Identification of Neural Networks That Contribute to Motion Sickness through Principal Components Analysis of Fos Labeling Induced by Galvanic Vestibular Stimulation

Motion sickness is a complex condition that includes both overt signs (e.g., vomiting) and more covert symptoms (e.g., anxiety and foreboding). The neural pathways that mediate these signs and symptoms are yet to identified. This study mapped the distribution of c-fos protein (Fos)-like immunoreactivity elicited during a galvanic vestibular stimulation paradigm that is known to induce motion sickness in felines. A principal components analysis was used to identify networks of neurons activated during this stimulus paradigm from functional correlations between Fos labeling in different nuclei. This analysis identified five principal components (neural networks) that accounted for greater than 95% of the variance in Fos labeling. Two of the components were correlated with the severity of motion sickness symptoms, and likely participated in generating the overt signs of the condition. One of these networks included neurons in locus coeruleus, medial, inferior and lateral vestibular nuclei, lateral nucleus tractus solitarius, medial parabrachial nucleus and periaqueductal gray. The second included neurons in the superior vestibular nucleus, precerebellar nuclei, periaqueductal gray, and parabrachial nuclei, with weaker associations of raphe nuclei. Three additional components (networks) were also identified that were not correlated with the severity of motion sickness symptoms. These networks likely mediated the covert aspects of motion sickness, such as affective components. The identification of five statistically independent component networks associated with the development of motion sickness provides an opportunity to consider, in network activation dimensions, the complex progression of signs and symptoms that are precipitated in provocative environments. Similar methodology can be used to parse the neural networks that mediate other complex responses to environmental stimuli.     

Monoaminergic and Neuropeptidergic Neurons Have Distinct Expression Profiles of Histone Deacetylases

Monoaminergic and neuropeptidergic neurons regulate a wide variety of behaviors, such as feeding, sleep/wakefulness behavior, stress response, addiction, and social behavior. These neurons form neural circuits to integrate different modalities of behavioral and environmental factors, such as stress, maternal care, and feeding conditions. One possible mechanism for integrating environmental factors through the monoaminergic and neuropeptidergic neurons is through the epigenetic regulation of gene expression via altered acetylation of histones. Histone deacetylases (HDACs) play an important role in altering behavior in response to environmental factors. Despite increasing attention and the versatile roles of HDACs in a variety of brain functions and disorders, no reports have detailed the localization of the HDACs in the monoaminergic and neuropeptidergic neurons. Here, we examined the expression profile of the HDAC protein family from HDAC1 to HDAC11 in corticotropin-releasing hormone, oxytocin, vasopressin, agouti-related peptide (AgRP), pro-opiomelanocortin (POMC), orexin, histamine, dopamine, serotonin, and noradrenaline neurons. Immunoreactivities for HDAC1,-2,-3,-5,-6,-7,-9, and -11 were very similar among the monoaminergic and neuropeptidergic neurons, while the HDAC4, -8, and -10 immunoreactivities were clearly different among neuronal groups. HDAC10 expression was found in AgRP neurons, POMC neurons, dopamine neurons and noradrenaline neurons but not in other neuronal groups. HDAC8 immunoreactivity was detected in the cytoplasm of almost all histamine neurons with a pericellular pattern but not in other neuropeptidergic and monoaminergic neurons. Thus, the differential expression of HDACs in monoaminergic and neuropeptidergic neurons may be crucial for the maintenance of biological characteristics and may be altered in response to environmental factors.     

Experience Does Not Equal Expertise in Recognizing Infrequent Incoming Gunfire: Neural Markers for Experience and Task Expertise at Peak Behavioral Performance

