A Dynamic Core Network and Global Efficiency in the Resting Human Brain.

Spontaneous brain activity is spatially and temporally organized in the absence of any stimulation or task in networks of cortical and subcortical regions that appear largely segregated when imaged at slow temporal resolution with functional magnetic resonance imaging (fMRI). When imaged at high temporal resolution with magneto-encephalography (MEG), these resting-state networks (RSNs) show correlated fluctuations of band-limited power in the beta frequency band (14-25 Hz) that alternate between epochs of strong and weak internal coupling. This study presents 2 novel findings on the fundamental issue of how different brain regions or networks interact in the resting state. First, we demonstrate the existence of multiple dynamic hubs that allow for across-network coupling. Second, dynamic network coupling and related variations in hub centrality correspond to increased global efficiency. These findings suggest that the dynamic organization of across-network interactions represents a property of the brain aimed at optimizing the efficiency of communication between distinct functional domains (memory, sensory-attention, motor). They also support the hypothesis of a dynamic core network model in which a set of network hubs alternating over time ensure efficient global communication in the whole brain.

Towards the virtual brain: network modeling of the intact and the damaged brain.

Neurocomputational models of large-scale brain dynamics utilizing realistic connectivity matrices have advanced our understanding of the operational network principles in the brain. In particular, spontaneous or resting state activity has been studied on various scales of spatial and temporal organization including those that relate to physiological, encephalographic and hemodynamic data. In this article we focus on the brain from the perspective of a dynamic network and discuss the role of its network constituents in shaping brain dynamics. These constituents include the brain's structural connectivity, the population dynamics of its network nodes and the time delays involved in signal transmission. In addition, no discussion of brain dynamics would be complete without considering noise and stochastic effects. In fact, there is mounting evidence that the interaction between noise and dynamics plays an important functional role in shaping key brain processes. In particular, we discuss a unifying theoretical framework that explains how structured spatio-temporal resting state patterns emerge from noise driven explorations of unstable or stable oscillatory states. Embracing this perspective, we explore the consequences of network manipulations to understand some of the brain's dysfunctions, as well as network effects that offer new insights into routes towards therapy, recovery and brain repair. These collective insights will be at the core of a new computational environment, the Virtual Brain, which will allow flexible incorporation of empirical data constraining the brain models to integrate, unify and predict network responses to incipient pathological processes.

White matter maturation reshapes structural connectivity in the late developing human brain.

From toddler to late teenager, the macroscopic pattern of axonal projections in the human brain remains largely unchanged while undergoing dramatic functional modifications that lead to network refinement. These functional modifications are mediated by increasing myelination and changes in axonal diameter and synaptic density, as well as changes in neurochemical mediators. Here we explore the contribution of white matter maturation to the development of connectivity between ages 2 and 18 y using high b-value diffusion MRI tractography and connectivity analysis. We measured changes in connection efficacy as the inverse of the average diffusivity along a fiber tract. We observed significant refinement in specific metrics of network topology, including a significant increase in node strength and efficiency along with a decrease in clustering. Major structural modules and hubs were in place by 2 y of age, and they continued to strengthen their profile during subsequent development. Recording resting-state functional MRI from a subset of subjects, we confirmed a positive correlation between structural and functional connectivity, and in addition observed that this relationship strengthened with age. Continuously increasing integration and decreasing segregation of structural connectivity with age suggests that network refinement mediated by white matter maturation promotes increased global efficiency. In addition, the strengthening of the correlation between structural and functional connectivity with age suggests that white matter connectivity in combination with other factors, such as differential modulation of axonal diameter and myelin thickness, that are partially captured by inverse average diffusivity, play an increasingly important role in creating brain-wide coherence and synchrony.

The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

Predicting human resting-state functional connectivity from structural connectivity.

In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks--including their spatial statistics and their persistence across time--can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.

Theoretical neuroanatomy and the connectivity of the cerebral cortex.

