The development of brain network hubs DOI Creative Commons
Stuart Oldham, Alex Fornito

Developmental Cognitive Neuroscience, Год журнала: 2018, Номер 36, С. 100607 - 100607

Опубликована: Дек. 13, 2018

Some brain regions have a central role in supporting integrated function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and consider shape function maturing brain. Here, we review evidence examining both structural connectivity networks, develop over prenatal, neonate, childhood, adolescent periods. The available suggests that hubs arise prenatal period show consistent spatial topography through development, but undergo protracted consolidation extends into late adolescence. In contrast, networks more variable topography, being predominantly located primary cortical areas early before moving association by childhood. These findings suggest while basic anatomical infrastructure may be established early, viability integrative capacity these undergoes extensive postnatal maturation. Not all are with this view however. We methodological factors might drive inconsistencies, which should addressed promote rigorous investigation development.

Язык: Английский

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

и другие.

Nature reviews. Neuroscience, Год журнала: 2017, Номер 19(1), С. 17 - 33

Опубликована: Дек. 14, 2017

Язык: Английский

Процитировано

831

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

и другие.

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). Through extensive international data sharing efforts, detailed system's connection patterns available humans multiple model organisms, including invertebrate [1.Chiang A-S. et al.Three-dimensional brain-wide wiring Drosophila at single-cell resolution.Curr. Biol. 2011; 21: 1-11Abstract Full Text PDF PubMed Scopus (324) Google Scholar], avian [2.Shanahan M. al.Large-scale organization forebrain: matrix theoretical analysis.Front. Comput. Neurosci. 2013; 7: 89Crossref (111) rodent [3.Oh S.W. al.A mesoscale connectome mouse brain.Nature. 2014; 508: 207Crossref (777) Scholar,4.Bota al.Architecture cerebral association underlying cognition.Proc. Natl. Acad. Sci. U.S.A. 2015; 112: E2093-E2101Crossref (90) Scholar, primate [5.Markov N.T. weighted directed interareal macaque cortex.Cereb. Cortex. 2012; 24: 17-36Crossref (272) Scholar,6.Majka P. al.Towards comprehensive atlas connections brain: Mapping tracer injection studies common marmoset reference digital template.J. Com. Neurol. 2016; 524: 2161-2181Crossref (33) Scholar. These diagrams system, termed (SC) or connectomes, represent physical elements [7.Sporns O. al.The human connectome: description brain.PLoS 2005; 1: e42Crossref (1521) Scholar]. offers opportunity articulate functions. SC possess distinctive nonrandom attributes, high clustering short path length, characteristic small-world architecture [8.Watts D.J. Strogatz S.H. Collective dynamics networks.Nature. 1998; 393: 440Crossref (0) Populations similar tend cluster together, forming specialized modules crosslinked hub nodes diverse connectional fingerprints [9.Young M.P. systems cortex.J. Roy. Soc. Lond. B. 1993; 252: 13-18Crossref Scholar,10.Kötter R. al.Connectional characteristics areas Walker's map prefrontal cortex.Neurocomputing. 2001; 38: 741-746Crossref (20) hubs disproportionately interconnected each other, putative core [11.Hagmann al.Mapping cortex.PLoS 2008; 6: e159Crossref (2384) Scholar] 'rich club' [12.van den Heuvel al.High-cost, high-capacity backbone global communication.Proc. 109: 11372-11377Crossref (361) architectural feature potentially signals sampled integrated [13.Zamora-López G. al.Cortical form module multisensory integration on top networks.Front. Neuroinform. 2010; 4: 1PubMed Finally, spatially embedded, finite metabolic material resources [14.Bullmore E. Sporns economy organization.Nat. Rev. 13: 336Crossref (1282) resulting increased prevalence shorter, low-cost [15.Horvát S. al.Spatial embedding cost constrain layout rodents primates.PLoS 14: e1002512Crossref Scholar,16.Roberts J.A. contribution geometry connectome.NeuroImage. 124: 379-393Crossref (60) attributes replicated range tracing techniques, suggesting principles phylogeny [17.Van al.Comparative connectomics.Trends. Cogn. 20: 345-361Abstract (118) imparts distinct signature coactivation patterns. Inter-regional promote signaling synchrony distant populations, giving rise coherent dynamics, measured as regional time series electromagnetic hemodynamic activity. Systematic pairs regions can (FC) networks. Over past decade, these recorded without task instruction stimulation; 'intrinsic' ' resting-state' FC thought reflect spontaneous activity [18.Biswal al.Functional motor cortex resting using echo-planar MRI.Magn. Reson. Med. 1995; 34: 537-541Crossref (5503) Intrinsic highly organized [19.Damoiseaux J. al.Consistent resting-state healthy subjects.Proc. 2006; 103: 13848-13853Crossref (2665) 20.Bellec al.Multi-level bootstrap analysis stable clusters fMRI.NeuroImage. 51: 1126-1139Crossref (170) 21.Thomas Yeo intrinsic connectivity.J. Neurophysiol. 106: 1125-1165Crossref (2040) reproducible [22.Gordon E.M. al.Precision mapping individual brains.Neuron. 2017; 95: 791-807Abstract (140) Scholar,23.Noble decade test-retest reliability connectivity: systematic review meta-analysis.NeuroImage. 2019; : 116157Crossref (5) comparable task-driven [24.Smith S.M. al.Correspondence brain's during activation rest.Proc. 2009; 13040-13045Crossref (2684) Scholar,25.Cole M.W. al.Intrinsic task-evoked architectures brain.Neuron. 83: 238-251Abstract (516) persistent nature rest makes ideal starting point study [26.Honey C.J. al.Can brain?.NeuroImage. 52: 766-776Crossref (291) Scholar,27.Damoiseaux J.S. Greicius M.D. Greater than sum parts: combining connectivity.Brain Struct. Funct. 