For a soldier, decisions to use force can happen rapidly and sometimes lead to undesired consequences. In many of these situations, there is a rapid assessment by the shooter that recognizes a threat and responds to it with return fire. But the neural processes underlying these rapid decisions are largely unknown, especially amongst those with extensive weapons experience and expertise. In this paper, we investigate differences in weapons experts and non-experts during an incoming gunfire detection task. Specifically, we analyzed the electroencephalography (EEG) of eleven expert marksmen/soldiers and eleven non-experts while they listened to an audio scene consisting of a sequence of incoming and non-incoming gunfire events. Subjects were tasked with identifying each event as quickly as possible and committing their choice via a motor response. Contrary to our hypothesis, experts did not have significantly better behavioral performance or faster response time than novices. Rather, novices indicated trends of better behavioral performance than experts. These group differences were more dramatic in the EEG correlates of incoming gunfire detection. Using machine learning, we found condition-discriminating EEG activity among novices showing greater magnitude and covering longer periods than those found in experts. We also compared group-level source reconstruction on the maximum discriminating neural correlates and found that each group uses different neural structures to perform the task. From condition-discriminating EEG and source localization, we found that experts perceive more categorical overlap between incoming and non-incoming gunfire. Consequently, the experts did not perform as well behaviorally as the novices. We explain these unexpected group differences as a consequence of experience with gunfire not being equivalent to expertise in recognizing incoming gunfire.     

Sequential behavior and stability properties of enzymatic neuron networks

The cycle structure of enzymatic neural networks may be characterized in terms of number of cycles exhibited, size of cycle state sets and cycle lengths. Simulation experiments show that the stability properties of these networks have some unusual features which are not exhibited by networks of two-state switching elements or by randomly constructed ecosystem models. The behavioral and structural stability of these systems decreases with their structural complexity, as measured by the number of components. The behavioral and structural stability of enzymatic neural networks also decreases with structural complexity, as measured by the number of excitase types, but only up to the middle level of excitases per neuron. This is the point of highest potential responsiveness of the system to environmental stimuli. Beyond this point the behavioral and structural stability increase. This is due to the fact that the number of possible states increases up to this point and decreases beyond it. The number of possible states, not the number of components, serves as the useful measure of complexity in these types of systems. The selection circuits learning algorithm has been used to evolve networks whose cycle structures have desired features.     

Theta band zero-lag long-range cortical synchronization via hippocampal dynamical relaying

Growing evidence suggests that synchronization among distributed neuronal networks underlie functional integration in the brain. Neural synchronization is typically revealed by a consistent phase delay between neural responses generated in two separated sources. But the influence of a third neuronal assembly in that synchrony pattern remains largely unexplored. We investigate here the potential role of the hippocampus in determining cortico-cortical theta synchronization in different behavioral states during motor quiescent and while animals actively explore the environment. To achieve this goal, the two states were modeled with a recurrent network involving the hippocampus, as a relay element, and two distant neocortical sites. We found that cortico-cortical neural coupling accompanied higher hippocampal theta oscillations in both behavioral states, although the highest level of synchronization between cortical regions emerged during motor exploration. Local field potentials recorded from the same brain regions qualitatively confirm these findings in the two behavioral states. These results suggest that zero-lag long-range cortico-cortical synchronization is likely mediated by hippocampal theta oscillations in lower mammals as a function of cognitive demands and motor acts.     

Motor Inhibition during Overt and Covert Actions: An Electrical Neuroimaging Study

Given ample evidence for shared cortical structures involved in encoding actions, whether or not subsequently executed, a still unsolved problem is the identification of neural mechanisms of motor inhibition, preventing “covert actions” as motor imagery from being performed, in spite of the activation of the motor system. The principal aims of the present study were the evaluation of: 1) the presence in covert actions as motor imagery of putative motor inhibitory mechanisms; 2) their underlying cerebral sources; 3) their differences or similarities with respect to cerebral networks underpinning the inhibition of overt actions during a Go/NoGo task. For these purposes, we performed a high density EEG study evaluating the cerebral microstates and their related sources elicited during two types of Go/NoGo tasks, requiring the execution or withholding of an overt or a covert imagined action, respectively. Our results show for the first time the engagement during motor imagery of key nodes of a putative inhibitory network (including pre-supplementary motor area and right inferior frontal gyrus) partially overlapping with those activated for the inhibition of an overt action during the overt NoGo condition. At the same time, different patterns of temporal recruitment in these shared neural inhibitory substrates are shown, in accord with the intended overt or covert modality of action performance. The evidence that apparently divergent mechanisms such as controlled inhibition of overt actions and contingent automatic inhibition of covert actions do indeed share partially overlapping neural substrates, further challenges the rigid dichotomy between conscious, explicit, flexible and unconscious, implicit, inflexible forms of motor behavioral control.     