Over recent years, a wealth of neuroanatomical information on the pattern of interconnections between segregated areas of the cerebral cortex has become available. Here, we describe a set of structural measures, based on graph theory, which can be used to analyze these anatomical patterns. We describe relationships between these structural measures and measures based on patterns of functional connectivity, i.e. patterns of correlations in neural activity. We find that networks capable of producing highly complex functional dynamics share common structural motifs. These motifs are also found in cortical connection matrices, which are characterized by the existence of densely linked groups of areas, low potential wiring length, and a high abundance of reciprocal connections and short cycles. An analysis of cortical functional connectivity demonstrates the existence of functional clusters of highly interactive areas, producing highly complex dynamics. The combined structural and functional analysis outlined in this chapter provides insight into the large-scale functional organization of distributed cortical systems.

Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices.

Neuroanatomy places critical constraints on the functional connectivity of the cerebral cortex. To analyze these constraints we have examined the relationship between structural features of networks (expressed as graphs) and the patterns of functional connectivity to which they give rise when implemented as dynamical systems. We selected among structurally varying graphs using as selective criteria a number of global information-theoretical measures that characterize functional connectivity. We selected graphs separately for increases in measures of entropy (capturing statistical independence of graph elements), integration (capturing their statistical dependence) and complexity (capturing the interplay between their functional segregation and integration). We found that dynamics with high complexity were supported by graphs whose units were organized into densely linked groups that were sparsely and reciprocally interconnected. Connection matrices based on actual neuroanatomical data describing areas and pathways of the macaque visual cortex and the cat cortex showed structural characteristics that coincided best with those of such complex graphs, revealing the presence of distinct but interconnected anatomical groupings of areas. Moreover, when implemented as dynamical systems, these cortical connection matrices generated functional connectivity with high complexity, characterized by the presence of highly coherent functional clusters. We also found that selection of graphs as they responded to input or produced output led to increases in the complexity of their dynamics. We hypothesize that adaptation to rich sensory environments and motor demands requires complex dynamics and that these dynamics are supported by neuroanatomical motifs that are characteristic of the cerebral cortex.

Connectivity and complexity: the relationship between neuroanatomy and brain dynamics.

Nervous systems facing complex environments have to balance two seemingly opposing requirements. First, there is a need quickly and reliably to extract important features from sensory inputs. This is accomplished by functionally segregated (specialized) sets of neurons, e.g. those found in different cortical areas. Second, there is a need to generate coherent perceptual and cognitive states allowing an organism to respond to objects and events, which represent conjunctions of numerous individual features. This need is accomplished by functional integration of the activity of specialized neurons through their dynamic interactions. These interactions produce patterns of temporal correlations or functional connectivity involving distributed neuronal populations, both within and across cortical areas. Empirical and computational studies suggest that changes in functional connectivity may underlie specific perceptual and cognitive states and involve the integration of information across specialized areas of the brain. The interplay between functional segregation and integration can be quantitatively captured using concepts from statistical information theory, in particular by defining a measure of neural complexity. Complexity measures the extent to which a pattern of functional connectivity produced by units or areas within a neural system combines the dual requirements of functional segregation and integration. We find that specific neuroanatomical motifs are uniquely associated with high levels of complexity and that such motifs are embedded in the pattern of long-range cortico-cortical pathways linking segregated areas of the mammalian cerebral cortex. Our theoretical findings offer new insight into the intricate relationship between connectivity and complexity in the nervous system.

Measures of degeneracy and redundancy in biological networks.

