In a recent study, USC researchers with backgrounds across engineering, neurology, speech technology, artificial intelligence (AI), linguistics and clinical psychiatry teamed up to develop 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. Funded by the Defense Advanced Research Projects Agency (DARPA), the research project is titled “PRECOG: Multimodal integration of neural and biobehavioral signals for predicting preconscious responses” and began in June 2023. This research project could provide psychiatrists with a tool to identify and potentially predict high-risk individuals through biological signals, offering a more standardized way to monitor mental health and identify timely intervention points to help prevent suicide. The PRECOG project has resulted in five papers published in major journals this year, with one additional paper currently in the publication process, covering additional biomarkers and computational breakthroughs. Three papers focused on neural signals measured using electroencephalography (EEG). One, titled “Neural Evidence of Disrupted Self-Referential Processing in Suicidal Depression,” was published in the Journal of Affective Disorders and led by PhD student Colin McDaniel, who is advised by Narayanan and Assal Habibi, who is an associate professor of psychology at USC Dornsife College of Letters, Arts and Sciences. Another, titled “Time-Resolved EEG Decoding Reveals Altered Neural Dynamics of Affective Semantic Evaluation in Depression and Suicidality,” was published in Communications Biology and led by PhD student Woojae Jeong, who is part of USC Biomedical Imaging Group (BIG) and advised by Richard Leahy. Leahy is the USC Leonard Silverman Chair and professor of electrical and computer engineering, biomedical engineering, and radiology. The third paper, titled “Neural Responses to Affective Sentences Reveal Signatures of Depression,” was published in Translational Psychiatry and led by PhD student Aditya Kommineni, who is also advised by Narayanan.The fifth paper, titled “A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal Ideation,” used electrodermal activity (EDA), known as skin conductance, to collect sweat data as a biomarker. The study was also led by Avramidis.

A new home test can detect type 2 diabetes or prediabetes with high accuracy. The test has been developed using artificial intelligence and large amounts of health data – and surprisingly, one of the steps involves measuring the length of your thigh.

A novel AI model can use information collected during routine sleep studies to identify patients’ long-term health risks, according to a new study published in Nature Communications. Developed by a multidisciplinary research team, the model uncovered hidden sleep patterns linked to risks including heart disease, cognitive decline and death. The findings also suggest that routine medical tests may contain substantially more physiologic information than current clinical practice extracts from them. In this case, AI identified meaningful signals in standard overnight sleep study data that are not captured by conventional summary measures alone.