Biology subjects

Kuperman, S.

Publications and source records attributed to Kuperman, S..

4 recordsLinked to original sources

bioRxiv2023

Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (Memory group) were compared with a matched Control group who did not have memory problems. The Random Forests model identified specific features from each domain that contributed to the classification of Memory vs. Control group (AUC=88.29%). Specifically, individuals from the Memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except some connections involving anterior cingulate cortex which were predominantly hypoconnected. Other significant contributing features were (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past 5 years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past 12 months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity collected [~]18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict alcohol-related memory problems that arise in later life.

neuroscience↗

bioRxiv2022

Alcohol reverses the effects of KCNJ6 (GIRK2) noncoding variants on excitability of human glutamatergic neurons

Synonymous and noncoding single nucleotide polymorphisms (SNPs) in the KCNJ6 gene, encoding G protein-gated inwardly rectifying potassium (GIRK2) channel subunit 2, have been linked with increased electroencephalographic frontal theta event-related oscillations (ERO) in subjects diagnosed with alcohol use disorder (AUD). To identify molecular and cellular mechanisms while retaining the appropriate genetic background, we generated induced excitatory glutamatergic neurons (iN) from iPSCs derived from four AUD-diagnosed subjects with KCNJ6 variants ( Affected: AF) and four control subjects without variants ( Unaffected: UN). Neurons were analyzed for changes in gene expression, morphology, excitability and physiological properties. Single cell RNA sequencing suggests that KCNJ6 AF variant neurons have altered patterns of synaptic transmission and cell projection morphogenesis. Results confirm that AF neurons express lower levels of GIRK2, have greater neurite area, and elevated excitability. Interestingly, exposure to intoxicating concentrations of ethanol induces GIRK2 expression and reverses functional effects in AF neurons. Ectopic overexpression of GIRK2 alone mimics the effect of ethanol to normalize induced excitability. We conclude that KCNJ6 variants decrease GIRK2 expression and increase excitability and that this effect can be minimized or reduced with ethanol.

neuroscience↗

bioRxiv2021

Impact of Binge Drinking During College on Resting State Functional Connectivity

AimThe current study aimed to examine the longitudinal effects of standard binge drinking (4+/5+ drinks for females/males in 2 hours) and extreme binge drinking (8+/10+ drinks for females/males in 2 hours) on resting state functional connectivity. Method119 college students with distinct alcohol bingeing patterns (35 non-bingeing controls, 44 standard bingers, and 40 extreme bingers) were recruited to ensure variability in bingeing frequency. Resting state fMRI scans were obtained at time 1 when participants were college freshmen and sophomores and again approximately two years later. On four occasions during the 2-year period between scans, participants reported monthly standard and extreme binge drinking for the past 6 months. Association between bingeing and change in functional connectivity was studied using both network-level and edge-level analysis. Network connectivity was calculated by aggregating multiple edges (a functional connection between any two brain regions) affiliated with the same network. The network-level analysis used mixed-effects models to assess the association between standard/extreme binge drinking and change in network connectivity, focusing on canonical networks often implicated in substance misuse. On the other hand, the edge-level analysis tested the relationship between bingeing and change in whole-brain connectivity edges using connectome-based predictive modeling (CPM). ResultsFor network-level analysis, higher standard bingeing was associated with a decrease in connectivity between Default Mode Network-Ventral Attention Network (DMN-VAN) from time 1 to time 2, controlling for the initial binge groups at time 1, longitudinal network changes, in-scanner motion and other demographic covariates. For edge-level analysis, the CPM failed to identify a generalizable predictive model of cumulative standard/extreme bingeing from change in connectivity edges. ConclusionsOur findings suggest that binge drinking is associated with abnormality in networks implicated in attention allocation and self-focused processes, which, in turn, have been implicated in rumination, craving, and relapse. More extensive alterations in functional connectivity might be observed with heavier or longer binge drinking pattern.

neuroscience↗

bioRxiv2019

Polygenic Prediction of Substance Use Disorders in Clinical and Population Samples

Genome-wide, polygenic risk scores (PRS) have emerged as a useful way to characterize genetic liability using genotypic data. There is growing evidence that PRS may prove useful to identify those at increased risk for developing certain diseases. The current utility of PRS in relation to alcohol use disorders (AUD) remains an open question. Using data from both a population-based sample [the FinnTwin12 (FT12) study] and a high risk sample [the Collaborative Study on the Genetics of Alcoholism (COGA)], we examined the association between PRSs derived from genome-wide association studies (GWASs) of 1) alcohol dependence/alcohol problems, 2) alcohol consumption, and 3) risky behaviors with AUD and other substance use disorder (SUD) symptoms. Individuals in the top 20%, 10%, and 5% of PRSs had increasingly greater odds of having an AUD compared to the lower end of the continuum in both COGA (80th % OR = 1.95; 90th % OR = 2.03; 95th % OR = 2.13) and FT12 (80th % OR = 1.77; 90th % OR = 2.27; 95th % OR = 2.39). Those in the top 5% reported greater levels of licit (alcohol and nicotine) and illicit (cannabis) SUD symptoms. PRSs can predict elevated risk for SUD in independent samples. However, clinical utility of these scores in their current form is modest. As these scores become more predictive of SUD, they may become useful to practitioners. Improvement in predictive ability will likely be dependent on increasing the size of well-phenotyped discovery samples.

genetics↗