Dartmouth Clinical and Translational Science Institute

Schizophrenia and schizoaffective disorder are schizophrenia spectrum disorders that cause significant disability. Among individuals who have schizophrenia or schizoaffective disorder, alcohol use disorder (AUD) is common, and it contributes to worse outcomes than for those who do not have co-occurring substance use disorder. Common neurobiological mechanisms, including dysfunction in brain reward circuitry, may explain the high rates of co-occurrence of schizophrenia and AUD or other substance use disorders. Optimal treatment combines pharmacologic intervention and other therapeutic modalities to address both the psychotic disorder and AUD. Further research on the etiology of these co-occurring disorders and on treatment of affected individuals is needed.

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 'chronic BE' model as well as in a 'relapse to chronic BE' 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.

Antipsychotics that are potent dopamine (DA) D2 receptor antagonists have been linked to elevated levels of nicotine dependence in smokers with schizophrenia. Because activation of D2 receptors mediates motivation for nicotine, we examined whether potent D2 antagonists would diminish nicotine's ability to stimulate reward processing-a mechanism that may drive compensatory increases in smoking. Smokers with schizophrenia (n = 184) were recruited and stratified into medication groups based on D2 receptor antagonist potency. The effects of smoking on reward function were assessed using a probabilistic reward task (PRT), administered pre- and post-smoking. The PRT used an asymmetrical reinforcement schedule to produce a behavioral response bias, previously found to increase under conditions (including smoking) that enhance mesolimbic DA signaling. Among the 98 participants with valid PRT data and pharmacotherapy that could be stratified into D2 receptor antagonism potency, a medication × smoking × block interaction emerged (P = .005). Post-hoc tests revealed a smoking × block interaction only for those not taking potent D2 antagonists (P = .007). This group exhibited smoking-related increases in response bias (P < .001) that were absent in those taking potent D2 antagonists (P > .05). Our findings suggest that potent D2 antagonists diminish the reward-enhancing effects of nicotine in smokers with schizophrenia. This may be a mechanism implicated in the increased rate of smoking often observed in patients prescribed these medications. These findings have important clinical implications for the treatment of nicotine dependence in schizophrenia.

The ventral striatum (VS) is a central node within a distributed network that controls appetitive behavior, and neuromodulation of the VS has demonstrated therapeutic potential for appetitive disorders. Local field potential (LFP) oscillations recorded from deep brain stimulation (DBS) electrodes within the VS are a pragmatic source of neural systems-level information about appetitive behavior that could be used in responsive neuromodulation systems. Here, we recorded LFPs from the bilateral nucleus accumbens core and shell (subregions of the VS) during limited access to palatable food across varying conditions of hunger and food palatability in male rats. We used standard statistical methods (logistic regression) as well as the machine learning algorithm lasso to predict aspects of feeding behavior using VS LFPs. We were able to predict the amount of food eaten, the increase in consumption following food deprivation, and the type of food eaten. Further, we were able to predict whether the initiation of feeding was imminent up to 42.5 seconds before feeding began and classify current behavior as either feeding or not-feeding. In classifying feeding behavior, we found an optimal balance between model complexity and performance with models using 3 LFP features primarily from the alpha and high gamma frequencies. As shown here, unbiased methods can identify systems-level neural activity linked to domains of mental illness with potential application to the development and personalization of novel treatments.

Individuals differ in their vulnerability to develop alcohol dependence, which is determined by innate and environmental factors. The corticostriatal circuit is heavily involved in the development of alcohol dependence and may contain neural information regarding vulnerability to drink excessively. In the current experiment, we hypothesized that we could characterize high and low alcohol-drinking rats (HD and LD, respectively) based on corticostriatal oscillations, and that these subgroups would differentially respond to corticostriatal brain stimulation. Male Sprague-Dawley rats (n = 13) were trained to drink 10% alcohol in a limited access paradigm. In separate sessions, local field potentials (LFPs) were recorded from the nucleus accumbens shell (NAcSh) and medial prefrontal cortex (mPFC). Based on training alcohol consumption levels, we classified rats using a median split as HD or LD. Then, using machine-learning, we built predictive models to classify rats as HD or LD by corticostriatal LFPs and compared the model performance from real data to the performance of models built on data permutations. Additionally, we explored the impact of NAcSh or mPFC stimulation on alcohol consumption in HD vs. LD. Corticostriatal LFPs were able predict HD vs. LD group classification with greater accuracy than expected by chance (>80% accuracy). Moreover, NAcSh stimulation significantly reduced alcohol consumption in HD, but not LD (p0.05). These data collectively show that the corticostriatal circuit is differentially involved in regulating alcohol intake in HD vs. LD rats, and suggests that corticostriatal activity may have the potential to predict a vulnerability to develop alcohol dependence in a clinical population.

