Nucleus Accumbens Deep Brain Stimulation in a Rat Model of Binge Eating

Binge eating (BE) is a difficult-to-treat behavior with high relapse rates, thus complicating several disorders including obesity. In this study, we tested the effects of high-frequency deep brain stimulation (DBS) in a rodent model of BE. We hypothesized that BE rats receiving high-frequency DBS in the nucleus accumbens (NAc) core would have reduced binge sizes compared with sham stimulation in both a \u27chronic BE\u27 model as well as in a \u27relapse to chronic BE\u27 model. Male Sprague-Dawley rats (N=18) were implanted with stimulating electrodes in bilateral NAc core, and they received either active stimulation (N=12) or sham stimulation (N=6) for the initial chronic BE experiments. After testing in the chronic BE state, rats did not engage in binge sessions for 1 month, and then resumed binge sessions (relapse to chronic BE) with active or sham stimulation (N=5-7 per group). A significant effect of intervention group was observed on binge size in the chronic BE state, but no significant difference between intervention groups was observed in the relapse to chronic BE experiments. This research, making use of both a chronic BE model as well as a relapse to chronic BE model, provides data supporting the hypothesis that DBS of the NAc core can decrease BE. Further research will be needed to learn how to increase the effect size and decrease deep brain stimulation-treatment outcome variability across the continuum of BE behavior

A Framework for the Game-theoretic Analysis of Censorship Resistance

We present a game-theoretic analysis of optimal solutions for interactions between censors and censorship resistance systems (CRSs) by focusing on the data channel used by the CRS to smuggle clients’ data past the censors. This analysis leverages the inherent errors (false positives and negatives) made by the censor when trying to classify traffic as either non-circumvention traffic or as CRS traffic, as well as the underlying rate of CRS traffic. We identify Nash equilibrium solutions for several simple censorship scenarios and then extend those findings to more complex scenarios where we find that the deployment of a censorship apparatus does not qualitatively change the equilibrium solutions, but rather only affects the amount of traffic a CRS can support before being blocked. By leveraging these findings, we describe a general framework for exploring and identifying optimal strategies for the censorship circumventor, in order to maximize the amount of CRS traffic not blocked by the censor. We use this framework to analyze several scenarios with multiple data-channel protocols used as cover for the CRS. We show that it is possible to gain insights through this framework even without perfect knowledge of the censor’s (secret) values for the parameters in their utility function

Eskasoni Cultural Journeys: A community-led approach to sustainable tourism development

Abstract Eskasoni First Nation is the largest Mi’kmaq-speaking community in the world. With a population of more than 4300, Eskasoni is one of the five Mi’kmaq communities located on Unama’ki (Cape Breton Island), Canada. The community has been engaged in tourism development for decades, with varying degrees of success, until a new approach was considered – that being the community-led product of Eskasoni Cultural Journeys (ECJ). Eskasoni Cultural Journeys started at a grassroots level with Elder buy-in and initial Elder training in tourism. Today Eskasoni Cultural Journeys offers boat tours on the Bras d’Or Lake and authentic interpretive programming along the trails of Goat Island. This research examined the development of Eskasoni Cultural Journeys, now a successful and award-winning program, which developed through a gradual community-led process and highlights an example of a community-led Aboriginal tourism project that has been successful in its development stages. Successes and challenges of the project related to four aspects of sustainability (social, cultural, economic and environmental) are outlined and recommendations offered for future development as well as considerations to ensure the project continues well in to the future

Associative Cortex Features in the First Olfactory Brain Relay Station

SummarySynchronized firing of mitral cells (MCs) in the olfactory bulb (OB) has been hypothesized to help bind information together in olfactory cortex (OC). In this survey of synchronized firing by suspected MCs in awake, behaving vertebrates, we find the surprising result that synchronized firing conveys information on odor value (“Is it rewarded?”) rather than odor identity (“What is the odor?”). We observed that as mice learned to discriminate between odors, synchronous firing responses to the rewarded and unrewarded odors became divergent. Furthermore, adrenergic blockage decreases the magnitude of odor divergence of synchronous trains, suggesting that MCs contribute to decision-making through adrenergic-modulated synchronized firing. Thus, in the olfactory system information on stimulus reward is found in MCs one synapse away from the sensory neuron

Maternal immune activation and adolescent alcohol exposure increase alcohol drinking and disrupt cortical-striatal-hippocampal oscillations in adult offspring

Maternal immune activation (MIA) is strongly associated with an increased risk of developing mental illness in adulthood, which often co-occurs with alcohol misuse. The current study aimed to begin to determine whether MIA, combined with adolescent alcohol exposure (AE), could be used as a model with which we could study the neurobiological mechanisms behind such co-occurring disorders. Pregnant Sprague-Dawley rats were treated with polyI:C or saline on gestational day 15. Half of the offspring were given continuous access to alcohol during adolescence, leading to four experimental groups: controls, MIA, AE, and Dual (MIA + AE). We then evaluated whether MIA and/or AE alter: (1) alcohol consumption; (2) locomotor behavior; and (3) cortical-striatal-hippocampal local field potentials (LFPs) in adult offspring. Dual rats, particularly females, drank significantly more alcohol in adulthood compared to all other groups. MIA led to reduced locomotor behavior in males only. Using machine learning to build predictive models from LFPs, we were able to differentiate Dual rats from control rats and AE rats in both sexes, and Dual rats from MIA rats in females. These data suggest that Dual “hits” (MIA + AE) increases substance use behavior and disrupts activity in reward-related circuits, and that this may be a valuable heuristic model we can use to study the neurobiological underpinnings of co-occurring disorders. Our future work aims to extend these findings to other addictive substances to enhance the translational relevance of this model, as well as determine whether amelioration of these circuit disruptions can reduce substance use behavior

Sex differences in the ability of corticostriatal oscillations to predict rodent alcohol consumption

Background: Although male and female rats differ in their patterns of alcohol use, little is known regarding the neural circuit activity that underlies these differences in behavior. The current study used a machine learning approach to characterize sex differences in local field potential (LFP) oscillations that may relate to sex differences in alcohol-drinking behavior. Methods: LFP oscillations were recorded from the nucleus accumbens shell and the rodent medial prefrontal cortex of adult male and female Sprague-Dawley rats. Recordings occurred before rats were exposed to alcohol (n = 10/sex × 2 recordings/rat) and during sessions of limited access to alcohol (n = 5/sex × 5 recordings/rat). Oscillations were also recorded from each female rat in each phase of estrous prior to alcohol exposure. Using machine learning, we built predictive models with oscillation data to classify rats based on: (1) biological sex, (2) phase of estrous, and (3) alcohol intake levels. We evaluated model performance from real data by comparing it to the performance of models built and tested on permutations of the data. Results: Our data demonstrate that corticostriatal oscillations were able to predict alcohol intake levels in males (p \u3c 0.01), but not in females (p = 0.45). The accuracies of models predicting biological sex and phase of estrous were related to fluctuations observed in alcohol drinking levels; females in diestrus drank more alcohol than males (p = 0.052), and the male vs. diestrus female model had the highest accuracy (71.01%) compared to chance estimates. Conversely, females in estrus drank very similar amounts of alcohol to males (p = 0.702), and the male vs. estrus female model had the lowest accuracy (56.14%) compared to chance estimates. Conclusions: The current data demonstrate that oscillations recorded from corticostriatal circuits contain significant information regarding alcohol drinking in males, but not alcohol drinking in females. Future work will focus on identifying where to record LFP oscillations in order to predict alcohol drinking in females, which may help elucidate sex-specific neural targets for future therapeutic development

Machine Learning Based Classification of Deep Brain Stimulation Outcomes in a Rat Model of Binge Eating Using Ventral Striatal Oscillations

Neuromodulation-based interventions continue to be evaluated across an array of appetitive disorders but broader implementation of these approaches remains limited due to variable treatment outcomes. We hypothesize that individual variation in treatment outcomes may be linked to differences in the networks underlying these disorders. Here, Sprague-Dawley rats received deep brain stimulation separately within each nucleus accumbens (NAc) sub-region (core and shell) using a within-animal crossover design in a rat model of binge eating. Significant reductions in binge size were observed with stimulation of either target but with significant variation in effectiveness across individuals. When features of local field potentials (LFPs) recorded from the NAc were used to classify the pre-defined stimulation outcomes (response or non-response) from each rat using a machine-learning approach (lasso), stimulation outcomes could be classified with greater accuracy than expected by chance (effect sizes: core = 1.13, shell = 1.05). Further, these LFP features could be used to identify the best stimulation target for each animal (core vs. shell) with an effect size = 0.96. These data suggest that individual differences in underlying network activity may relate to the variable outcomes of circuit based interventions, and measures of network activity could have the potential to individually guide the selection of an optimal stimulation target to improve overall treatment response rates

Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network

We present the first gene regulatory network (GRN) that pertains to post-developmental gene expression. Specifically, we mapped a transcription regulatory network of Caenorhabditis elegans metabolic gene promoters using gene-centered yeast one-hybrid assays. We found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs) compared with other gene-centered regulatory networks, and that these NHRs organize into functional network modules.The NHR family has greatly expanded in nematodes; C. elegans has 284 NHRs, whereas humans have only 48. We show that the NHRs in the metabolic GRN have metabolic phenotypes, suggesting that they do not simply function redundantly.The mediator subunit MDT-15 preferentially interacts with NHRs that occur in the metabolic GRN.We describe an NHR circuit that responds to nutrient availability and propose a model for the evolution and organization of NHRs in C. elegans metabolic regulatory networks

Updating Photon-Based Normal Tissue Complication Probability Models for Pneumonitis in Patients With Lung Cancer Treated With Proton Beam Therapy

Purpose: No validated models for predicting the risk of radiation pneumonitis (RP) with proton beam therapy (PBT) currently exist. Our goal was to externally validate and recalibrate multiple established photon-based normal tissue complication probability models for RP in a cohort with locally advanced nonsmall cell lung cancer treated with contemporary doses of chemoradiation using PBT. Methods and Materials: The external validation cohort consisted of 99 consecutive patients with locally advanced nonsmall cell lung cancer treated with chemoradiation using PBT. RP was retrospectively scored at 3 and 6 months posttreatment. We evaluated the performance of the photon Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) pneumonitis model, the QUANTEC model adjusted for clinical risk factors, and the newer Netherlands updated QUANTEC model. A closed testing procedure was performed to test the need for model updating, either by recalibration-in-the-large (re-estimation of intercept), recalibration (re-estimation of intercept/slope), or model revision (re-estimation of all coefficients). Results: There were 21 events (21%) of ≥grade 2 RP. The closed testing procedure on the PBT data set did not detect major deviations between the models and the data and recommended adjustment of the intercept only for the photon-based Netherlands updated QUANTEC model (intercept update: –1.2). However, an update of the slope and revision of the model coefficients were not recommended by the closed testing procedure, as the deviations were not significant within the power of the data. Conclusions: The similarity between the dose-response relationship for PBT and photons for normal tissue complications has been an assumption until now. We demonstrate that the preexisting, widely used photon based models fit our PBT data well with minor modifications. These now-validated and updated normal tissue complication probability models can aid in individualizing selection of the most optimal treatment technique for a particular patient