Integration of sensory and motor processing underlying social behaviour in túngara frogs

Social decision making involves the perception and processing of social stimuli, the subsequent evaluation of that information in the context of the individual's internal and external milieus to produce a decision, and then culminates in behavioural output informed by that decision. We examined brain networks in an anuran communication system that relies on acoustic signals to guide simple, stereotyped motor output. We used egr-1 mRNA expression to measure neural activation in male túngara frogs, Physalaemus pustulosus, following exposure to conspecific and heterospecific calls that evoke competitive or aggressive behaviour. We found that acoustically driven activation in auditory brainstem nuclei is transformed into activation related to sensory-motor interactions in the diencephalon, followed by motor-related activation in the telencephalon. Furthermore, under baseline conditions, brain nuclei typically have correlated egr-1 mRNA levels within brain divisions. Hearing conspecific advertisement calls increases correlations between anatomically distant brain divisions; no such effect was observed in response to calls that elicit aggressive behaviour. Neural correlates of social decision making thus take multiple forms: (i) a progressive shift from sensory to motor encoding from lower to higher stages of neural processing and (ii) the emergence of correlated activation patterns among sensory and motor regions in response to behaviourally relevant social cues.     

Individual differences in estrogen receptor alpha in select brain nuclei are associated with individual differences in aggression

Steroid hormones play an important role in modulating social behavior in many species. Estrogens are thought to act on an interconnected network of hypothalamic and limbic brain areas to affect aggressive behavior, although the specific nuclei unknown remain unspecified. We show that individual variation in estrogen receptor alpha (ERalpha) immunoreactivity in the lateral septum (LS), ventral bed nucleus of the stria terminalis (vBNST), and anterior hypothalamus (AHA) of CD-1 mice is positively correlated with aggressive behavior. When males were treated with fadrozole (an aromatase inhibitor), aggressive behavior was reduced, although castration did not reduce aggression. These results suggest that estrogens modulate aggressive behavior by acting on a circuit that includes the LS, vBNST, and AHA and that the source of estrogens is non-gonadal. Fadrozole also decreased c-fos expression in the lateral septum following aggressive encounters. Although the effects of estrogen on aggression appear to involve regulation of neuronal activity in the LS, additional processes are likely involved. These results suggest that estrogen acts in a specific subset of a complex network of nuclei to affect aggressive behavior.     

Gene expression in the social behavior network of the wire‐tailed manakin (Pipra filicauda) brain

The vertebrate basal forebrain and midbrain contain a set of interconnected nuclei that control social behavior. Conserved anatomical structures and functions of these nuclei have now been documented among fish, amphibians, reptiles, birds and mammals, and these brain regions have come to be known as the vertebrate social behavior network (SBN). While it is known that nuclei (nodes) of the SBN are rich in steroid and neuropeptide activity linked to behavior, simultaneous variation in the expression of neuroendocrine genes among several SBN nuclei has not yet been described in detail. In this study, we use RNA‐seq to profile gene expression across seven brain regions representing five nodes of the vertebrate SBN in a passerine bird, the wire‐tailed manakin Pipra filicauda. Using weighted gene co‐expression network analysis, we reconstructed sets of coregulated genes, showing striking patterns of variation in neuroendocrine gene expression across the SBN. We describe regional variation in gene networks comprising a broad set of hormone receptors, neuropeptides, steroidogenic enzymes, catecholamines and other neuroendocrine signaling molecules. Our findings show heterogeneous patterns of brain gene expression across nodes of the avian SBN and provide a foundation for future analyses of how the regulation of gene networks may mediate social behavior. These results highlight the importance of region‐specific sampling in studies of the mechanisms of behavior.     