Degeneracy, the ability of elements that are structurally different to perform the same function, is a prominent property of many biological systems ranging from genes to neural networks to evolution itself. Because structurally different elements may produce different outputs in different contexts, degeneracy should be distinguished from redundancy, which occurs when the same function is performed by identical elements. However, because of ambiguities in the distinction between structure and function and because of the lack of a theoretical treatment, these two notions often are conflated. By using information theoretical concepts, we develop here functional measures of the degeneracy and redundancy of a system with respect to a set of outputs. These measures help to distinguish the concept of degeneracy from that of redundancy and make it operationally useful. Through computer simulations of neural systems differing in connectivity, we show that degeneracy is low both for systems in which each element affects the output independently and for redundant systems in which many elements can affect the output in a similar way but do not have independent effects. By contrast, degeneracy is high for systems in which many different elements can affect the output in a similar way and at the same time can have independent effects. We demonstrate that networks that have been selected for degeneracy have high values of complexity, a measure of the average mutual information between the subsets of a system. These measures promise to be useful in characterizing and understanding the functional robustness and adaptability of biological networks.

BDNF-dependent enhancement of exocytosis in cultured cortical neurons requires translation but not transcription.

Neurotrophic factors, such as brain-derived neurotrophic factor (BDNF), are involved in acute modulation of synaptic plasticity. Different modes of action of BDNF have been described with time courses ranging from seconds to hours, but the sequence of cellular processes responsible for BDNF-dependent modulation of synaptic plasticity is unknown. We have used optical imaging of the styryl dye, FM1-43, which selectively labels synaptic vesicles, to investigate potential presynaptic effects of BDNF. Addition of BDNF to cultured cortical neurons for 3 h produced a significant enhancement of exocytosis upon modest depolarization. BDNF had no effect on exocytosis either immediately or after incubation for 30 min. BDNF-dependent enhancement of exocytosis was blocked by the tyrosine kinase inhibitor, K252a, but not by K252b, consistent with signalling via the TrkB receptor. Having demonstrated that the BDNF-dependent enhancement of synaptic vesicle release was present only after 1 h, we investigated whether de novo gene transcription and/or protein synthesis were involved. Addition of the inhibitors of RNA synthesis, actinomycin D, or 5,6-dichloro-1-beta-D-ribofuranosyl benzimidazole (DRB), did not affect the enhancement of exocytosis produced by BDNF. However, the effect of BDNF was blocked by the inhibitors of translation, cycloheximide or anisomycin. Our results indicate a rapid BDNF-dependent enhancement of neurotransmitter release that requires translation but not transcription.

Behavioral constraints in the development of neuronal properties: a cortical model embedded in a real-world device.

The ability of organisms to categorize diverse and often novel stimuli depends on ongoing interactions with their environment. In a modality such as vision, categorization requires the generation of both selective and invariant responses of cortical neurons to complex visual stimuli. How does behavior contribute to shaping the responses of these neurons? Analysis of this question is made difficult by the complex multilevel interactions between many neural and behavioral variables. To mitigate this difficulty, we studied the development and ongoing plasticity of pattern-selective neuronal responses by means of synthetic neural modeling. For this purpose, we constructed Darwin V, which consists of a simulated neuronal model embedded in a real-world device that is capable of motion and autonomous behavior. The neuronal model consists of four major components: a visual system (containing cortical and subcortical networks); a taste system based on conductance; sets of motor neurons capable of triggering behavior; and a diffuse ascending (value) system. The modeled visual cortex consists of two areas: a topographic map responsive to elementary features connected to a higher-order map composed of initially non-selective neuronal units. During behavior over time in its environment, Darwin V encounters numerous objects consisting of black metal cubes displaying different patterns of white blobs and stripes. Initially, the lack of specific higher-order visual responses does not allow visual pattern discrimination, and appetitive and aversive behaviors are triggered by the 'taste' (surface conductivity of objects) alone. In the course of sensory experience, however, changes occur in visual and sensorimotor connection strengths, with two major consequences. First, units within the higher visual area acquire responses that are both pattern selective and translation invariant. Second, as a result of the operation of the value system, these responses become linked to appropriate behaviors. Analysis of Darwin V after such changes indicates that the continuity of self-generated movements is essential for the development of pattern-selective and translation-invariant responses. The concomitant development of a preference for foveal over parafoveal objects was found to be due to increased behavioral interactions with object cubes gripped by the centrally mounted effector (snout) of Darwin V. Finally, even after development of higher-order visual responses, visual responses to more frequently encountered objects continued to be enhanced, while other responses were diminished. Overall, the detailed study of Darwin V over multiple levels of organization provides a heuristically revealing example of the crucial role played by behavioral and environmental interactions in the development of complex responses by specialized neurons.

