Pathophysiology Informed Biomarkers of Treatment Response in Early Psychosis (PIB)

病理生理学为早期精神病 (PIB) 治疗反应提供生物标志物

• 批准号: 10612356
• 负责人: Cameron S. Carter
• 金额: --
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10612356
• 关键词: Adjuvant; Admission activity; Anti-Inflammatory Agents; Artificial Intelligence; Behavioral; Biological Markers; Brain; Caring; Clinical; Clinical assessments; Clozapine; Cognition; Cognitive; Coordinated Specialty Care; Data; Decision Making; Development; Diffusion Magnetic Resonance Imaging; Disease; Dopamine; Drug Prescriptions; Early identification; Effectiveness; Enrollment; Evidence based practice; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Image; Image Analysis; Individual; Intervention; Link; Magnetic Resonance Imaging; Measurement; Measures; Methods; Midbrain structure; Modeling; Noise; Parietal; Parkinson Disease; Participant; Patients; Performance; Pharmaceutical Preparations; Phase; Positron-Emission Tomography; Prediction of Response to Therapy; Prefrontal Cortex; Process; Property; Protocols documentation; Psychoses; Psychotherapy; Psychotic Disorders; Publishing; Recovery; Research; Sampling; Scanning; Schizophrenia; Series; System; Testing; Time; Treatment outcome; candidate marker; clinical decision-making; clinically significant; cognitive control; deep learning; duration of untreated psychosis; early psychosis; experience; gray matter; high risk; imaging biomarker; indexing; ineffective therapies; insight; learning strategy; medication compliance; neural; neuroimaging; neuroinflammation; neuromelanin; neurophysiology; novel; personalized medicine; precision medicine; predicting response; psychosocial; reduce symptoms; responders and non-responders; standard of care; substance use; support tools; tool; transfer learning; treatment responders; treatment response; water diffusion; white matter

The introduction of Coordinated Specialty Care (CSC) has transformed the standard of care and elevated treatment outcome goals for young individuals experiencing the initial stages of a psychotic illness (EP). The response to treatment for EP individuals receiving CSC, however, remains highly variable. A substantial proportion show minimal symptom reduction despite receiving the full range of evidence-based practices comprising this treatment model. Currently, clinicians have no way to predict which EP individuals entering CSC will respond to treatment and published data show that expert clinicians perform no better than chance. Early identification of treatment non-responders has very high clinical significance and would inform and enhance clinical decision making during the first few months of care. Surprisingly, little research has been conducted on baseline predictors of treatment outcomes in EP individuals entering CSC. During the past two decades, considerable progress has been made using neuroimaging to investigate pathophysiological processes during the early phases of illness. Furthermore, limited data suggest that fMRI measures of brain activity and PET measures of increased dopamine synthesis are related to treatment outcomes in EP. We have recently demonstrated in a moderately large sample of EP patients entering CSC that the ability to activate the frontal parietal (FP) cognitive control network (measured using fMRI during the AX- CPT task) is a significant predictor of who will meet responder criterion after one year of CSC. We propose to replicate and extend this result by examining the predictive ability of this and two other promising MRI based measures linked to pathophysiological processes related to psychosis: 1) free water diffusion tensor imaging (FW) - a putative biomarker of neuroinflammation that is increased in EP individuals, and 2) midbrain neuromelanin (NM) scans, which index midbrain dopamine, shown to be decreased in Parkinson's disease and increased in schizophrenia. Each of these measures will be used individually to predict responder status for EP participants entering CSC. In addition to these analyses we will use novel deep learning methods to optimize the prediction of treatment response in EP individuals entering CSC and to obtain new insights into the mechanisms underlying these effects. Our goal is to leverage recent progress in the development of MRI based imaging biomarkers to develop a precision medicine tool that can identify early psychosis patients entering CSC who are at high risk for non-response and thereby inform treatment decision making for all patients in order to optimize the recovery of young individuals following the onset of psychotic illness.

协调专科护理（CSC）的引入已经改变了护理标准和