chronic-intermittent-ethanol-vapor-exposure-paired-with-two-bottle

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Alcohol use disorder (AUD) is a chronic alcohol-related disorder that typically presents as uncontrolled drinking and preoccupation with alcohol. A key ...

Purdue University

Purdue Institute for Integrative Neuroscience

Purdue Institute of Inflammation, Immunology, and Infectious Disease

Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural "hubs" involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking.

Leveraging Neural Networks in Preclinical Alcohol Research.

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.

The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches.

Numerous brain regions have been identified as contributing to withdrawal behaviors, but it is unclear the way in which these brain regions as a whole lead to withdrawal. The search for a final common brain pathway that is involved in withdrawal remains elusive. To address this question, we implanted osmotic minipumps containing either saline, nicotine (24 mg/kg/d), cocaine (60 mg/kg/d), or methamphetamine (4 mg/kg/d) for one week in male C57BL/6J mice. After one week, the minipumps were removed and brains collected 8 h (saline, nicotine, and cocaine) or 12 h (methamphetamine) after removal. We then performed single-cell whole-brain imaging of neural activity during the withdrawal period when brains were collected. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical-driven to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway associated with withdrawal.

Characterization of the Brain Functional Architecture of Psychostimulant Withdrawal Using Single-Cell Whole-Brain Imaging.

Complex social behaviors are governed by a neural network theorized to be the social decision-making network (SDMN). However, this theoretical network is not tested on functional grounds. Here, we assess the organization of regions in the SDMN using c-Fos, to generate functional connectivity models during specific social interactions in a socially monogamous rodent, the prairie voles (). Male voles displayed robust selective affiliation toward a female partner, while exhibiting increased threatening, vigilant, and physically aggressive behaviors toward novel males and females. These social interactions increased c-Fos levels in eight of the thirteen brain regions of the SDMN. Each social encounter generated a distinct correlation pattern between individual brain regions. Thus, hierarchical clustering was used to characterize interrelated regions with similar c-Fos activity resulting in discrete network modules. Functional connectivity maps were constructed to emulate the network dynamics resulting from each social encounter. Our partner functional connectivity network presents similarities to the theoretical SDMN model, along with connections in the network that have been implicated in partner-directed affiliation. However, both stranger female and male networks exhibited distinct architecture from one another and the SDMN. Further, the stranger-evoked networks demonstrated connections associated with threat, physical aggression, and other aversive behaviors. Together, this indicates that distinct patterns of functional connectivity in the SDMN can be detected during select social encounters.

Pair Bond-Induced Affiliation and Aggression in Male Prairie Voles Elicit Distinct Functional Connectivity in the Social Decision-Making Network.

Autism spectrum disorder (ASD) affects ~2% of the population in the US, and monogenic forms of ASD often result in the most severe manifestation of the disorder. Recently, SCN2A has emerged as a leading gene associated with ASD, of which abnormal sleep pattern is a common comorbidity. SCN2A encodes the voltage-gated sodium channel Na1.2. Predominantly expressed in the brain, Na1.2 mediates the action potential firing of neurons. Clinical studies found that a large portion of children with SCN2A deficiency have sleep disorders, which severely impact the quality of life of affected individuals and their caregivers. The underlying mechanism of sleep disturbances related to Na1.2 deficiency, however, is not known. Using a gene-trap Scn2a-deficient mouse model (Scn2a), we found that Scn2a deficiency results in increased wakefulness and reduced non-rapid-eye-movement (NREM) sleep. Brain region-specific Scn2a deficiency in the suprachiasmatic nucleus (SCN) containing region, which is involved in circadian rhythms, partially recapitulates the sleep disturbance phenotypes. At the cellular level, we found that Scn2a deficiency disrupted the firing pattern of spontaneously firing neurons in the SCN region. At the molecular level, RNA-sequencing analysis revealed differentially expressed genes in the circadian entrainment pathway including core clock genes Per1 and Per2. Performing a transcriptome-based compound discovery, we identified dexanabinol (HU-211), a putative glutamate receptor modulator, that can partially reverse the sleep disturbance in mice. Overall, our study reveals possible molecular and cellular mechanisms underlying Scn2a deficiency-related sleep disturbances, which may inform the development of potential pharmacogenetic interventions for the affected individuals.

Deficiency of autism-related Scn2a gene in mice disrupts sleep patterns and circadian rhythms.