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.     

Psychiatric disorder‐related abnormal behavior and habenulointerpeduncular pathway defects in Wnt1‐cre and Wnt1‐GAL4 double transgenic mice

The neural crest is a unique structure in vertebrates. Wnt1‐cre and Wnt1‐GAL4 double transgenic (dTg) mice have been used in a variety of studies concerning neural crest cell lineages in which the Cre/loxP or GAL4/UAS system was applied. Here, we show psychiatric disorder‐related behavioral abnormalities and histologic alterations in a neural crest‐derived brain region in dTg mice. The dTg mice exhibited increased locomotor activity, decreased social interaction, and impaired short‐term spatial memory and nesting behavior. The choline acetyltransferase‐ and vesicular glutamate transporter 2‐immunoreactive habenulointerpeduncular fiber tracts that project from the medial habenular nucleus of the epithalamus to the interpeduncular nucleus of the midbrain tegmentum appeared irregular in the dTg mice. Both the medial habenula nucleus and the interpeduncular nucleus were confirmed to be derived from the neural crest. The findings of this study suggest that neural crest‐derived cells have pathogenic roles in the development of psychiatric disorders and that the dTg mouse could be a useful animal model for studying the pathophysiology of mental illness such as autism and schizophrenia. Scientists that use the dTg mice as a cre‐transgenic deleter line should be cautious in its possible toxicity, especially if behavioral analyses are to be performed.     

Vision First? The Development of Primary Visual Cortical Networks Is More Rapid Than the Development of Primary Motor Networks in Humans

The development of cortical functions and the capacity of the mature brain to learn are largely determined by the establishment and maintenance of neocortical networks. Here we address the human development of long-range connectivity in primary visual and motor cortices, using well-established behavioral measures--a Contour Integration test and a Finger-tapping task--that have been shown to be related to these specific primary areas, and the long-range neural connectivity within those. Possible confounding factors, such as different task requirements (complexity, cognitive load) are eliminated by using these tasks in a learning paradigm. We find that there is a temporal lag between the developmental timing of primary sensory vs. motor areas with an advantage of visual development; we also confirm that human development is very slow in both cases, and that there is a retained capacity for practice induced plastic changes in adults. This pattern of results seems to point to human-specific development of the "canonical circuits" of primary sensory and motor cortices, probably reflecting the ecological requirements of human life.     

Lessons for circuit function from large insects: towards understanding the neural basis of motor flexibility

Motor behaviors result from information processing that occurs in multiple neural networks acting at all levels from the initial selection of the behavior to its final generation. A long-standing research interest is how single neural networks can help generate different motor behaviors, that is, the origin of motor flexibility. Modern experimental techniques allow studying neural network activity during the production of multiple motor behaviors. Recent data provide strong evidence that the neural networks controlling insect legs are individually modified in task-dependent and finely tuned fashions. Understanding the mechanistic basis of these neural network modifications will be of particular interest in the upcoming years.     

Integrating behavioral and neural data in a model of zebrafish network interaction

The spinal neural networks of larval zebrafish (Danio rerio) generate a variety of movements such as escape, struggling, and swimming. Various mechanisms at the neural and network levels have been proposed to account for switches between these behaviors. However, there are currently no detailed demonstrations of such mechanisms. This makes determining which mechanisms are plausible extremely difficult. In this paper, we propose a detailed biologically plausible model of the interactions between the swimming and escape networks in the larval zebrafish, while taking into account anatomical and physiological evidence. We show that the results of our neural model generate the expected behavior when used to control a hydrodynamic model of carangiform locomotion. As a result, the model presented here is a clear demonstration of a plausible mechanism by which these distinct behaviors can be controlled. Interestingly, the networks are anatomically overlapping, despite clear differences in behavioral function and physiology.     

Biological pattern generation: the cellular and computational logic of networks in motion

In 1900, Ramón y Cajal advanced the neuron doctrine, defining the neuron as the fundamental signaling unit of the nervous system. Over a century later, neurobiologists address the circuit doctrine: the logic of the core units of neuronal circuitry that control animal behavior. These are circuits that can be called into action for perceptual, conceptual, and motor tasks, and we now need to understand whether there are coherent and overriding principles that govern the design and function of these modules. The discovery of central motor programs has provided crucial insight into the logic of one prototypic set of neural circuits: those that generate motor patterns. In this review, I discuss the mode of operation of these pattern generator networks and consider the neural mechanisms through which they are selected and activated. In addition, I will outline the utility of computational models in analysis of the dynamic actions of these motor networks.     