The regulation of social recognition, social communication and aggression: vasopressin in the social behavior neural network

Neuropeptides in the arginine vasotocin/arginine vasopressin (AVT/AVP) family play a major role in the regulation of social behavior by their actions in the brain. In mammals, AVP is found within a circuit of recriprocally connected limbic structures that form the social behavior neural network. This review examines the role played by AVP within this network in controlling social processes that are critical for the formation and maintenance of social relationships: social recognition, social communication and aggression. Studies in a number of mammalian species indicate that AVP and AVP V1a receptors are ideally suited to regulate the expression of social processes because of their plasticity in response to factors that influence social behavior. The pattern of AVP innervation and V1a receptors across the social behavior neural network may determine the potential range and intensity of social responses that individuals display in different social situations. Although fundamental information on how social behavior is wired in the brain is still lacking, it is clear that different social behaviors can be influenced by the actions of AVP in the same region of the network and that AVP can act within multiple regions of this network to regulate the expression of individual social behaviors. The existing data suggest that AVP can influence social behavior by modulating the interpretation of sensory information, by influencing decision making and by triggering complex motor outputs. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior.     

Oxytocin receptors modulate a social salience neural network in male prairie voles

Social behavior is regulated by conserved neural networks across vertebrates. Variation in the organization of neuropeptide systems across these networks is thought to contribute to individual and species diversity in network function during social contexts. For example, oxytocin (OT) is an ancient neuropeptide that binds to OT receptors (OTRs) in the brain and modulates social and reproductive behavior across vertebrate species, including humans. Central OTRs exhibit extraordinarily diverse expression patterns that are associated with individual and species differences in social behavior. In voles, OTR density in the nucleus accumbens (NAc)—a region important for social and reward learning—is associated with individual and species variation in social attachment behavior. Here we test whether OTRs in the NAc modulate a social salience network (SSN)—a network of interconnected brain nuclei thought to encode valence and incentive salience of sociosensory cues—during a social context in the socially monogamous male prairie vole. Using a selective OTR antagonist, we test whether activation of OTRs in the NAc during sociosexual interaction and mating modulates expression of the immediate early gene product Fos across nuclei of the SSN. We show that blockade of endogenous OTR signaling in the NAc during sociosexual interaction and mating does not strongly modulate levels of Fos expression in individual nodes of the network, but strongly modulates patterns of correlated Fos expression between the NAc and other SSN nuclei.     

Mesoscopic neuron population modeling of normal/epileptic brain dynamics

Simulations of EEG data provide the understanding of how the limbic system exhibits normal and abnormal states of the electrical activity of the brain. While brain activity exhibits a type of homeostasis of excitatory and inhibitory mesoscopic neuron behavior, abnormal neural firings found in the seizure state exhibits brain instability due to runaway oscillatory entrained neural behavior. We utilize a model of mesoscopic brain activity, the KIV model, where each network represents the areas of the limbic system, i.e., hippocampus, sensory cortex, and the amygdala. Our model initially demonstrates oscillatory entrained neural behavior as the epileptogenesis, and then by increasing the external weights that join the three networks that represent the areas of the limbic system, seizure activity entrains the entire system. By introducing an external signal into the model, simulating external electrical titration therapy, the modeled seizure behavior can be ‘rebalanced’ back to its normal state.     

Ovarian steroids and serotonin neural function

The serotonin neural system originates from ten nuclei in the mid- and hindbrain regions. The cells of the rostral nuclei project to almost every area of the forebrain, including the hypothalamus, limbic regions, basal ganglia, thalamic nuclei, and cortex. The caudal nuclei project to the spinal cord and interact with numerous autonomic and sensory systems. This article reviews much of the available literature from basic research and relevant clinical research that indicates that ovarian steroid hormones, estrogens and progestins, affect the function of the serotonin neural system. Experimental results in nonhuman primates from this laboratory are contrasted with studies in rodents and humans. The sites of action of ovarian hormones on the serotonin neural system include effects within serotonin neurons as well as effects on serotonin afferent neurons and serotonin target neurons. Therefore, information on estrogen and progestin receptor-containing neurons was synthesized with information on serotonin afferent and efferent circuits. The ability of estrogens and progestins to alter the function of the serotonin neural system at various levels provides a cellular mechanism whereby ovarian hormones can impact mood, cognition, pain, and numerous other autonomic functions.     