A major limitation of the most widely used current animal models of alcohol dependence is that they use forced exposure to ethanol including ethanol-containing liquid diet and chronic intermittent ethanol (CIE) vapor to produce clinically relevant blood alcohol levels (BAL) and addiction-like behaviors. We recently developed a novel animal model of voluntary induction of alcohol dependence using ethanol vapor self-administration (EVSA). However, it is unknown whether EVSA leads to an escalation of alcohol drinking per se, and whether such escalation is associated with neuroadaptations in brain regions related to stress, reward, and habit. To address these issues, we compared the levels of alcohol drinking during withdrawal between rats passively exposed to alcohol (CIE) or voluntarily exposed to EVSA and measured the number of Fos+ neurons during acute withdrawal (16 h) in key brain regions important for stress, reward, and habit-related processes. CIE and EVSA rats exhibited similar BAL and similar escalation of alcohol drinking and motivation for alcohol during withdrawal. Acute withdrawal from EVSA and CIE recruited a similar number of Fos+ neurons in the Central Amygdala (CeA), however, acute withdrawal from EVSA recruited a higher number of Fos+ neurons in every other brain region analyzed compared to acute withdrawal from CIE. In summary, while the behavioral measures of alcohol dependence between the voluntary (EVSA) and passive (CIE) model were similar, the recruitment of neuronal ensembles during acute withdrawal was very different. The EVSA model may be particularly useful to unveil the neuronal networks and pharmacology responsible for the voluntary induction and maintenance of alcohol dependence and may improve translational studies by providing preclinical researchers with an animal model that highlights the volitional aspects of alcohol use disorder.

Voluntary and forced exposure to ethanol vapor produces similar escalation of alcohol drinking but differential recruitment of brain regions related to stress, habit, and reward in male rats.

Bioelectricity is defined as endogenous electrical signaling mediated by the dynamic distribution of charged molecules. Recently, increasing evidence has revealed that cellular bioelectric signaling is critical for regulating embryonic development, regeneration, and congenital diseases. However, systematic real-time  dynamic electrical activity monitoring of whole organisms has been limited, mainly due to the lack of a suitable model system and voltage measurement tools for  biology. Here, we addressed this gap by utilizing a genetically stable zebrafish line, Tg (: ASAP1), and ASAP1 (Accelerated sensor of action potentials 1), a genetically encoded voltage indicator (GEVI). With light-sheet microscopy, we systematically investigated cell membrane potential (Vm) signals during different embryonic stages. We found cells of zebrafish embryos showed local membrane hyperpolarization at the cleavage furrows during the cleavage period of embryogenesis. This signal appeared before cytokinesis and fluctuated as it progressed. In contrast, whole-cell transient hyperpolarization was observed during the blastula and gastrula stages. These signals were generally limited to the superficial blastomere, but they could be detected within the deeper cells during the gastrulation period. Moreover, the zebrafish embryos exhibit tissue-level cell Vm signals during the segmentation period. Middle-aged somites had strong and dynamic Vm fluctuations starting at about the 12-somite stage. These embryonic stage-specific characteristic cellular bioelectric signals suggest that they might play a diverse role in zebrafish embryogenesis that could underlie human congenital diseases.

Zebrafish Embryos Display Characteristic Bioelectric Signals during Early Development.

Binge drinking can lead to various negative consequences and in non-experimental settings, alcohol usually contains flavoring, which may promote increased binge drinking. Preclinical models of binge-like drinking have been well established, however, the influence of flavor on alcohol preference and binge-like drinking has not been fully explored.

Article	Year
Leveraging Neural Networks in Preclinical Alcohol Research. Brain sciences\| PubMed ID: 32825739	2020
The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Frontiers in systems neuroscience\| PubMed ID: 33967705	2021
Characterization of the Brain Functional Architecture of Psychostimulant Withdrawal Using Single-Cell Whole-Brain Imaging. eNeuro\| PubMed ID: 34580158	2021
Pair Bond-Induced Affiliation and Aggression in Male Prairie Voles Elicit Distinct Functional Connectivity in the Social Decision-Making Network. Frontiers in neuroscience\| PubMed ID: 34720866	2021
Deficiency of autism-related Scn2a gene in mice disrupts sleep patterns and circadian rhythms. Neurobiology of disease\| PubMed ID: 35301122	2022
Voluntary and forced exposure to ethanol vapor produces similar escalation of alcohol drinking but differential recruitment of brain regions related to stress, habit, and reward in male rats... Neuropharmacology\| PubMed ID: 36334765	2023
Zebrafish Embryos Display Characteristic Bioelectric Signals during Early Development. Cells\| PubMed ID: 36429015	2022
Prior experience with flavored alcohol increases preference for flavored alcohol but flavor does not influence binge-like drinking behavior in mice. Physiology & behavior\| PubMed ID: 37336280	2023

Adam Kimbrough

Biography

JoVE Journal Publications

Other Publications