Comparing brain connectivity metrics: a didactic tutorial with a toy model and experimental data

Beauchene, Christine; Roy, Subhradeep; Moran, Rosalyn J.; Leonessa, Alexander; Abaid, Nicole

doi:10.1088/1741-2552/aad96e

Cited by 14 publications

(10 citation statements)

References 38 publications

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“…This structural constraint on potential causal relations results in patterns of activity reflecting communication among units. Such activity can be measured by techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG), among others (Beauchene, Roy, Moran, Leonessa, & Abaid, 2018;Sporns, 2013b). In light of the complexity of observed activity patterns and in response to questions regarding their generative mechanisms, investigators have developed mathematical models of neuronal communication.…”

Section: Communication Modelsmentioning

confidence: 99%

Models of communication and control for brain networks: distinctions, convergence, and future outlook

Srivastava¹,

Nozari²,

Kim³

et al. 2020

Network Neuroscience

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Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.

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Section: Communication Modelsmentioning

confidence: 99%

Models of communication and control for brain networks: distinctions, convergence, and future outlook

Srivastava¹,

Nozari²,

Kim³

et al. 2020

Network Neuroscience

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“…Despite the different concepts and the many time-seriesanalysis techniques, a discussion about their relative merit lasting for more than 15 years indicates that there is probably no single approach which is best suited to characterize properties of interactions between physiological systems (Smirnov and Andrzejak, 2005;Ansari-Asl et al, 2006;Kreuz et al, 2007;Paluš and Vejmelka, 2007;Smirnov et al, 2007;Osterhage et al, 2007aOsterhage et al, ,b, 2008Vejmelka and Paluš, 2008;Wendling et al, 2009;Florin et al, 2011;Wang et al, 2014;Zhou et al, 2014;Hirata et al, 2016;Stokes and Purdon, 2017;Xiong et al, 2017;Barnett et al, 2018;Beauchene et al, 2018;Dhamala et al, 2018;Krakovská et al, 2018;Bakhshayesh et al, 2019).…”

Section: Data-driven Assessment Of Pairwise Interaction Propertiesmentioning

confidence: 99%

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

2020

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The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions—namely strength, direction, and functional form—from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.

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“…For this purpose, six graph-based global metrics are considered: Transitivity, Global Efficiency, Modularity, Density, Betweenness Centrality and Assortativity. Such metrics are indeed widely used to characterize brain connectivity [40], and could provide a reliable measure of the quality of generated data. In Fig.…”

Section: Evaluation Of Synthetic Data Based On Graphs Featuresmentioning

confidence: 99%

Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis

Barile

Marzullo

Stamile³

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and objective: Machine learning frameworks have demonstrated their potentials in dealing with complex data structures, achieving remarkable results in many areas, including brain imaging. However, a large collection of data is needed to train these models. This is particularly challenging in the biomedical domain since, due to acquisition accessibility, costs and pathology related variability, available datasets are limited and usually imbalanced. To overcome this challenge, generative models can be used to generate new data. Methods: In this study, a framework based on generative adversarial network is proposed to create synthetic structural brain networks in Multiple Sclerosis (MS). The dataset consists of 29 relapsingremitting and 19 secondary-progressive MS patients. T1 and diffusion tensor imaging (DTI) acquisitions were used to obtain the structural brain network for each subject. Evaluation of the quality of newly generated brain networks is performed by (i) analysing their structural properties and (ii) studying their impact on classification performance. Results: We demonstrate that advanced generative models could be directly applied to the structural brain networks. We quantitatively and qualitatively show that newly generated data do not present significant differences compared to the real ones. In addition, augmenting the existing dataset with generated samples leads to an improvement of the classification performance ( 1 81%) with respect to the baseline approach ( 1 66%). Conclusions: Our approach defines a new tool for biomedical application when connectome-based data augmentation is needed, providing a valid alternative to usual image-based data augmentation techniques.

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Comparing brain connectivity metrics: a didactic tutorial with a toy model and experimental data

Cited by 14 publications

References 38 publications

Models of communication and control for brain networks: distinctions, convergence, and future outlook

Models of communication and control for brain networks: distinctions, convergence, and future outlook

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis

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