Network neuroscience

Nature Neuroscience, Journal Year: 2017, Volume and Issue: 20(3), P. 353 - 364

Published: Feb. 23, 2017

Language: Английский

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Networks beyond pairwise interactions: Structure and dynamics

Physics Reports, Journal Year: 2020, Volume and Issue: 874, P. 1 - 92

Published: June 13, 2020

The complexity of many biological, social and technological systems stems from the richness interactions among their units. Over past decades, a great variety complex has been successfully described as networks whose interacting pairs nodes are connected by links. Yet, in face-to-face human communication, chemical reactions ecological systems, can occur groups three or more cannot be simply just terms simple dyads. Until recently, little attention devoted to higher-order architecture real systems. However, mounting body evidence is showing that taking structure these into account greatly enhance our modeling capacities help us understand predict emerging dynamical behaviors. Here, we present complete overview field beyond pairwise interactions. We first discuss methods represent give unified presentation different frameworks used describe highlighting links between existing concepts representations. review measures designed characterize models proposed literature generate synthetic structures, such random growing simplicial complexes, bipartite graphs hypergraphs. introduce rapidly research on topology. focus novel emergent phenomena characterizing landmark processes, diffusion, spreading, synchronization games, when extended elucidate relations topology properties, conclude with summary empirical applications, providing an outlook current conceptual frontiers.

Language: Английский

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Small-World Brain Networks Revisited

The Neuroscientist, Journal Year: 2016, Volume and Issue: 23(5), P. 499 - 516

Published: Sept. 21, 2016

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by combination high clustering and short path length; about 10 this metric complex topology began to be widely applied analysis neuroimaging other neuroscience data as part rapid growth new field connectomics. Here, we review briefly foundational concepts graph theoretical estimation generation networks. We take stock some key developments in past decade consider detail implications recent studies using high-resolution tract-tracing methods map anatomical networks macaque mouse. In doing so, draw attention important methodological distinction between topological binary or unweighted graphs, which have provided popular but simple approach brain past, weighted retain more biologically relevant information are appropriate increasingly sophisticated on connectivity emerging from contemporary imaging studies. conclude highlighting possible future trends further development small-worldness deeper broader understanding functional value strong weak links areas mammalian cortex.

Language: Английский

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A roadmap for the computation of persistent homology

EPJ Data Science, Journal Year: 2017, Volume and Issue: 6(1)

Published: Aug. 9, 2017

Persistent homology (PH) is a method used in topological data analysis (TDA) to study qualitative features of that persist across multiple scales. It robust perturbations input data, independent dimensions and coordinates, provides compact representation the input. The computation PH an open area with numerous important fascinating challenges. field evolving rapidly, new algorithms software implementations are being updated released at rapid pace. purposes our article (1) introduce theory computational methods for broad range scientists (2) provide benchmarks state-of-the-art PH. We give friendly introduction PH, navigate pipeline eye towards applications, use synthetic real-world sets evaluate currently available open-source Based on benchmarking, we indicate which best suited different types sets. In accompanying tutorial, guidelines make publicly all scripts wrote processed version benchmarking.

Language: Английский

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Multi-scale brain networks

NeuroImage, Journal Year: 2016, Volume and Issue: 160, P. 73 - 83

Published: Nov. 11, 2016

The network architecture of the human brain has become a feature increasing interest to neuroscientific community, largely because its potential illuminate cognition, variation over development and aging, alteration in disease or injury. Traditional tools approaches study this have focused on single scales-of topology, time, space. Expanding beyond narrow view, we focus review pertinent questions novel methodological advances for multi-scale brain. We separate our exposition into content related topological structure, temporal spatial structure. In each case, recount empirical evidence such structures, survey network-based reveal these outline current frontiers open questions. Although predominantly peppered with examples from neuroimaging, hope that account will offer an accessible guide any neuroscientist aiming measure, characterize, understand full richness brain's multiscale structure-irrespective species, imaging modality, resolution.

