Resilience after Trauma Exposure (BRITE)
There is strong evidence that maltreatment during childhood results in differences across multiple circuits in the developing brain. The extent that affected circuits are involved in psychopathology, however, is unclear. Determining which circuits are involved is necessary to develop interventions that target specific neurobiological systems. The primary aims of the proposed studies are to clarify the involvement of multiple brain features (e.g., structures and functional activations) in maltreatment-related psychopathology. The first is to use machine learning to develop a model that integrates structural and task-based functional MRI data to differentiate adolescents with a history of maltreatment from controls. Identifying the features that best differentiate these groups will determine which circuits are implicated in maltreatment-related psychopathology. These models will be constructed with data from an existing high-dimensional database that was obtained by project mentors. The second aim is to obtain pilot data on the association between variations in brain regions and the severity of psychopathology in a sample of maltreated adolescents. When this research is completed, the brain features most affected by maltreatment will be identified and the relation between these brain features and observable symptoms will be quantified. Such knowledge will provide a set of targets for treatment and assist in our classification of maltreatment-related mental illness. These aims will be accomplished with machine learning. Machine learning is a set of computer-based learning methods in which meaningful theoretical models are derived from empirical data. This project is funded by a K08 award through the National Institute of Mental Health (PI: Matt Price)
Active Collaborative Projects
These are projects that are being conducted at other institutions in which CREST is an active collaborator.
Quality of Care in Child Mental Health Service Settings
Assuring children access to the highest quality mental health care is a top national priority. Yet, quality of care continues to be highly variable in traditional service settings. The project aims to address this challenge through using a tablet based application to help providers administer TF-CBT. This is project is being conducted at the Medical University of South Carolina and is led by Dr. Kenneth Ruggiero. CREST is a collaborating partner on the project.
- Bounce Back Now: A low-cost
intervention to facilitate post-disaster recovery
Disasters confront individuals with a wide range of stressors, including threat of death or injury, loss of loved ones, limited access to basic needs, and financial strain due to property damage or disruptions in employment. The availability of brief, effective, free, and highly accessible interventions to facilitate personal and community resilience and rapid and sustained recovery is potentially of tremendous value to disaster-affected communities and disaster response agencies. This study will evaluate Bounce Back Now (BBN), a novel, scalable, and highly sustainable technology-based intervention. This is project is being conducted at the Medical University of South Carolina and is led by Dr. Kenneth Ruggiero. CREST is a collaborating partner on the project.
Last modified May 12 2019 11:32 AM