Activity level, kinesiophobia, and sports injury-related anxiety in professional athletes after anterior cruciate ligament reconstruction.

Alcohol Use Disorder (AUD) is a common and debilitating mental health condition marked by excessive habitual drinking and loss of control over drinking. (). According to the 2024 National Survey on Drug Use and Health, 27.9 million people ages 12 and older had AUD in the past year in the United States (). Moreover, estimates suggest that alcohol played a role in at least 7.1% of emergency department visits (), and an analysis of death certificates showed that deaths involving alcohol among people ages 16 and older accounted for 99,017 (). These alarming facts highlight the critical importance of identifying objective and accurate biomarkers predictive of AUD diagnosis and outcomes. Such insights could contribute to preventive strategies and treatments, potentially intervening before the addiction fully develops. Over the past several decades, AUD neuroimaging research using magnetic resonance imaging (MRI) () (), functional MRI (fMRI) (), and electroencephalogram (EEG) () () () recordings has focused on developing predictive models and identifying biomarkers associated with the disorder. Studies using structural MRI have found cortical thinning and volume reductions in frontal and temporal regions associated with AUD, which have been used as input features for machine learning (ML) algorithms such as support vector machines (SVM) and random forests to create significant AUD classifiers () (). fMRI has been used to detect altered brain connectivity patterns, particularly in the default mode and reward networks (). EEG-based approaches using features such as spectral power values or event-related potential (ERP) have produced significant models that predict people with AUD (). Despite significant advances, the ability to reliably detect individuals with AUD or accurately predict vulnerability based on biomarkers remains limited, largely due to the absence of objective, scalable diagnostic tools and the heterogeneity of clinical presentations and brain development (; ). Moreover, while existing studies show encouraging results, they are often constrained by small sample sizes (), inconsistent data processing pipelines, and a lack of external validation, all of which hinder generalizability across populations () (). Our current work focuses on classifying AUD individuals using a substantial amount of EEG data from the Collaborative Study on the Genetics of Alcoholism (COGA) (). COGA is a landmark, longitudinal, multi-site research project aimed at identifying genetic, neurobiological, and environmental factors that contribute to the development and persistence of AUD and related conditions (). EEG offers a noninvasive, cost-effective method for capturing brain activity with high temporal resolution and has shown promise in identifying neurophysiological markers of AUD (). Among the various neuroimaging paradigms, resting-state assessment is particularly attractive for clinical applications due to its portability, simplicity, minimal task demands, reproducibility, and consistency across imaging and electrophysiological modalities ().