Microbial short-chain fatty acids regulate drug seeking and transcriptional control in a model of cocaine seeking.

Cocaine use disorder represents a public health crisis with no FDA-approved medications for its treatment. A growing body of research has detailed the important connections between the brain and the resident population of bacteria in the gut, the gut microbiome, in psychiatric disease models. Acute depletion of gut bacteria results in enhanced reward in a mouse cocaine place preference model, and repletion of bacterially-derived short-chain fatty acid (SCFA) metabolites reverses this effect. However, the role of the gut microbiome and its metabolites in modulating cocaine-seeking behavior after prolonged abstinence is unknown. Given that relapse prevention is the most clinically challenging issue in treating substance use disorders, studies examining the effects of microbiome manipulations in relapse-relevant models are critical. Here, male Sprague-Dawley rats received either untreated water or antibiotics to deplete the gut microbiome and its metabolites. Rats were trained to self-administer cocaine and subjected to either within-session threshold testing to evaluate motivation for cocaine or 21 days of abstinence followed by a cue-induced cocaine-seeking task to model relapse behavior. Microbiome depletion did not affect cocaine acquisition on an fixed-ratio 1 schedule. However, microbiome-depleted rats exhibited significantly enhanced motivation for low dose cocaine on a within-session threshold task. Similarly, microbiome depletion increased cue-induced cocaine-seeking following prolonged abstinence and altered transcriptional regulation in the nucleus accumbens. In the absence of a normal microbiome, repletion of bacterially-derived SCFA metabolites reversed the behavioral and transcriptional changes associated with microbiome depletion. These findings suggest that gut bacteria, via their metabolites, are key regulators of drug-seeking behaviors, positioning the microbiome as a potential translational research target.

Improving case duration accuracy of orthopedic surgery using bidirectional encoder representations from Transformers (BERT) on Radiology Reports.

PURPOSE: A major source of inefficiency in the operating room is the mismatch between scheduled versus actual surgical time. The purpose of this study was to demonstrate a proof-of-concept study for predicting case duration by applying natural language processing (NLP) and machine learning that interpret radiology reports for patients undergoing radius fracture repair. METHODS: Logistic regression, random forest, and feedforward neural networks were tested without NLP and with bag-of-words. Another NLP method tested used feedforward neural networks and Bidirectional Encoder Representations from Transformers specifically pre-trained on clinical notes (ClinicalBERT). A total of 201 cases were included. The data were split into 70% training and 30% test sets. The average root mean squared error (RMSE) were calculated (and 95% confidence interval [CI]) from 10-fold cross-validation on the training set. The models were then tested on the test set to determine proportion of times surgical cases would have scheduled accurately if ClinicalBERT was implemented versus historic averages. RESULTS: The average RMSE was lowest using feedforward neural networks using outputs from ClinicalBERT (25.6 min, 95% CI: 21.5-29.7), which was significantly (P < 0.001) lower than the baseline model (39.3 min, 95% CI: 30.9-47.7). Using the feedforward neural network and ClinicalBERT on the test set, the percentage of accurately predicted cases, which was defined by the actual surgical duration within 15% of the predicted surgical duration, increased from 26.8 to 58.9% (P < 0.001). CONCLUSION: This proof-of-concept study demonstrated the successful application of NLP and machine leaning to extract features from unstructured clinical data resulting in improved prediction accuracy for surgical case duration.

Tracking persistent postoperative opioid use: a proof-of-concept study demonstrating a use case for natural language processing.