Complexity and coherency: integrating information in the brain.

The brains of higher mammals are extraordinary integrative devices. Signals from large numbers of functionally specialized groups of neurons distributed over many brain regions are integrated to generate a coherent, multimodal scene. Signals from the environment are integrated with ongoing, patterned neural activity that provides them with a meaningful context. We review recent advances in neurophysiology and neuroimaging that are beginning to reveal the neural mechanisms of integration. In addition, we discuss concepts and measures derived from information theory that lend a theoretical basis to the notion of complexity as integration of information and suggest new experimental tests of these concepts.

Potassium ion- and nitric oxide-induced exocytosis from populations of hippocampal synapses during synaptic maturation in vitro.

The development of mechanisms of neurotransmitter release is an important component in the formation of functional synaptic connections. Synaptic neurotransmitter release can be modulated by nitric oxide, a compound shown to have a variety of physiologic functions in the nervous system. The goal of this study was to determine whether, during synaptic maturation, nitric oxide is capable of affecting exocytosis of synaptic vesicles, and to compare its effects with those elicited by strongly depolarizing stimuli. To address these questions we examined vesicle release from large numbers of individual synapses of hippocampal neurons between five and 13 days in culture. Synaptic vesicles were labelled by uptake of the styrylpyridinium dye N-(3-triethylammoniumpropyl)-4-(4-(dibutylamino)styryl)pyridinium dibromide (FM1-43) and their release was monitored by fluorescence imaging. Across populations of developing synapses, there was a good correspondence between FM1-43 staining and synapsin immunocytochemistry. A marked heterogeneity was observed in the ability to release vesicles both after potassium and nitric oxide stimulation. In less mature populations of synapses, the rate of potassium- and nitric oxide-induced exocytosis gradually increased, while at later stages nitric oxide-induced responses levelled off and potassium-induced responses continued to rise. Application of nitric oxide donors did not trigger any detectable changes in intracellular calcium. Combined immunocytochemical analysis of cultured hippocampal neurons for neuronal nitric oxide synthase and synapsin revealed that nitric oxide synthase was present within neurites of cultured hippocampal neurons, largely distributed in a bead-like pattern which partially overlapped presynaptic sites. Stimulation of the N-methyl-D-aspartate receptor while blocking propagation of action potentials with tetrodotoxin resulted in exocytosis from numerous individually resolved sites. Preincubation of neurons with an nitric oxide synthase inhibitor or addition of an nitric oxide scavenger eliminated these responses indicating a role for nitric oxide in N-methyl-D-aspartate-stimulated exocytosis. Using fluorescence imaging of individually resolved synaptic sites, we provide direct evidence for an effect of nitric oxide on vesicular neurotransmitter release in intact neurons. Nitric oxide is capable to produce this effect at all stages of synaptic development and acts independently of calcium influx. We show that nitric oxide synthase is present at synaptic sites and endogenously produced nitric oxide is sufficient to cause exocytosis. Taken together, these experiments suggest a possible role for nitric oxide in calcium-independent transmitter release in populations of synapses at all stages of maturation.

A complexity measure for selective matching of signals by the brain.