213: 525-533Crossref Here first show direct one-to-one links limited inherently obscured networked survey modern quantitative methods move away correlations conceptualizing emerging focus strengths, limitations, commonalities. posit next steps network-level take account heterogeneity enriching microscale transcriptomic, neuromodulatory information. close theories uniform brain, vary parallel cytoarchitectonic representational Early emphasized weights. weights correlated [28.Honey C. al.Predicting connectivity.Proc. 2035-2040Crossref (1543) also Furthermore, structurally connected display greater unconnected Scholar,29.Shen K. al.Information processing functionally defined 32: 17465-17476Crossref (63) Scholar (Figure 1A). More globally, networks, particularly visual somatomotor circumscribed dense anatomical [29.Shen 30.Van Den al.Functionally linked brain.Hum. Brain Mapp. 30: 3127-3141Crossref (625) 31.Alves P.N. al.An improved neuroanatomical default-mode reconciles previous neuroimaging neuropathological findings.Commun. 2: 1-14Crossref While perfect. Even best-case estimates place correlation R2 ≈ 0.5 which means considerable variance (at least half) unexplained simple 1:1 structure. discrepancy widens case 1B ). A salient example homotopic corresponding structures two hemispheres. typically strongest subset [32.Mišić landscape One. 9: e111007Crossref (14) all supported callosal projection [33.Shen al.Stable long-range interhemispheric coordination projections.Proc. 6473-6478Crossref (52) strong may observed even individuals no [34.Uddin L.Q. al.Residual split-brain revealed fMRI.Neuroreport. 19: 703Crossref (96) 35.O'Reilly J.X. al.Causal effect disconnection lesions rhesus monkeys.Proc. 110: 13982-13987Crossref (106) 36.Layden E.A. al.Interhemispheric zebra finch absent corpus callosum normal ontogeny.NeuroImage. Crossref (1) examples illustrate sustained communication via indirect manifest FC. discordance pronounced mesoscopic scale. commonly meta-analytic recovered [37.Mišić al.Cooperative competitive spreading connectome.Neuron. 86: 1518-1529Abstract Scholar,38.Betzel R.F. al.Diversity meso-scale non-human connectomes.Nat. Commun. 2018; 346Crossref (21) 1C). reproducibly independent component community detection [39.Power J.D. 72: 665-678Abstract (1499) data-driven [20.Bellec Scholar,21.Thomas both recordings application diffusion-weighted covariance yields contiguous Scholar,40.Betzel modular networks: accounting wiring.Net. 42-68Crossref For example, fail identify default mode-like network, perhaps parts anatomically inter-connected differences. evidence assortative mixing, whereby (e.g., degrees) likely connected, whereas same true [50.Lim al.Discordant two-layer multiplex network.Sci. Rep. 2885Crossref (2) At scale, communities assortative, while disassortative [38.Betzel In words, affinity dissimilar attributes. As result, tuning algorithms sensitive improves match Altogether, rich body work demonstrates spans scales, edges their arrangement. Why FC? Functional arise connections, courses synapses removed other. propensity correlate driven only them, inputs they receive sensory organs entire [27.Damoiseaux Scholar,51.Bettinardi R.G. al.How sculpts function: unveiling structure.Chaos. 27: 047409Crossref (12) corollary much less distance-dependent connections. Anatomical subject material, spatial, constraints Scholar]; pressures reduced probability weight increasing spatial separation Although distance-dependence FC, weaker, ensuring differences configurations. section consider emergent property links. seen so far, exists nontrivial perfectly aligned. number emerged embody link, statistical [41.Mišić al.Network-level structure-function neocortex.Cereb. 26: 3285-3296Crossref (153) Scholar,42.Messé A. al.Relating relative contributions anatomy, stationary non-stationarities.PLoS. 10: e1003530Crossref Scholar,43.Graham D. Rockmore packet switching brain.J. 23: 267-276Crossref (18) 44.Goñi al.Resting-brain predicted analytic measures 111: 833-838Crossref (208) 45.Crofts J.J. Higham communicability measure applied networks.J. Interf. 411-414Crossref (61) [46.Honey al.Network shapes scales.Proc. 2007; 104: 10240-10245Crossref (941) 47.Breakspear Dynamic large-scale activity.Nat. 340Crossref (147) 48.Sanz-Leon al.Mathematical framework modeling Virtual Brain.NeuroImage. 385-430Crossref 49.Deco al.Key role coupling, delay, noise fluctuations.Proc. 10302-10307Crossref (372) Though implementation assumptions, emphasize collective, transcends geometric dependence dyadic relationships. briefly strategies, interpretation predictive utility, most importantly, what teach us about Perhaps simplest way statistically. Varying rank regression useful, canonical [52.Deligianni F. al.NODDI tensor-based microstructural indices predictors connectivity.PLoS 11: e0153404Crossref (13) partial squares objective simultaneously combinations maximally [53.McIntosh A.R. Mišić Multivariate analyses data.Annu. Psychol. 64: 499-525Crossref (73) 2). An appealing such modes. particular configuration subnetwork give Taking further, artificial learn recent variant word2vec algorithm build low-dimensional representation train deep edge-wise [54.Rosenthal relations embedded vector 2178Crossref (3) offer associate assuming specific mode interaction Communication science telecommunication engineering conceptualize superposition elementary events [43.Graham Scholar,55.Avena-Koenigsberger al.Communication networks.Nat. 17Crossref (92) By explicitly formulating inter-regional signaling, open important questions, namely: biologically realistic model, well does fit network? focused centralized shortest routing, discrete travel set source node prespecified target node. recently, attention shifted decentralized mechanisms where diffuse through [56.Mišić convergence zone hippocampus.PLoS. e1003982Crossref Scholar,57.Atasoy al.Human connectome-specific harmonic waves.Nat. 10340Crossref often broadcast fronts Scholar,58.Abdelnour diffusion accurately networks.NeuroImage. 90: 335-347Crossref (71) Scholar,59.Worrell J.C. al.Optimized sensory-motor integration.Net. 415-430Crossref Others considered neither fully nor decentralized, ensembles [45.Crofts Scholar,60.Avena-Koenigsberger al.Path tradeoff efficiency resilience connectome.Brain 222: 603-618Crossref (17) multiplexed strategies involving [44.Goñi Scholar,61.Avena-Koenigsberger routing networks.PLoS 15: e1006833Crossref 62.Betzel al.Structural, genetic factors interregional probed electrocorticography.Nat. Biomed. Eng. 63.Vazquez-Rodriguez al.Gradients tethering neocortex.Proc. 116: 21219-21227Crossref (6) consensus that, given topological proximity possible utilize either al.Re

