Depression is heterogeneous. Current assessments to diagnose are imprecise, and studies to find genetic and environmental links have been too small to identify the various causes and types of depression. We are researching the different types of depression and the different courses of the disease to identify precise, objective tools for diagnoses and make predictions about the courses. Below, we share our digital sensing and genetics studies researching the courses and trajectories of depression.
Digital Sensing Studies
Digital Mental Health Study
PI: Freimer
The Digital Mental Health Study (DMHS) is designed to help revolutionize the detection and treatment of depression. The study is the largest of its kind today, and is conducted in collaboration with Apple. The research uses powerful sensor technology including iPhone, Apple Watch and Beddit sleep-monitoring devices. The main phase of the study, which began in January 2022, involves more than 3,000 participants from UCLA Health and the UCLA student body who are engaged in the study for approximately one year.
Making the connection between objective, quantifiable data and symptoms of anxiety and depression could give health care providers the power to prevent the onset of depressive episodes, track the effectiveness of treatment and identify causes of depression.
OPTIMA & ILIAD
PI: Craske
The OPTIMA study — Operationalizing Digital Phenotyping in the Measurement of Anhedonia — is part of UCLA’s work as one of 12 performers in the Wellcome-Leap Multi-Channel Psych program, a cross-collaborative effort to better understand anhedonic depression, a severe and inhibitive kind of depression which affects the ability to feel pleasure or enjoyment. Up to 100 of the OPTIMA participants are invited to participate in a companion study called ILIAD (Investigating LIFUP In Anhedonic Depression) which uses low-intensity focused ultrasound pulsation (LIFUP) to stimulate brain activity in areas of the brain associated with anhedonia. Participating in ILIAD adds 5 weeks to the participant's engagement in the study.
Learn more about OPTIMA and ILIAD
Combining Voice & Genetic Information to Detect Heterogeneity in Major Depressive Disorder*
PI: Flint
This study combines information from voice recordings and genetics to identify subtypes of depression and develop robust predictors of mood, severity of illness and other clinical indicators.
Learn more about this voice-based depression study
Genetics Studies
Depression Grand Challenge faculty have an extensive body of genetics research studies focused on different populations. Below is a selection of currently active projects.
Populations Underrepresented in Mental Illness Association Studies (PUMAS)*
PI: Freimer
An international collaboration of investigators from the US, South America and Africa aim to discover new genes for schizophrenia and bipolar disorder, dramatically increasing the diversity of genetic discovery efforts, an important step towards reducing health disparities.
A Latin American Biobank for Large-Scale Genetics Research on Severe Mental Illness (LAB-SMI)*
PI: Freimer
Latin American descended populations, which constitute the fastest growing ethnicity in the United States, are poorly represented in psychiatric genetics research. This study will reverse this underrepresentation, accelerating discoveries and reducing health disparities by creating the Latin American Biobank for Severe Mental Illness (LAB-SMI), consisting of 50,000 SMI cases and 50,000 population controls ascertained by screening electronic medical records from the Paisa region of Colombia.
Identifying the genetic causes of depression in a deeply phenotyped population from South Korea*
PI: Flint
This study is collecting samples from 10,000 South Korean women with recurrent major depressive disorder and 10,000 matched controls. All participants will be genotyped to identify the location of genetic risk factors for major depressive disorder in a genome-wide association analysis.
Learn more about the study taking place in South Korea
Improving the interpretability of genetic studies of major depressive disorder to identify risk genes*
PI: Flint
This study aims to advance the understanding of major depressive disorder through the analysis of electronic health records (EHR), biobanks and associated genetic data.
Learn more about this EHR study
*An asterisk indicates projects that are housed outside the Depression Grand Challenge because they did not require or, at the time of submission for funding, predated an existing DGC capability or resource.
