SBIR-STTR Award

Mood disorders, like many psychiatric conditions, have their onset in emerging adulthood and often last a lifetime. Mental illnesses have an excessively large loading of Disability Adjusted Life Years, making early intervention crucial for both individuals and society. Differentiating bipolar disorder (BD) from major depressive disorder (MDD) based on the Diagnostic and Statistical Manual (DSM) can be especially challenging if clear mania is absent. In fact, BD patients go an average of 6-10 years without the proper diagnosis, with 70% misdiagnosed with MDD instead. The challenge of identifying BD patients among depressed individuals is complex but critical because diagnosis determines treatment. The use of antidepressant (AD) medications in patients with BD can lead to worsening of illness. We have recently shown that, using resting fMRI, we can predict future medication class response with high accuracy (> 90%). In this project we will build on this work to generalize to a new MRI scanner and clinical assessment protocol. In addition we will develop, in consultation with multiple psychiatrists, a cloud-based tool to analyze and report the results from the brain imaging protocol and machine learning analysis, in a timely, meaningful, and interpretable manner. Results are expected to be an important step forward in the eventual development of clinical useful markers of mental illness. 2

Public Health Relevance Statement:
Project Narrative Patients with mood disorders will often spend months to years on the wrong medication, which can also make them worse (e.g., putting individuals with bipolar disorder on antidepressants). There is a great need to develop biomarkers of treatment response in mental illness. This work will build on recent work from our group showing over 90% accuracy using resting fMRI predictors to further generalize the results to multiple scanners and develop an online portal to process and provide reporting of results (classification results) as well as processed data and citation information.

Project Terms:
accurate diagnosis; Algorithms; Antidepressive Agents; Back; base; Biological; Biological Markers; biomedical informatics; Bipolar Disorder; Brain; Brain imaging; Businesses; Classification; Clinic; Clinical assessments; clinical care; Clinical Decision Support Systems; clinical development; Clinical Trials; cloud based; Collaborations; Combination Medication; commercialization; Communities; Community Surveys; Complex; computerized data processing; Consultations; cost; Data; data acquisition; Data Analyses; Data Collection; Data Set; data sharing; data tools; data warehouse; Demography; Depressed mood; depressive symptoms; Development; Diagnosis; Diagnostic and Statistical Manual of Mental Disorders; Differential Diagnosis; disability-adjusted life years; Disease remission; Early Intervention; effective therapy; emerging adulthood; Ensure; Evaluation; experience; Face; Feedback; Financial cost; Functional Magnetic Resonance Imaging; Future; Generations; Geographic Locations; Goals; high risk; Image; imaging biomarker; Individual; Intuition; Lead; London; Machine Learning; machine learning method; Magnetic Resonance Imaging; Major Depressive Disorder; Manic; member; Mental Depression; Mental disorders; Minority; Modernization; Mood Disorders; mortality risk; neuroimaging; New Mexico; novel; Obsessive-Compulsive Disorder; Ontario; Patients; Pharmaceutical Preparations; Phase; Positioning Attribute; Post-Traumatic Stress Disorders; precision medicine; Procedures; Process; Protocols documentation; Psychiatric Diagnosis; Psychiatrist; quality assurance; Readiness; Recording of previous events; Reporting; Research; response; Rest; Running; Scanning; Site; Societies; Standardization; Suicide; support tools; Surveys; Symptoms; Technology Transfer; Therapeutic; Time; tool; Training; treatment planning; treatment response; treatment strategy; Universities; Validation; web portal; Work

Mood disorders, like many psychiatric conditions, have their onset in emerging adulthood and often last alifetime. Mental illnesses have an excessively large loading of Disability Adjusted Life Years, making earlyintervention crucial for both individuals and society. Differentiating bipolar disorder (BD) from major depressivedisorder (MDD) based on the Diagnostic and Statistical Manual (DSM) can be especially challenging if clearmania is absent. In fact, BD patients go an average of 6-10 years without the proper diagnosis, with 70%misdiagnosed with MDD instead. The challenge of identifying BD patients among depressed individuals iscomplex but critical because diagnosis determines treatment. The use of antidepressant (AD) medications inpatients with BD can lead to worsening of illness. We have recently shown that, using resting fMRI, we canpredict future medication class response with high accuracy (> 90%). In this project we will build on this work togeneralize to a new MRI scanner and clinical assessment protocol. In addition we will develop, in consultationwith multiple psychiatrists, a cloud-based tool to analyze and report the results from the brain imaging protocoland machine learning analysis, in a timely, meaningful, and interpretable manner. Results are expected to be animportant step forward in the eventual development of clinical useful markers of mental illness.2

Public Health Relevance Statement:
Project Narrative Patients with mood disorders will often spend months to years on the wrong medication, which can also make them worse (e.g., putting individuals with bipolar disorder on antidepressants). There is a great need to develop biomarkers of treatment response in mental illness. This work will build on recent work from our group showing over 90% accuracy using resting fMRI predictors to further generalize the results to multiple scanners and develop an online portal to process and provide reporting of results (classification results) as well as processed data and citation information.

Project Terms: