Reaching beyond the classical receptive field of V1 neurons: horizontal or feedback axons?

It is commonly assumed that the orientation-selective surround field of neurons in primary visual cortex (V1) is due to interactions provided solely by intrinsic long-range horizontal connections. We review evidence for and against this proposition and conclude that horizontal connections are too slow and cover too little visual field to subserve all the functions of suppressive surrounds of V1 neurons in the macaque monkey. We show that the extent of visual space covered by horizontal connections corresponds to the region of low contrast summation of the receptive field center mechanism. This region encompasses the classically defined receptive field center and the proximal surround. Beyond this region, feedback connections are the most likely substrate for surround suppression. We present evidence that inactivation of higher order areas leads to a major decrease in the strength of the suppressive surround of neurons in lower order areas, supporting the hypothesis that feedback connections play a major role in center-surround interactions.     

Cortical interactions in the beta2-rhythm during examination stress

Two groups of students: with higher and lower amplitudes of the baseline alpha-rhythm differed in the level of anxiety during examinations and the level of cortical interactions. Cognitive tasks were presented in three experimental conditions: before the examinations, immediately after the examinations, and on a usual day of academic semester (control). Subjects with initially higher alpha-rhythm amplitude had lower level of anxiety in a stress situation. Cognitive performance of these subjects before examinations was associated with an increase in the number of functional connections between different cortical areas. In the subjects with initially lower alpha-rhythm amplitude, the level of cortical interactions before examinations was higher, but presentation of the cognitive task led to a decrease in the number of intracortical connections. However, the localization of these connections was task-specific.     

Survival of photoreceptor neurons in the compound eye of Drosophila depends on connections with the optic ganglia.

The importance of retinal innervation for the normal development of the optic ganglia in Drosophila is well documented. However, little is known about retrograde effects of the optic lobe on the adult photoreceptor cells (R-cells). We addressed this question by examining the survival of R-cells in mutant flies where R-cells do not connect to the brain. Although imaginal R-cells develop normally in the absence of connections to the optic lobes, we find that their continued survival requires these connections. Genetic mosaic studies with the disconnected (disco) mutation demonstrate that survival of R-cells does not depend on the genotype of the eye, but is correlated with the presence of connections to the optic ganglia. These results suggest the existence of retrograde interactions in the Drosophila visual system reminiscent of trophic interactions found in vertebrates.     

An exponential core in the heart of the yeast protein interaction network

Protein interactions in the budding yeast have been shown to form a scale-free network, a feature of other organized networks such as bacterial and archaeal metabolism and the World Wide Web. Here, we study the connections established by yeast proteins and discover a preferential attachment between essential proteins. The essential-essential connections are long ranged and form a subnetwork where the giant component includes 97% of these proteins. Unexpectedly, this subnetwork displays an exponential connectivity distribution, in sharp contrast to the scale-free topology of the complete network. Furthermore, the wide phylogenetic extent of these core proteins and interactions provides evidence that they represent the ancestral state of the yeast protein interaction network. Finally, we propose that this core exponential network may represent a generic scaffold around which organism-specific and taxon-specific proteins and interactions coalesce.     

Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ～5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.     

Development of axon-target specificity of ponto-cerebellar afferents

The function of neuronal networks relies on selective assembly of synaptic connections during development. We examined how synaptic specificity emerges in the pontocerebellar projection. Analysis of axon-target interactions with correlated light-electron microscopy revealed that developing pontine mossy fibers elaborate extensive cell-cell contacts and synaptic connections with Purkinje cells, an inappropriate target. Subsequently, mossy fiber-Purkinje cell connections are eliminated resulting in granule cell-specific mossy fiber connectivity as observed in mature cerebellar circuits. Formation of mossy fiber-Purkinje cell contacts is negatively regulated by Purkinje cell-derived BMP4. BMP4 limits mossy fiber growth in vitro and Purkinje cell-specific ablation of BMP4 in mice results in exuberant mossy fiber-Purkinje cell interactions. These findings demonstrate that synaptic specificity in the pontocerebellar projection is achieved through a stepwise mechanism that entails transient innervation of Purkinje cells, followed by synapse elimination. Moreover, this work establishes BMP4 as a retrograde signal that regulates the axon-target interactions during development.     

Interneurons, spike timing, and perception.

Rhythmic gamma oscillations at 30-70 Hz in cortical and hippocampal slices depend on a maintained excitation and on interactions between interneurons and pyramidal cells. These interactions include gap-junctional connections between inhibitory cells and fast excitatory and inhibitory chemical synapses. Spike timing with precision in the range of several ms may be assured by biphasic signaling mechanisms operating at these different connections. Such temporal precision may be important in cognitive processing.     

