Biology subjects

Clark, D. A.

Publications and source records attributed to Clark, D. A..

5 recordsLinked to original sources

bioRxiv2020

Connectomic Alterations Linked to Transdiagnostic Risk for Psychopathology at the Transition to Adolescence

BACKGROUNDConvergent research identifies a general factor ("P factor") that confers transdiagnostic risk for psychopathology. However, brain functional connectivity patterns that underpin the P factor remain poorly understood, especially at the transition to adolescence when many serious mental disorders have their onset. OBJECTIVEIdentify a distributed connectome-wide neurosignature of the P factor and assess the generalizability of this neurosignature in held out samples. DESIGN, SETTING, AND PARTICIPANTSThis study used data from the full baseline wave of the Adolescent Brain and Cognitive Development (ABCD) national consortium study, a prospective, population-based study of 11,875 9- and 10-year olds. Data for this study were collected from September 1, 2016 to November 15, 2018 at 21 research sites across the United States. MAIN OUTCOMES AND MEASURESWe produced whole brain functional connectomes for 5,880 youth with high quality resting state scans. We then constructed a low rank basis set of 250 components that captures interindividual connectomic differences. Multi-level regression modeling was used to link these components to the P factor, and leave-one-site-out cross-validation was used to assess generalizability of P factor neurosignatures to held out subjects across 19 ABCD sites. RESULTSThe set of 250 connectomic components was highly statistically significantly related to the P factor, over and above nuisance covariates alone (ANOVA nested model comparison, incremental R-squared 6.05%, {chi}2(250) =412.1, p<4.6x10-10). In addition, two individual connectomic components were statistically significantly related to the P factor after Bonferroni correction for multiple comparisons (t(5511)= 4.8, p<1.4x10-06; t(5121)= 3.9, p<9.7x10-05). Functional connections linking control networks and default mode network were prominent in the P factor neurosignature. In leave-one-site-out cross-validation, the P factor neurosignature generalized to held out subjects (average correlation between actual and predicted P factor scores across 19 held out sites=0.13; pPERMUTATION<0.0001). Additionally, results remained significant after a number of robustness checks. CONCLUSIONS AND RELEVANCEThe general factor of psychopathology is associated with connectomic alterations involving control networks and default mode network. Brain imaging combined with network neuroscience can identify distributed and generalizable signatures of transdiagnostic risk for psychopathology during emerging adolescence.

neuroscience↗

bioRxiv2020

Polygenic Risk Score for Smoking is associated with Externalizing Psychopathology and Disinhibited Personality Traits but not Internalizing Psychopathology in Adolescence

ImportanceLarge consortia of genome wide association studies have yielded more accurate polygenic risk scores (PRS) that aggregate the small effects of many genetic variants to characterize the genetic architecture of disorders and provide a personalized measure of genetic risk. ObjectiveWe examined whether a PRS for smoking measured genetic risk for general behavioral disinhibition by estimating its associations with externalizing and internalizing psychopathology and related personality traits. We examined these associations at multiple time points in adolescence using more refined phenotypes defined by stable characteristics across time and at young ages, which reduced potential confounds associated with cumulative exposure to substances and reverse causality. MethodsRandom intercept panel models were fit to symptoms of conduct disorder, oppositional defiant disorder, major depressive disorder (MDD), and teacher ratings of externalizing and internalizing problems and personality traits at ages 11, 14, and 17 years-old in the Minnesota Twin Family Study (N = 3225). ResultsThe smoking PRS had strong associations with the random intercept factors for all the externalizing measures (mean standardized {beta} = .27), agreeableness ({beta}=-.22, 95% CI: -.28, -.16), and conscientiousness ({beta}=-.19, 95% CI: -.24, -.13), but was not significantly associated with the internalizing measures (mean {beta} = .06) or extraversion ({beta}=.01, 95% CI: -.05, .07). After controlling for smoking at age 17, the associations with the externalizing measures (mean {beta} = .13) and personality traits related to behavioral control (mean {beta} = -.10) remained statistically significant. Conclusions and RelevanceThe smoking PRS measures genetic influences that contribute to a spectrum of phenotypes related to behavioral disinhibition including externalizing psychopathology and normal-range personality traits related to behavioral control, but not internalizing psychopathology. Continuing to identify the correlates and delineate the mechanisms of the genetic influences associated with disinhibition could have substantial impact in mitigating a variety of public health problems (e.g., mental health, academic achievement, criminality). Key PointsO_ST_ABSQuestionC_ST_ABSDoes a polygenic risk scores (PRS) for smoking measure genetic risk for behavioral disinhibition in general? FindingsThe smoking PRS was associated with externalizing psychopathology and personality traits related to behavioral control, but not internalizing psychopathology and extraversion during adolescence, even after controlling for smoking status. MeaningThe smoking PRS measures genetic influences on behavioral disinhibition in general which is associated with a variety of important outcomes including mental health, academic success, and criminality.

