Biology subjects

Ly, A.

Publications and source records attributed to Ly, A..

21 records · Page 2Linked to original sources

bioRxiv2021

Ventral tegmental area glutamate neurons mediate the nonassociative consequences of traumatic stress

Exposure to trauma is a risk factor for the development of a number of mood disorders, and may enhance vulnerability to future adverse life events. Recent data implicate ventral tegmental area (VTA) glutamate neuronal activity as functionally important for signaling aversive or threating stimuli. However, it is unknown whether VTA glutamate neurons regulate transsituational outcomes that result from stress and whether these neurons are sensitive to stressor controllability. This work established an operant mouse paradigm to examine the impact of stressor controllability on VTA glutamate neuron function and stressor outcome. Uncontrollable (inescapable) stress, but not physically identical controllable (escapable) stress, produced social avoidance in male mice. Cell-type-specific calcium recordings showed that both controllable and uncontrollable stressors increased VTA glutamate neuronal activity. Chemogenetic reduction of VTA glutamate neuron activity prevented the behavioral sequelae of uncontrollable stress. Our results provide causal evidence that mice can be used to model stressor controllability and that VTA glutamate neurons may contribute to transsituational stressor outcomes, such as social avoidance and exaggerated fear that are observed within trauma-related disorders.

neuroscience↗

bioRxiv2020

Evidence accumulation and associated error-related brain activity as computationally informed prospective predictors of substance use in emerging adulthood

RationaleSubstance use peaks during the developmental period known as emerging adulthood (ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. ObjectivesWe test whether lower efficiency of evidence accumulation (EEA), a computationally-characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults. Methods and ResultsIn an fMRI substudy within a sociobehavioral longitudinal study (n=106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18-21 are both prospectively related to greater substance use during ages 22-26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neuroimaging correlates contain unique predictive information about substance use involvement. ConclusionsThese findings highlight EEA as a computationally-characterized neurocognitive risk factor for substance use during a critical developmental period, with clear links to both neuroimaging measures and well-established formal theories of brain function.

neuroscience↗

bioRxiv2018

Identifying Parkinson’s disease and parkinsonism cases using routinely-collected healthcare data: a systematic review

BackgroundPopulation-based, prospective studies can provide important insights into Parkinsons disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely-collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.\n\nMethodsWe searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely-collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.\n\nResultsWe identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPVs ranged from 56-90% in hospital datasets, 53-87% in prescription datasets, 81-90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPVs ranged from 36-88% in hospital datasets, 40-74% in prescription datasets, and was 94% in mortality datasets. Sensitivities ranged from 15-73% in single datasets for PD and 43-63% in single datasets for parkinsonism.\n\nConclusionsIn many settings, routinely-collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.

epidemiology↗