Ontogeny of rhythmic motor networks in the stomatogastric nervous system

We used the lobster Homarus gammarus to study the ontogeny of neural networks involved in rhythmic behaviours. Since in the adult the neural networks belonging to the stomatogastric nervous system and controlling the rhythmic movements of the foregut are well characterised, we have studied them during ontogeny. While this foregut develops slowly throughout embryonic and larval stages, the neuronal population of these motor networks is quantitatively established since the mid-embryonic period. Moreover, in the embryo, this neural population is organised into a single functional network that displays a unique motor output. By contrast, in the adult the same neuronal elements are organised into three neural networks that express independent motor programs. Our results indicate that the multiple adult networks are partitioned progressively from a single embryonic network during development. Accepted: 23 May 1999     

Hand function: peripheral and central constraints on performance.

The hand is one of the most fascinating and sophisticated biological motor systems. The complex biomechanical and neural architecture of the hand poses challenging questions for understanding the control strategies that underlie the coordination of finger movements and forces required for a wide variety of behavioral tasks, ranging from multidigit grasping to the individuated movements of single digits. Hence, a number of experimental approaches, from studies of finger movement kinematics to the recording of electromyographic and cortical activities, have been used to extend our knowledge of neural control of the hand. Experimental evidence indicates that the simultaneous motion and force of the fingers are characterized by coordination patterns that reduce the number of independent degrees of freedom to be controlled. Peripheral and central constraints in the neuromuscular apparatus have been identified that may in part underlie these coordination patterns, simplifying the control of multi-digit grasping while placing certain limitations on individuation of finger movements. We review this evidence, with a particular emphasis on how these constraints extend through the neuromuscular system from the behavioral aspects of finger movements and forces to the control of the hand from the motor cortex.     

Working Memory Cells' Behavior May Be Explained by Cross-Regional Networks with Synaptic Facilitation

Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.     

Role of endocannabinoids in brain development.

In addition to those functions that have been extensively addressed in this special issue, such as nociception, motor activity, neuroendocrine regulation, immune function and others, the endogenous cannabinoid system seems to play also a role in neural development. This view is based on a three-fold evidence. A first evidence emerges from neurotoxicological studies that showed that synthetic and plant-derived cannabinoids, when administered to pregnant rats, produced a variety of changes in the maturation of several neurotransmitters and their associated-behaviors in their pups, changes that were evident at different stages of brain development. A second evidence comes from studies that demonstrated the early appearance of elements of the endogenous cannabinoid system (receptors and ligands) during the brain development. The atypical location of these elements during fetal and early postnatal periods favours the notion that this system may play a role in specific molecular events related to neural development. Finally, a third evidence derives from studies using cultures of fetal glial or neuronal cells. Cannabinoid receptors are present in some of these cultured cells and their activation produced a set of cellular effects consistent with a role of this system in the process of neural development. All this likely supports that endocannabinoids, early synthesized in nervous cells, play a role in events related to development, by acting through the activation of second messenger-coupled cannabinoid receptors.     

Activity in the lateral prefrontal cortex reflects multiple steps of future events in action plans

To achieve a behavioral goal in a complex environment, we must plan multiple steps of motor behavior. On planning a series of actions, we anticipate future events that will occur as a result of each action and mentally organize the temporal sequence of events. To investigate the involvement of the lateral prefrontal cortex (PFC) in such multistep planning, we examined neuronal activity in the PFC of monkeys performing a maze task that required the planning of stepwise cursor movements to reach a goal. During the preparatory period, PFC neurons reflected each of all forthcoming cursor movements, rather than arm movements. In contrast, in the primary motor cortex, most neuronal activity reflected arm movements but little of cursor movements during the preparatory period, as well as during movement execution. Our data suggest that the PFC is involved primarily in planning multiple future events that occur as a consequence of behavioral actions.     

On the Relationships between Generative Encodings,Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.