A USC study developed an assessment that classifies depression and suicidal ideation by analyzing biomarkers such as sweat, brain signals and eye movements, and using AI to build this classification system. Suicide rates among veterans are 1.5 times higher than those of the general public, according to a USC Suzanne Dworak-Peck School of Social Work study. Researchers note that mental illnesses like depression lack a standard testing method, as diagnoses rely on self-reporting tools. The study was led by Shrikanth Narayanan and funded by the Defense Advanced Research Projects Agency (DARPA). The research project, titled “PRECOG: Multimodal integration of neural and biobehavioral signals for predicting preconscious responses,” began in June 2023. The PRECOG project has resulted in five papers published in major journals this year, with one additional paper currently in the publication process. One paper, titled “Deep Learning Characterizes Depression and Suicidal Ideation in Young Adults From Eye Movements,” was published in npj Digital Medicine. Three papers focused on neural signals measured using electroencephalography (EEG), including one titled “Neural Evidence of Disrupted Self-Referential Processing in Suicidal Depression,” published in the Journal of Affective Disorders.