Modular and Hierarchically Modular Organization of Brain Networks

Meunier, David; Lambiotte, Renaud; Bullmore, Edward T.

doi:10.3389/fnins.2010.00200

Cited by 1,060 publications

(948 citation statements)

References 78 publications

(112 reference statements)

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“…Functional brain networks emerge as modules of highly interconnected nodes, with relatively few long-distance edges integrating across modules. This topologically modular organization is characteristic of the brain and other complex systems and enables flexible behavior based on specialized processes in locally segregated modules as well as more globally integrated functions through fast communication across modules (23). The strength of modular organization can be quantified using the modularity metric.…”

Section: Resultsmentioning

confidence: 99%

Ongoing dynamics in large-scale functional connectivity predict perception

Sadaghiani

Poline

Kleinschmidt

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

251

220

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Most brain activity occurs in an ongoing manner not directly locked to external events or stimuli. Regional ongoing activity fluctuates in unison with some brain regions but not others, and the degree of long-range coupling is called functional connectivity, often measured with correlation. Strength and spatial distributions of functional connectivity dynamically change in an ongoing manner over seconds to minutes, even when the external environment is held constant. Direct evidence for any behavioral relevance of these continuous large-scale dynamics has been limited. Here, we investigated whether ongoing changes in baseline functional connectivity correlate with perception. In a continuous auditory detection task, participants perceived the target sound in roughly one-half of the trials. Very long (22-40 s) interstimulus intervals permitted investigation of baseline connectivity unaffected by preceding evoked responses. Using multivariate classification, we observed that functional connectivity before the target predicted whether it was heard or missed. Using graph theoretical measures, we characterized the difference in functional connectivity between states that lead to hits vs. misses. Before misses compared with hits and task-free rest, connectivity showed reduced modularity, a measure of integrity of modular network structure. This effect was strongest in the default mode and visual networks and caused by both reduced within-network connectivity and enhanced across-network connections before misses. The relation of behavior to prestimulus connectivity was dissociable from that of prestimulus activity amplitudes. In conclusion, moment to moment dynamic changes in baseline functional connectivity may shape subsequent behavioral performance. A highly modular network structure seems beneficial to perceptual efficiency.functional connectivity | brain networks | dynamics | graph theory | classification T he brain is highly active in a continuous manner, and much of neural activity is not directly locked to external events or stimuli. This continuous brain activity is spatiotemporally organized into a functional connectivity architecture that comprises several large-scale networks. Large-scale networks span different cerebral lobes and include subcortical structures (1). The regions comprised in such networks commonly coactivate together in response to task demands (2), but they also show correlated and spontaneous activity fluctuations when no changes occur in the external environment. The functional connectivity architecture ensuing from these activity cofluctuations largely persists across all mental states, including various tasks, resting wakefulness, and sleep, albeit showing some degree of modulation across these states (3, 4).Strength and spatial distributions of functional connectivity within this architecture are not, however, stationary across time. At the spatial level of large-scale networks, functional connectivity shows prominent changes over the range of seconds to minutes (5). These so-called in...

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Section: Resultsmentioning

confidence: 99%

Ongoing dynamics in large-scale functional connectivity predict perception

Sadaghiani

Poline

Kleinschmidt

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

251

220

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“…This hierarchical scaling of communication is a well-known pattern in circuit design called Rent's rule [4], where p ¼ 1/D w is Rent's exponent. 1 This pattern is not unique to circuits and has been shown to occur in many biological networks [12][13][14][15]. Vascular systems correspond to a special case, where w i ¼ 1 for all i.…”

Section: Unified Model Of Network Scalingmentioning

confidence: 99%

“…The scaling of white and grey matter [54] and communication modularity [14] in the brain, of flow through river networks that minimize transportation costs [55], of energy use and GDP in countries [56], and the pace of life and population in cities [45] are all additional examples that a unifying scaling theory might explain. Because cost and performance, i.e.…”

Section: (B) Implications For Evolutionary Transitionsmentioning

confidence: 99%

Energy and time determine scaling in biological and computer designs

Moses

Bezerra

Edwards³

et al. 2016

Phil. Trans. R. Soc. B

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One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'. Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy-time minimization principle may govern the design of many complex systems that process energy, materials and information.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

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“…On the other hand, it also requires the stable maintenance of task goals (7) and reconfigurations of the network based on these goals. Given these characteristics and the organization of brain networks into modules with distinct functional properties (27,28), we hypothesized that task-specific processes, whose involvement varies from trial to trial, are reflected in dynamic adjustments of more peripheral components of the brain network. We further hypothesized that task goals are represented in a stable network core that we expected to be densely connected and to have direct access to large portions of the network (29,30).…”

mentioning

confidence: 99%

Predicting errors from reconfiguration patterns in human brain networks

Ekman

Derrfuß

Tittgemeyer

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

157

143

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Task preparation is a complex cognitive process that implements anticipatory adjustments to facilitate future task performance.Little is known about quantitative network parameters governing this process in humans. Using functional magnetic resonance imaging (fMRI) and functional connectivity measurements, we show that the large-scale topology of the brain network involved in task preparation shows a pattern of dynamic reconfigurations that guides optimal behavior. This network could be decomposed into two distinct topological structures, an error-resilient core acting as a major hub that integrates most of the network's communication and a predominantly sensory periphery showing more flexible network adaptations. During task preparation, core-periphery interactions were dynamically adjusted. Task-relevant visual areas showed a higher topological proximity to the network core and an enhancement in their local centrality and interconnectivity. Failure to reconfigure the network topology was predictive for errors, indicating that anticipatory network reconfigurations are crucial for successful task performance. On the basis of a unique network decoding approach, we also develop a general framework for the identification of characteristic patterns in complex networks, which is applicable to other fields in neuroscience that relate dynamic network properties to behavior. graph theory | attention | cognitive control T he human brain forms a highly complex network that is organized into a large number of specialized regions. During goal-directed behavior, like the preparation of an upcoming task, relevant cortical regions are anticipatorily modulated (1-5), which has been shown to facilitate the detection and analysis of task-relevant stimuli (6-13).However, little is known about how these task-specific adjustments are integrated across distinct brain regions and how preparatory mechanisms are reflected in a large-scale network topology (14-16). It has been shown that attention can modulate interarea correlations between distant cortical regions, independent from changes in regional blood flow (17-19). However, these studies were usually limited to a small selection of cortical regions (2,7,15,(18)(19)(20). With recent developments in functional connectivity analysis, it has become possible to study the role of large-scale networks for cognitive processing and to quantify network properties using global and local graph theoretical measures (21-26).On the one hand, task preparation involves dynamic adjustments in regions that carry out computations that are specific to a given task. On the other hand, it also requires the stable maintenance of task goals (7) and reconfigurations of the network based on these goals. Given these characteristics and the organization of brain networks into modules with distinct functional properties (27, 28), we hypothesized that task-specific processes, whose involvement varies from trial to trial, are reflected in dynamic adjustments of more peripheral components of the brain network. We...

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Modular and Hierarchically Modular Organization of Brain Networks

Cited by 1,060 publications

References 78 publications

Ongoing dynamics in large-scale functional connectivity predict perception

Ongoing dynamics in large-scale functional connectivity predict perception

Energy and time determine scaling in biological and computer designs

Predicting errors from reconfiguration patterns in human brain networks

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