BACKGROUND: Large language models have been gaining tremendous popularity since the introduction of ChatGPT in late 2022. Perioperative pain providers should leverage natural language processing (NLP) technology and explore pertinent use cases to improve patient care. One example is tracking persistent postoperative opioid use after surgery. Since much of the relevant data may be 'hidden' within unstructured clinical text, NLP models may prove to be advantageous. The primary objective of this proof-of-concept study was to demonstrate the ability of an NLP engine to review clinical notes and accurately identify patients who had persistent postoperative opioid use after major spine surgery. METHODS: Clinical documents from all patients that underwent major spine surgery during July 2015-August 2021 were extracted from the electronic health record. The primary outcome was persistent postoperative opioid use, defined as continued use of opioids greater than or equal to 3 months after surgery. This outcome was ascertained via manual clinician review from outpatient spine surgery follow-up notes. An NLP engine was applied to these notes to ascertain the presence of persistent opioid use-this was then compared with results from clinician manual review. RESULTS: The final study sample consisted of 965 patients, in which 705 (73.1%) were determined to have persistent opioid use following surgery. The NLP engine correctly determined the patients' opioid use status in 92.9% of cases, in which it correctly identified persistent opioid use in 95.6% of cases and no persistent opioid use in 86.1% of cases. DISCUSSION: Access to unstructured data within the perioperative history can contextualize patients' opioid use and provide further insight into the opioid crisis, while at the same time improve care directly at the patient level. While these goals are in reach, future work is needed to evaluate how to best implement NLP within different healthcare systems for use in clinical decision support.

A Retrospective Analysis Investigating Whether Case Volume Experience of the Anesthesiologist Correlates with Intraoperative Efficiency for Joint Arthroplasty.

The objective of this retrospective study was to determine if there was an association between anesthesiology experience (e.g. historic case volume) and operating room (OR) efficiency times for lower extremity joint arthroplasty cases. The primary outcome was time from patient in the OR to anesthesia ready (i.e. after spinal or general anesthesia induction was complete). The secondary outcomes included time from anesthesia ready to surgical incision, and time from incision to closing completed. Mixed effects linear regression was performed, in which the random effect was the anesthesiology attending provider. There were 4,575 patients undergoing hip or knee arthroplasty included. There were 82 unique anesthesiology providers, in which the median [quartile] frequency of cases performed was 79 [45, 165]. On multivariable mixed effects linear regression - in which the primary independent variable (anesthesiologist case volume history for joint arthroplasty anesthesia) was log-transformed - the estimate for log-transformed case volume was - 0.91 (95% confidence interval [CI] -1.62, -0.20, P = 0.01). When modeling time from incision to closure complete, the estimate for log-transformed case volume was - 2.07 (95% -3.54, -0.06, P = 0.01). Thus, when comparing anesthesiologists with median case volume (79 cases) versus those with the lowest case volume (10 cases), the predicted difference in times added up to only approximately 6 min. If the purpose of faster anesthesia workflows was to open up more OR time to increase surgical volume in a given day, this study does not support the supposition that anesthesiologists with higher joint arthroplasty case volume would improve throughput.

Meditation-induced bloodborne factors as an adjuvant treatment to COVID-19 disease.

The COVID-19 pandemic has resulted in significant morbidity and mortality worldwide. Management of the pandemic has relied mainly on SARS-CoV-2 vaccines, while alternative approaches such as meditation, shown to improve immunity, have been largely unexplored. Here, we probe the relationship between meditation and COVID-19 disease and directly test the impact of meditation on the induction of a blood environment that modulates viral infection. We found a significant inverse correlation between length of meditation practice and SARS-CoV-2 infection as well as accelerated resolution of symptomology of those infected. A meditation "dosing" effect was also observed. In cultured human lung cells, blood from experienced meditators induced factors that prevented entry of pseudotyped viruses for SARS-CoV-2 spike protein of both the wild-type Wuhan-1 virus and the Delta variant. We identified and validated SERPINA5, a serine protease inhibitor, as one possible protein factor in the blood of meditators that is necessary and sufficient for limiting pseudovirus entry into cells. In summary, we conclude that meditation can enhance resiliency to viral infection and may serve as a possible adjuvant therapy in the management of the COVID-19 pandemic.

Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing.

The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.

A Point-Based Risk Calculator for Mortality After Hepatectomy.

