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Browsing by Author "Brislin, Sarah J."
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Item Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features(MDPI, 2023-05-18) Kamarajan, Chella; Pandey, Ashwini K.; Chorlian, David B.; Meyers, Jacquelyn L.; Kinreich, Sivan; Pandey, Gayathri; Subbie-Saenz de Viteri, Stacey; Zhang, Jian; Kuang, Weipeng; Barr, Peter B.; Aliev, Fazil; Anokhin, Andrey P.; Plawecki, Martin H.; Kuperman, Samuel; Almasy, Laura; Merikangas, Alison; Brislin, Sarah J.; Bauer, Lance; Hesselbrock, Victor; Chan, Grace; Kramer, John; Lai, Dongbing; Hartz, Sarah; Bierut, Laura J.; McCutcheon, Vivia V.; Bucholz, Kathleen K.; Dick, Danielle M.; Schuckit, Marc A.; Edenberg, Howard J.; Porjesz, Bernice; Psychiatry, School of MedicineMemory 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.Item The Collaborative Study on the Genetics of Alcoholism: Overview(Wiley, 2023) Agrawal, Arpana; Brislin, Sarah J.; Bucholz, Kathleen K.; Dick, Danielle; Hart, Ronald P.; Johnson, Emma C.; Meyers, Jacquelyn; Salvatore, Jessica; Slesinger, Paul; COGA Collaborators; Almasy, Laura; Foroud, Tatiana; Goate, Alison; Hesselbrock, Victor; Kramer, John; Kuperman, Samuel; Merikangas, Alison K.; Nurnberger, John I.; Tischfield, Jay; Edenberg, Howard J.; Porjesz, Bernice; Medical and Molecular Genetics, School of MedicineAlcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.