ASU Scholarship Showcase

Illicit psychostimulant addiction remains a significant problem worldwide, despite decades of research into the neural underpinnings and various treatment approaches. The purpose of this review is to provide a succinct overview of the neurocircuitry involved in drug addiction, as well as the acute and chronic effects of cocaine and amphetamines within this circuitry in humans. Investigational pharmacological treatments for illicit psychostimulant addiction are also reviewed. Our current knowledge base clearly demonstrates that illicit psychostimulants produce lasting adaptive neural and behavioral changes that contribute to the progression and maintenance of addiction. However, attempts at generating pharmacological treatments for psychostimulant addiction have historically focused on intervening at the level of the acute effects of these drugs. The lack of approved pharmacological treatments for psychostimulant addiction highlights the need for new treatment strategies, especially those that prevent or ameliorate the adaptive neural, cognitive, and behavioral changes caused by chronic use of this class of illicit drugs.

Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

Attention deficit/hyperactivity disorder (ADHD) is a risk factor for tobacco use and dependence. This study examines the responsiveness to nicotine of an adolescent model of ADHD, the spontaneously hypertensive rat (SHR). The conditioned place preference (CPP) procedure was used to assess nicotine-induced locomotion and conditioned reward in SHR and the Wistar Kyoto (WKY) control strain over a range of nicotine doses (0.0, 0.1, 0.3 and 0.6 mg/kg). Prior to conditioning, SHRs were more active and less biased toward one side of the CPP chamber than WKY rats. Following conditioning, SHRs developed CPP to the highest dose of nicotine (0.6 mg/kg), whereas WKYs did not develop CPP to any nicotine dose tested. During conditioning, SHRs displayed greater locomotor activity in the nicotine-paired compartment than in the saline-paired compartment across conditioning trials. SHRs that received nicotine (0.1, 0.3, 0.6 mg/kg) in the nicotine-paired compartment showed an increase in locomotor activity between conditioning trials. Nicotine did not significantly affect WKY locomotor activity. These findings suggest that the SHR strain is a suitable model for studying ADHD-related nicotine use and dependence, but highlights potential limitations of the WKY control strain and the CPP procedure for modeling ADHD-related nicotine reward.

Positive allosteric modulators (PAMs) of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors are a diverse class of compounds that increase fast excitatory transmission in the brain. AMPA PAMs have been shown to facilitate long-term potentiation, strengthen communication between various cortical and subcortical regions, and some of these compounds increase the production and release of brain-derived neurotrophic factor (BDNF) in an activity-dependent manner. Through these mechanisms, AMPA PAMs have shown promise as broad spectrum pharmacotherapeutics in preclinical and clinical studies for various neurodegenerative and psychiatric disorders. In recent years, a small collection of preclinical animal studies has also shown that AMPA PAMs may have potential as pharmacotherapeutic adjuncts to extinction-based or cue-exposure therapies for the treatment of drug addiction. The present paper will review this preclinical literature, discuss novel data collected in our laboratory, and recommend future research directions for the possible development of AMPA PAMs as anti-addiction medications.

The group I metabotropic glutamate receptors (mGluR1a and mGluR5) are important modulators of neuronal structure and function. Although these receptors share common signaling pathways, they are capable of having distinct effects on cellular plasticity. We investigated the individual effects of mGluR1a or mGluR5 activation on dendritic spine density in medium spiny neurons in the nucleus accumbens (NAc), which has become relevant with the potential use of group I mGluR based therapeutics in the treatment of drug addiction. We found that systemic administration of mGluR subtype-specific positive allosteric modulators had opposite effects on dendritic spine densities. Specifically, mGluR5 positive modulation decreased dendritic spine densities in the NAc shell and core, but was without effect in the dorsal striatum, whereas increased spine densities in the NAc were observed with mGluR1a positive modulation. Additionally, direct activation of mGluR5 via CHPG administration into the NAc also decreased the density of dendritic spines. These data provide insight on the ability of group I mGluRs to induce structural plasticity in the NAc and demonstrate that the group I mGluRs are capable of producing not just distinct, but opposing, effects on dendritic spine density.

Studies utilizing selective pharmacological antagonists or targeted gene deletion have demonstrated thattype 5 metabotropic glutamate receptors (mGluR5) are critical mediators and potential therapeutic targets for the treatment of numerous disorders of the central nervous system (CNS), including depression, anxiety, drug addiction, chronic pain, Fragile X syndrome, Parkinson’s disease, and gastroesophageal reflux disease. However, in recent years, the development of positive allosteric modulators (PAMs) of the mGluR5 receptor have revealed that allosteric activation of this receptor may also be of potential therapeutic benefit for the treatment of other CNS disorders, including schizophrenia, cognitive deficits associated with chronic drug use, and deficits in extinction learning. Here we summarize the discovery and characterization of various mGluR5 PAMs, with an emphasis on those that are systemically active. We will also review animal studies showing that these molecules have potential efficacy as novel antipsychotic agents. Finally, we will summarize findings that suggest that mGluR5 PAMs have pro-cognitive effects such as the ability toenhance synaptic plasticity, improve performance in various learning and memory tasks, including extinction of drug-seeking behavior, and reverse cognitive deficits produced by chronic drug use.

Glutamate plays a pivotal role in drug addiction, and the N-methyl-D-aspartate (NMDA) glutamate receptor subtype serves as a molecular target for several drugs of abuse. In this review, we will provide an overview of NMDA receptor structure and function, followed by a review of the mechanism of action, clinical efficacy, and side effect profile of NMDA receptor ligands that are currently in use or being explored for the treatment of drug addiction. These ligands include the NMDA receptor modulators memantine and acamprosate, as well as the partial NMDA agonist D-cycloserine. Data collected to date suggest that direct NMDA receptor modulators have relatively limited efficacy in the treatment of drug addiction, and that partial agonism of NMDA receptors may have some efficacy with regards to extinction learning during cue exposure therapy. However, the lack of consistency in results to date clearly indicates that additional studies are needed, as are studies examining novel ligands with indirect mechanisms for altering NMDA receptor function.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.