A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains DOI Creative Commons
Milou Straathof, Michel R.T. Sinke, Rick M. Dijkhuizen

et al.

Journal of Cerebral Blood Flow & Metabolism, Journal Year: 2018, Volume and Issue: 39(2), P. 189 - 209

Published: Oct. 30, 2018

The mammalian brain is composed of densely connected and interacting regions, which form structural functional networks. An improved understanding the structure–function relation crucial to understand underpinnings function plasticity after injury. It currently unclear how connectivity strength relates strength. We obtained an overview recent papers that report on correspondences between quantitative measures in brain. included network studies was measured with resting-state fMRI, either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven 28 showed a positive relationship. Large inter-study variations were found comparing diffusion-based (correlation coefficient (r) ranges: 0.18–0.82) tracer-based (r = 0.24–0.74). Two datasets demonstrated lower correlations 0.22 r 0.30) than 0.49 0.65). robust relationship supports hypothesis provides hardware from emerges. However, methodological differences complicate comparison across studies, emphasize need for validation standardization studies.

Language: Английский

Communication dynamics in complex brain networks DOI
Andrea Avena‐Koenigsberger, Bratislav Mišić, Olaf Sporns

et al.

Nature reviews. Neuroscience, Journal Year: 2017, Volume and Issue: 19(1), P. 17 - 33

Published: Dec. 14, 2017

Language: Английский

Citations

814

Linking Structure and Function in Macroscale Brain Networks DOI Creative Commons
Laura E. Suárez, Ross D. Markello, Richard F. Betzel

et al.

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

Citations

672

A cross-disorder connectome landscape of brain dysconnectivity DOI
Martijn P. van den Heuvel, Olaf Sporns

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

Published: May 24, 2019

Language: Английский

Citations

395

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature DOI Creative Commons

David S. Grayson,

Damien A. Fair

NeuroImage, Journal Year: 2017, Volume and Issue: 160, P. 15 - 31

Published: Feb. 2, 2017

Language: Английский

Citations

391

A Network Model of the Emotional Brain DOI
Luiz Pessoa

Trends in Cognitive Sciences, Journal Year: 2017, Volume and Issue: 21(5), P. 357 - 371

Published: March 28, 2017

Language: Английский

Citations

375

Deschloroclozapine, a potent and selective chemogenetic actuator enables rapid neuronal and behavioral modulations in mice and monkeys DOI
Yuji Nagai, Naohisa Miyakawa, Hiroyuki Takuwa

et al.

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(9), P. 1157 - 1167

Published: July 6, 2020

Language: Английский

Citations

301

Chemogenetic Interrogation of a Brain-wide Fear Memory Network in Mice DOI Creative Commons
Gisella Vetere, Justin W. Kenney, Lina Tran

et al.

Neuron, Journal Year: 2017, Volume and Issue: 94(2), P. 363 - 374.e4

Published: April 1, 2017

Language: Английский

Citations

268

An Open Resource for Non-human Primate Imaging DOI Creative Commons
Michael P. Milham, Lei Ai, Bonhwang Koo

et al.

Neuron, Journal Year: 2018, Volume and Issue: 100(1), P. 61 - 74.e2

Published: Sept. 27, 2018

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational cross-species comparative neuroscience. Unfortunately, the technological methodological advances past two decades have outpaced accrual data, which particularly challenging given relatively few centers necessary facilities capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human magnetic resonance imaging (MRI) datasets openly sharing them via International Neuroimaging Data-sharing Initiative (INDI). Here, we present rationale, design, procedures for PRIME-DE consortium, as well initial release, consisting 25 independent data collections aggregated across 22 sites (total = 217 primates). We also outline unique pitfalls challenges should be considered in analysis MRI datasets, including providing automated quality assessment contributed datasets.

Language: Английский

Citations

241

The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations DOI Creative Commons

Janita Turchi,

Catie Chang, Frank Q. Ye

et al.

Neuron, Journal Year: 2018, Volume and Issue: 97(4), P. 940 - 952.e4

Published: Feb. 1, 2018

Language: Английский

Citations

219

The central extended amygdala in fear and anxiety: Closing the gap between mechanistic and neuroimaging research DOI
Andrew S. Fox, Alexander J. Shackman

Neuroscience Letters, Journal Year: 2017, Volume and Issue: 693, P. 58 - 67

Published: Nov. 30, 2017

Language: Английский

Citations

197