Impact of Chronic Stress on Brain Structure and Function: Implications for Emotional Health

Chronic stress has a profound impact on brain structure and function, significantly influencing emotional health. This paper explores how prolonged exposure to stress alters key brain regions such as the hippocampus, prefrontal cortex, and amygdala, leading to cognitive impairments and emotional dysregulation. By integrating neuroscience with artificial intelligence, we propose a machine learning-based approach—utilizing Convolutional Neural Networks (CNN) and Support Vector Machines (SVM)—to detect structural changes in the brain through MRI and fMRI data. A dataset of neuroimaging scans was analyzed to identify patterns of atrophy and hyperactivity associated with chronic stress. The proposed models demonstrated high accuracy in classifying stress levels, with CNN achieving 91.3% accuracy and SVM achieving 87.6%. The study also highlights the most affected brain regions and significant biomarkers contributing to stress classification. These findings support the use of AI models for early detection and intervention strategies. The research concludes that machine learning not only enhances diagnostic accuracy but also provides a path toward personalized treatment approaches for stress-related neurological conditions.