Naloxone's dose-dependent displacement of [11C]carfentanil and duration of receptor occupancy in the rat brain.

The continuous rise in opioid overdoses in the United States is predominantly driven by very potent synthetic opioids, mostly fentanyl and its derivatives (fentanyls). Although naloxone (NLX) has been shown to effectively reverse overdoses by conventional opioids, there may be a need for higher or repeated doses of NLX to revert overdoses from highly potent fentanyls. Here, we used positron emission tomography (PET) to assess NLX's dose-dependence on both its rate of displacement of [11C]carfentanil ([11C]CFN) binding and its duration of mu opioid receptor (MOR) occupancy in the male rat brain. We showed that clinically relevant doses of intravenously (IV) administered NLX (0.035 mg/kg, Human Equivalent Dose (HED) 0.4 mg; 0.17 mg/kg, HED 2 mg) rapidly displaced the specific binding of [11C]CFN in the thalamus in a dose-dependent manner. Brain MOR occupancy by IV NLX was greater than 90% at 5 min after NLX administration for both doses, but at 27.3 min after 0.035 mg/kg dose and at 85 min after 0.17 mg/kg NLX, only 50% occupancy remained. This indicates that the duration of NLX occupancy at MORs is short-lived. Overall, these results show that clinically relevant doses of IV NLX can promptly displace fentanyls at brain MORs, but repeated or higher NLX doses may be required to prevent re-narcotization following overdoses with long-acting fentanyls.

Discovery of Highly Potent Adenosine A1 Receptor Agonists: Targeting Positron Emission Tomography Probes.

Adenosine receptor (AR) radiotracers for positron emission tomography (PET) have provided knowledge on the in vivo biodistribution of ARs in the central nervous system (CNS), which is of therapeutic interest for various neuropsychiatric disorders. Additionally, radioligands that can image changes in endogenous adenosine levels in different physiological and pathological conditions are still lacking. The binding of known antagonist adenosine A1 receptor (A1R) radiotracer, [11C]MDPX, failed to be inhibited by elevated endogenous adenosine in a rodent PET study. Since most of the known AR PET radiotracers were antagonists, we propose that an A1R agonist radioligand may possess higher sensitivity to measure changes in endogenous adenosine concentration. Herein, we report our latest findings toward the development of a full agonist adenosine A1 radioligand for PET. Based on a 3,5-dicyanopyridine template, 16 new derivatives were designed and synthesized to optimize both binding affinity and functional activity, resulting in two full agonists (compounds 27 and 29) with single-digit nanomolar affinities and good subtype selectivity (A1/A2A selectivity of ∼1000-fold for compound 27 and 29-fold for compound 29). Rapid O-[11C]methylation provided [11C]27 and [11C]29 in high radiochemical yields and radiochemical purity. However, subsequent brain PET imaging in rodents showed poor brain permeability for both radioligands. An in vivo PET study using knockout mice for MDR 1a/a, BCRP, and MRP1 indicated that these compounds might be substrates for brain efflux pumps. In addition, in silico evaluation using multiparameter optimization identified high molecular weight and high polar surface area as the main molecular descriptors responsible for low brain penetration. These results will provide further insight toward development of full agonist adenosine A1 radioligands and also highly potent CNS A1AR drugs.

Phenotypic characterization of frontal cortex microglia in a rat model of post-traumatic stress disorder.

INTRODUCTION: Post-traumatic stress disorder (PTSD) is an anxiety disorder induced by psychologically traumatic events. Using a rat model, this study aimed to determine whether psychological trauma alters relative expression between pro-inflammatory and anti-inflammatory markers in microglia. To meet this goal, expression of genes encoding i-NOS, arginase, TNF-α, interleukin-10, CD74, and Mannose Receptor C was analyzed on multiple days following trauma exposure. METHODS: Single-prolonged stress (SPS) was used to model PTSD in male Sprague-Dawley rats. Twenty-four rats (12 Controls and 12 SPS-exposed) were sacrificed on Days 1, 3, and 7 post-SPS. Twenty-four (12 Controls and 12 SPS-exposed) additional rats were exposed to classical fear conditioning on Day 7, and fear extinction on Days 8, 9, 10, 15, 16, and 17. Freezing behavior was measured to assess fear resolution. Microglial isolates were collected from the frontal cortex, and RNA was extracted. Changes in relative expression of target genes were quantified via RT-PCR. RESULTS: SPS rats showed significant decreases in IL-10 and TNF-α expression and increases in the i-NOS:Arginase and TNF-α:IL-10 ratios compared to Controls on Day 1, but not on Day 3 or Day 7 for any of the dependent variables. Day 17 SPS rats showed a significant decrease in IL-10 expression and an increase in the TNF-α:IL-10 ratio, further characterized by a significant inverse relationship between IL-10 expression and fear persistence. CONCLUSION: Psychological trauma impacts the immunological phenotype of microglia of the frontal cortex. Consequently, future studies should further evaluate the mechanistic role of microglia in PTSD pathology.

Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types.

In recent years, great strides have been made for causal structure learning in the high-dimensional setting and in the mixed data-type setting when there are both discrete and continuous variables. However, due to the complications involved with modeling continuous-discrete variable interactions, the intersection of these two settings has been relatively understudied. The current paper explores the problem of efficiently extending causal structure learning algorithms to high-dimensional data with mixed data-types. First, we characterize a model over continuous and discrete variables. Second, we derive a degenerate Gaussian (DG) score for mixed data-types and discuss its asymptotic properties. Lastly, we demonstrate the practicality of the DG score on learning causal structures from simulated data sets.

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods.

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis.

MOTIVATION: Integration of data from different modalities is a necessary step for multi-scale data analysis in many fields, including biomedical research and systems biology. Directed graphical models offer an attractive tool for this problem because they can represent both the complex, multivariate probability distributions and the causal pathways influencing the system. Graphical models learned from biomedical data can be used for classification, biomarker selection and functional analysis, while revealing the underlying network structure and thus allowing for arbitrary likelihood queries over the data. RESULTS: In this paper, we present and test new methods for finding directed graphs over mixed data types (continuous and discrete variables). We used this new algorithm, CausalMGM, to identify variables directly linked to disease diagnosis and progression in various multi-modal datasets, including clinical datasets from chronic obstructive pulmonary disease (COPD). COPD is the third leading cause of death and a major cause of disability and thus determining the factors that cause longitudinal lung function decline is very important. Applied on a COPD dataset, mixed graphical models were able to confirm and extend previously described causal effects and provide new insights on the factors that potentially affect the longitudinal lung function decline of COPD patients. AVAILABILITY AND IMPLEMENTATION: The CausalMGM package is available on http://www.causalmgm.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Preclinical Evaluation of the First Adenosine A1 Receptor Partial Agonist Radioligand for Positron Emission Tomography Imaging.

Central adenosine A1 receptor (A1R) is implicated in pain, sleep, substance use disorders, and neurodegenerative diseases, and is an important target for pharmaceutical development. Radiotracers for A1R positron emission tomography (PET) would enable measurement of the dynamic interaction of endogenous adenosine and A1R during the sleep-awake cycle. Although several human A1R PET tracers have been developed, most are xanthine-based antagonists that failed to demonstrate competitive binding against endogenous adenosine. Herein, we explored non-nucleoside (3,5-dicyanopyridine and 5-cyanopyrimidine) templates for developing an agonist A1R PET radiotracer. We synthesized novel analogues, including 2-amino-4-(3-methoxyphenyl)-6-(2-(6-methylpyridin-2-yl)ethyl)pyridine-3,5-dicarbonitrile (MMPD, 22b), a partial A1R agonist of sub-nanomolar affinity. [11C]22b showed suitable blood-brain barrier (BBB) permeability and test-retest reproducibility. Regional brain uptake of [11C]22b was consistent with known brain A1R distribution and was blocked significantly by A1R but not A2AR ligands. [11C]22b is the first BBB-permeable A1R partial agonist PET radiotracer with the promise of detecting endogenous adenosine fluctuations.

Comparison of strategies for scalable causal discovery of latent variable models from mixed data.

Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data ("Mixed Data"), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inference algorithms can identify important relationships from biomedical data; however, handling the challenges of causal inference over mixed data with unmeasured confounders in a scalable way is still an open problem. Despite recent advances into causal discovery strategies that could potentially handle these challenges; individually, no study currently exists that comprehensively compares these approaches in this setting. In this paper, we present a comparative study that addresses this problem by comparing the accuracy and efficiency of different strategies in large, mixed datasets with latent confounders. We experiment with two extensions of the Fast Causal Inference algorithm: a maximum probability search procedure we recently developed to identify causal orientations more accurately, and a strategy which quickly eliminates unlikely adjacencies in order to achieve scalability to high-dimensional data. We demonstrate that these methods significantly outperform the state of the art in the field by achieving both accurate edge orientations and tractable running time in simulation experiments on datasets with up to 500 variables. Finally, we demonstrate the usability of the best performing approach on real data by applying it to a biomedical dataset of HIV-infected individuals.

Scoring Bayesian Networks of Mixed Variables.

