2023
DOI: 10.1101/2022.12.30.522330
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Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features

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 (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 … Show more

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Cited by 1 publication
(5 citation statements)
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“…For example, we reported that ML models combining EEG and AUD associated genetic variants outperformed other models based on a single type of data, suggesting that each contributed unique and significant information 136 . Using a Random Forests method, we found that EEG hyperconnectivity across the default mode network regions, PGS for AUD, alcohol consumption and related health consequences, elevated neuroticism, increased harm avoidance, and fewer positive life events could all be used to classify individuals who would develop alcohol induced memory problems 20 years later 31 . More recently, COGA has used ML algorithms to predict the difference in AUD recovery status, identifying several discriminative features, including PGS related to alcohol use, personality and psychopathology, psychosocial factors and electrophysiological indicators including lower default mode network and fusiform connectivity and higher insula connectivity 137 (Figure S3).…”
Section: How Do Neural Signatures Associated With Aud Help Elucidate ...mentioning
confidence: 89%
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“…For example, we reported that ML models combining EEG and AUD associated genetic variants outperformed other models based on a single type of data, suggesting that each contributed unique and significant information 136 . Using a Random Forests method, we found that EEG hyperconnectivity across the default mode network regions, PGS for AUD, alcohol consumption and related health consequences, elevated neuroticism, increased harm avoidance, and fewer positive life events could all be used to classify individuals who would develop alcohol induced memory problems 20 years later 31 . More recently, COGA has used ML algorithms to predict the difference in AUD recovery status, identifying several discriminative features, including PGS related to alcohol use, personality and psychopathology, psychosocial factors and electrophysiological indicators including lower default mode network and fusiform connectivity and higher insula connectivity 137 (Figure S3).…”
Section: How Do Neural Signatures Associated With Aud Help Elucidate ...mentioning
confidence: 89%
“…Functional Connectivity (eLORETA lagged connectivity) Phenotypic findings : Dysregulation in network communication in Default Mode Network (DMN) seen in those who developed alcohol‐related memory problems 20 year later. 31 Genetic findings : PGS for AUD, along with other multimodal features (including functional connectivity in default mode network), are associated with alcohol‐related memory problems. 31 …”
Section: Coga 'S Measures Of Brain Functionmentioning
confidence: 99%
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