Biology subjects

Bowring, A.

Publications and source records attributed to Bowring, A..

6 recordsLinked to original sources

bioRxiv2026

Prenatal alcohol and cannabinoid co-exposure increases ethanol-seeking in adult offspring and produces sex-dependent medial prefrontal cannabinoid receptor 1 expression.

Background: Alcohol and cannabinoids are frequently used during pregnancy, however their combined effects on offspring outcomes are currently under-investigated. In this study, co-exposure was compared to single-drug and drug-free exposures for influences on adult alcohol-seeking and cannabinoid system proteomics. Methods: Pregnant C57BL/6J mice received ethanol vapor (ALC), CP-55,940 (0.75 mg/kg; CB), both (ALC+CB), or neither on gestational days 12-15. Beginning on Postnatal Day 200, male and female offspring underwent home-cage ethanol drinking (N=108) and operant self-administration, progressive-ratio, extinction, and reinstatement testing (N=96). CNR1 and CNRIP1 protein abundance was quantified in the medial prefrontal cortex (mPFC) and dorsomedial striatum (N=57) and compared to within-subject drinking behaviors. Results: Prenatal CB exposure increased cumulative home-cage ethanol intake, primarily in males. At 40% ethanol under progressive-ratio schedules, ALC+CB males consumed more than all other male groups, whereas all exposed female groups consumed more than controls. ALC+CB males also showed greater extinction responding than all other male groups and greater reinstatement intake relative to baseline than CON and ALC males. ALC+CB increased mPFC CNR1 in males relative to all other groups, whereas CB and ALC+CB reduced mPFC CNR1 in females. Higher mPFC CNR1 was associated with fixed-ratio and 40% progressive-ratio intake, extinction, and reinstatement exclusively in males. Conclusions: Prenatal co-exposure produced a persistent male phenotype of enhanced ethanol seeking that was distinct from either exposure alone and accompanied by elevated mPFC CNR1. This behavioral and molecular convergence justifies future investigations manipulating corticostriatal cannabinoid signaling to attenuate exposure-induced ethanol consumption in late adulthood.

developmental biology↗

bioRxiv2025

Early Life Outcomes of Prenatal Exposure to Alcohol and Synthetic Cannabinoids in Mice

This study explores the effects of prenatal co-exposure to alcohol and synthetic cannabinoids on offspring viability, physical development, and neurobehavioral outcomes in young adulthood. The aim is to identify distinct outcomes of co-exposure compared to single-drug exposures and to examine potential sex-specific vulnerabilities in motor coordination and exploratory behaviors. Pregnant C57Bl/6J mice were assigned to one of four treatment groups: Control, Alcohol-exposed, Cannabinoid-exposed, or Alcohol+Cannabinoid-exposed, with drug administration occurring between Gestational Days 12-15. Offspring were first evaluated at birth for survival, physical malformations, and developmental delays. Subsequently, young adult offspring were assessed for motor coordination using rotarod tests and exploratory behavior using open field tests. Our results indicate that alcohol and cannabinoid co-exposure significantly reduced offspring survival and litter sizes compared to controls. Non-viable offspring displayed craniofacial abnormalities, limb malformations, and developmental delays. Behavioral assessments in young adulthood demonstrated that all forms of prenatal drug exposure impaired motor coordination in males, while alcohol and cannabinoid exposures independently produced impairments in females. In the open field test, co-exposed male offspring exhibited reduced center exploration, indicative of anxiety-like behavior. Co-exposed offspring, regardless of sex, demonstrated hyperactivity, characterized by increased speed and distance traveled. Together, these findings underscore the heightened risks associated with prenatal polysubstance exposure, which exacerbates offspring mortality and induces sex-specific neurobehavioral deficits. This study highlights the distinct outcomes associated with prenatal co-exposure, and the need for future research to investigate underlying mechanisms driving these developmental disruptions and sex-specific susceptibilities. Structured AbstractO_ST_ABSPurposeC_ST_ABSThis study investigates the effects of prenatal co-exposure to alcohol and synthetic cannabinoids on offspring viability, physical development, and neurobehavioral outcomes in young adulthood. The goal of this investigation is to determine whether prenatal co-exposure produces distinct outcomes from single-drug exposures, including sex-specific vulnerabilities in motor coordination and exploratory behaviors. MethodsPregnant C57Bl/6J mice were randomly assigned to one of four treatment groups: drug-free controls, alcohol (ALC)-exposed, cannabinoid (CP-55,940, CB)-exposed or ALC+CB-exposed, with drug exposure occurring between Gestational Days 12-15. Offspring viability, physical malformations, and developmental delays were first assessed at birth. Then, behavioral evaluations, including rotarod and open field tests, were conducted on young adult offspring (Postnatal Days 100-120). ResultsALC+CB exposure significantly decreased litter survival (p = 0.006) and offspring viability compared to controls. Non-viable offspring exhibited craniofacial abnormalities, limb malformations, and developmental delays. Assessments of rotarod performance revealed that all exposures reduced motor coordination in males compared to controls (p < 0.05), while ALC and CB exposures alone produced this outcome in females. Open field tests indicated that ALC+CB exposure reduced time in the center of the arena in male offspring exclusively, while this same exposure increased hyperactivity compared to single-drug and control groups, independent of sex (p < 0.05). ConclusionsPrenatal co-exposure to alcohol and synthetic cannabinoids exacerbates offspring mortality and induces sex-specific deficits in neurobehavioral motor outcomes. These findings highlight the distinct risks of polysubstance exposure during pregnancy and underscore the need for targeted interventions to mitigate the effects of prenatal polysubstance exposure on offspring health outcomes. HighlightsO_LIAlcohol and cannabinoid co-exposure significantly reduces offspring survival. C_LIO_LIReduced survival is associated with offspring craniofacial and limb malformations. C_LIO_LIPrenatal exposures produce sex-specific impairments in motor coordination. C_LIO_LICo-exposed offspring display hyperactivity in young adulthood. C_LI