In this paper we outline two novel scoring methods for learning Bayesian networks in the presence of both continuous and discrete variables, that is, mixed variables. While much work has been done in the domain of automated Bayesian network learning, few studies have investigated this task in the presence of both continuous and discrete variables while focusing on scalability. Our goal is to provide two novel and scalable scoring functions capable of handling mixed variables. The first method, the Conditional Gaussian (CG) score, provides a highly efficient option. The second method, the Mixed Variable Polynomial (MVP) score, allows for a wider range of modeled relationships, including non-linearity, but it is slower than CG. Both methods calculate log likelihood and degrees of freedom terms, which are incorporated into a Bayesian Information Criterion (BIC) score. Additionally, we introduce a structure prior for efficient learning of large networks and a simplification in scoring the discrete case which performs well empirically. While the core of this work focuses on applications in the search and score paradigm, we also show how the introduced scoring functions may be readily adapted as conditional independence tests for constraint-based Bayesian network learning algorithms. Lastly, we describe ways to simulate networks of mixed variable types and evaluate our proposed methods on such simulations.

Outcomes of Community-Based Prenatal Education Programs for Pregnant Women in Rural Texas.

A prenatal, evidenced-based education program was implemented in 7 rural counties and provided by trained staff at the Texas Department of State Health Services. This was implemented to address health disparities, in regard to birth outcomes, in rural minorities of Southeast Texas. The participants were given a preassessment (N = 382) and a postassessment (N = 326) of relevant health knowledge and a follow-up assessment (N = 149) to document the outcomes of their birth as well as health practices they were employing as new parents. The assessment results were analyzed to determine the effectiveness of the programs on improving health outcomes and knowledge.

A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images.

We describe two modifications that parallelize and reorganize caching in the well-known Greedy Equivalence Search (GES) algorithm for discovering directed acyclic graphs on random variables from sample values. We apply one of these modifications, the Fast Greedy Search (FGS) assuming faithfulness, to an i.i.d. sample of 1,000 units to recover with high precision and good recall an average degree 2 directed acyclic graph (DAG) with one million Gaussian variables. We describe a modification of the algorithm to rapidly find the Markov Blanket of any variable in a high dimensional system. Using 51,000 voxels that parcellate an entire human cortex, we apply the FGS algorithm to Blood Oxygenation Level Dependent (BOLD) time series obtained from resting state fMRI.

Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints.

Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score multiple models, outputting the most probable one; however, they are often computationally infeasible to apply when modeling latent variables. We introduce a hybrid method that derives a Bayesian probability that the set of independence tests associated with a given causal model are jointly correct. Using this constraint-based scoring method, we are able to score multiple causal models, which possibly contain latent variables, and output the most probable one. The structure-discovery performance of the proposed method is compared to an existing constraint-based method (RFCI) using data generated from several previously published Bayesian networks. The structural Hamming distances of the output models improved when using the proposed method compared to RFCI, especially for small sample sizes.

Causal Clustering for 1-Factor Measurement Models.

Many scientific research programs aim to learn the causal structure of real world phenomena. This learning problem is made more difficult when the target of study cannot be directly observed. One strategy commonly used by social scientists is to create measurable "indicator" variables that covary with the latent variables of interest. Before leveraging the indicator variables to learn about the latent variables, however, one needs a measurement model of the causal relations between the indicators and their corresponding latents. These measurement models are a special class of Bayesian networks. This paper addresses the problem of reliably inferring measurement models from measured indicators, without prior knowledge of the causal relations or the number of latent variables. We present a provably correct novel algorithm, FindOneFactorClusters (FOFC), for solving this inference problem. Compared to other state of the art algorithms, FOFC is faster, scales to larger sets of indicators, and is more reliable at small sample sizes. We also present the first correctness proofs for this problem that do not assume linearity or acyclicity among the latent variables.

Translating a gene expression signature for multiple myeloma prognosis into a robust high-throughput assay for clinical use.

BACKGROUND: Widespread adoption of genomic technologies in the management of heterogeneous indications, including Multiple Myeloma, has been hindered by concern over variation between published gene expression signatures, difficulty in physician interpretation and the challenge of obtaining sufficient genetic material from limited patient specimens. METHODS: Since 2006, the 70-gene prognostic signature, developed by the University of Arkansas for Medical Sciences (UAMS) has been applied to over 4,700 patients in studies performed in 4 countries and described in 17 peer-reviewed publications. Analysis of control sample and quality control data compiled over a 12-month period was performed. RESULTS: Over a 12 month period, the 70-gene prognosis score (range 0-100) of our multiple myeloma cell-line control sample had a standard deviation of 2.72 and a coefficient of variance of 0.03. The whole-genome microarray profile used to calculate a patient's GEP70 score can be generated with as little as 15 ng of total RNA; approximately 30,000 CD-138+ plasma cells. Results from each GEP70 analysis are presented as either low (70-gene score <45.2) or high (≥45.2) risk for relapse (newly diagnosed setting) or shorter overall survival (relapse setting). A personalized and outcome-annotated gene expression heat map is provided to assist in the clinical interpretation of the result. CONCLUSIONS: The 70-gene assay, commercialized under the name 'MyPRS®' (Myeloma Prognostic Risk Score) and performed in Signal Genetics' CLIA-certified high throughput flow-cytometry and molecular profiling laboratory is a reproducible and standardized method of multiple myeloma prognostication.

