AGE-DEPENDENT EFFECTS OF EEDQ ON COCAINE-INDUCED LOCOMOTOR ACTIVITY AND D2 RECEPTOR SUPERSENSITIVITY

The neurochemical changes occurring between the preweanling period and adolescence could be crucial for understanding the role development plays in the manifestation of psychotic behaviors. N-ethoxycarbonyl-2-ethoxy-1,2-dihydroquinoline (EEDQ) fully attenuates the DA agonist-induced behaviors of adult rats, while potentiating the DA agonist-induced locomotor activity of preweanling rats. My specific hypotheses were as follows: (1) Systemically administered EEDQ would block the cocaine-induced locomotor activity of adult rats. (2) Systemically administered EEDQ would potentiate the cocaine-induced locomotion of preweanling rats. (3) EEDQ would increase the Emax values (a measure of D2 receptor sensitivity) of preweanling rats, but not adolescent or adult rats. And, (4) EEDQ would reduce dorsal striatal β-arrestin-2 (ARRB2) and GRK6 levels (measures of D2 receptor sensitivity) of preweanling rats. Behavioral results were as expected, because EEDQ attenuated the locomotion of adult and adolescent rats, while EEDQ potentiated locomotor activity of preweanling rats. EEDQ enhanced the GTPγS binding of preweanling rats, while depressing ARRB2 levels. These results are consistent with the overarching hypothesis that EEDQ causes DA supersensitivity in preweanling rats. Thus, it is here proposed that EEDQ inactivates a significant number of D2 receptors in preweanling rats, but that the remaining D2 receptors are supersensitive and capable of mediating a potentiated locomotor response

Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex.

The dopaminergic system has been shown to control the amount of noise in the prefrontal cortex (PFC) and likely plays an important role in working memory and the pathophysiology of schizophrenia. We developed a model that takes into account the known receptor distributions of D1 and D2 receptors, the changes these receptors have on neuron response properties, as well as identified circuitry involved in working memory. Our model suggests that D1 receptor under-stimulation in supragranular layers gates internal noise into the PFC leading to cognitive symptoms as has been proposed in attention disorders, while D2 over-stimulation gates noise into the PFC by over-activation of cortico-striatal projecting neurons in infragranular layers. We apply this model in the context of a memory-guided saccade paradigm and show deficits similar to those observed in schizophrenic patients. We also show set-shifting impairments similar to those observed in rodents with D1 and D2 receptor manipulations. We discuss how the introduction of noise through changes in D1 and D2 receptor activation may account for many of the symptoms of schizophrenia depending on where this dysfunction occurs in the PFC

Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex.

The dopaminergic system has been shown to control the amount of noise in the prefrontal cortex (PFC) and likely plays an important role in working memory and the pathophysiology of schizophrenia. We developed a model that takes into account the known receptor distributions of D1 and D2 receptors, the changes these receptors have on neuron response properties, as well as identified circuitry involved in working memory. Our model suggests that D1 receptor under-stimulation in supragranular layers gates internal noise into the PFC leading to cognitive symptoms as has been proposed in attention disorders, while D2 over-stimulation gates noise into the PFC by over-activation of cortico-striatal projecting neurons in infragranular layers. We apply this model in the context of a memory-guided saccade paradigm and show deficits similar to those observed in schizophrenic patients. We also show set-shifting impairments similar to those observed in rodents with D1 and D2 receptor manipulations. We discuss how the introduction of noise through changes in D1 and D2 receptor activation may account for many of the symptoms of schizophrenia depending on where this dysfunction occurs in the PFC

Spatial Transformations in Frontal Cortex During Memory-Guided Head-Unrestrained Gaze Shifts

