INVESTIGATING NEUROPSYCHOLOGICAL BIOMARKERS ASSOCIATED WITH ANXIETY, DEPRESSION, AND COGNITIVE DECLINE

Abstract

Anxiety, depression, and cognitive decline are among the leading contributors to global disability, significantly affecting emotional well-being, cognitive performance, and quality of life across all age grosups. Despite substantial advances in psychiatric diagnostics and neuroimaging technologies, early identification of individuals at risk remains challenging due to the lack of reliable neuropsychological biomarkers capable of accurately predicting disease onset and progression. This study investigates the association between neuropsychological biomarkers and the severity of anxiety, depression, and cognitive decline to enhance early diagnosis and support personalized therapeutic interventions. A quantitative cross-sectional research design is proposed, integrating standardized neuropsychological assessments with validated psychological instruments and cognitive performance measures. The study is designed to collect data from healthcare organizations in Lahore, Pakistan, using structured questionnaires and standardized neuropsychological tests. Data analysis is intended to include descriptive statistics, Pearson correlation, multiple regression, and structural equation modeling to examine the relationships among executive functioning, working memory, processing speed, attention, emotional regulation, anxiety, depression, and cognitive impairment. Contemporary neuroscientific evidence indicates that impairments in executive functioning, episodic memory, attentional control, and emotional regulation consistently predict worsening psychiatric symptoms and accelerated cognitive decline. The integration of multidimensional neuropsychological biomarkers offers greater diagnostic precision than traditional symptom-based clinical evaluations. The study contributes to the growing literature on biomarker-informed mental health assessment and provides practical implications for clinicians, psychologists, neurologists, and healthcare policymakers in developing evidence-based screening and intervention strategies. Future longitudinal investigations employing multimodal biomarkers, artificial intelligence, and neuroimaging techniques are recommended to strengthen predictive accuracy and improve personalized mental healthcare.