UCSF engineers are developing technology with the potential to do something unprecedented: collect objective data that will help identify early indicators of depression. Their work and the potential it has to change health care was recently recognized with a UCSF Weill Institute for Neurosciences Catalyst Award. The awards support promising translational neuroscience projects by providing seed funding and industry advisor mentorship, with the goal to advance projects that could be commercially developed. 

A team led by UCSF investigators Reza Abbasi-Asl and Maryam Bijanzadeh, is building a brain-and-body sensing system anyone can wear at home — much like an EEG headset and a smartwatch — that collects data about how patients think, feel, and react over time. They want to use advanced machine learning to find patterns in mood changes that will give people real-time insights into their mental state and empower them to get critical mental health care before their situations worsen.