Structural and functional brain networks: from connections to cognition.
01 Nov 2013-Science (American Association for the Advancement of Science)-Vol. 342, Iss: 6158, pp 1238411-1238411
TL;DR: It is concluded that the emergence of dynamic functional connectivity, from static structural connections, calls for formal (computational) approaches to neuronal information processing that may resolve the dialectic between structure and function.
Abstract: How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.
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TL;DR: A number of methods for detecting modules in both structural and functional brain networks are surveyed and their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics are considered.
Abstract: The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization...
1,048 citations
TL;DR: It is demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies.
Abstract: Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface; (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website (http://www.nitrc.org/projects/gretna/).
884 citations
Cites background from "Structural and functional brain net..."
..., 2010) in both typical and atypical population is therefore fundamental since they provide invaluable insights into how the collective of the human brain elements is topologically organized to promote cognitive demands (Park and Friston, 2013) and how the topology dynamically reorganizes to respond to various brain disorders (Bullmore and Sporns, 2009; He and Evans, 2010; Rubinov and Bullmore, 2013)....
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...…since they provide invaluable insights into how the collective of the human brain elements is topologically organized to promote cognitive demands (Park and Friston, 2013) and how the topology dynamically reorganizes to respond to various brain disorders (Bullmore and Sporns, 2009; He and Evans,…...
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TL;DR: It is argued that exposure to acute stress prompts a reallocation of resources to a salience network, promoting fear and vigilance, at the cost of an executive control network after stress subsides, which normalizes emotional reactivity and enhances higher-order cognitive processes important for long-term survival.
607 citations
TL;DR: The development of new imaging techniques allows a better understanding of the complexity of brain organization and it is now possible to reliably investigate a new type of diaschisis defined as the changes of structural and functional connectivity between brain areas distant to the lesion.
Abstract: After a century of false hopes, recent studies have placed the concept of diaschisis at the centre of the understanding of brain function. Originally, the term 'diaschisis' was coined by von Monakow in 1914 to describe the neurophysiological changes that occur distant to a focal brain lesion. In the following decades, this concept triggered widespread clinical interest in an attempt to describe symptoms and signs that the lesion could not fully explain. However, the first imaging studies, in the late 1970s, only partially confirmed the clinical significance of diaschisis. Focal cortical areas of diaschisis (i.e. focal diaschisis) contributed to the clinical deficits after subcortical but only rarely after cortical lesions. For this reason, the concept of diaschisis progressively disappeared from the mainstream of research in clinical neurosciences. Recent evidence has unexpectedly revitalized the notion. The development of new imaging techniques allows a better understanding of the complexity of brain organization. It is now possible to reliably investigate a new type of diaschisis defined as the changes of structural and functional connectivity between brain areas distant to the lesion (i.e. connectional diaschisis). As opposed to focal diaschisis, connectional diaschisis, focusing on determined networks, seems to relate more consistently to the clinical findings. This is particularly true after stroke in the motor and attentional networks. Furthermore, normalization of remote connectivity changes in these networks relates to a better recovery. In the future, to investigate the clinical role of diaschisis, a systematic approach has to be considered. First, emerging imaging and electrophysiological techniques should be used to precisely map and selectively model brain lesions in human and animals studies. Second, the concept of diaschisis must be applied to determine the impact of a focal lesion on new representations of the complexity of brain organization. As an example, the evaluation of remote changes in the structure of the connectome has so far mainly been tested by modelization of focal brain lesions. These changes could now be assessed in patients suffering from focal brain lesions (i.e. connectomal diaschisis). Finally, and of major significance, focal and non-focal neurophysiological changes distant to the lesion should be the target of therapeutic strategies. Neuromodulation using transcranial magnetic stimulation is one of the most promising techniques. It is when this last step will be successful that the concept of diaschisis will gain all the clinical respectability that could not be obtained in decades of research.
605 citations
Cites background from "Structural and functional brain net..."
...As we will discuss later, it is important to notice at this point that both structural and functional connectivity measures do not inform on direction and strength of the relationship between brain regions and cannot differentiate excitatory and inhibitory polysynaptic effects (Park and Friston, 2013), which makes the mechanistic comprehension of brain function after focal lesions of the connectome problematic....
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...…measures do not inform on direction and strength of the relationship between brain regions and cannot differentiate excitatory and inhibitory polysynaptic effects (Park and Friston, 2013), which makes the mechanistic comprehension of brain function after focal lesions of the connectome problematic....
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TL;DR: This Review surveys important aspects of communication dynamics in brain networks and proposes that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.
Abstract: Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.
592 citations
References
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
39,297 citations
TL;DR: It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
Abstract: An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
8,766 citations
TL;DR: Once Deff is estimated from a series of NMR pulsed-gradient, spin-echo experiments, a tissue's three orthotropic axes can be determined and the effective diffusivities along these orthotropic directions are the eigenvalues of Deff.
5,641 citations
TL;DR: This Review looks at some key brain theories in the biological and physical sciences from the free-energy perspective, suggesting that several global brain theories might be unified within a free- energy framework.
Abstract: A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories — optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.
4,866 citations
TL;DR: It is concluded that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.”
Abstract: Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.”
4,768 citations
"Structural and functional brain net..." refers background in this paper
...Considerable correspondence can be found between ICNs and task-related neurocognitive modules (23, 24)....
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