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

Proceedings of the National Academy of Sciences, Journal Year: 2018, Volume and Issue: 115(21)

Published: May 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

Language: Английский

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Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

NeuroImage, Journal Year: 2022, Volume and Issue: 249, P. 118870 - 118870

Published: Jan. 1, 2022

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides important tool for quantitative mapping structural connectivity using measures or tissue microstructure. Over last two decades, study brain dMRI has played a prominent role neuroimaging research landscape. In this paper, we provide high-level overview how used to enable analysis health and disease. We focus on types analyses tractography, including: 1) tract-specific refers typically hypothesis-driven studies particular anatomical fiber tracts, 2) connectome-based more data-driven generally entire brain. first review methodology involved three main processing steps are common across most approaches including methods correction, segmentation quantification. For each step, aim describe methodological choices, their popularity, potential pros cons. then have matter, focusing applications neurodevelopment, aging, neurological disorders, mental neurosurgery. conclude that, while there been considerable advancements technologies breadth applications, nevertheless remains no consensus about "best" researchers should remain cautious when interpreting results clinical applications.

Language: Английский

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Graph Neural Networks in Network Neuroscience

IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal Year: 2022, Volume and Issue: 45(5), P. 5833 - 5848

Published: Sept. 26, 2022

Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional connectivities were developed to create a comprehensive road map of neuronal activities in human -namely graph. Relying on its non-euclidean data type, graph neural network (GNN) provides clever way learning deep structure it is rapidly becoming state-of-the-art leading enhanced performance various neuroscience tasks. Here we review current GNN-based methods, highlighting ways that they have been used several applications related graphs such as missing synthesis disease classification. We conclude by charting path toward better application GNN models field for neurological disorder diagnosis population integration. The list papers cited our work available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.

Language: Английский

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Network geometry

Nature Reviews Physics, Journal Year: 2021, Volume and Issue: 3(2), P. 114 - 135

Published: Jan. 29, 2021

Language: Английский

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Null models in network neuroscience

Nature reviews. Neuroscience, Journal Year: 2022, Volume and Issue: 23(8), P. 493 - 504

Published: May 31, 2022

Language: Английский

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Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: June 9, 2021

Abstract Dynamical brain state transitions are critical for flexible working memory but the network mechanisms incompletely understood. Here, we show that performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses control theory. The stability relates to dopamine D1 receptor gene expression while influenced by D2 modulation. Individuals schizophrenia altered properties, including more diverse energy landscape decreased representations. Our results demonstrate relevance signaling steering whole-brain dynamics during link these processes pathophysiology.

Language: Английский

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Brain network communication: concepts, models and applications

Nature reviews. Neuroscience, Journal Year: 2023, Volume and Issue: 24(9), P. 557 - 574

Published: July 12, 2023

Language: Английский

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Local structure-function relationships in human brain networks across the lifespan

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: April 19, 2022

Abstract A growing number of studies have used stylized network models communication to predict brain function from structure. Most focused on a small set applied globally. Here, we compare large at both global and regional levels. We find that globally most predictors perform poorly. At the level, performance improves but heterogeneously, in terms variance explained optimal model. Next, expose synergies among by using pairs jointly FC. Finally, assess age-related differences coupling across human lifespan. decreases magnitude structure-function with age. these are driven reduced sensorimotor regions, while higher-order cognitive systems preserve local Our results describe patterns cortex how this may change

Language: Английский

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