Язык: Английский

Процитировано

686

Questions and controversies in the study of time-varying functional connectivity in resting fMRI DOI Creative Commons
Daniel J. Lurie, Daniel Kessler, Danielle S. Bassett

и другие.

Network Neuroscience, Год журнала: 2019, Номер 4(1), С. 30 - 69

Опубликована: Дек. 16, 2019

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge the spatiotemporal organization these interactions critical for establishing solid understanding brain's functional architecture and relationship between neural dynamics cognition in health disease. possibility studying through careful analysis neuroimaging data has catalyzed substantial interest methods that estimate time-resolved fluctuations connectivity (often referred to as "dynamic" or time-varying connectivity; TVFC). At same time, debates have emerged regarding application TVFC analyses resting fMRI data, about statistical validity, physiological origins, cognitive behavioral relevance TVFC. These other unresolved issues complicate interpretation findings limit insights can be gained from this promising new research area. This article brings together scientists with variety perspectives on review current literature light issues. We introduce core concepts, define key terms, summarize controversies open questions, present forward-looking perspective how rigorously productively applied investigate wide range questions systems neuroscience.

Язык: Английский

Процитировано

558

Human cognition involves the dynamic integration of neural activity and neuromodulatory systems DOI
James M. Shine, Michael Breakspear, Peter T. Bell

и другие.

