Addiction Related

Research into addiction is increasingly leveraging advanced computational methods to identify and characterize brain networks associated with addictive behaviors. Current efforts focus on developing data-driven models, employing machine learning techniques like Long Short-Term Memory networks and graph neural networks, to analyze functional neuroimaging data (e.g., fMRI, LFP) and pinpoint dynamic patterns within these networks. This work aims to improve our understanding of the neurobiological mechanisms underlying addiction, ultimately contributing to the development of more effective diagnostic tools and treatment strategies.