A Synergistic Model for Monitoring Brain´s Changes: A Case Study

It is known that the early detection of chronic diseases significantly increases the life span of the elderly and improves the quality of life in general. Since the brain is the most valuable part of the human body, it is very important for the physicians to know the stages of aging and changes that take place during these stages and possible implications associated with them in order to more effectively treat their patients. In addition, fMRI provides information (size, density, location, etc) from brain images for the active regions during thinking and/or performing certain tasks. Thus, in this paper a monitoring brain-aging model based on a synergy of methodologies, like image segmentation, registration, local global (L-G) graphs and stochastic Petri net (SPN) graphs is presented. In particular, the synergistic brain-aging model uses fMRI images to detect, extract and associate the way that the brain regions interact regarding thinking and/or executing certain tasks. These brain-region images are extracted and geometrically are represented and associated with the L-G graphs. Then the use of SPN graphs models the regions´ functionality. Thus, comparing the L-G and SPN graphs extracted from fMRI images taken in different periods from the same subject, the model has the capability to detect changes and associate them in order the medical expert to monitor the health status and provide a diagnosis or prognosis regarding a human subject. Sets of L-G and SPN graph models generated in time for each particular subject are available in an L-G/SPN graph Database. Here the synergistic model and its proof of concept are presented.