The smell of success and failure: the role of intrinsic and extrinsic chemical signals on the social behavior of crayfish

Animals commonly modify their behavior in the presence of aconspecific or in response to signals. This is particularlytrue in the context of aggressive exchanges, which animals useto form networks of social relationships and to communicatesocial status associated with those relationships. Althoughhierarchical structures are a widespread phenomenon that hasbeen studied extensively, the dynamic communication processes,specifically chemical communication in this review, is relativelyoverlooked. In particular, it is the exchange of informationduring agonistic interactions that mediates hierarchies and/oralters the outcomes of agonistic interactions. Given the theoreticalappeal of these interactions, and the evolutionary importanceand taxonomic diversity associated with social hierarchies,it is not surprising that the sensory mechanisms involved inthe formation and maintenance of hierarchical structures havereceived recent attention. In crayfish, dominance is thoughtto be largely determined by physical superiority, where encountersare largely dyadic and fighting behavior is highly stereotyped.However, recent evidence has shown that the outcome of dyadicencounters are dependent upon a number of factors other thanphysical size, that include the exchange of chemical informationduring encounters, previous social history, and the intrinsicneurochemical state of opponents. We have attempted to providea comprehensive analysis of the extrinsic chemical processes(previous history, sensory communication, etc.) and intrinsicchemical processes (neurochemical state) that produce and maintaindominance relations and social hierarchies in crayfish. We hopethat this review will bring together a global picture of theprocesses that determine a crayfish's social standing and howintrinsic and extrinsic chemicals have substantial effects onaggressive states and agonistic bouts.     

Multimodal functional network connectivity: an EEG-fMRI fusion in network space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.     

Arginine vasotocin neuronal phenotypes, telencephalic fiber varicosities, and social behavior in butterflyfishes (Chaetodontidae): potential similarities to birds and mammals

The neuropeptide arginine vasopressin (AVP) influences many social behaviors through its action in the forebrain of mammals. However, the function of the homologous arginine vasotocin (AVT) in the forebrain of fishes, specifically the telencephalon remains unresolved. We tested whether the density of AVT-immunoreactive (-ir) fiber varicosities, somata size or number of AVT-ir neuronal phenotypes within the forebrain were predictive of social behavior in reproductive males of seven species of butterflyfishes (family Chaetodontidae) in four phylogenetic clades. Similar to other fishes, the aggressive (often territorial) species in most cases had larger AVT-ir cells within the gigantocellular preoptic cell group. Linear discriminant function analyses demonstrated that the density of AVT-ir varicosities within homologous telencephalic nuclei to those important for social behavior in mammals and birds were predictive of aggressive behavior, social affiliations, and mating system. Of note, the density of AVT-ir varicosities within the ventral nucleus of the ventral telencephalon, thought to be homologous to the septum of other vertebrates, was the strongest predictor of aggressive behavior, social affiliation, and mating system. These results are consistent with the postulate that AVT within the telencephalon of fishes plays an important role in social behavior and may function in a similar manner to that of AVT / AVP in birds and mammals despite having cell populations solely within the preoptic area.     

Localizing Brain Regions Associated with Female Mate Preference Behavior in a Swordtail

Female mate choice behavior is a critical component of sexual selection, yet identifying the neural basis of this behavior is largely unresolved. Previous studies have implicated sensory processing and hypothalamic brain regions during female mate choice and there is a conserved network of brain regions (Social Behavior Network, SBN) that underlies sexual behaviors. However, we are only beginning to understand the role this network has in pre-copulatory female mate choice. Using in situ hybridization, we identify brain regions associated with mate preference in female Xiphophorus nigrensis, a swordtail species with a female choice mating system. We measure gene expression in 10 brain regions (linked to sexual behavior, reward, sensory integration or other processes) and find significant correlations between female preference behavior and gene expression in two telencephalic areas associated with reward, learning and multi-sensory processing (medial and lateral zones of the dorsal telencephalon) as well as an SBN region traditionally associated with sexual response (preoptic area). Network analysis shows that these brain regions may also be important in mate preference and that correlated patterns of neuroserpin expression between regions co-vary with differential compositions of the mate choice environment. Our results expand the emerging network for female preference from one that focused on sensory processing and midbrain sexual response centers to a more complex coordination involving forebrain areas that integrate primary sensory processing and reward.     

Modeling the Impact of Lesions in the Human Brain

Lesions of anatomical brain networks result in functional disturbances of brain systems and behavior which depend sensitively, often unpredictably, on the lesion site. The availability of whole-brain maps of structural connections within the human cerebrum and our increased understanding of the physiology and large-scale dynamics of cortical networks allow us to investigate the functional consequences of focal brain lesions in a computational model. We simulate the dynamic effects of lesions placed in different regions of the cerebral cortex by recording changes in the pattern of endogenous (“resting-state”) neural activity. We find that lesions produce specific patterns of altered functional connectivity among distant regions of cortex, often affecting both cortical hemispheres. The magnitude of these dynamic effects depends on the lesion location and is partly predicted by structural network properties of the lesion site. In the model, lesions along the cortical midline and in the vicinity of the temporo-parietal junction result in large and widely distributed changes in functional connectivity, while lesions of primary sensory or motor regions remain more localized. The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions.     

Social Regulation of Gene Expression in Threespine Sticklebacks

Identifying genes that are differentially expressed in response to social interactions is informative for understanding the molecular basis of social behavior. To address this question, we described changes in gene expression as a result of differences in the extent of social interactions. We housed threespine stickleback (Gasterosteus aculeatus) females in either group conditions or individually for one week, then measured levels of gene expression in three brain regions using RNA-sequencing. We found that numerous genes in the hindbrain/cerebellum had altered expression in response to group or individual housing. However, relatively few genes were differentially expressed in either the diencephalon or telencephalon. The list of genes upregulated in fish from social groups included many genes related to neural development and cell adhesion as well as genes with functions in sensory signaling, stress, and social and reproductive behavior. The list of genes expressed at higher levels in individually-housed fish included several genes previously identified as regulated by social interactions in other animals. The identified genes are interesting targets for future research on the molecular mechanisms of normal social interactions.     

Shaping embodied neural networks for adaptive goal-directed behavior

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.     

Using multilayer network analysis to explore the temporal dynamics of collective behavior

Social organisms often show collective behaviors such as group foraging or movement.Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals.When social interactions change over time,collective behaviors may change because these behaviors emerge from interactions among individuals.Despite the importance of,and growing interest in,the temporal dynamics of social interactions,it is not clear how to quantify changes in interactions over time or measure their stability.Furthermore,the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent.Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors.We found that social interactions changed over time at a constant rate.Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed.Individuals that maintained a large and stable number of connections,despite changes in network structure,were the boldest individuals in the group.Therefore,social interactions and boldness are linked across time,but group collective behavior is not influenced by the stability of the social network.Our work demonstrates that dynamic social networks can be modeled in a multilayer framework.This approach may reveal biologically important temporal changes to social structure in other systems.     

Learning related interactions among neuronal systems involved in memory processes

Functional neuroimaging techniques using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have provided new insights in our understanding of brain function from the molecular to the systems level. While subtraction strategy based data analyses have revealed the involvement of distributed brain regions in memory processes, covariance analysis based data analysis strategies allow functional interactions between brain regions of a neuronal network to be assessed. The focus of this chapter is to (1) establish the functional topography of episodic and working memory processes in young and old normal volunteers, (2) to assess functional interactions between modules of networks of brain regions by means of covariance based analyses and systems level modelling and (3) to relate neuroimaging data to the underpinning neural networks. Male normal young and old volunteers without neurological or psychiatric illness participated in neuroimaging studies (PET, fMRI) on working and episodic memory. Distributed brain areas are involved in memory processes (episodic and working memory) in young volunteers and show much of an overlap with respect to the network components. Systems level modelling analyses support the hypothesis of bihemispheric, asymmetric networks subserving memory processes and revealed both similarities in general and differences in the interactions between brain regions during episodic encoding and retrieval as well as working memory. Changes in memory function with ageing are evident from studies in old volunteers activating more brain regions compared to young volunteers and revealing more and stronger influences of prefrontal regions. We finally discuss the way in which the systems level models based on PET and fMRI results have implications for the understanding of the underlying neural network functioning of the brain.     

