The goal of this Mentored Clinical Scientist Development Award (K08) is to provide the candidate with extensive training in 1) machine learning, 2) neuroimaging and 3) developmental psychopathology to conduct independent investigations on the neurobiology that underlies psychopathology that results from childhood maltreatment. 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.
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 proposal builds on the candidate's prior training in clinical psychology, neurobiological models of traumatic stress, and advanced computational methods to develop expertise in: 1) computational methods that include machine learning, genetic algorithms, and artificial neural networks, 2) the collection of multi-modal neuroimaging data and 3) the effect of maltreatment on development. Additional professional development training will include grantsmanship, manuscript preparation, and research ethics. To achieve these aims, the candidate has assembled an accomplished mentorship team of experts that span a range of disciplines including developmental psychopathology, computational science, and neuroscience. The training and mentorship will allow the candidate to conduct a series of research studies with two primary aims. 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 i s 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. The experience and data obtained from this project will position the candidate to pursue future NIH funding to further examine the neurobiology of maltreatment- related psychopathology in adolescence.
Child abuse prior to adolescence is associated with marked impairment across the lifespan and is a significant public health concern. There is considerable need to understand how abuse in childhood affects the developing brain and how these changes correspond to observable maladaptive behaviors during adolescence. This project aims to quantify the contribution of brain circuits to maltreatment-related psychopathology in order to improve diagnosis and identify targets for intervention.
