Since its development in the 70’s and 80’s, the technique of magnetic resonance imaging (MRI) has substantially contributed to the study of the human brain. The implementation of specialized sequences of different modalities (structural, functional, chemical), as well as specific analytical approaches reflect on the high productivity in human neuroscience. The recent advent of Artificial Intelligence together with the implementation of multimodal approaches to examine different tissues simultaneously, and the exponential growth of Data Science, have opened the possibility of exploring more complex aspects of brain structure and function. This process, however, involves challenges at both the computational and statistical levels. In this symposium we will present some of the leading methodologies used to characterize morphological and functional aspects of the human brain in normal subjects and neurological patients. We will discuss the use of promising brain markers/indicators extracted from functional (fMRI), structural (T1), and diffusion (DWI) images to characterize brain function, learning-related plasticity, and for clinical diagnosis. We will also review some critical algorithms of machine learning, and their application in the detection and identification of subtle brain changes in both normal individuals and patients. Finally, we will address some of the challenges associated with this complex approach and the limitations of the technique.