The main theme of this proposal is the identification of brain signatures associated with hedonic eating behaviors (HEB) and the role of inflammatory mediators in shaping these brain signatures. I will also evaluate the feasibility of using a targeted intervention (Cognitive Behavioral Therapy [CBT]) to counteract the hypothesized alterations within the extended reward network in this subgroup of obese subjects. This area of study is significant because increases in the hedonic component of food intake, which are no longer driven by homeostatic needs, are likely to play an important pathophysiological role in some obese individuals, but the mechanisms that bias the brain towards this alteration in ingestive behavior are incompletely understood. Here I will use inflammatory gene expression profiles to show that adverse experiences alter brain signatures within the extended reward network through the process of neuroinflammation. The proposal begins by characterizing brain signatures related to HEB. Then gene expression profiles as measured by peripheral blood mononuclear cells and plasma cytokines are identified and correlated with brain signatures and HEB. CBT will be used to investigate the therapeutic effect linked to strengthening inhibitory influences of prefrontal regions within the extended reward network, and reducing increased sympathetic nervous system modulation of the immune system, thereby offering a cost effective way of counteracting HEB. Advanced and sophisticated multivariate analytic techniques will be used to integrate the data from multiple neuroimaging sources, gene profiles, and behavioral data in order to determine the unique variance of adverse environmental factors in determining signatures associated with HEB. This may serve as a sensitive measure of central biomarkers in obesity related to HEB. A model that accounts for sex and race differences will increase the validity of the model, and will help identify disadvantaged groups who are at increased risk for obesity associated with HEB. I have a background in psychology and seek the following training goals from this award: (1) knowledge in the pathophysiology of obesity; (2) genomic analysis techniques including bioinformatics techniques; (3) knowledge and expertise in metabolomics data analysis; (4) specific expertise in diffusion tensor MRI analysis; (5) advanced data driven multivariate techniques using topological network and system biological analytical techniques (e.g. cluster and classification analysis); and (6) expertise in applying behavioral therapy to obesity. The work environment at the Center for Neurobiology of Stress provides an excellent infrastructure for training in neuro-genetic investigations of obesity. I will attend courses, workshops, and meetings in order to obtain a comprehensive understanding of the underlying biological sequelae of obesity. Regular individual and group meetings with mentors (Mayer [primary], Pisegna, Li, Cole, Labus, Naliboff [co-mentors]) have been set up. I plan to apply for a R03 grant by Year 3 and a R01 grant by Year 5. Long term, I plan to establish an independent research career in brain-gut interactions associated with health disparities in obesity.
With obesity becoming a major health epidemic in the United States, and with concerns being raised about the validity and effectiveness of obesity treatments, this project investigates increases in the hedonic related food addiction component of food intake which are no longer driven by homeostatic needs, and are thus likely to play an important pathophysiological role in some obese individuals. In order for effective and personalized obesity treatments to be developed for hedonic ingestion, this project addresses a comprehensive biopsychosocial bidirectional brain-immune mechanistic model that characterizes the influence of adverse environment factors on prefrontal inhibitory control of brain signatures in the extended reward network, while accounting for sex and race differences. Using advanced automated and mathematical analytic techniques allows to integrate information from multimodal neuroimaging data and metadata sets (genomics, clinical behaviors, psychosocial environmental factors), in order to provide a powerful and sensitive biomarker that will increase biological readouts of hedonic eating behaviors and thus bring to the forefront those disadvantaged groups and individuals who are at increased risk for this type of obesity
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