The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula,Maryland and Delaware

The dependence of downstream waters on upstream ecosystems necessitates an improved understanding of watershed-scale hydrological interactions including connections between wetlands and streams. An evaluation of such connections is challenging when, (1) accurate and complete datasets of wetland and stream locations are often not available and (2) natural variability in surface-water extent influences the frequency and duration of wetland/stream connectivity. The Upper Choptank River watershed on the Delmarva Peninsula in eastern Maryland and Delaware is dominated by a high density of small, forested wetlands. In this analysis, wetland/stream surface water connections were quantified using multiple wetland and stream datasets, including headwater streams and depressions mapped from a lidar-derived digital elevation model. Surface-water extent was mapped across the watershed for spring 2015 using Landsat-8, Radarsat-2 and Worldview-3 imagery. The frequency of wetland/stream connections increased as a more complete and accurate stream dataset was used and surface-water extent was included, in particular when the spatial resolution of the imagery was finer (i.e., <10 m). Depending on the datasets used, 12–60% of wetlands by count (21–93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50–94% of the watershed contributing direct surface water runoff to streamflow. This finding suggests that our interpretation of the frequency and duration of wetland/stream connections will be influenced not only by the spatial and temporal characteristics of wetlands, streams and potential flowpaths, but also by the completeness, accuracy and resolution of input datasets.     

Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas

Cognition is based on the integrated functioning of hierarchically organized cortical processing streams in a manner yet to be clarified. Because integration fundamentally depends on convergence and the complementary notion of divergence of the neuronal connections, we analysed integration by measuring the degree of convergence/divergence through the connections in the network of cortical areas. By introducing a new index, we explored the complementary convergent and divergent nature of connectional reciprocity and delineated the backward and forward cortical sub-networks for the first time. Integrative properties of the areas defined by the degree of convergence/divergence through their afferents and efferents exhibited distinctive characteristics at different levels of the cortical hierarchy. Areas previously identified as hubs exhibit information bottleneck properties. Cortical networks largely deviate from random graphs where convergence and divergence are balanced at low reciprocity level. In the cortex, which is dominated by reciprocal connections, balance appears only by further increasing the number of reciprocal connections. The results point to the decisive role of the optimal number and placement of reciprocal connections in large-scale cortical integration. Our findings also facilitate understanding of the functional interactions between the cortical areas and the information flow or its equivalents in highly recurrent natural and artificial networks.     

Specification and connectivity of neuronal subtypes in the sensory lineage

During the development of the nervous system, many different types of neuron are produced. As well as forming the correct type of neuron, each must also establish precise connections. Recent findings show that, because of shared gene programmes, neuronal identity is intimately linked to and coordinated with axonal behaviour. Peripheral sensory neurons provide an excellent system in which to study these interactions. This review examines how neuronal diversity is created in the PNS and describes proteins that help to direct the diversity of neuronal subtypes, cell survival, axonal growth and the establishment of central patterns of modality-specific connections.     

A stimulus-dependent connectivity analysis of neuronal networks

We present an analysis of interactions among neurons in stimulus-driven networks that is designed to control for effects from unmeasured neurons. This work builds on previous connectivity analyses that assumed connectivity strength to be constant with respect to the stimulus. Since unmeasured neuron activity can modulate with the stimulus, the effective strength of common input connections from such hidden neurons can also modulate with the stimulus. By explicitly accounting for the resulting stimulus-dependence of effective interactions among measured neurons, we are able to remove ambiguity in the classification of causal interactions that resulted from classification errors in the previous analyses. In this way, we can more reliably distinguish causal connections among measured neurons from common input connections that arise from hidden network nodes. The approach is derived in a general mathematical framework that can be applied to other types of networks. We illustrate the effects of stimulus-dependent connectivity estimates with simulations of neurons responding to a visual stimulus. This research was supported by the National Science Foundation grants DMS-0415409 and DMS-0748417.     

Interplay between brain BDNF and glutamatergic systems: A brief state of the evidence and association with the pathogenesis of depression

The excitatory neurotransmitter glutamate system and the brain-derived neurotrophic factor (BDNF) system are principally involved in phenomena of cellular and synaptic plasticity. These systems are interacting, and disclosing mechanisms of such interactions is critically important for understanding the machinery of neuroplasticity and its modulation in normal and pathological situations. The short state of evidence in this review addresses experimentally confirmed connections of these mechanisms and their potential relation to the pathogenesis of depression. The connections between the two systems are numerous and bidirectional, providing for mutual regulation of the glutamatergic and BDNF systems. The available data suggest that it is complex and well-coordinating nature of these connections that secures optimal synaptic and cellular plasticity in the normal brain. Both systems are associated with the pathogenesis of depression, and the disturbance of tight and well-balanced associations between them results in unfavorable changes in neuronal plasticity underlying depressive disorders and other mood diseases.     

Caudato-thalamic connections in cats

We studied interactions between thenucl. caudatus and ventrolateral nucleus of the thalamus in cats. With the use of a retrograde axon transport technique, we demonstrated the existence of direct connections between these important motor centers. Using electrophysiological techniques allowed us to support this conclusion and detail the peculiarities of these connections.     

Driving Protein Conformational Cycles in Physiology and Disease: “Frustrated” Amino Acid Interaction Networks Define Dynamic Energy Landscapes

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or “conformational cycle,” required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.     

