Parameterizing neural power spectra into periodic and aperiodic components

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(12), P. 1655 - 1665

Published: Nov. 23, 2020

Language: Английский

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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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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. 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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. 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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

Language: Английский

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Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Network Neuroscience, Journal Year: 2019, Volume and Issue: 4(1), P. 30 - 69

Published: Dec. 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.

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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Generative modeling of brain maps with spatial autocorrelation

NeuroImage, Journal Year: 2020, Volume and Issue: 220, P. 117038 - 117038

Published: June 22, 2020

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in maps across multiple modalities. Evaluating the significance these findings requires establishing statistical expectations under a null hypothesis interest. Through generative modeling synthetic data that instantiate specific hypothesis, quantitative benchmarks can be derived for arbitrarily complex measures. Here, we present model, provided as an open-access software platform, generates surrogate with autocorrelation (SA) matched to SA target map. is prominent and ubiquitous property violates assumptions independence conventional tests. Our method simulate maps, constrained by empirical data, preserve cortical, subcortical, parcellated, dense maps. We characterize how impacts p-values pairwise map comparisons. Furthermore, demonstrate SA-preserving used gene set enrichment analyses test hypotheses interest related topography. utility testing analyses, underscore need disambiguate meaningful from chance associations studies organization.

Language: Английский

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Resting brain dynamics at different timescales capture distinct aspects of human behavior

Nature Communications, Journal Year: 2019, Volume and Issue: 10(1)

Published: May 24, 2019

Abstract Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The counterparts static connectivity (FC), resolution several minutes, have been studied but correlates dynamic measures FC few seconds unclear. Here, using fMRI and 58 phenotypic from Human Connectome Project, we find that captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported loneliness life satisfaction) equally well explained by FC. Furthermore, behaviorally relevant emerges interconnections across all networks, rather than within between pairs networks. Our findings shed new light on cognitive processes involved distinct facets behavior.

Language: Английский

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Comparing spatial null models for brain maps

NeuroImage, Journal Year: 2021, Volume and Issue: 236, P. 118052 - 118052

Published: April 19, 2021

Technological and data sharing advances have led to a proliferation of high-resolution structural functional maps the brain. Modern neuroimaging research increasingly depends on identifying correspondences between topographies these maps; however, most standard methods for statistical inference fail account their spatial properties. Recently, multiple been developed generate null distributions that preserve autocorrelation brain yield more accurate estimates. Here, we comprehensively assess performance ten published frameworks in analyses data. To test efficacy situations with known ground truth, first apply them series controlled simulations examine impact resolution family-wise error rates. Next, use each framework two empirical datasets, investigating when testing (1) correspondence (e.g., correlating activation maps) (2) distribution feature within partition quantifying specificity an map intrinsic network). Finally, investigate how differences implementation models may performance. In agreement previous reports, find naive do not consistently elevated false positive rates unrealistically liberal While spatially-constrained yielded realistic, conservative estimates, even suffer from inflated variable across analyses. Throughout our results, observe minimal parcellation model Altogether, findings highlight need continued development statistically-rigorous comparing maps. The present report provides harmonised benchmarking future advancements.

Language: Английский

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The relationship between spatial configuration and functional connectivity of brain regions

eLife, Journal Year: 2018, Volume and Issue: 7

Published: Feb. 16, 2018

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated extent to which patterns coupling strength multiple neural populations relates behaviour. For example, used ‘functional fingerprints’ characterise individuals' activity. Here, we investigate exact spatial arrangement cortical regions interacts with measures connectivity. We find that shape and location interact strongly modelling connectivity, present evidence functional predictive non-imaging behaviour lifestyle. believe that, many cases, cross-subject variations configuration are being interpreted as changes Therefore, a better understanding these effects important when interpreting relationship imaging data cognitive traits.

Language: Английский

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Macroscopic gradients of synaptic excitation and inhibition in the neocortex

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 21(3), P. 169 - 178

Published: Feb. 6, 2020

Language: Английский

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neuromaps: structural and functional interpretation of brain maps

Nature Methods, Journal Year: 2022, Volume and Issue: 19(11), P. 1472 - 1479

Published: Oct. 6, 2022

Abstract Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Comparing experimentally generated these facilitates cross-disciplinary scientific discovery. Although recent data sharing initiatives increase the accessibility maps, often shared in disparate coordinate systems, precluding systematic accurate comparisons. Here we introduce neuromaps, a toolbox for accessing, transforming analyzing structural functional annotations. We implement functionalities generating high-quality transformations between four standard systems. The includes curated biological ontologies human brain, such as molecular, microstructural, electrophysiological, developmental ontologies. Robust quantitative assessment map-to-map similarity is enabled via suite spatial autocorrelation-preserving null models. neuromaps combines open-access with transparent functionality standardizing comparing providing workflow comprehensive annotation enrichment analysis brain.

Language: Английский

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