Linking Structure and Function in Macroscale Brain Networks

Trends in Cognitive Sciences, Год журнала: 2020, Номер 24(4), С. 302 - 315

Опубликована: Фев. 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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Язык: Английский

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Graph theory methods: applications in brain networks

Dialogues in Clinical Neuroscience, Год журнала: 2018, Номер 20(2), С. 111 - 121

Опубликована: Июнь 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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On the nature and use of models in network neuroscience

Nature reviews. Neuroscience, Год журнала: 2018, Номер 19(9), С. 566 - 578

Опубликована: Июль 12, 2018

Язык: Английский

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The physics of brain network structure, function and control

Nature Reviews Physics, Год журнала: 2019, Номер 1(5), С. 318 - 332

Опубликована: Март 27, 2019

Язык: Английский

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Principles of dynamic network reconfiguration across diverse brain states

NeuroImage, Год журнала: 2017, Номер 180, С. 396 - 405

Опубликована: Авг. 3, 2017

Язык: Английский

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Specificity and robustness of long-distance connections in weighted, interareal connectomes

Proceedings of the National Academy of Sciences, Год журнала: 2018, Номер 115(21)

Опубликована: Май 8, 2018

Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections short, reflecting spatial energetic constraints. Nonetheless, a small number of span long distances, consistent with the notion that functionality these must outweigh cost. While precise function long-distance is not known, leading hypothesis they act to reduce topological distance between brain areas facilitate efficient interareal communication. However, this implies non-specificity we contend unlikely. Instead, propose serve diversify inputs outputs, thereby promoting complex dynamics. Through analysis five network datasets, show play only minor roles in reducing average distance. contrast, short-range neighbors exhibit marked differences connectivity profiles, suggesting enhance dissimilarity regional outputs. Next, -- isolation profiles non-random levels similarity, communication pathways formed redundancies may promote robustness. Finally, use linearization Wilson-Cowan dynamics simulate covariance structure neural activity absence connections, common measure diversity decreases. Collectively, our findings suggest necessary for supporting diverse

Язык: Английский

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Distance-dependent consensus thresholds for generating group-representative structural brain networks

Network Neuroscience, Год журнала: 2018, Номер 3(2), С. 475 - 496

Опубликована: Дек. 10, 2018

Large-scale structural brain networks encode white matter connectivity patterns among distributed areas. These connection are believed to support cognitive processes and, when compromised, can lead neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of is construct group-representative from multisubject cohorts. Doing so amplifies signal noise ratios provides a clearer picture network organization. Here, we show that current approaches generating sparse overestimate proportion short-range connections present in as result, fail match subject-level along wide range statistics. We an alternative preserves connection-length distribution individual subjects. have used this method previous papers generate networks, though date its performance has not been appropriately benchmarked compared against other methods. As result simple modification, generated using successfully recapitulate properties, outperforming similar by better preserving features promote integrative function rather than segregative. The developed here holds promise future studies investigating basic organizational large-scale networks.

Язык: Английский

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Multimodal network dynamics underpinning working memory

Nature Communications, Год журнала: 2020, Номер 11(1)

Опубликована: Июнь 15, 2020

Working memory (WM) allows information to be stored and manipulated over short time scales. Performance on WM tasks is thought supported by the frontoparietal system (FPS), default mode (DMS), interactions between them. Yet little known about how these systems their relate individual differences in performance. We address this gap knowledge using functional MRI data acquired during performance of a 2-back task, as well diffusion tensor imaging collected same individuals. show that strength FPS DMS task engagement inversely correlated with performance, modulated activation regions but not regions. Next, we use clustering algorithm identify two distinct subnetworks FPS, find display distinguishable patterns gene expression. Activity one subnetwork positively associated FPS-DMS interactions, while activity second negatively associated. Further, pattern structural linkages explains differential capacity influence interactions. To determine whether observations could provide mechanistic account large-scale neural underpinnings WM, build computational model composed coupled oscillators. Modulating amplitude causes expected change thereby offering support for mechanism which tunes Broadly, our study presents holistic regional activity, together humans.

Язык: Английский

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The neuropeptidergic connectome of C. elegans

Neuron, Год журнала: 2023, Номер 111(22), С. 3570 - 3589.e5

Опубликована: Ноя. 1, 2023

Efforts are ongoing to map synaptic wiring diagrams, or connectomes, understand the neural basis of brain function. However, chemical synapses represent only one type functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles all nervous systems. By integrating single-cell anatomical gene-expression datasets with biochemical analysis receptor-ligand interactions, we have generated a draft connectome neuropeptide C. elegans system. This network characterized high connection density, extended cascades, autocrine foci, decentralized topology, large, highly interconnected core containing three constituent communities sharing similar patterns input connectivity. Intriguingly, several key hubs little-studied neurons that appear specialized for peptidergic neuromodulation. We anticipate neuropeptidergic will serve as prototype how networks neuromodulatory organized.

Язык: Английский

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Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography

Nature Biomedical Engineering, Год журнала: 2019, Номер 3(11), С. 902 - 916

Опубликована: Май 27, 2019

Язык: Английский

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