned. This observation suggests that the concept is ill defined. Using the example of the organization of the primate visual cortical system,
Claus Christian Hilgetag +1 more
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Epileptic brain network mechanisms and neuroimaging techniques for the brain network
Neural Regeneration Research, 2023 Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy ...
Yi Guo, Zhonghua Lin, Zhen Fan, Xin Tian
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Hodge Laplacian of Brain Networks
IEEE Transactions on Medical Imaging, 2023 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.
D. Vijay Anand, Moo K. Chung
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