Multilayer modeling and analysis of human brain networks

Domenico, Manlio De

doi:10.1093/gigascience/gix004

Cited by 179 publications

(139 citation statements)

References 69 publications

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“…Multilayer network analysis (Muldoon & Bassett, 2016;De Domenico, 2017) is a novel graph-theoretic model of networks where nodes are connected across time and space. Multilayer networks can be decomposed into modules that span time and space using a multilayer modularity algorithm (Mucha, Richardson, Macon, Porter, & Onnela, 2010) that estimates the spatiotemporal segregation of nodes forming a subset of non-overlapping modules or networks.…”

Section: Introductionmentioning

confidence: 99%

Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

Pedersen

Zalesky

Omidvarnia

et al. 2018

Preprint

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Large-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative di↵erent brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between di↵erent fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics -defined as the di↵erence in variance between time-resolved fMRI connectivity signals and phase randomized surrogates-; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

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Section: Introductionmentioning

confidence: 99%

Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

Pedersen

Zalesky

Omidvarnia

et al. 2018

Preprint

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“…More recently, the multilayer network studies [176][177][178] have also been proposed. Since Study of brain networks is a part of brain science, which incorporates a wide range of disciplines such as neuroscience and graph theory.…”

Section: Discussionmentioning

confidence: 99%

Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

et al. 2017

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It is well known that most brain disorders are complex diseases, such as Alzheimer's disease (AD) and schizophrenia (SCZ). In general, brain regions and their interactions can be modeled as complex brain network, which describe highly efficient information transmission in a brain. Therefore, complex brain network analysis plays an important role in the study of complex brain diseases. With the development of noninvasive neuroimaging and electrophysiological techniques, experimental data can be produced for constructing complex brain networks. In recent years, researchers have found that brain networks constructed by using neuroimaging data and electrophysiological data have many important topological properties, such as small-world property, modularity, and rich club. More importantly, many brain disorders have been found to be associated with the abnormal topological structures of brain networks. These findings provide not only a new perspective to explore the pathological mechanisms of brain disorders, but also guidance for early diagnosis and treatment of brain disorders. The purpose of this survey is to provide a comprehensive overview for complex brain network analysis and its applications to brain disorders.

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“…The MuxViz toolkit[DDPA15], from the domain of complex systems, utilizes several variants of node‐link visualizations. They are also used in other domains that depend on complex systems theory [GB15, BBB*16, DD17].…”

Section: Survey Of Multilayer Graph Visualizationsmentioning

confidence: 99%

The State of the Art in Multilayer Network Visualization

McGee

Ghoniem

Mélançon

et al. 2019

Computer Graphics Forum

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Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.

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Multilayer modeling and analysis of human brain networks

Cited by 179 publications

References 69 publications

Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey

The State of the Art in Multilayer Network Visualization

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