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.

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.

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.

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.

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.

Value-dependent selection in the brain: simulation in a synthetic neural model.

Many forms of learning depend on the ability of an organism to sense and react to the adaptive value of its behavior. Such value, if reflected in the activity of specific neural structures (neural value systems), can selectively increase the probability of adaptive behaviors by modulating synaptic changes in the circuits relevant to those behaviors. Neuromodulatory systems in the brain are well suited to carry out this process since they respond to evolutionarily important cues (innate value), broadcast their responses to widely distributed areas of the brain through diffuse projections, and release substances that can modulate changes in synaptic strength. The main aim of this paper is to show that, if value-dependent modulation is extended to the inputs of neural value systems themselves, initially neutral cues can acquire value. This process has important implications for the acquisition of behavioral sequences. We have used a synthetic neural model to illustrate value-dependent acquisition of a simple foveation response to a visual stimulus. We then examine the improvement that ensues when the connections to the value system are themselves plastic and thus become able to mediate acquired value. Using a second-order conditioning paradigm, we demonstrate that auditory discrimination can occur in the model in the absence of direct positive reinforcement and even in the presence of slight negative reinforcement. The discriminative responses are accompanied by value-dependent plasticity of receptive fields, as reflected in the selective augmentation of unit responses to valuable sensory cues. We then consider the time-course during learning of the responses of the value system and the transfer of these responses from one sensory modality to another. Finally, we discuss the relation of value-dependent learning to models of reinforcement learning. The results obtained from these simulations can be directly related to various reported experimental findings and provide additional support for the application of selectional principles to the analysis of brain and behavior.

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.

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.

Cholinesterases and peanut agglutinin binding related to cell proliferation and axonal growth in embryonic chick limbs.

Embryonic cholinesterases are assigned important functions during morphogenesis. Here we describe the expression of butyrylcholinesterase and acetylcholinesterase, and the binding of peanut agglutinin, and relate the results to mitotic activity in chick wing and leg buds from embryonic day 4 to embryonic day 9. During early stages, butyrylcholinesterase is elevated in cells under the apical ectodermal ridge and around invading motoraxons, while acetylcholinesterase is found in the chondrogenic core, on motoraxons and along the ectoderm. Peanut agglutinin binds to the apical ectodermal ridge and most prominently to the chondrogenic core. Measurements of thymidine incorporation and enzyme activities were consistent with our histological findings. Butyrylcholinesterase is concentrated near proliferative zones and periods, while acetylcholinesterase is associated with low proliferative activity. At late stages of limb development, acetylcholinesterase is concentrated in muscles and nonexistent within bones, while butyrylcholinesterase shows an inverse pattern. Thus, as in other systems, in limb formation butyrylcholinesterase is a transmitotic marker preceding differentiation, acetylcholinesterase is found on navigating axons, while peanut agglutinin appears in non-invaded regions. These data suggest roles for cholinesterases as positive regulators and peanut-agglutinin-binding proteins as negative regulators of neural differentiation.

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.

Turing structures in an enzyme-induction system with gap junction-mediated non-linear diffusion.

Two cells, each containing a reaction system modeling genetic induction, are coupled by diffusion. The substrate is moving through gap junctions, the number of which is regulated by the adjacent cells. This leads to a non-linear substrate diffusion term in the rate equations. Stability analysis reveals the conditions for the emergence of stable asymmetric solutions (dissipative structures). Due to non-linear diffusion rigid restrictions on the ratio of the two diffusion constants no longer exist. We demonstrate that substances operating as regulators of intercellular communication and participating in cellular metabolism may exhibit morphogenetic functions.

Oscillations in theoretical models of induction.

A two-variable model for the genetic regulatory mechanism of induction is proposed. In a feedforward step an autocatalytically accumulated substrate induces the transcription of its own degrading enzyme. The differential equations for enzyme and substrate are treated analytically and it is found that in a defined parameter range the system becomes unstable and shows structurally stable limit cycle oscillations. The system behaves like an activator-inhibitor model and instability is likely to arise if the transcription process is slow. In a slightly modified system oscillations inside a cell are generated if an external parameter (extracellular substrate concentration) exceeds a certain threshold and all other parameters are unchanged. Possible biological implications of these results are destabilization of metabolic units by transport processes and feedforward catalysis.

