The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure resting state cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury, disorders of consciousness and different brain pathological conditions. FDG-PET is widely employed in developed economies. However, in less developed economies, as in Latin-America, the spreading of this approach is limited. The main reason is the high cost of this technologies that prevents the installation of cyclotron facilities in the proximity of the hospitals to produce the FDG radionuclides needed for the PET scanning. In addition, the tracer preparation cost that is also high compared to alternative neuroimaging technologies, such as Magnetic Resonance Imaging (MRI). These two limitations have resulted in a low penetration of the FDG-PET technology in developing countries. Previously, we assessed the possibility of creating functional MRI activity maps comparable to FDG-PET cerebral metabolism maps. These maps are of clinical interst, specially for centers without access to FDG-PET. This approach also overcomes the problem of recognizing individual networks of independent component selection in fMRI resting state analysis, a common approach in resting state fMRI analysis. The results showed significant levels of similarity between FDG-PET and the fMRI based maps. These results point towards the possibility of using fMRI resting state to estimate metabolic activity. Unfortunately, this work has the major limitation that was developed for 3 Tesla magnetic resonators. An expensive technology with low penetration in developed countries. In this project, a novel computational method to generate similar FDG-PET cerebral metabolic maps out of brain activity measurements using functional magnetic resonance imaging (fMRI) at low magnetic field (1.5T), a more widely used technology already available in low income countries, will be developed. This work will provide a new simplified framework of brain activity analysis for the neurology community that could be used to study brain pathologies in local populations and which can be adapted to the current technological scenario of developing countries.