We constantly orient our line of sight (i.e., gaze) to external objects in our environment. One of the central questions in sensorimotor neuroscience concerns how visual input (registered on retina) is transformed into appropriate signals that drive gaze shift, comprised of coordinated movement of the eyes and the head. In this dissertation I investigated the function of a node in the frontal cortex, known as the frontal eye field (FEF) by investigating the spatial transformations that occur within this structure. FEF is implicated as a key node in gaze control and part of the working memory network. I recorded the activity of single FEF neurons in head-unrestrained monkeys as they performed a simple memory-guided gaze task which required delayed gaze shifts (by a few hundred milliseconds) towards remembered visual stimuli. By utilizing an elaborate analysis method which fits spatial models to neuronal response fields, I identified the spatial code embedded in neuronal activity related to vision (visual response), memory (delay response), and gaze shift (movement response). First (Chapter 2), spatial transformations that occur within the FEF were identified by comparing spatial codes in visual and movement responses. I showed eye-centered dominance in both neuronal responses (and excluded head- and space-centered coding); however, whereas the visual response encoded target position, the movement response encoded the position of the imminent gaze shift (and not its independent eye and head components), and this was observed even within single neurons. In Chapter 3, I characterized the time-course for this target-to-gaze transition by identifying the spatial code during the intervening delay period. The results from this study highlighted two major transitions within the FEF: a gradual transition during the visual-delay-movement extent of delay-responsive neurons, followed by a discrete transition between delay-responsive neurons and pre-saccadic neurons that exclusively fire around the time of gaze movement. These results show that the FEF is involved in memory-based transformations in gaze control; but instead of encoding specific movement parameters (eye and head) it encodes the desired gaze endpoint. The representations of the movement goal are subject to noise and this noise accumulates at different stages related to different mechanisms

Role of Superior Colliculus in Visual to Movement Spatial Transformations During Memory-Guided and Reactive Head-Unrestrained Gaze Shifts

The fundamental process in the brain which allows the generation of what is known as behavior, is the transformation of sensory or internally generated information to commands for movement. For example, shifting the line of gaze to look at and interact with our environment requires transformation of visual information into proper contraction of eye and neck muscles. In this thesis I studied the transformation of visual signals to movement commands in the primates superior colliculus, a key structure in sensory integration and gaze movement generation. In the first chapter the frames of reference and the spatial information encoded by the visual and motor activity of superior colliculus, in different neuron types, are investigated in a memory delay task, and the results provide support for visuomotor transformation process that occurs between and within neurons during the memory delay task. In the second chapter the focus of study is on reactive gaze shift task and we show that the spatial information occurs during the burst of activity of single neurons even in such a short interval and without a presence of a memory delay. In the last (third) chapter, I compared the visual and motor spatial coding and their transformation between the reactive and the memory delay tasks and found that although similarities exist, there are important differences in neural activity profiles and the spatial codes and the extent of visual to movement transformation. Together the findings in this dissertation suggest that the process of visual to movement transformation occurs between and within neurons in SC regardless of the duration of the gaze shift or the task, however task demands influence both the activity and spatial coding of neurons which are consequently translated in the differences in behavior in each tas

Bifurcation Analysis of Large Networks of Neurons

The human brain contains on the order of a hundred billion neurons, each with several thousand synaptic connections. Computational neuroscience has successfully modeled both the individual neurons as various types of oscillators, in addition to the synaptic coupling between the neurons. However, employing the individual neuronal models as a large coupled network on the scale of the human brain would require massive computational and financial resources, and yet is the current undertaking of several research groups. Even if one were to successfully model such a complicated system of coupled differential equations, aside from brute force numerical simulations, little insight may be gained into how the human brain solves problems or performs tasks. Here, we introduce a tool that reduces large networks of coupled neurons to a much smaller set of differential equations that governs key statistics for the network as a whole, as opposed to tracking the individual dynamics of neurons and their connections. This approach is typically referred to as a mean-field system. As the mean-field system is derived from the original network of neurons, it is predictive for the behavior of the network as a whole and the parameters or distributions of parameters that appear in the mean-field system are identical to those of the original network. As such, bifurcation analysis is predictive for the behavior of the original network and predicts where in the parameter space the network transitions from one behavior to another. Additionally, here we show how networks of neurons can be constructed with a mean-field or macroscopic behavior that is prescribed. This occurs through an analytic extension of the Neural Engineering Framework (NEF). This can be thought of as an inverse mean-field approach, where the networks are constructed to obey prescribed dynamics as opposed to deriving the macroscopic dynamics from an underlying network. Thus, the work done here analyzes neuronal networks through both top-down and bottom-up approaches