The particles are in our blood, brains and guts – and scientists are only beginning to learn what they do

A patient who types with his brain thanks to a Neuralink implant is using AI chatbots to speed things up.

Apathy is common in neurological disease, associated with poor prognosis and limited treatments. Current models posit that goal-directed actions are reduced because costs or effort outweigh the expect...

Building reproducible systems across labs is possible, even in large-scale neuroscience—with rigor and collaboration.

A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Rakesh et al. used data from the Adolescent Brain Cognitive Development cohort study to evaluate the relationship between state-level income inequality, brain structure and function, and mental health in young people.

Men-led papers receive more media coverage than women’s, new study finds

YouTube video by Linkin Park

Single-cell immunoprofiling uncovered systemic immune cell signatures of neurological MOG antibody–associated disease.