The C-terminal region of E1A: a molecular tool for cellular cartography

The adenovirus E1A proteins function via protein-protein interactions. By making many connections with the cellular protein network, individual modules of this virally encoded hub reprogram numerous aspects of cell function and behavior. Although many of these interactions have been thoroughly studied, those mediated by the C-terminal region of E1A are less well understood. This review focuses on how this region of E1A affects cell cycle progression, apoptosis, senescence, transformation, and conversion of cells to an epithelial state through interactions with CTBP1/2, DYRK1A/B, FOXK1/2, and importin-α. Furthermore, novel potential pathways that the C-terminus of E1A influences through these connections with the cellular interaction network are discussed.     

When two become one: the limits of causality analysis of brain dynamics

Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.     

Adhesive Interactions and the Patterning of Nerve Connections: An Experimental and Theoretical Approach

Adhesive interactions have long been proposed to play a centralrole in the patterning of neural structures and their interconnections.Many of these ideas are based upon experiments on the projectionfrom the eye to the optic tectum (the retinotectal projection)in lowervertebrates. In order to test the feasibility of suchproposals, a detailed model, based largely on adhesive interactionsbetween cells, has been developed. Computer simulations of themodelshow that simple adhesive interactions are sufficient toexplain much of the literature on the patterning of the amphibianretinotectal projection. Aspects of the model have been experimetallytested through the use of antibodies to known adhesive molecules.The results of these experiments appear consistent with therole of adhesion in the patterning of the connections and withthe predictions of the model. Although such experiments demonstratethe power of adhesive cell interactions in the patterning ofnerve connections, additional experiments and simulations demonstratethat some other non-adhesive processes may play a role. In particular,the addition of a process that is dependent on the activityof the neurons allows the model to better fit the literature.An activity-dependent competition between neurons for adhesivesites on the target cells appears to be sufficient to play thisrole.     

Synaptic interactions between nonspiking local interneurones in the terminal abdominal ganglion of the crayfish

Nonspiking local interneurones are the important premotor elements in arthropod motor control systems. We have analyzed the synaptic interactions between nonspiking interneurones in the crayfish terminal (6th) abdominal ganglion using simultaneous intracellular recordings. Only 15% of nonspiking interneurones formed bi-directional excitatory connections. In 77% of connections, however, the nonspiking interneurones showed a one-way inhibitory interaction. In these cases, the presynaptic nonspiking interneurones received excitatory synaptic inputs from the sensory afferents innervating hairs on the surface of the uropods and the postsynaptic nonspiking interneurones received inhibitory synaptic inputs that were partly mediated by the inputs to the presynaptic nonspiking interneurones. The membrane hyperpolarization of the postsynaptic nonspiking interneurones mediated by the presynaptic nonspiking interneurones was reduced in amplitude when the hyperpolarizing current was injected into the postsynaptic interneurones, or when the external bathing solution was replaced with one containing low calcium and high magnesium concentrations. The role of these interactions in the circuits controlling the movements of the terminal appendages is discussed.Abbreviations AL antero-lateral - epsp excitatory postsynaptic potential - ipsp inhibitory postsynaptic potential - PL postero-lateral     

Synaptic interactions among neurons that coordinate swimmeret and abdominal movements in the crayfish

1. Many interneurons in the crayfish (Procambarus clarkii) abdominal nervous system influence two behaviors, abdominal positioning and swimmeret movements. Such neurons are referred to as dual output cells. Other neurons which influence either one behavior or the other are single output cells. 2. Extensive synaptic interactions were observed between both dual and single output neurons involved in the control of abdominal positioning and swimmeret movements. Over 60% of all neuron pairs examined displayed interactions. Pairs of agonist neurons displayed excitatory interactions, while pairs of antagonists had inhibitory interactions. This pattern of interaction was observed in about 75% of interactive neuron pairs whether abdominal positioning or swimmeret outputs were considered. 3. Evidence for both serial and parallel connectivity, as well as, reciprocal or looping connections was observed. Looping connections can be found both between the abdominal positioning and swimmeret systems and within each system. 4. Most (28/34) single output neurons were not presynaptic to dual output neurons. No single output neurons were found to excite dual output neurons to spiking, although inhibitory interactions and weak excitations were observed. 5. Abdominal positioning inhibitors displayed properties consistent with a role in mediating some of the coordination between the swimmeret and abdominal positioning systems. 6. None of the dual output neurons examined influenced the swimmeret motoneurons directly.     

Poly- and mononeuronal innervation in a model for the development of neuromuscular connections

In the normal development of connections between motor neurons and muscle fibres, an initial stage of polyneuronal innervation is followed by withdrawal of connections until each muscle fibre is innervated by a single axon. However, polyneuronal innervation has been found to persist after prolonged nerve conduction block, in spite of the resumption of normal neuromuscular activity. Here we analyse in detail a model proposed for the withdrawal of nerve connections in developing muscle, based on competition between nerve terminals. The model combines competition for a pre-synaptic resource with competition for a post-synaptic resource. Using bifurcation and phase space analysis, we show that polyneuronal innervation, as well as mononeuronal innervation, can be stable. The model accounts for the development of mononeuronal innervation and for persistent polyneuronal innervation after prolonged nerve conduction block, which appears as a consequence of the general competitive interactions operating during normal development.