UF1MH136062

Ace-D: Accelerating cognition-guided signatures to enhance translation in depression - Project summary/abstract

The lack of accessible, individual-level measurements suited to inform clinical decision-making is a critically unmet need in depression, the leading cause of disability globally.

Persistent cognitive impairments from depression are a major contributor to disability.

Our objective is to optimize, validate, and deploy a clinical cognitive signature using behavioral measures that have a basis in neural mechanisms, enabling individualized assessment at scale suited to personalized clinical prognostic and treatment selection decisions.

We will extend our pioneering work in identifying a cognitive phenotype of depression derived from computerized behavioral ‘WebNeuro’ tasks that align with the RDoC cognitive control construct, to be complemented by a novel, research-based smartphone ‘Biaffect’ application for finer-grained, passively sampled behavioral metrics.

In Aim 1, we will optimize a clinical cognitive signature for individual-level predictions based on our already identified cognitive phenotype.

We leverage our unique, large existing multi-modal datasets with common cognitive data elements totaling 3,082 participants.

These datasets span participants with major depressive disorder assessed pre-post treatment with pharmacotherapy and behavioral therapy, pre-post naturalistic trajectories, and matched healthy participants from the same sites.

We will systematically optimize a clinical cognitive signature by generating trial-by-trial individualized scores on cognitive control tasks, with refined norms, and evaluate these scores in predictive models.

We will also refine the mechanistic understanding of the clinical cognitive signature in the subset of participants who also have neuroimaging data.

In Aim 2, we will evaluate the clinical cognitive signature in combination with digital phenotyping at scale in a new prospective sample of 1,200 adults with depression, to be recruited remotely.

To enhance generalizability, the sample will span a broad range of symptom severity and functional impairment.

We will complement WebNeuro with the Biaffect technology, both suited for remote administration, to quantify finer-grained individual variations in behavior throughout the day.

This new cohort will complete repeat assessments for symptom and disability outcome predictions over 8 weeks with a 6-month follow-up.

In Aim 3, we will validate the clinical cognitive signature for prospective stratification in a randomized clinical trial with 160 participants from the Stanford Bay Area and Chicago sites.

We will prospectively identify participants with a prominent clinical cognitive signature (designated as C+) and those with a relative absence of the signature (designated as C-).

Participants will be randomly assigned to receive sertraline plus guanfacine or sertraline plus placebo.

Guanfacine is chosen because it has been shown to ameliorate cognitive deficits in depression based on the published preliminary findings from our team.

The expected end product will be a clinically validated cognitive signature using a behavioral assessment tool that can be readily scaled and translated into routine clinical practice to inform prognostic and tailored treatment decisions.

The project will generate a unique fair-compliant dataset enabling future scaling using machine learning and AI.

The Leland Stanford Junior University

THE MISSION OF THE NATIONAL INSTITUTE OF MENTAL HEALTH (NIMH) IS TO TRANSFORM THE UNDERSTANDING AND TREATMENT OF MENTAL ILLNESSES THROUGH BASIC AND CLINICAL RESEARCH, PAVING THE WAY FOR PREVENTION, RECOVERY, AND CURE. WE FULFILL THIS MISSION BY SUPPORTING AND CONDUCTING RESEARCH ON MENTAL ILLNESSES, HEALTH SERVICES, AND THE UNDERLYING BASIC SCIENCE OF THE BRAIN AND BEHAVIOR; SUPPORTING THE TRAINING OF SCIENTISTS TO CARRY OUT BASIC AND CLINICAL MENTAL HEALTH RESEARCH; AND COMMUNICATING WITH SCIENTISTS, PATIENTS, PROVIDERS, AND THE PUBLIC ABOUT MENTAL HEALTH RESEARCH ADVANCES AND PRIORITIES. IN MAY 2024, NIMH RELEASED ITS STRATEGIC PLAN FOR RESEARCH. THE STRATEGIC PLAN BUILDS ON THE SUCCESSES OF PREVIOUS NIMH STRATEGIC PLANS BY PROVIDING A FRAMEWORK FOR SCIENTIFIC RESEARCH AND EXPLORATION, AND ADDRESSING NEW CHALLENGES IN MENTAL HEALTH.THE NEW STRATEGIC PLAN OUTLINES FOUR HIGH-LEVEL GOALS: GOAL 1: DEFINE THE BRAIN MECHANISMS UNDERLYING COMPLEX BEHAVIORS GOAL 2: EXAMINE MENTAL ILLNESS TRAJECTORIES ACROSS THE LIFESPAN GOAL 3: STRIVE FOR PREVENTION AND CURES GOAL 4: STRENGTHEN THE PUBLIC HEALTH IMPACT OF NIMH-SUPPORTED RESEARCH THESE FOUR GOALS FORM A BROAD ROADMAP FOR THE INSTITUTES RESEARCH PRIORITIES OVER THE NEXT FIVE YEARS, BEGINNING WITH THE FUNDAMENTAL SCIENCE OF THE BRAIN AND BEHAVIOR, AND EXTENDING THROUGH EVIDENCE-BASED SERVICES THAT IMPROVE PUBLIC HEALTH OUTCOMES.

93.242 - Mental Health Research Grants

National Institute of Mental Health (NIMH) [HHS - NIH]

Palo Alto, California 94304 United States

Single Zip Code

Individually Measured Phenotypes to Advance Computational Translation in Mental Health (IMPACT-MH) (U01 Clinical Trial Optional) (RFA-MH-23-105)