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Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features
Journal article   Open access   Peer reviewed

Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features

Chella Kamarajan, Ashwini K Pandey, David B Chorlian, Jacquelyn L Meyers, Sivan Kinreich, Gayathri Pandey, Stacey Subbie-Saenz de Viteri, Jian Zhang, Weipeng Kuang, Peter B Barr, …
Behavioral sciences, Vol.13(5), 427
05/18/2023
DOI: 10.3390/bs13050427
PMCID: PMC10215235
PMID: 37232664
url
https://doi.org/10.3390/bs13050427View
Published (Version of record) Open Access

Abstract

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 (the 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 the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the 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 five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve 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 data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.
 alcohol use disorder (AUD) random forests EEG source functional connectivity alcohol-related memory problems default mode network

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