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). 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

Language: Английский

---

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(12), P. 1644 - 1654

Published: Oct. 19, 2020

Language: Английский

---

High-amplitude cofluctuations in cortical activity drive functional connectivity

Proceedings of the National Academy of Sciences, Journal Year: 2020, Volume and Issue: 117(45), P. 28393 - 28401

Published: Oct. 22, 2020

Significance Despite widespread applications, the origins of functional connectivity remain elusive. Here we analyze human neuroimaging data. We decompose resting-state across time to assess contributions moment-to-moment activity cofluctuations overall pattern. show that is driven by a small number high-amplitude frames. these frames are underpinned specific mode brain activity; topography this gets modulated during in-scanner tasks; and encode personalized, subject-specific information. In summary, our parameter-free method provides an exact mathematical link between frame-wise cofluctuations, creating opportunities for studying both static time-varying networks.

Language: Английский

---

Behavior needs neural variability

Neuron, Journal Year: 2021, Volume and Issue: 109(5), P. 751 - 766

Published: Feb. 17, 2021

Language: Английский

---

Machine learning in resting-state fMRI analysis

Magnetic Resonance Imaging, Journal Year: 2019, Volume and Issue: 64, P. 101 - 121

Published: June 4, 2019

Language: Английский

---

Functional connectivity predicts changes in attention observed across minutes, days, and months

Proceedings of the National Academy of Sciences, Journal Year: 2020, Volume and Issue: 117(7), P. 3797 - 3807

Published: Feb. 4, 2020

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same capture fluctuations individuals remains unclear. Here, five independent studies, we demonstrate the connectome-based predictive model (CPM), validated function, generalizes attentional state from data collected minutes, days, weeks, months. Furthermore, CPM is sensitive within-subject induced by propofol as well sevoflurane, such show signatures stronger states when awake than under deep sedation light anesthesia. Together, results reflect variability attention.

Language: Английский

---

LSD alters dynamic integration and segregation in the human brain

NeuroImage, Journal Year: 2020, Volume and Issue: 227, P. 117653 - 117653

Published: Dec. 17, 2020

Investigating changes in brain function induced by mind-altering substances such as LSD is a powerful method for interrogating and understanding how mind interfaces with brain, connecting novel psychological phenomena their neurobiological correlates. known to increase measures of complexity, potentially reflecting correlate the especially rich phenomenological content psychedelic-induced experiences. Yet although subjective stream consciousness constant ebb flow, no studies date have investigated influences dynamics functional connectivity human brain. Focusing on two fundamental network properties integration segregation, here we combined graph theory dynamic from resting-state MRI examine time-resolved effects networks Our main finding that experience are non-uniform time: makes globally segregated sub-states more complex, weakens relationship between anatomical connectivity. On regional level, reduces anterior medial prefrontal cortex, specifically during states high segregation. Time-specific were correlated different aspects experiences; particular, ego dissolution was predicted increased small-world organisation state global integration. These results reveal nuanced, temporally-specific picture altered complexity under psychedelics than has previously been reported.

Language: Английский

---

The impact of the human thalamus on brain-wide information processing

Nature reviews. Neuroscience, Journal Year: 2023, Volume and Issue: 24(7), P. 416 - 430

Published: May 26, 2023

Language: Английский

---

How to Interpret Resting-State fMRI: Ask Your Participants

Journal of Neuroscience, Journal Year: 2021, Volume and Issue: 41(6), P. 1130 - 1141

Published: Feb. 10, 2021

Resting-state fMRI (rsfMRI) reveals brain dynamics in a task-unconstrained environment as subjects let their minds wander freely. Consequently, resting navigate rich space of cognitive and perceptual states (i.e., ongoing experience). How this experience shapes rsfMRI summary metrics (e.g., functional connectivity) is unknown, yet likely to contribute uniquely within- between-subject differences. Here we argue that understanding the role requires access standardized, temporally resolved, scientifically validated first-person descriptions those experiences. We suggest best practices for obtaining via introspective methods appropriately adapted use research. conclude with set guidelines fusing these two data types answer pressing questions about etiology rsfMRI.

Language: Английский

---

A parsimonious description of global functional brain organization in three spatiotemporal patterns

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(8), P. 1093 - 1103

Published: July 28, 2022

Language: Английский

---