We have previously derived a theoretical measure of neural complexity (CN) in an attempt to characterize functional connectivity in the brain. CN measures the amount and heterogeneity of statistical correlations within a neural system in terms of the mutual information between subsets of its units. CN was initially used to characterize the functional connectivity of a neural system isolated from the environment. In the present paper, we introduce a related statistical measure, matching complexity (CM), which reflects the change in CN that occurs after a neural system receives signals from the environment. CM measures how well the ensemble of intrinsic correlations within a neural system fits the statistical structure of the sensory input. We show that CM is low when the intrinsic connectivity of a simulated cortical area is randomly organized. Conversely, CM is high when the intrinsic connectivity is modified so as to differentially amplify those intrinsic correlations that happen to be enhanced by sensory input. When the input is represented by an individual stimulus, a positive value of CM indicates that the limited mutual information between sensory sheets sampling the stimulus and the rest of the brain triggers a large increase in the mutual information between many functionally specialized subsets within the brain. In this way, a complex brain can deal with context and go "beyond the information given."

Neural cell adhesion molecule (N-CAM) inhibits astrocyte proliferation after injury to different regions of the adult rat brain.

After a penetrating lesion in the central nervous system, astrocytes enlarge, divide, and participate in creating an environment that adversely affects neuronal regeneration. We have recently shown that the neural cell adhesion molecule (N-CAM) partially inhibits the division of early postnatal rat astrocytes in vitro. In the present study, we demonstrate that addition of N-CAM, the third immunoglobulin-like domain of N-CAM, or a synthetic decapeptide corresponding to a putative homophilic binding site in N-CAM partially inhibits astrocyte proliferation after a stab lesion in the adult rat brain. Animals were lesioned in the cerebral cortex, hippocampus, or striatum with a Hamilton syringe and needle at defined stereotaxic positions. On one side, the lesions were concomitantly infused with N-CAM or with one of the N-CAM-related molecules. As a control, a peptide of the same composition as the N-CAM decapeptide but of random sequence was infused on the contralateral side of the brain. We consistently found that the population of dividing astrocytes was significantly smaller on the side in which N-CAM or one of the N-CAM-related molecules was infused than on the opposite side. The inhibition was greatest in the cortical lesion sites (approximately 50%) and was less pronounced in the hippocampus (approximately 25%) and striatum (approximately 20%). Two weeks after the lesion, the cerebral cortical sites infused with N-CAM continued to exhibit a significantly smaller population of dividing astrocytes than the sites on the opposite side. When N-CAM and basic fibroblast growth factor, which is known to stimulate astrocyte division in vitro, were coinfused into cortical lesion sites, astrocyte proliferation was still inhibited. These results suggest the hypothesis that, by reducing glial proliferation, N-CAM or its peptides may help create an environment that is more suitable for neuronal regeneration.

The neural cell adhesion molecule (N-CAM) inhibits proliferation in primary cultures of rat astrocytes.

Cell proliferation is a key primary process during neural development and also plays an important role in the regenerative response of neural tissue to injury. It has been reported that glial cell proliferation is, at least in part, controlled by a neuronal signal, possibly involving cell surface molecules. We report here that the addition of purified rat neural cell adhesion molecule (N-CAM) to primary cultures of rat forebrain astrocytes inhibits their proliferation. This inhibitory effect can be elicited in cultures grown in chemically defined serum-free medium or in medium that had been supplemented with growth factors. Polyclonal antibodies to N-CAM or their Fab' fragments elicited a similar inhibitory effect. The magnitude of the inhibitory effect of N-CAM was dependent on cell density: it was maximal at low cell densities and weakened progressively as cells approached confluency. Synthetic peptides with sequences identical to a putative homophilic binding region of N-CAM mimicked the effect of purified N-CAM, while peptides of the same length and amino acid composition but with a randomized sequence did not. The addition of N-CAM antisense oligonucleotides to primary astrocyte cultures for 48 h resulted in reduced levels of N-CAM expression. After N-CAM levels on astrocytes were diminished by this treatment, the antiproliferative effect of N-CAM added to the medium was significantly reduced. The combined results suggest that N-CAM homophilic binding may be involved in the control of glial cell proliferation.