This work develops advanced statistical modeling techniques to build efficient and scalable computational frameworks for investigating decision-making in behavioral tasks, integrating data across multiple tasks, uncovering latent neural dynamics, and elucidating brain–behavior relationships. The overarching goals are to identify and quantify distinctive decision-making patterns associated with mental health conditions and diverse cognitive strategies, to localize brain regions activated by specific tasks, and to determine the causal effects of brain activity on behavior.