Leanne Williams receives $18 million National Institutes of Health grant to diagnose and treat depression

Leanne Williams, PhD, a professor of psychiatry and behavioral sciences, has been awarded a five-year, $18.86 million grant, part of the National Institute for Health’s Individually Measured Phenotypes to Advance Computational Translation in Mental Health initiative, to develop a diagnosis and treatment tool for depressive disorders.

Williams, the Vincent V.C. Woo Professor and the director of the Stanford Center for Precision Mental Health and Wellness, will be the project leader; the co-principal investigators are Jun Ma, MD, PhD, and Olu Ajilore, MD, PhD, of the University of Illinois, Chicago. Additional Stanford Medicine investigators include Laura Hack, MD, PhD, Trevor Hastie, PhD, Booil Jo, PhD, Ruth O'Hara, PhD, Peter van Roessel, MD, PhD, and Alan Schatzberg, MD.

Only one-third of patients with depression improve with current assessment and treatment approaches. This project has the potential to double that number, Williams said.

“Our team is driven by the urgent need for better tools to understand and treat depression,” Williams said. “It’s not just about seeing depression as a whole, but understanding how it uniquely affects each individual’s brain. Imagine being able to tailor treatments based on how depression affects someone’s thinking — that’s the promise of this study. We’re not just aiming to improve outcomes; we’re aiming to transform the way depression is diagnosed and treated, one individual at a time.”

Relying on a pool of more than 4,500 participants, the team will use brain imaging, computerized tests and a novel smartphone app — which measures swipe speed, keystroke dynamics and message length — to specify what they call cognitive biotypes for depression.

The researchers then plan to develop a tool that can be used at the first instance of major depression — or early as possible after diagnosis — to help pinpoint the specific type of depression (biotype), provide personalized predictions and guide treatment choices, whether by a primary care physician or a specialist. They expect to refine the tool using machine learning and artificial intelligence — making significant advancements in individualized psychiatric treatment and risk prediction.

“By advancing a clinical cognitive signature to personalize treatments, we address an urgent public need,” Williams said. “Depression, with its staggering lifetime prevalence of 20.6% in the U.S. and affecting 280 million people globally, is a leading cause of disability and imposes an economic burden of $326.2 billion. With our project, we aim to develop individualized, brain-based assessments at scale, enhancing clinical decision-making and improving outcomes for millions affected by depression worldwide.”