BACKGROUND: Preoperative risk stratification for hepatectomy patients can aid clinical decision making. The objective of this retrospective cohort study was to determine postoperative mortality risk factors and develop a score-based risk calculator using a limited number of preoperative predictors to estimate mortality risk in patients undergoing hepatectomy. METHODS: Data were collected from patients that underwent hepatectomy from the National Surgical Quality Improvement Program dataset from 2014 to 2020. Baseline characteristics were compared between survival and 30-day mortality cohorts using the χ 2 test. Next, the data were split into a training set to build the model and a test set to validate the model. A multivariable logistic regression model modeling 30-day postoperative mortality was trained on the training set using all available features. Next, a risk calculator using preoperative features was developed for 30-day mortality. The results of this model were converted into a score-based risk calculator. A point-based risk calculator was developed that predicted 30-day postoperative mortality in patients who underwent hepatectomy surgery. RESULTS: The final dataset included 38,561 patients who underwent hepatectomy. The data were then split into a training set from 2014 to 2018 (n = 26,397) and test set from 2019 to 2020 (n = 12,164). Nine independent variables associated with postoperative mortality were identified and included age, diabetes, sex, sodium, albumin, bilirubin, serum glutamic-oxaloacetic transaminase (SGOT), international normalized ratio, and American Society of Anesthesiologists classification score. Each of these features were then assigned points for a risk calculator based on their odds ratio. A univariate logistic regression model using total points as independent variables were trained on the training set and then validated on the test set. The area under the receiver operating characteristics curve on the test set was 0.719 (95% confidence interval, 0.681-0.757). CONCLUSIONS: Development of risk calculators may potentially allow surgical and anesthesia providers to provide a more transparent plan to support patients planned for hepatectomy.

Development and benchmarking of machine learning models to classify patients suitable for outpatient lower extremity joint arthroplasty.

STUDY OBJECTIVE: Performing hip or knee arthroplasty as an outpatient surgery has been shown to be operationally and financially beneficial for selected patients. By applying machine learning models to predict patients suitable for outpatient arthroplasty, health care systems can better utilize resources efficiently. The goal of this study was to develop predictive models for identifying patients likely to be discharged same-day following hip or knee arthroplasty. DESIGN: Model performance was assessed with 10-fold stratified cross-validation, evaluated over baseline determined by the proportion of eligible outpatient arthroplasty over sample size. The models used for classification were logistic regression, support vector classifier, balanced random forest, balanced bagging XGBoost classifier, and balanced bagging LightGBM classifier. SETTING: The patient records were sampled from arthroplasty procedures at a single institution from October 2013 to November 2021. PATIENTS: The electronic intake records of 7322 knee and hip arthroplasty patients were sampled for the dataset. After data processing, 5523 records were kept for model training and validation. INTERVENTIONS: None. MEASUREMENTS: The primary measures for the models were the F1-score, area under the receiver operating characteristic curve (ROCAUC), and area under the precision-recall curve. To measure feature importance, the SHapley Additive exPlanations value (SHAP) were reported from the model with the highest F1-score. RESULTS: The best performing classifier (balanced random forest classifier) achieved an F1-score of 0.347: an improvement of 0.174 over baseline and 0.031 over logistic regression. The ROCAUC for this model was 0.734. Using SHAP, the top determinant features of the model included patient sex, surgical approach, surgery type, and body mass index. CONCLUSIONS: Machine learning models may utilize electronic health records to screen arthroplasty procedures for outpatient eligibility. Tree-based models demonstrated superior performance in this study.

A Neural Network Model Using Pain Score Patterns to Predict the Need for Outpatient Opioid Refills Following Ambulatory Surgery: Algorithm Development and Validation.

