INTERESTED IN PARTICIPATING IN RESEARCH?
RSVP: Reward, Salience, and Value Processing in At-Risk Youth
The purpose of our overall study is to better understand salience processing and symptoms in individuals who may be clinically high risk (CHR) for psychosis. This study also looks to improve neural circuitry and psychological processes for CHR young adults, including positive and negative symptomology. This study uses brain imaging and behavioral tasks to investigate these topics.
About 88 people ages 16-30 will participate in this study at the University of Pennsylvania. This study involves 3 visits in total over the course of about 2 years, each about a year apart.
ProNET: Psychosis Risk Outcomes Network
The purpose of this study is to establish a collaborative multi-site network to rapidly recruit and characterize clinical phenotype and biomarkers in a large number of CHR participants, in order to dissect the heterogeneity of the CHR syndrome and predict differential outcomes, and to ultimately improve the ability to identify youth at-risk of developing psychosis and understand why some young people develop psychosis and others do not.
About 74 people ages 12-30 will participate in this study at the University of Pennsylvania. This study involves about 15 visits in total over the course of about 2 years, with varying procedures at each visit.
ProCAN: Psychosis Risk Outcomes Compound Assessment Network
This project, expected to begin enrollment in early 2025, will evaluate the potential of pharmacological interventions to improve biomarker and clinical measures identified in ProNET as important indicators of psychosis risk.
Current Projects in the Lab for Motivation in Psychiatry
Dr. Wolf is also a Collaborating Investigator on multiple projects, including:
THE NEUROIMAGING BRAIN CHART SOFTWARE SUITE
This study proposes to refine, integrate and disseminate the NeuroImaging Brain Chart (NIBCh) software toolbox and machine learning (ML) model library, an ecosystem of software allowing mapping of multimodal imaging data into a compact multidimensional coordinate system of informative neuroimaging signatures implemented by our library of ML models (https://neuroimagingchart.com/).
A TRANSLATIONAL AND NEUROCOMPUTATIONAL EVALUATION OF A D1R PARTIAL AGONIST FOR SCHIZOPHRENIA (PENN PI RAQUEL GUR)
This multi-site study, currently in the data analysis phase, tested in early-stage schizophrenia the dose-related effects of a potentially pro-cognitive dopamine D1R/D5R partial agonist, PF-06412562, on neuroimaging biomarkers based on translational and computational neuroscience understanding of the role of D1R/D5Rs in cortical systems.
MAPPING HETEROGENEITY OF NEUROANATOMICAL IMAGING SIGNATURES OF PSYCHOSIS VIA PATTERN ANALYSIS (PI CHRISTOS DAVATZIKOS)
This project aims to use advanced pattern analysis and machine learning methods to structural MRI data, in order to elucidate patterns of neuroanatomical change in psychosis, and use those to derive diagnostic and predictive indices on an individual patient basis.