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For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.
Multiplexed single-cell experimental techniques like mass cytometry measure 40 or more features and enable deep characterization of well-known and novel cell populations. However, traditional data analysis techniques rely extensively on human experts or prior knowledge, and novel machine learning algorithms may generate unexpected population groupings. Marker enrichment modeling (MEM) creates quantitative identity labels based on features enriched in a population relative to a reference. While developed for cell type analysis, MEM labels can be generated for a wide range of multidimensional data types, and MEM works effectively with output from expert analysis and diverse machine learning algorithms. MEM is implemented as an R package and includes three steps: (1) calculation of MEM values that quantify each feature's relative enrichment in the population, (2) reporting of MEM labels as a heatmap or as a text label, and (3) quantification of MEM label similarity between populations. The protocols here show MEM analysis using datasets from immunology and oncology. These MEM implementations provide a way to characterize population identity and novelty in the context of computational and expert analyses. © 2018 by John Wiley & Sons, Inc.
Copyright © 2018 John Wiley & Sons, Inc.
Before insulin can stimulate myocytes to take up glucose, it must first move from the circulation to the interstitial space. The continuous endothelium of skeletal muscle (SkM) capillaries restricts insulin's access to myocytes. The mechanism by which insulin crosses this continuous endothelium is critical to understand insulin action and insulin resistance; however, methodological obstacles have limited understanding of endothelial insulin transport in vivo. Here, we present an intravital microscopy technique to measure the rate of insulin efflux across the endothelium of SkM capillaries. This method involves development of a fully bioactive, fluorescent insulin probe, a gastrocnemius preparation for intravital microscopy, an automated vascular segmentation algorithm, and the use of mathematical models to estimate endothelial transport parameters. We combined direct visualization of insulin efflux from SkM capillaries with modeling of insulin efflux kinetics to identify fluid-phase transport as the major mode of transendothelial insulin efflux in mice. Model-independent experiments demonstrating that insulin movement is neither saturable nor affected by insulin receptor antagonism supported this result. Our finding that insulin enters the SkM interstitium by fluid-phase transport may have implications in the pathophysiology of SkM insulin resistance as well as in the treatment of diabetes with various insulin analogs.
BACKGROUND - The developmental propensity model of antisocial behavior posits that several dispositional characteristics of children transact with the environment to influence the likelihood of learning antisocial behavior across development. Specifically, greater dispositional negative emotionality, greater daring, and lower prosociality-operationally, the inverse of callousness- and lower cognitive abilities are each predicted to increase risk for developing antisocial behavior.
METHODS - Prospective tests of key predictions derived from the model were conducted in a high-risk sample of 499 twins who were assessed on dispositions at 10-17 years of age and assessed for antisocial personality disorder (APD) symptoms at 22-31 years of age. Predictions were tested separately for parent and youth informants on the dispositions using multiple regressions that adjusted for oversampling, nonresponse, and clustering within twin pairs, controlling demographic factors and time since the first assessment.
RESULTS - Consistent with predictions, greater numbers of APD symptoms in adulthood were independently predicted over a 10-15 year span by higher youth ratings on negative emotionality and daring and lower youth ratings on prosociality, and by parent ratings of greater negative emotionality and lower prosociality. A measure of working memory did not predict APD symptoms.
CONCLUSIONS - These findings support future research on the role of these dispositions in the development of antisocial behavior.
© 2017 Association for Child and Adolescent Mental Health.
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as "scanner effects", can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
Copyright © 2017 Elsevier Inc. All rights reserved.