Non-Gaussian methods and high-pass filters in the estimation of effective connections.

We consider several alternative ways of exploiting non-Gaussian distributional features, including some that can in principle identify direct, positive feedback relations (graphically, 2-cycles) and combinations of methods that can identify high dimensional graphs. All of the procedures are implemented in the TETRAD freeware (Ramsey et al., 2013). We show that in most cases the limited accuracy of the several non-Gaussian methods in the Smith et al. (2011) simulations can be attributed to the high-pass Butterworth filter used in that study. Without that filter, or with the filter in the widely used FSL program (Jenkinson et al., 2012), the directional accuracies of several of the non-Gaussian methods are at or near ceiling in many conditions of the Smith et al. simulation. We show that the improvement of an apparently Gaussian method (Patel et al., 2006) when filtering is removed is due to non-Gaussian features of that method introduced by the Smith et al. implementation. We also investigate some conditions in which multi-subject data help with causal structure identification using higher moments, notably with non-stationary time series or with 2-cycles. We illustrate the accuracy of the methods with more complex graphs with and without 2-cycles, and with a 500 node graph; to illustrate applicability and provide a further test we apply the methods to an empirical case for which aspects of the causal structure are known. Finally, we note a number of cautions and issues that remain to be investigated, and some outstanding problems for determining the structure of effective connections from fMRI data.

Bayesian networks for fMRI: a primer.

Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches are more attractive than other functional connectivity analyses in that they can often operate on larger sets of nodes and run searches over a wide range of candidate networks. An important study by Smith et al. (2011) illustrated that many Bayesian network approaches did not perform well in identifying the directionality of connections in simulated single-subject data. Since then, new Bayesian network approaches have been developed that have overcome the failures in the Smith work. Additionally, an important discovery was made that shows a preprocessing step used in the Smith data puts some of the Bayesian network methods at a disadvantage. This work provides a review of Bayesian network analyses, focusing on the methods used in the Smith work as well as methods developed since 2011 that have improved estimation performance. Importantly, only approaches that have been specifically designed for fMRI data perform well, as they have been tailored to meet the challenges of fMRI data. Although this work does not suggest a single best model, it describes the class of models that perform best and highlights the features of these models that allow them to perform well on fMRI data. Specifically, methods that rely on non-Gaussianity to direct causal relationships in the network perform well.

Atypical effective connectivity of social brain networks in individuals with autism.

Failing to engage in joint attention is a strong marker of impaired social cognition associated with autism spectrum disorder (ASD). The goal of this study was to localize the source of impaired joint attention in individuals with ASD by examining both behavioral and fMRI data collected during various tasks involving eye gaze, directional cuing, and face processing. The tasks were designed to engage three brain networks associated with social cognition [face processing, theory of mind (TOM), and action understanding]. The behavioral results indicate that even high-functioning individuals with ASD perform less accurately and more slowly than neurotypical (NT) controls when processing eyes, but not when processing a directional cue (an arrow) that did not involve eyes. Behavioral differences between the NT and ASD groups were consistent with differences in the effective connectivity of FACE, TOM, and ACTION networks. An independent multiple-sample greedy equivalence search was used to examine these social brain networks and found that whereas NTs produced stable patterns of response across tasks designed to engage a given brain network, ASD participants did not. Moreover, ASD participants recruited all three networks in a manner highly dissimilar to that of NTs. These results extend a growing literature that describes disruptions in general brain connectivity in individuals with autism by targeting specific networks hypothesized to underlie the social cognitive impairments observed in these individuals.

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study.

Smith et al. report a large study of the accuracy of 38 search procedures for recovering effective connections in simulations of DCM models under 28 different conditions. Their results are disappointing: no method reliably finds and directs connections without large false negatives, large false positives, or both. Using multiple subject inputs, we apply a previously published search algorithm, IMaGES, and novel orientation algorithms, LOFS, in tandem to all of the simulations of DCM models described by Smith et al. (2011). We find that the procedures accurately identify effective connections in almost all of the conditions that Smith et al. simulated and, in most conditions, direct causal connections with precision greater than 90% and recall greater than 80%.

Network modelling methods for FMRI.

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution.