Linking Structure and Function in Macroscale Brain Networks

Trends in Cognitive Sciences, Journal Year: 2020, Volume and Issue: 24(4), P. 302 - 315

Published: Feb. 25, 2020

The emergence of network neuroscience allows researchers to quantify the link between organizational features neuronal networks and spectrum cortical functions.Current models indicate that structure function are significantly correlated, but correspondence is not perfect because reflects complex multisynaptic interactions in structural networks.Function cannot be directly estimated from structure, must inferred by higher-order interactions. Statistical, communication, biophysical have been used translate brain function.Structure–function coupling regionally heterogeneous follows molecular, cytoarchitectonic, functional hierarchies. Structure–function relationships a fundamental principle many naturally occurring systems. However, research suggests there an imperfect connectivity brain. Here, we synthesize current state knowledge linking macroscale discuss different types assess this relationship. We argue do include requisite biological detail completely predict function. Structural reconstructions enriched with local molecular cellular metadata, concert more nuanced representations functions properties, hold great potential for truly multiscale understanding structure–function relationship central concept natural sciences engineering. Consider how conformation protein determines its chemical properties and, ultimately, folding into 3D promotes among amino acids, allowing chemically interact other molecules endowing it Conversely, disruption protein's results loss Tellingly, said denatured, highlighting idea changing has fundamentally altered nervous system analogously shaped arrangement neurons populations. synaptic projections forms hierarchy (see Glossary) nested increasingly polyfunctional neural circuits support perception, cognition, action. Modern imaging technology permits high-throughput reconstruction across spatiotemporal scales species (Box 1). 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Language: Английский

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Novel electrode technologies for neural recordings

Nature reviews. Neuroscience, Journal Year: 2019, Volume and Issue: 20(6), P. 330 - 345

Published: March 4, 2019

Language: Английский

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Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Frontiers in Neuroscience, Journal Year: 2019, Volume and Issue: 13

Published: June 6, 2019

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in mid-1990s and attracted increasing attention attempts to discover neural underpinnings cognition neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective among various units. Computational methods, especially graph theory-based have recently played a significant role understanding architecture. Objectives: Thanks emergence theoretical analysis, main purpose current paper is systematically review how properties can emerge through distinct neuronal units cognitive applications fMRI. Moreover, this article provides an overview existing effective methods used construct network, along with their advantages pitfalls. Methods: systematic review, databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, SpringerLink employed for exploring evolution computational 1990 present, focusing on theory. The Cochrane Collaboration's tool was assess risk bias individual studies. Results: Our results show that theory its implications neuroscience researchers since 2009 (as Human Connectome Project launched), because prominent capability characterizing behavior complex systems. Although approach be generally applied either during rest task performance, date, most articles focused resting-state connectivity. Conclusions: This insight into utilize measures make neurobiological inferences regarding mechanisms underlying well different

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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Gradients of structure–function tethering across neocortex

Proceedings of the National Academy of Sciences, Journal Year: 2019, Volume and Issue: 116(42), P. 21219 - 21227

Published: Sept. 30, 2019

The white matter architecture of the brain imparts a distinct signature on neuronal coactivation patterns. Interregional projections promote synchrony among distant populations, giving rise to richly patterned functional networks. A variety statistical, communication, and biophysical models have been proposed study relationship between structure function, but link is not yet known. In present report we seek relate structural connection profiles individual areas. We apply simple multilinear model that incorporates information about spatial proximity, routing, diffusion regions predict their connectivity. find structure–function relationships vary markedly across neocortex. Structure function correspond closely in unimodal, primary sensory, motor regions, diverge transmodal cortex, particularly default mode salience divergence systematically follows cytoarchitectonic hierarchies. Altogether, results demonstrate networks do align uniformly brain, gradually uncouple higher-order polysensory

Language: Английский

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A cross-disorder connectome landscape of brain dysconnectivity

Nature reviews. Neuroscience, Journal Year: 2019, Volume and Issue: 20(7), P. 435 - 446

Published: May 24, 2019

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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Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(12), P. 1644 - 1654

Published: Oct. 19, 2020

Language: Английский

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Consistency and differences between centrality measures across distinct classes of networks

PLoS ONE, Journal Year: 2019, Volume and Issue: 14(7), P. e0220061 - e0220061

Published: July 26, 2019

The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity node influence, or be influenced by, other via its connection topology. Many measures have been proposed, but degree they offer unique information, and such whether it is advantageous use multiple define roles, unclear. Here we calculate correlations between 17 across 212 diverse real-world networks, examine how these relate variations in density global topology, investigate can clustered into distinct classes according their profiles. We find that generally positively correlated each other, strength varies modularity plays key role driving cross-network variations. Data-driven clustering based on profiles distinguish including topological cores highly central peripheries less nodes. Our findings illustrate topology shapes pattern demonstrate comparative approach inform interpretation nodal complex networks.

Language: Английский

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