Doxorubicin forms the basis of chemotherapy regimens for several malignancies, including triple negative breast cancer (TNBC). Here, we present a coupled experimental/modeling approach to establish an in vitro pharmacokinetic/pharmacodynamic model to describe how the concentration and duration of doxorubicin therapy shape subsequent cell population dynamics. This work features a series of longitudinal fluorescence microscopy experiments that characterize (1) doxorubicin uptake dynamics in a panel of TNBC cell lines, and (2) cell population response to doxorubicin over 30 days. We propose a treatment response model, fully parameterized with experimental imaging data, to describe doxorubicin uptake and predict subsequent population dynamics. We found that a three compartment model can describe doxorubicin pharmacokinetics, and pharmacokinetic parameters vary significantly among the cell lines investigated. The proposed model effectively captures population dynamics and translates well to a predictive framework. In a representative cell line (SUM-149PT) treated for 12 hours with doxorubicin, the mean percent errors of the best-fit and predicted models were 14% (±10%) and 16% (±12%), which are notable considering these statistics represent errors over 30 days following treatment. More generally, this work provides both a template for studies quantitatively investigating treatment response and a scalable approach toward predictions of tumor response in vivo.
PURPOSE - Some X-ray contrast agents contain exchangeable protons that give rise to exchange-based effects on MRI, including chemical exchange saturation transfer (CEST). However, CEST has poor specificity to explicit exchange parameters. Spin-lock sequences at high field are also sensitive to chemical exchange. Here, we evaluate whether spin-locking techniques can detect the contrast agent iohexol in vivo after intravenous administration, and their potential for measuring changes in tissue pH.
METHODS - Two metrics of contrast based on R , the spin lattice relaxation rate in the rotating frame, were derived from the behavior of R at different locking fields. Solutions containing iohexol at different concentrations and pH were used to evaluate the ability of the two metrics to quantify exchange effects. Images were also acquired from rat brains bearing tumors before and after intravenous injections of iohexol to evaluate the potential of spin-lock techniques for detecting the agent and pH variations.
RESULTS - The two metrics were found to depend separately on either agent concentration or pH. Spin-lock imaging may therefore provide specific quantification of iohexol concentration and the iohexol-water exchange rate, which reports on pH.
CONCLUSIONS - Spin-lock techniques may be used to assess the dynamics of intravenous contrast agents and detect extracellular acidification. Magn Reson Med 79:298-305, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
© 2017 International Society for Magnetic Resonance in Medicine.
Single particle tracking (SPT) experiments have provided the scientific community with invaluable single-molecule information about the dynamic regulation of individual receptors, transporters, kinases, lipids, and molecular motors. SPT is an alternative to ensemble averaging approaches, where heterogeneous modes of motion might be lost. Quantum dots (QDs) are excellent probes for SPT experiments due to their photostability, high brightness, and size-dependent, narrow emission spectra. In a typical QD-based SPT experiment, QDs are bound to the target of interest and imaged for seconds to minutes via fluorescence video microscopy. Single QD spots in individual frames are then linked to form trajectories that are analyzed to determine their mean square displacement, diffusion coefficient, confinement index, and instantaneous velocity. This chapter describes a generalizable protocol for the single particle tracking of membrane neurotransmitter transporters on cell membranes with either unmodified extracellular antibody probes and secondary antibody-conjugated quantum dots or biotinylated extracellular antibody probes and streptavidin-conjugated quantum dots in primary neuronal cultures. The neuronal cell culture, the biotinylation protocol and the quantum dot labeling procedures, as well as basic data analysis are discussed.
Historically, admissions committees for biomedical Ph.D. programs have heavily weighed GRE scores when considering applications for admission. The predictive validity of GRE scores on graduate student success is unclear, and there have been no recent investigations specifically on the relationship between general GRE scores and graduate student success in biomedical research. Data from Vanderbilt University Medical School's biomedical umbrella program were used to test to what extent GRE scores can predict outcomes in graduate school training when controlling for other admissions information. Overall, the GRE did not prove useful in predicating who will graduate with a Ph.D., pass the qualifying exam, have a shorter time to defense, deliver more conference presentations, publish more first author papers, or obtain an individual grant or fellowship. GRE scores were found to be moderate predictors of first semester grades, and weak to moderate predictors of graduate GPA and some elements of a faculty evaluation. These findings suggest admissions committees of biomedical doctoral programs should consider minimizing their reliance on GRE scores to predict the important measures of progress in the program and student productivity.