TY - JOUR
T1 - Predicting Alcohol-Related Memory Problems in Older Adults
T2 - A Machine Learning Study with Multi-Domain Features
AU - Kamarajan, Chella
AU - Pandey, Ashwini K.
AU - Chorlian, David B.
AU - Meyers, Jacquelyn L.
AU - Kinreich, Sivan
AU - Pandey, Gayathri
AU - Subbie-Saenz de Viteri, Stacey
AU - Zhang, Jian
AU - Kuang, Weipeng
AU - Barr, Peter B.
AU - Aliev, Fazil
AU - Anokhin, Andrey P.
AU - Plawecki, Martin H.
AU - Kuperman, Samuel
AU - Almasy, Laura
AU - Merikangas, Alison
AU - Brislin, Sarah J.
AU - Bauer, Lance
AU - Hesselbrock, Victor
AU - Chan, Grace
AU - Kramer, John
AU - Lai, Dongbing
AU - Hartz, Sarah
AU - Bierut, Laura J.
AU - McCutcheon, Vivia V.
AU - Bucholz, Kathleen K.
AU - Dick, Danielle M.
AU - Schuckit, Marc A.
AU - Edenberg, Howard J.
AU - Porjesz, Bernice
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
KW - EEG source functional connectivity
KW - alcohol use disorder (AUD)
KW - alcohol-related memory problems
KW - default mode network
KW - random forests
UR - http://www.scopus.com/inward/record.url?scp=85160235976&partnerID=8YFLogxK
U2 - 10.3390/bs13050427
DO - 10.3390/bs13050427
M3 - Article
C2 - 37232664
AN - SCOPUS:85160235976
SN - 2076-328X
VL - 13
JO - Behavioral Sciences
JF - Behavioral Sciences
IS - 5
M1 - 427
ER -