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Professor

The brain-gut project is an open, single-centre, case-control characterisation study (IBS n=100, HC n=40), followed by open label dietary intervention for a subgroup of subjects with IBS-D (n=60). 
Irritable bowel syndrome (IBS) is a common clinical label for medically unexplained gastrointestinal (GI) symptoms, recently described as a disturbance of the brain-gut-microbiota (BGM) axis. To gain a better understanding of the mechanisms underlying the poorly understood etiology of IBS, we have designed a multifaceted study that aim to stratify the complex interaction and dysfunction between the brain, the gut and the microbiota in patients with IBS.
In this study, deep phenotyping data from patients with IBS (n=100) and healthy age- (between 18 and 65) and gender-matched controls (n=40) will be collected between May 2019 and December 2021. Psychometric tests, questionnaires, human biological tissue/samples (blood, faeces, saliva and GI biopsies from antrum, duodenum and sigmoid colon), assessment of the stomachs accommodation and emptying using transabdominal ultrasound, peripheral neuronal activity (vagus), and functional and structural magnetic resonance imaging (MRI) of the brain, are included in the investigation of each participant. A subgroup of 60 patients with IBS-D will be further included in a 12-week low FODMAP dietary intervention-study to determine short and long-term effects of diet on gastrointestinal symptoms (GI), gut microbiota composition and functions, molecular GI signatures, cognitive, emotional and social functions, and structural and functional brain signatures. Deep machine learning (artificial intelligence methods), prediction tools and big data analyses will be used for multivariate analyses allowing disease stratification and diagnostic biomarker detection. Our study protocol has been published in 2020. 
To our knowledge, this is the first study to i) employ unsupervised machine learning techniques and incorporate systems-based interactions between the central and the peripheral components of the brain-gut-microbiota axis at the levels of the multi-omics, microbiota profiles, and brain connectome of a cohort of 100 patients with IBS and matched controls; ii ) study long?term safety and efficacy of the low-FODMAP diet on changes in nutritional status, gut microbiota composition and metabolites; and to iii) investigate changes in the brain and gut connectome after 12 weeks strict low-FODMAP-diet in patients with IBS.

The clinical data collection finished June 2021. A comprehensive datamanagment plan and data prosessing of our brain-gut analytics database is ongoing. The database consist of complex multi-organ and longitudinal clinical data.

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.

Brain-Gut-Microbiota Interaction in Irritable Bowel Syndrome: A Multidimensional Approach

Effects of Child Welfare Services

EUI - Modeling interacting agents - 
4. år - tilleggsstipend

EUI - Modeling interacting agents

0 projects

HELSEVEL-Gode og effektive helse-, omsorgs- og velferdstjenester

Effects of Child Welfare Services

Awarded: NOK 15.0 mill.

Project Period: 2019-2024

Location: Oslo

FRIHUMSAM-Fri prosj.st. hum og sam

EUI - Modeling interacting agents - 4. år - tilleggsstipend

Awarded: NOK 0.17 mill.

Project Period: 2013-2014

Location: Ukjent Fylke

FRIHUMSAM-Fri prosj.st. hum og sam

EUI - Modeling interacting agents

Awarded: NOK 1.9 mill.

Project Period: 2010-2014

Location: Ukjent Fylke