The Neurobiology of Stress and Its Impact on Cognitive Function: A Review of Biomarkers and Early Detection Using Machine Learning Models

Stress is a pervasive neurobiological phenomenon that exerts profound effects on cognitive performance, influencing attention,
memory, executive function, and emotional regulation. Chronic activation of the “hypothalamic–pituitary–adrenal (HPA)” axis
disrupts homeostatic mechanisms, leading to structural and functional alterations in brain regions such as the prefrontal cortex,
hippocampus, and amygdala. These neuroadaptive changes, mediated by glucocorticoid exposure, neuroinflammation, and
neurotransmitter imbalances, have been strongly correlated with cognitive decline, anxiety disorders, and neurodegenerative
conditions. Despite extensive neurobiological research, early and objective detection of stress-related cognitive impairment
remains limited by the subjective nature of psychological assessments and the complex interplay of biological and behavioural
factors. This study conducts a systematic review of neurobiological markers associated with stress encompassing hormonal “(e.g.,
cortisol, ACTH), neurochemical (e.g., BDNF, serotonin, dopamine), electrophysiological (e.g., EEG spectral patterns)”, and
neuroimaging-based indicators (e.g., fMRI connectivity) and evaluates their integration with machine learning (ML) approaches
for early diagnosis. The paper proposes a hybrid ML-based predictive framework combining multimodal biomarker data with
deep learning models to enhance classification accuracy and interpretability. Comparative analysis of existing studies
demonstrates that ML algorithms, particularly convolutional and recurrent neural networks, can effectively capture complex
nonlinear relationships between stress biomarkers and cognitive outcomes. The findings suggest that a data-driven
neurobiological model could revolutionize early detection, personalized intervention, and cognitive resilience monitoring. This
review contributes to the growing intersection of neuroscience, computational psychiatry, and artificial intelligence by outlining
how machine learning can serve as a bridge between biological mechanisms and clinical prediction in stress-related cognitive
dysfunction