Nature Neuroscience, Год журнала: 2019, Номер 22(2), С. 289 - 296

Опубликована: Янв. 12, 2019

Язык: Английский

Процитировано

485

The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core DOI Creative Commons
Gustavo Deco, Morten L. Kringelbach, Viktor Jirsa

и другие.

Scientific Reports, Год журнала: 2017, Номер 7(1)

Опубликована: Июнь 2, 2017

In the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but underlying mechanisms are not understood. We use connectome based computational brain modeling to reveal fundamental principles how generates large-scale observable by noninvasive neuroimaging. used structural and neuroimaging data construct whole- models. With this novel approach, we that operates at maximum metastability, i.e. in a switching. addition, investigate cortical heterogeneity across areas. Optimization spectral characteristics each local region revealed dynamical core which is driving rest whole brain. Brain modelling goes beyond correlational analysis reveals non-trivial non-invasive observations. Our findings significantly pertain important role connectomics understanding function.

Язык: Английский

Процитировано

483

Colloquium: Criticality and dynamical scaling in living systems DOI
Miguel A. Muñoz

Reviews of Modern Physics, Год журнала: 2018, Номер 90(3)

Опубликована: Июль 10, 2018

A celebrated and controversial hypothesis conjectures that some biological systems --parts, aspects, or groups of them-- may extract important functional benefits from operating at the edge instability, halfway between order disorder, i.e. in vicinity critical point a phase transition. Criticality has been argued to provide with an optimal balance robustness against perturbations flexibility adapt changing conditions, as well confer on them computational capabilities, huge dynamical repertoires, unparalleled sensitivity stimuli, etc. Criticality, its concomitant scale invariance, can be conjectured emerge living result adaptive evolutionary processes that, for reasons fully elucidated, select it template upon which higher layers complexity rest. This is very suggestive proposes criticality could constitute general common organizing strategy biology stemming physics transitions. However, despite thrilling implications, this still embryonic state well-founded theory and, such, elicited healthy skepticism. From experimental side, advent high-throughput technologies created new prospects exploration systems, empirical evidence favor proliferated, examples ranging endogenous brain activity gene-expression patterns, flocks birds insect-colony foraging, name but few...

Язык: Английский

Процитировано

476

Gradients of structure–function tethering across neocortex DOI Creative Commons
Bertha Vázquez-Rodríguez, Laura E. Suárez, Ross D. Markello

и другие.

Proceedings of the National Academy of Sciences, Год журнала: 2019, Номер 116(42), С. 21219 - 21227

Опубликована: Сен. 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

Язык: Английский

Процитировано

470

Dopamine and glutamate in schizophrenia: biology, symptoms and treatment DOI Open Access
Robert A. McCutcheon, John H. Krystal, Oliver Howes

и другие.

World Psychiatry, Год журнала: 2020, Номер 19(1), С. 15 - 33

Опубликована: Янв. 10, 2020

Glutamate and dopamine systems play distinct roles in terms of neuronal signalling, yet both have been proposed to contribute significantly the pathophysiology schizophrenia. In this paper we assess research that has implicated aetiology disorder. We examine evidence from post‐mortem, preclinical, pharmacological vivo neuroimaging studies. Pharmacological preclinical studies implicate systems, imaging system consistently identified elevated striatal synthesis release capacity Imaging glutamate other aspects on produced less consistent findings, potentially due methodological limitations heterogeneity Converging indicates genetic environmental risk factors for schizophrenia underlie disruption glutamatergic dopaminergic function. However, while influences may directly dysfunction, few variants system, indicating aberrant signalling is likely be predominantly factors. discuss neural circuits through which two interact, how their cause psychotic symptoms. also mechanisms existing treatments operate, recent highlighted opportunities development novel therapies. Finally, consider outstanding questions field, including what remains unknown regarding nature function schizophrenia, needs achieved make progress developing new treatments.

Язык: Английский

Процитировано

463

Human consciousness is supported by dynamic complex patterns of brain signal coordination DOI Creative Commons
Athéna Demertzi, Enzo Tagliazucchi, Stanislas Dehaene

и другие.

Science Advances, Год журнала: 2019, Номер 5(2)

Опубликована: Фев. 1, 2019

Dynamic patterns of brain activity at rest distinguish conscious and unconscious states in humans.

Язык: Английский

Процитировано

455

On the nature and use of models in network neuroscience DOI
Danielle S. Bassett, Perry Zurn, Joshua I. Gold

и другие.

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

Опубликована: Июль 12, 2018

Язык: Английский

Процитировано

380