fMRI-based characterization of brain activity patterns during weight loss and body weight maintenance: relation to hormonal mechanisms regulating energy homeostasis

• Applicant: Professor Dr. John-Dylan Haynes
• Subject Area: Pediatric and Adolescent Medicine ; Endocrinology, Diabetology, Metabolism ; Human Cognitive and Systems Neuroscience
• Term: from 2009 to 2014
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 101434729

Obesity is one of the major epidemics of the 21st century. However, neural mechanisms contributing to this condition remain poorly understood. Recent approaches suggest a link between hormonal factors such as leptin and activity of dopaminergic brain regions such as the striatum involved in the perception of reward and stimulus-response habit learning. Previous studies indicate that the dorsal striatum plays an important role in both habit learning and food cue-reactivity. A second key area in the development and maintenance of obesity appears to be the prefrontal cortex linked to goal-directed behavior and initiation and inhibition of behavior. It is suspected that activation/deactivation of mesolimbic and feedforwardly connected regions such as orbitofrontal and dorsolateral cortex following highcalorie food-cue stimulation is modulated by leptin levels and may be one of the neural mechanisms contributing to the maintenance of obesity. Therefore, a first goal of this project is to investigate how a lifestyle intervention associated with weight loss and possible weight regain relates to changes in food cue-reactivity and changes in executive functioning as measured by a delay of gratification task, and how these changes are mediated by hormonal as well as psychological factors. Theses goals will be achieved by measuring functional magnetic resonance imaging (fMRI) signals during food-related behavioral tasks. A further question that will be addressed is whether or not the analysis of disorder-associated neuronal processes can benefit from the application of multivariate pattern recognition procedures or classifiers. These multivariate algorithms are able to extract information much more efficiently than conventional univariate techniques because they can take into account the full information contained in the spatial pattern of brain activity. Multivariate approaches tend to be more sensitive in signal detection than univariate approaches which in turn makes them highly suitable for the application in neurological- or psychodiagnosis based on fMRI activation patterns. The unique approach of this project consists in investigating simultaneously both key mechanisms, hormonal and neural, known to contribute to obesity, and hence may substantially advance our understanding of this condition.

• DFG Programme: Clinical Research Units
• Subproject of: KFO 218: Hormonal Regulation of Body Weight Maintenance
• Participating Person: Dr. Yvonne Rothemund