BACKGROUND: Expansion of clinical guidance tools is crucial to identify patients at risk of requiring an opioid refill after outpatient surgery. OBJECTIVE: The objective of this study was to develop machine learning algorithms incorporating pain and opioid features to predict the need for outpatient opioid refills following ambulatory surgery. METHODS: Neural networks, regression, random forest, and a support vector machine were used to evaluate the data set. For each model, oversampling and undersampling techniques were implemented to balance the data set. Hyperparameter tuning based on k-fold cross-validation was performed, and feature importance was ranked based on a Shapley Additive Explanations (SHAP) explainer model. To assess performance, we calculated the average area under the receiver operating characteristics curve (AUC), F1-score, sensitivity, and specificity for each model. RESULTS: There were 1333 patients, of whom 144 (10.8%) refilled their opioid prescription within 2 weeks after outpatient surgery. The average AUC calculated from k-fold cross-validation was 0.71 for the neural network model. When the model was validated on the test set, the AUC was 0.75. The features with the highest impact on model output were performance of a regional nerve block, postanesthesia care unit maximum pain score, postanesthesia care unit median pain score, active smoking history, and total perioperative opioid consumption. CONCLUSIONS: Applying machine learning algorithms allows providers to better predict outcomes that require specialized health care resources such as transitional pain clinics. This model can aid as a clinical decision support for early identification of at-risk patients who may benefit from transitional pain clinic care perioperatively in ambulatory surgery.

An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation.

BACKGROUND: Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration. OBJECTIVE: The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration. METHODS: We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R2, root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance. RESULTS: A total of 3189 patients who underwent spine surgery were included. The institution's current method of predicting case times has a very poor coefficient of determination with actual times (R2=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R2 of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R2 of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model. CONCLUSIONS: Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.

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.

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.

Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center.

BACKGROUND: Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression. METHODS: Data were collected from patients at an ambulatory surgery center. The primary outcome measurement was determined to have a value of 1 (versus 0) if they met both criteria: (1) surgery ends by 5 pm and (2) patient is discharged from the recovery room by 7 pm. We developed models to determine if a procedure would meet both criteria if it were scheduled at 1 pm, 2 pm, 3 pm, or 4 pm. We implemented regression, random forest, balanced random forest, balanced bagging, neural network, and support vector classifier, and included the following features: surgery, surgeon, service line, American Society of Anesthesiologists score, age, sex, weight, and scheduled case duration. We evaluated model performance with Synthetic Minority Oversampling Technique (SMOTE). We compared the following performance metrics: F1 score, area under the receiver operating characteristic curve (AUC), specificity, sensitivity, precision, recall, and Matthews correlation coefficient. RESULTS: Among 13,447 surgical procedures, the median total perioperative time (actual case duration and PACU length stay) was 165 minutes. When SMOTE was not used, when predicting whether surgery will end by 5 pm and patient will be discharged by 7 pm, the average F1 scores were best with random forest, balanced bagging, and balanced random forest classifiers. When SMOTE was used, these models had improved F1 scores compared to no SMOTE. The balanced bagging classifier performed best with F1 score of 0.78, 0.80, 0.82, and 0.82 when predicting our outcome if cases were to start at 1 pm, 2 pm, 3 pm, or 4 pm, respectively. CONCLUSIONS: We demonstrated improvement in predicting the outcome at a range of start times when using ensemble learning versus regression techniques. Machine learning may be adapted by operating room management to allow for a better determination whether an add-on case at an outpatient surgery center could be appropriately booked.

Leptin Protects Against the Development and Expression of Cocaine Addiction-Like Behavior in Heterogeneous Stock Rats.

Cocaine affects food intake, metabolism and bodyweight. It has been hypothesized that feeding hormones like leptin play a role in this process. Preclinical studies have shown a mutually inhibitory relationship between leptin and cocaine, with leptin also decreasing the rewarding effects of cocaine intake. But prior studies have used relatively small sample sizes and did not investigate individual differences in genetically heterogeneous populations. Here, we examined whether the role of individual differences in bodyweight and blood leptin level are associated with high or low vulnerability to addiction-like behaviors using data from 306 heterogeneous stock rats given extended access to intravenous self-administration of cocaine and 120 blood samples from 60 of these animals, that were stored in the Cocaine Biobank. Finally, we tested a separate cohort to evaluate the causal effect of exogenous leptin administration on cocaine seeking. Bodyweight was reduced due to cocaine self-administration in males during withdrawal and abstinence, but was increased in females during abstinence. However, bodyweight was not correlated with addiction-like behavior vulnerability. Blood leptin levels after ∼6 weeks of cocaine self-administration did not correlate with addiction-like behaviors, however, baseline blood leptin levels before any access to cocaine negatively predicted addiction-like behaviors 6 weeks later. Finally, leptin administration in a separate cohort of 59 animals reduced cocaine seeking in acute withdrawal and after 7 weeks of protracted abstinence. These results demonstrate that high blood leptin level before access to cocaine may be a protective factor against the development of cocaine addiction-like behavior and that exogenous leptin reduces the motivation to take and seek cocaine. On the other hand, these results also show that blood leptin level and bodyweight changes in current users are not relevant biomarkers for addiction-like behaviors.