animal behavior and cognition↗

bioRxiv2021

Isolating the Sources of Pipeline-Variability in Group-Level Task-fMRI results

While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group-level results of a task-fMRI investigation (Bowring et al., 2019, BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL and SPM) across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e.g. choice of first-level signal model had the most impact for the ds000001 dataset, while first-level noise model was more influential for ds000109 dataset). We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group-level results were largely unaffected by which software package is used to model the low-frequency fMRI drifts.

neuroscience↗

bioRxiv2020

Spatial Confidence Sets for Standardized Effect Size Images

Current statistical inference methods for task-fMRI suffer from two fundamental limitations. First, the focus is solely on detection of non-zero signal or signal change, a problem that is exasperated for large scale studies (e.g. UK Biobank, N = 40, 000+) where the null hypothesis fallacy causes even trivial effects to be determined as significant. Second, for any sample size, widely used cluster inference methods only indicate regions where a null hypothesis can be rejected, without providing any notion of spatial uncertainty about the activation. In this work, we address these issues by developing spatial Confidence Sets (CSs) on clusters found in thresholded Cohens d effect size images. We produce an upper and lower CS to make confidence statements about brain regions where Cohens d effect sizes have exceeded and fallen short of a non-zero threshold, respectively. The CSs convey information about the magnitude and reliability of effect sizes that is usually given separately in a t-statistic and effect estimate map. We expand the theory developed in our previous work on CSs for %BOLD change effect maps (Bowring et al., 2019) using recent results from the bootstrapping literature. By assessing the empirical coverage with 2D and 3D Monte Carlo simulations resembling fMRI data, we find our method is accurate in sample sizes as low as N = 60. We compute Cohens d CSs for the Human Connectome Project working memory taskfMRI data, illustrating the brain regions with a reliable Cohens d response for a given threshold. By comparing the CSs with results obtained from a traditional statistical voxelwise inference, we highlight the improvement in activation localization that can be gained with the Confidence Sets.

neuroscience↗

bioRxiv2019

Spatial Confidence Sets for Raw Effect Size Images

The mass-univariate approach for functional magnetic resonance imagery (fMRI) analysis remains a widely used and fundamental statistical tool within neuroimaging. However, this method suffers from at least two fundamental limitations: First, with sample sizes growing to 4, 5 or even 6 digits, the entire approach is undermined by the null hypothesis fallacy, i.e. with sufficient sample size, there is high enough statistical power to reject the null hypothesis everywhere, making it difficult if not impossible to localize effects of interest. Second, with any sample size, when cluster-size inference is used a significant p-value only indicates that a cluster is larger than chance, and no notion of spatial uncertainty is provided. Therefore, no perception of confidence is available to express the size or location of a cluster that could be expected with repeated sampling from the population.\n\nIn this work, we address these issues by extending on a method proposed by Sommerfeld, Sain, and Schwartzman (2018) to develop spatial Confidence Sets (CSs) on clusters found in thresholded raw effect size maps. While hypothesis testing indicates where the null, i.e. a raw effect size of zero, can be rejected, the CSs give statements on the locations where raw effect sizes exceed, and fall short of, a non-zero threshold, providing both an upper and lower CS.\n\nWhile the method can be applied to any parameter in a mass-univariate General Linear Model, we motivate the method in the context of BOLD fMRI contrast maps for inference on percentage BOLD change raw effects. We propose several theoretical and practical implementation advancements to the original method in order to deliver an improved performance in small-sample settings. We validate the method with 3D Monte Carlo simulations that resemble fMRI data. Finally, we compute CSs for the Human Connectome Project working memory task contrast images, illustrating the brain regions that show a reliable %BOLD change for a given %BOLD threshold.

neuroscience↗

bioRxiv2018

Exploring the Impact of Analysis Software on Task fMRI Results

A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this methodological plurality comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable.\n\nIn this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 - 0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.

neuroscience↗