Language: Английский

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Graph theory methods: applications in brain networks

Dialogues in Clinical Neuroscience, Journal Year: 2018, Volume and Issue: 20(2), P. 111 - 121

Published: June 30, 2018

Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, from molecular to behavioral scales, are ever increasing in size complexity. These developments lead strong demand for appropriate tools methods that model analyze network data, such as those provided by graph theory. This brief review surveys some of the most commonly used neurobiologically insightful measures techniques. Among these, detection communities or modules, identification central elements facilitate communication signal transfer, particularly salient. A number emerging trends growing use generative models, dynamic (time-varying) multilayer well application algebraic topology. Overall, theory centrally important understanding architecture, development, evolution networks.La neurociencia de la red es un campo próspero y rápida expansión. Los datos empíricos sobre las redes cerebrales, desde niveles moleculares hasta conductuales, son cada vez más grandes en tamaño complejidad. Estos desarrollos llevan una fuerte demanda herramientas métodos apropiados que modelen analicen los cerebral, como proporcionados por teoría grafos. Esta breve revisión examina algunas medidas técnicas gráficas comúnmente empleadas neurobiológicamente discriminadoras. Entre estas, particularmente importantes detección módulos o comunidades redes, identificación elementos centrales facilitan comunicación transferencia señales. Algunas tendencias emergentes el empleo creciente modelos generativos, dinámicas (de tiempo variable) multicapa, así aplicación topología algebraica. En general, grafos especialmente para comprender arquitectura, desarrollo evolución cerebrales.La des réseaux est domaine florissant qui s'étend rapidement. Les données empiriques sur les cérébraux, l'échelle moléculaire à comportementale, ne cessent d'augmenter volume et complexité. Ces développements génèrent une demande forte d'outils méthodes appropriés pour modéliser analyser comme celles fournies par théorie graphes. Dans cette rapide analyse, nous examinons certaines techniques mesures graphes plus couramment utilisées signifiantes neurobiologiquement. Parmi elles, détection modules ou communautés l'identification éléments réseau facilite le transfert du signal, sont particulièrement marquantes. tendances émergentes, note l'utilisation croissante modèles génératifs, dynamiques (variables avec temps) multi-couches, ainsi l'application topologie algébrique. Globalement, essentielles comprendre l'architecture, développement l'évolution cérébraux.

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Topological Data Analysis

Annual Review of Statistics and Its Application, Journal Year: 2017, Volume and Issue: 5(1), P. 501 - 532

Published: Dec. 14, 2017

Topological data analysis (TDA) can broadly be described as a collection of methods that find structure in data. These include clustering, manifold estimation, nonlinear dimension reduction, mode ridge estimation and persistent homology. This paper reviews some these methods.

Language: Английский

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Cognitive Network Neuroscience

Journal of Cognitive Neuroscience, Journal Year: 2015, Volume and Issue: 27(8), P. 1471 - 1491

Published: March 24, 2015

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure function of human brain in novel ways using simple concepts mathematical representations. neuroscience is a rapidly growing field providing considerable insight into structural connectivity, functional connectivity while at rest, changes networks over time (dynamics), how these properties differ clinical populations. In addition, number studies have begun quantify network characteristics variety cognitive processes provide context for understanding cognition from perspective. this review, we outline contributions neuroscience. We describe methodology as applied particular case neuroimaging data review its uses investigating range functions including sensory processing, language, emotion, attention, control, learning, memory. conclusion, discuss current frontiers specific challenges must overcome integrate complementary disciplines Increased communication between neuroscientists scientists could lead significant discoveries under an emerging scientific intersection known

Language: Английский

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On the nature and use of models in network neuroscience

Nature reviews. Neuroscience, Journal Year: 2018, Volume and Issue: 19(9), P. 566 - 578

Published: July 12, 2018

Language: Английский

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The physics of brain network structure, function and control

Nature Reviews Physics, Journal Year: 2019, Volume and Issue: 1(5), P. 318 - 332

Published: March 27, 2019

Language: Английский

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