Machine learning approach to predicting persistent opioid use following lower extremity joint arthroplasty.

BACKGROUND: The objective of this study is to develop predictive models for persistent opioid use following lower extremity joint arthroplasty and determine if ensemble learning and an oversampling technique may improve model performance. METHODS: We compared various predictive models to identify at-risk patients for persistent postoperative opioid use using various preoperative, intraoperative, and postoperative data, including surgical procedure, patient demographics/characteristics, past surgical history, opioid use history, comorbidities, lifestyle habits, anesthesia details, and postoperative hospital course. Six classification models were evaluated: logistic regression, random forest classifier, simple-feedforward neural network, balanced random forest classifier, balanced bagging classifier, and support vector classifier. Performance with Synthetic Minority Oversampling Technique (SMOTE) was also evaluated. Repeated stratified k-fold cross-validation was implemented to calculate F1-scores and area under the receiver operating characteristics curve (AUC). RESULTS: There were 1042 patients undergoing elective knee or hip arthroplasty in which 242 (23.2%) reported persistent opioid use. Without SMOTE, the logistic regression model has an F1 score of 0.47 and an AUC of 0.79. All ensemble methods performed better, with the balanced bagging classifier having an F1 score of 0.80 and an AUC of 0.94. SMOTE improved performance of all models based on F1 score. Specifically, performance of the balanced bagging classifier improved to an F1 score of 0.84 and an AUC of 0.96. The features with the highest importance in the balanced bagging model were postoperative day 1 opioid use, body mass index, age, preoperative opioid use, prescribed opioids at discharge, and hospital length of stay. CONCLUSIONS: Ensemble learning can dramatically improve predictive models for persistent opioid use. Accurate and early identification of high-risk patients can play a role in clinical decision making and early optimization with personalized interventions.

Drugs and Bugs: The Gut-Brain Axis and Substance Use Disorders.

Substance use disorders (SUDs) represent a significant public health crisis. Worldwide, 5.4% of the global disease burden is attributed to SUDs and alcohol use, and many more use psychoactive substances recreationally. Often associated with comorbidities, SUDs result in changes to both brain function and physiological responses. Mounting evidence calls for a precision approach for the treatment and diagnosis of SUDs, and the gut microbiome is emerging as a contributor to such disorders. Over the last few centuries, modern lifestyles, diets, and medical care have altered the health of the microbes that live in and on our bodies; as we develop, our diets and lifestyle dictate which microbes flourish and which microbes vanish. An increase in antibiotic treatments, with many antibiotic interventions occurring early in life during the microbiome's normal development, transforms developing microbial communities. Links have been made between the microbiome and SUDs, and the microbiome and conditions that are often comorbid with SUDs such as anxiety, depression, pain, and stress. A better understanding of the mechanisms influencing behavioral changes and drug use is critical in developing novel treatments for SUDSs. Targeting the microbiome as a therapeutic and diagnostic tool is a promising avenue of exploration. This review will provide an overview of the role of the gut-brain axis in a wide range of SUDs, discuss host and microbe pathways that mediate changes in the brain's response to drugs, and the microbes and related metabolites that impact behavior and health within the gut-brain axis.

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

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.