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.

Neural modeling and functional neuroimaging.

Two research areas that so far have had little interaction with one another are functional neuroimaging and computational neuroscience. The application of computational models and techniques to the inherently rich data sets generated by "standard" neurophysiological methods has proven useful for interpreting these data sets and for providing predictions and hypotheses for further experiments. We suggest that both theory- and data-driven computational modeling of neuronal systems can help to interpret data generated by functional neuroimaging methods, especially those used with human subjects. In this article, we point out four sets of questions, addressable by computational neuroscientists whose answere would be of value and interest to those who perform functional neuroimaging. The first set consist of determining the neurobiological substrate of the signals measured by functional neuroimaging. The second set concerns developing systems-level models of functional neuroimaging data. The third set of questions involves integrating functional neuroimaging data across modalities, with a particular emphasis on relating electromagnetic with hemodynamic data. The last set asks how one can relate systems-level models to those at the neuronal and neural ensemble levels. We feel that there are ample reasons to link functional neuroimaging and neural modeling, and that combining the results from the two disciplines will result in furthering our understanding of the central nervous system. © 1994 Wiley-Liss, Inc. This Article is a US Goverment work and, as such, is in the public domain in the United State of America.

Solving Bernstein's problem: a proposal for the development of coordinated movement by selection.

In recent years, many established concepts in the theory of human motor development have undergone profound change, and our knowledge has increased greatly. Nevertheless, some outstanding problems remain unsolved. A central problem concerns the redundancy of effective movements, first pointed out by N. A. Bernstein. The human motor system is mechanically complex and can make use of a large number of degrees of freedom. The controlled operation of such a system requires a reduction of mechanical redundancy, effectively by reducing the number of degrees of freedom. More recent work has shown that this problem is hard to solve explicitly by computing solutions to the equations of motion of the system. Equally challenging to traditional computational approaches is the fact the motor systems show remarkable adaptability and flexibility in the presence of changing biomechanical properties of motor organs during development and when faced with different environmental conditions or tasks. Solutions to these problems would have a large impact on a variety of issues in child development. In this article, we stress the importance of the somatic selection of neuronal groups in maps for the progressive transformation of a primary movement repertoire into a set of motor synergies and adaptive action patterns. We present results from computer simulations of a simple motor system that works according to such selectional principles. This approach suggests a provisional solution to Bernstein's problem and provides new parameters to guide experimental approaches to the development of sensorimotor coordination.

Behaviorally based modeling and computational approaches to neuroscience.

The almost incredible advances that have recently occurred in the power of computers available to scientists in all disciplines have encouraged an explosion of neural network and behavioral models. Some of these have been constrained more by the imagination of the programmer than by rude biological facts. Their relevance for the experimental neuroscientist thus varies from case to case. Some models (e.g. Grillner's model of lamprey swimming movements) are so closely based on known neuroanatomy and neurophysiology that it becomes possible to generate and test precise experimental predictions. Other models (such as MURPHY and NOMAD) use neurobiological principles in their architectures, but do not portray any particular organism. Although it is harder to relate the study of these models of the study of real animals, they fulfill an important explanatory role. They make possible insights into how behavior is controlled by neuronal activity that would be unobtainable in real animals using present methods. Thus, even the excesses of neural modeling have provided a useful impetus to what is undoubtedly a most promising approach to integrating data from the various disciplines concerned with behavior and the mind. The problems have been pointed out by many authors (see citations in our introduction), and a phase of more critical evaluation appears to have begun. We hope that our brief survey of models based on widely different theoretical approaches, but all aimed at explaining behavior, will encourage critical comparisons to be made. As in more mature fields, such as thermodynamics, we can expect that more complete models will force an evaluation of theoretical hypotheses against the entire body of available evidence, rather than just a few pertinent test cases. Such evaluation will make possible a much more rigorous exclusion of invalid or inconsistent theoretical ideas. From such studies, a much smaller, but more robust, set of basic principles can be expected to emerge. From the perspective afforded by our own modeling studies, it appears essential that modeling be informed by a general theory of brain function. In this work, the theory of neuronal group selection provides a useful basis for further work by virtue of its consistency with basic evolutionary and physiological principles and the power of the selection paradigm to shape neural networks in behaviorally adaptive directions.