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

Author:

Kamarajan Chella1ORCID,Pandey Ashwini K.1ORCID,Chorlian David B.1,Meyers Jacquelyn L.1,Kinreich Sivan1,Pandey Gayathri1,Subbie-Saenz de Viteri Stacey1,Zhang Jian1,Kuang Weipeng1,Barr Peter B.1,Aliev Fazil2,Anokhin Andrey P.3,Plawecki Martin H.4,Kuperman Samuel5,Almasy Laura6,Merikangas Alison6,Brislin Sarah J.2ORCID,Bauer Lance7,Hesselbrock Victor7,Chan Grace57ORCID,Kramer John5,Lai Dongbing4ORCID,Hartz Sarah3ORCID,Bierut Laura J.3,McCutcheon Vivia V.3ORCID,Bucholz Kathleen K.3,Dick Danielle M.2,Schuckit Marc A.8,Edenberg Howard J.4,Porjesz Bernice1

Affiliation:

1. Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA

2. Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA

3. Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA

4. Indiana University School of Medicine, Indianapolis, IN 46202, USA

5. Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA

6. The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA

7. Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA

8. Department of Psychiatry, University of California, San Diego, CA 92103, USA

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.

Funder

National Institute on Alcohol Abuse

Alcoholism

National Institute on Drug Abuse

Publisher

MDPI AG

Subject

Behavioral Neuroscience,General Psychology,Genetics,Development,Ecology, Evolution, Behavior and Systematics

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