genetics↗

bioRxiv2020

Polygenic Risk Scores Predict the Development of Alcohol and Nicotine Use Problems from Adolescence through Young Adulthood

ObjectiveMolecular genetic studies of alcohol and nicotine have identified many genome-wide loci. We examined the predictive utility of drinking and smoking polygenic scores (PGS) for alcohol and nicotine use from late childhood to early adulthood, substance-specific versus broader-liability PGS effects, and if PGS performance varied between consumption versus pathological use. MethodsLatent growth curve models with structured residuals were used to assess the predictive utility of drinks per week and regular smoking PGS for measures of alcohol and nicotine consumption and problematic use from age 14 to 34. PGSs were generated from the largest discovery sample for alcohol and nicotine use to date (i.e., GSCAN), and examined for associations with alcohol and nicotine use in the Minnesota Twin Family Study (N=3225). ResultsThe drinking PGS was a significant predictor of age 14 problematic alcohol use and increases in problematic use during young adulthood. The smoking PGS was a significant predictor for all nicotine use outcomes. After adjusting for the effects of both PGSs, the smoking PGS demonstrated incremental predictive utility for most alcohol use outcomes and remained a significant predictor of nicotine use trajectories. ConclusionsHigher PGS for drinking and smoking were associated with more problematic levels of substance use longitudinally. The smoking PGS seems to capture both nicotine-specific and non-specific genetic liability for substance use, and may index genetic risk for broader externalizing behavior. Validation of PGS within longitudinal designs may have important clinical implications should future studies support the clinical utility of PGS for substance use disorders.

genetics↗

bioRxiv2020

Integrating spatial capture-recapture models with variable individual identifiability

Spatial capture-recapture (SCR) models have become the preferred tool for estimating densities of carnivores. Within this family of models are variants requiring identification of all individuals in each encounter (SCR), a subset of individuals only (generalized spatial mark-resight, gSMR), or no individual identification (spatial count or spatial presence-absence). Although each technique has been shown through simulation to yield unbiased results, the consistency and relative precision of estimates across methods in real-world settings are seldom considered. We tested a suite of models ranging from those only requiring detections of unmarked individuals to others that integrate remote camera, physical capture, genetic, and global positioning system (GPS) data into a hybrid model, to estimate population densities of black bears, bobcats, cougars, and coyotes. For each species we genotyped fecal DNA collected with detection dogs during a 20-day period. A subset of individuals from each species was affixed with GPS collars bearing unique markings and resighted by remote cameras over 140 days contemporaneous with scat collection. Camera-based gSMR models produced density estimates that differed by less than 10% from genetic SCR for bears, cougars, and coyotes once important sources of variation (sex or behavioral status) were controlled for. For bobcats, SCR estimates were 33% higher than gSMR. The cause of the discrepancies in estimates was likely attributable to challenges designing a study compatible for species with disparate home range sizes and the difficulty of collecting sufficient data in a timeframe in which demographic closure could be assumed. Unmarked models estimated densities that varied greatly from SCR, but estimates became more consistent in models wherein more individuals were identifiable. Hybrid models containing all data sources exhibited the most precise estimates for all species. For studies in which only sparse data can be obtained and the strictest model assumptions are unlikely to be met, we suggest researchers use caution making inference from models lacking individual identity. For best results, we further recommend the use of methods requiring at least a subset of the population is marked and that multiple datasets are incorporated when possible.

ecology↗

bioRxiv2020

Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters

Insects find food, mates, and egg-laying sites by tracking odor plumes swept by complex wind patterns. Previous studies have shown that moths and flies localize plumes by surging upwind at odor onset and turning cross- or downwind at odor offset. Less clear is how, once within the expanding cone of the odor plume, insects use their brief encounters with individual odor packets, whose location and timing are random, to progress towards the source. Experiments and theory have suggested that the timing of odor encounters might assist navigation, but connecting behaviors to individual encounters has been challenging. Here, we imaged complex odor plumes simultaneous with freely-walking flies, allowing us to quantify how behavior is shaped by individual odor encounters. Combining measurements, dynamical models, and statistical inference, we found that within the plume cone, individual encounters did not trigger reflexive surging, casting, or counterturning. Instead, flies turned stochastically with stereotyped saccades, whose direction was biased upwind by the timing of prior odor encounters, while the magnitude and rate of saccades remained constant. Odor encounters did not strongly affect walking speed. Instead, flies used encounter timing to modulate the rate of transitions between walks and stops. When stopped, flies initiated walks using information from multiple odor encounters, suggesting that integrating evidence without losing position was part of the strategy. These results indicate that once within the complex odor plume, where odor location and timing are unpredictable, animals navigate with biased random walks shaped by the entire sequence of encounters.

neuroscience↗