Effect of Intestinal Microbiota on Brain Activity: ANN Analysis of Functional Magnetic Resonance Imaging

Abstract

Modern biotope genomics has been able to determine the characteristics of each individual. The intestinal microbiota has a direct effect on the brain and behavioral activity of the individual. Any change in it modifies the mechanisms of emotional signaling, behavioral and visceral nociceptive reflexes. The resulting signaling mechanisms of the gut microbiota and probiotics including variations in microbiota production influence brain activity. Studies on rodents with reported vagal signaling in response to intestinal and probiotic pathogens. Diet, genetic factors, environmental factors may play a role in these alterations. The result is that is proven by functional brain imaging. The resulting effect of this interaction can be recorded in specific areas in the brain using functional magnetic resonance imaging. Depending on different biotopes in different people, levels of stress or relaxation are recorded. The analysis of these variations allows having an idea of the effect carried by each alteration in biotope and drugs administered to soothe stress for example. However, these effects are far from accurate. Human physiology is much more complex to draw hasty conclusions. If the effect is much more pronounced in women is that they are more emotional, sex is a factor to consider. In this study, we propose an intelligent system for analyzing these variables. The analysis of the MRI image is very complex due to it is a question of defining the exact contours of the activated brain zones. Given the complexity of the system, an artificial neural network analysis with deep learning is proposed. The constructed system is supervised learning. Input variables are (Biotope classification, genetic factor, diet factor, sex) and an output variable that expresses the degree of effect on activation of brain areas in relation to the centers of emotions recorded in functional fMRI. The established algorithm randomly introduces values ​​to the input factors of the system to read predict the emotional effect on the individual.