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.

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 < 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.

BACKGROUND/OBJECTIVES: Physical inactivity, sedentary lifestyle, and impaired neuromuscular function increases fall risk and fractures in our aging population. Mind-body modalities, improve strength, balance and coordination, mitigating these risks. This study examined whether a manualized Medical Qigong protocol measurably improves balance, gait, and health self-confidence among older adults. DESIGN: Randomized prospective cohort pre-post study with wait time control. SETTING: Two martial arts centers in Massachusetts and Arizona. PARTICIPANTS: Ninety-five adults age ≥ 50 (mean age 68.6 y.o., range 51 - 96) were randomly assigned to an immediate start group (N=53) or four-week delayed start group (N=43). INTERVENTION: A 10 form qigong protocol taught over 12 weekly classes. MEASURMENTS: Primary outcome measures were the Community Balance and Mobility Scale (CBMS) and Activities-Specific Balance Confidence (ABC) Scale. Data was collected at baseline, one-month and four-months. RESULTS: Both groups at both sites demonstrated improved balance and gait (CBMS + 11.9 points, p< .001). This effect was strongest in patients in their 60s (CBMS +12.9 p <.01) and 70s (CBMS + 14.3, p<.001) , was equal across genders and socioeconomic status. Balance self-confidence did not significantly change (ABC + .9, p=.48), though several elements within ABC trended towards improvement (e.g., walk up/down ramp (p=.07), bend over/pick up (p=.09)). Falls in the past year was inversely correlated with balance self-confidence. (p=.01). CONCLUSION: A 12-week manualized Medical Qigong protocol significantly improved balance and gait and modestly improved balance self-confidence among older adults. Medical Qigong may be a useful clinical intervention for older adults at heightened risk for falls and related injuries.

The longitudinal impact of fractures associated with osteoporosis on costs and health-related quality of life is not well understood. The objective of this study was to characterize these outcomes over an 18-mo period following 4 common fracture types. Patients aged over 50 yr at 7 study sites with a diagnosis of incident hip, distal radius, proximal humerus, or vertebral fracture were enrolled in the International Costs and Utilities Related to Osteoporotic Fracture Study US. Data collection by questionnaire occurred at baseline (within 6 wk of fracture), 4-, 12-, and 18-mo post-fracture. Direct, indirect, and total costs were estimated over an 18-mo period and are reported in 2020 US Dollars. Health utilities were measured using EuroQol EQ-5D and the SF-6D. We performed longitudinal regression models of estimated costs adjusted for age and sex. We enrolled 284 patients with single fragility fractures (58 hip, 50 distal radius, 32 proximal humerus, 144 vertebral). Mean ages were 68.1 yr for distal radius and proximal humerus and 76 yr for hip patients. Most participants were women (76%-84% women). Over the 18-mo study period, direct costs (Including initial fracture and fracture related follow-up costs) were $18 495 for hip, $3451 for distal forearm, $6009 for humerus, and $9274 for vertebral fracture. Mean indirect costs were $9250 for hip, $1772 for distal radius, $4195 for humerus, and $4084 for vertebral fracture. Adjusted mean EQ-5D differences (95% CI) at 18-mo for those surviving/reporting compared with baseline were: hip -0.162 (95% CI, -0.22 to -0.103), distal radius -0.017 (95% CI, -0.056 to 0.022), proximal humerus -0.064 (95% CI, -0.103 to -0.0248), and vertebral -0.044 (95% CI, -0.083 to -0.0048). Substantial direct and indirect costs are observed in the 18-mo following 4 common osteoporotic fractures. Significant changes in health utility persisted for all fractures other than distal radius fractures regardless of the health utility measure used.

