Thursday, November 21, 2024
11/21/2024
Project Summary Disorganization in psychosis has important clinical implications but is under-studied. Several lines of evidence suggest that disorganization reflects higher genetic loading and worse outcomes, and is sensitive to treatment response and relapse. We will use computational linguistics to measure disorganization in a sensitive, objective, efficient, reproducible, and repeatable way. Speech will be elicited with open-ended and structured tasks from 270 people with schizophrenia spectrum disorders and mood disorders with psychotic features, generating ~30,000 sentences across the sample. Findings will be validated in an existing independent dataset. We will measure psychosis symptoms, functioning, and cognition in both samples. Incoherence and inefficiency will be labeled for individual sentences and rated for the overall participant. Our Specific Aims are as follows: (1) Develop deep-learning methods to classify sentence-level disorganization; (2) Integrate across computational features to predict participant-level disorganization; (3) Predict key participant characteristics using linguistic features. An integrated training plan will combine hands-on experience through these research aims with mentorship, coursework, self-study, seminars, and conferences to achieve the following Training Goals: (1) Proficiency in computational linguistics and machine learning methods, (2) Expertise in validating clinically-relevant biomarkers, and (3) Development as a physician-scientist. This work provides the foundation needed to develop cutting-edge computational methods into biomarkers of disorganization and key psychosis outcomes. We lay the groundwork for future studies that leverage these features as early markers of treatment response and relapse, and that use these features to connect behavioral phenotypes with underlying biology. The proposed project builds on my existing expertise to develop the technical proficiency and expertise in psychosis biomarker research I need to lead new discoveries in this area as an independent physician- investigator.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
