Publications by authors named "Alexei Taylor"

Background: Alcohol, the most consumed drug in the United States, is associated with various psychological disorders and abnormal personality traits. Despite extensive research on adolescent alcohol consumption, the impact of early alcohol sipping patterns on changes in personality and mental health over time remains unclear. There is also limited information on the latent trajectory of early alcohol sipping, beginning as young as 9-10 years old.

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The purpose of this study it to build a machine learning model to predict dietary lapses with comparable accuracy, sensitivity, and specificity to previous literature while recovering predictor interactions. The sample for the current study consisted of merged data from two separate studies of individuals with obesity/overweight (total N = 87). Participants completed six ecological momentary assessment surveys per day where they were asked about 16 risk factors of lapse and if they had lapsed from their dietary prescriptions since the previous survey.

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Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional.

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Brain-age prediction has emerged as a novel approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures.

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Cognitive capacities afford contingent associations between sensory information and behavioral responses. We studied this problem using an olfactory delayed match to sample task whereby a sample odor specifies the association between a subsequent test odor and rewarding action. Multi-neuron recordings revealed representations of the sample and test odors in olfactory sensory and association cortex, which were sufficient to identify the test odor as match or non-match.

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