Introduction: Rural communities often lack access to healthcare, have limited resources and infrastructure, and may experience suboptimal translation of evidence-based interventions into practice or measurement of translational research impact. The Consortium of Rural States (CORES), comprising eight Clinical and Translational Science Award (CTSA) hubs, is a research consortium that focuses on clinical and translational research impacting rural health. Methods: Utilizing the Translational Science Benefits Model (TSBM) framework, each CTSA hub's evaluation lead co-created an inventory of rural-focused activities, projects, and initiatives that occurred at their respective site during the funding period 2021-2023. Variables included program area; activity type and description; target population; activity status; outputs; and short-term outcomes. The evaluators then mapped site outcomes according to the four TSBM domains (clinical, community, economic, policy) and 30 subcategories (benefits). Results: 184 rural-focused activities, projects and initiatives were identified across the hubs. All rural-focused efforts involved impacts in the Community and Clinical domains of the TSBM, with >60% focusing on Community impacts. These results suggest an opportunity gap to better define Economic and Policy-level impacts in the context of rural-focused initiatives. Discussion: This work demonstrates a novel mapping of the TSBM to rural health research settings and explores the nuances of using the concepts and domains of the TSBM as a coding tool. This work gives the Consortium insight on the types of projects and impacts that are supported and how to prioritize more exploration of the full range of translational science benefits in rural health initiatives going forward.

Abstract Individuals differ in their vulnerability to develop alcohol dependence that are determined by innate and environmental factors. The corticostriatal circuit is heavily involved in the development of alcohol dependence and may contain neural information regarding vulnerability to drink excessively. In the current experiment, we hypothesized that we could characterize high and low alcohol-drinking rats (HD and LD, respectively) based on corticostriatal oscillations, and that these subgroups would differentially respond to corticostriatal brain stimulation. Rats were trained to drink 10% alcohol in a limited access paradigm. In separate sessions, local field potentials (LFPs) were recorded from the nucleus accumbens shell (NAcSh) and medial prefrontal cortex (mPFC) of male Sprague-Dawley rats (n=13). Based on training alcohol consumption levels, we classified rats using a median split as HD or LD. Then, using machine-learning, we built predictive models to classify rats as HD or LD by corticostriatal LFPs and compared the model performance from real data to the performance of models built on data permutations. Additionally, we explored the impact of NAcSh or mPFC stimulation on alcohol consumption in HD vs. LD. Corticostriatal LFPs were able predict HD vs. LD group classification with greater accuracy than expected by chance (>80% accuracy). Additionally, NAcSh stimulation significantly reduced alcohol consumption in HD, but not LD (p<0.05), while mPFC stimulation did not alter drinking behavior in either HD or LD (p>0.05). These data collectively show that the corticostriatal circuit is differentially involved in regulating alcohol intake in HD vs. LD rats, and suggests that corticostriatal activity may have the potential to predict a vulnerability to develop alcohol dependence in a clinical population.

Abstract 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 as predictors of the pre-defined stimulation outcomes (response or non-response) from each rat using a machine-learning approach (lasso), stimulation outcomes could be predicted 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 contribute to the variable outcomes of circuit based interventions and that measures of network activity have the potential to individually guide the selection of an optimal stimulation target and improve overall treatment response rates.

Abstract 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 X 2 recordings/rat) and during sessions of limited access to alcohol (n=5/sex X 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 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<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 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.

Abstract The ventral striatum (VS) is a central node within a distributed network that controls appetitive behavior, and neuromodulation of the VS has demonstrated therapeutic potential for appetitive disorders. Local field potential (LFP) oscillations recorded from deep brain stimulation electrodes within the VS are a pragmatic source of neural systems-level information about appetitive behavior that could be used in responsive neuromodulation systems. Here, we recorded LFPs from the bilateral nucleus accumbens core and shell (subregions of the VS) during limited access to palatable food across varying conditions of hunger and food palatability in male rats. We used standard statistical methods (logistic regression) as well as the machine learning algorithm lasso to predict aspects of feeding behavior using VS LFPs. These models were able to predict the amount of food eaten, the increase in consumption following food deprivation, and the type of food eaten. Further, we were able to predict whether the initiation of feeding was imminent up to 42.5 seconds before feeding began and classify current behavior as either feeding or not-feeding. In classifying this behavior, we found an optimal balance between model complexity and performance with models using 3 LFP features primarily from the alpha and high gamma frequencies. As shown here, unbiased methods can identify systems-level neural activity linked to symptoms of mental illness with potential application to the development and personalization of novel treatments. Author Summary As neuropsychiatry begins to leverage the power of computational methods to understand disease states and to develop better therapies, it is vital that we acknowledge the trade-offs between model complexity and performance. We show that computational methods can elucidate a neural signature of feeding behavior and show how these methods could be used to discover neural patterns related to other behaviors and used as therapeutic targets. Further, our results helps to contextualize both the limitations and potential of applying computational methods to neuropsychiatry by showing how changing the data being used to train predictive models (e.g., population vs. individual data) can have a large impact on how model performance generalizes across time, internal states and individuals.