There is increasing awareness that the brain and the gut are intimately linked, and that massive and diverse community of bacteria in the digestive tract aid digestion, regulates immune homeostasis and interacts with the central nervous system. In a clinical setting there has been great research interest in perturbations and dysfunction of the brain-gut-microbiota axis in functional gastrointestinal disorders. These new insights have been especially relevant to our understanding and treatment strategies of the widespread condition labelled irritable bowel syndrome (IBS). The project addresses research challenges in the following five key areas (cf. the 4 Big Questions in Nature Outlooks, May 2016): (i) Brain structure and function and gastrointestinal motility in IBS, (ii) Cognition and emotion in IBS, (iii) Microbiota profiles in IBS, (iv) The importance of diet in driving IBS, (v) Patient phenotypes and identification of clinically useful molecular and imaging-derived biomarkers. For each of these areas, specific research questions will be asked and organised as Work Packages. For image analysis we will apply next-generation techniques behind an ongoing revolution in both clinical and preclinical imaging: the machine learning methods deep neural networks (DNN) and convolutional neural networks (CNN). In medical fields, deep learning techniques have recently been shown to outperform traditional image analysis approaches in a variety of applications. Deep learning was named a Method to watch by Nature in 2016. With top international collaborators and being a multidisciplinary team of gastroenterologists, neuroscientists, nutritionist, clinical neuropsychologist, imaging specialists, geneticists, microbiologist, and data analysts, including three early career researchers, and access to an outstanding imaging infrastructure and a Norwegian Centre for Functional Gastrointestinal Disorders, we are in a very good position to advance the clinical science of IBS.
Project leader: Trygve Hausken
Institution: Institutt for Klinisk Medisin