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

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 Cocaine and Oxycodone Biobanks, Two Repositories from Genetically Diverse and Behaviorally Characterized Rats for the Study of Addiction.

The rat oxycodone and cocaine biobanks contain samples that vary by genotypes (by using genetically diverse genotyped HS rats), phenotypes (by measuring addiction-like behaviors in an advanced SA model), timepoints (samples are collected longitudinally before, during, and after SA, and terminally at three different timepoints in the addiction cycle: intoxication, withdrawal, and abstinence or without exposure to drugs through age-matched naive rats), samples collected (organs, cells, biofluids, feces), preservation (paraformaldehyde-fixed, snap-frozen, or cryopreserved) and application (proteomics, transcriptomics, microbiomics, metabolomics, epigenetics, anatomy, circuitry analysis, biomarker discovery, etc.Substance use disorders (SUDs) are pervasive in our society and have substantial personal and socioeconomical costs. A critical hurdle in identifying biomarkers and novel targets for medication development is the lack of resources for obtaining biological samples with a detailed behavioral characterization of SUD. Moreover, it is nearly impossible to find longitudinal samples. As part of two ongoing large-scale behavioral genetic studies in heterogeneous stock (HS) rats, we have created two preclinical biobanks using well-validated long access (LgA) models of intravenous cocaine and oxycodone self-administration (SA) and comprehensive characterization of addiction-related behaviors. The genetic diversity in HS rats mimics diversity in the human population and includes individuals that are vulnerable or resilient to compulsive-like responding for cocaine or oxycodone. Longitudinal samples are collected throughout the experiment, before exposure to the drug, during intoxication, acute withdrawal, and protracted abstinence, and include naive, age-matched controls. Samples include, but are not limited to, blood plasma, feces and urine, whole brains, brain slices and punches, kidney, liver, spleen, ovary, testis, and adrenal glands. Three preservation methods (fixed in formaldehyde, snap-frozen, or cryopreserved) are used to facilitate diverse downstream applications such as proteomics, metabolomics, transcriptomics, epigenomics, microbiomics, neuroanatomy, biomarker discovery, and other cellular and molecular approaches. To date, >20,000 samples have been collected from over 1000 unique animals and made available free of charge to non-profit institutions through https://www.cocainebiobank.org/ and https://www.oxycodonebiobank.org/.

Depletion of the Microbiome Alters the Recruitment of Neuronal Ensembles of Oxycodone Intoxication and Withdrawal.

Substance use disorders have a complex etiology. Genetics, the environment, and behavior all play a role in the initiation, escalation, and relapse of drug use. Recently, opioid use disorder has become a national health crisis. One aspect of opioid addiction that has yet to be fully examined is the effects of alterations of the microbiome and gut-brain axis signaling on central nervous system activity during opioid intoxication and withdrawal. The effect of microbiome depletion on the activation of neuronal ensembles was measured by detecting Fos-positive (Fos+) neuron activation during intoxication and withdrawal using a rat model of oxycodone dependence. Daily oxycodone administration (2 mg/kg) increased pain thresholds and increased Fos+ neurons in the basolateral amygdala (BLA) during intoxication, with a decrease in pain thresholds and increase in Fos+ neurons in the periaqueductal gray (PAG), central nucleus of the amygdala (CeA), locus coeruleus (LC), paraventricular nucleus of the thalamus (PVT), agranular insular cortex (AI), bed nucleus of the stria terminalis (BNST), and lateral habenula medial parvocellular region during withdrawal. Microbiome depletion produced widespread but region- and state-specific changes in neuronal ensemble activation. Oxycodone intoxication and withdrawal also increased functional connectivity among brain regions. Microbiome depletion resulted in a decorrelation of this functional network. These data indicate that microbiome depletion by antibiotics produces widespread changes in the recruitment of neuronal ensembles that are activated by oxycodone intoxication and withdrawal, suggesting that the gut microbiome may play a role in opioid use and dependence. Future studies are needed to better understand the molecular, neurobiological, and behavioral effects of microbiome depletion on addiction-like behaviors.