Mental health disorders, such as depression, anxiety, and mood disorders, are prevalent worldwide and often go undiagnosed until they significantly affect an individual’s quality of life. Traditional diagnostic methods rely heavily on subjective assessments, which can lead to delayed diagnoses and limited treatment options. Recent advances in brain imaging and computational techniques present an opportunity to improve mental health diagnosis, intervention, and management. This research leverages cutting-edge technologies, including machine learning algorithms and neuroimaging (such as diffusion tensor imaging), to analyze large datasets from sources like UK Biobank. The study investigates how brain activity, particularly in white matter pathways, relates to mental health conditions. By identifying specific neural markers associated with mental health disorders, the project aims to develop more objective, accurate diagnostic tools and personalized treatment strategies.The scientific foundation of this project lies in the growing body of evidence suggesting that disruptions in brain networks, especially in the sensorimotor and memory systems, contribute to the development and progression of mental health disorders. By integrating brain imaging data with behavioral and cognitive assessments, this research seeks to create predictive models that can detect mental health conditions earlier and more accurately than traditional methods.