Dissociated functional pathways for appetitive and consummatory reproductive behaviors in male Syrian hamsters

In many species, including Syrian hamsters, the generation of male reproductive behavior depends critically on the perception of female odor cues from conspecifics in the environment. The behavioral response to these odors is mediated by a network of steroid-sensitive ventral forebrain nuclei including the medial amygdala (MA), posterior bed nucleus of the stria terminalis (BNST) and medial preoptic area (MPOA). Previous studies have demonstrated that each of these three nuclei is required for appropriate sexual behavior and that MA preferentially sends female odor information directly to BNST and MPOA. It is unknown, however, how the functional connections between MA and BNST and/or MPOA are organized to generate different aspects of reproductive behavior. Therefore, the following experiments used the asymmetrical pathway lesion technique to test the role of the functional connections between MA and BNST and/or MPOA in odor preference and copulatory behaviors. Lesions that functionally disconnected MA from MPOA eliminated copulatory behavior but did not affect odor preference. In contrast, lesions that functionally disconnected MA from BNST eliminated preference for volatile female odors but did not affect preference for directly contacted odors or copulatory behavior. These results therefore demonstrate a double dissociation in the functional connections required for attraction to volatile sexual odors and copulation and, more broadly, suggest that appetitive and consummatory reproductive behaviors are mediated by distinct neural pathways.     

Beyond topology: coevolution of structure and flux in metabolic networks

Interactions between the structure of a metabolic network and its functional properties underlie its evolutionary diversification, but the mechanism by which such interactions arise remains elusive. Particularly unclear is whether metabolic fluxes that determine the concentrations of compounds produced by a metabolic network, are causally linked to a network's structure or emerge independently of it. A direct empirical study of populations where both structural and functional properties vary among individuals’ metabolic networks is required to establish whether changes in structure affect the distribution of metabolic flux. In a population of house finches (Haemorhous mexicanus), we reconstructed full carotenoid metabolic networks for 442 individuals and uncovered 11 structural variants of this network with different compounds and reactions. We examined the consequences of this structural diversity for the concentrations of plumage‐bound carotenoids produced by flux in these networks. We found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure. Flux was partitioned similarly among compounds in individuals of the same network structure: within each network, compound concentrations were closely correlated. The highest among‐individual variation in flux occurred in networks with the strongest among‐compound correlations, suggesting that changes in the magnitude, but not the distribution of flux, underlie individual differences in compound concentrations on a static network structure. These findings indicate that the distribution of flux in carotenoid metabolism closely follows network structure. Thus, evolutionary diversification and local adaptations in carotenoid metabolism may depend more on the gain or loss of enzymatic reactions than on changes in flux within a network structure.     

Spontaneous prediction error generation in schizophrenia

Goal-directed human behavior is enabled by hierarchically-organized neural systems that process executive commands associated with higher brain areas in response to sensory and motor signals from lower brain areas. Psychiatric diseases and psychotic conditions are postulated to involve disturbances in these hierarchical network interactions, but the mechanism for how aberrant disease signals are generated in networks, and a systems-level framework linking disease signals to specific psychiatric symptoms remains undetermined. In this study, we show that neural networks containing schizophrenia-like deficits can spontaneously generate uncompensated error signals with properties that explain psychiatric disease symptoms, including fictive perception, altered sense of self, and unpredictable behavior. To distinguish dysfunction at the behavioral versus network level, we monitored the interactive behavior of a humanoid robot driven by the network. Mild perturbations in network connectivity resulted in the spontaneous appearance of uncompensated prediction errors and altered interactions within the network without external changes in behavior, correlating to the fictive sensations and agency experienced by episodic disease patients. In contrast, more severe deficits resulted in unstable network dynamics resulting in overt changes in behavior similar to those observed in chronic disease patients. These findings demonstrate that prediction error disequilibrium may represent an intrinsic property of schizophrenic brain networks reporting the severity and variability of disease symptoms. Moreover, these results support a systems-level model for psychiatric disease that features the spontaneous generation of maladaptive signals in hierarchical neural networks.