Connectivity studies using resting-state functional magnetic resonance imaging (rsfMRI) data have enhanced our knowledge on the organization of large-scale structural and functional brain networks, which consist of spatially distributed, but functionally linked regions that continuously share information with each other. Brain's energy is largely consumed at rest during spontaneous neuronal activity (~20%), while task-related increases in metabolism energy are minor (<5%). Spontaneous low-frequency correlated fluctuations in blood oxygenation level dependent (BOLD) rsfMRI signals at the level of large-scale neural systems are not noise, but orderly and organized in a series of functional networks that permanently maintain a high level of temporal coherence among brain areas that are structurally segregated and functionally linked in resting-state networks (RSNs). Some RSNs are functionally organized as dynamically competing systems both at rest and during task-related experiments. The default mode network (DMN), the most important RSN, is involved in realization of tasks like memory retrieval, emotional process, and social cognition. Cortical connectivity at rest was reportedly altered in several neurological and psychiatric disorders. Most recently, human brain function has been imaged in fMRI, and thereby accessing both sides of the mind-brain interface (subjective experience and objective observations) has simultaneously been performed. As such, functional neuroimaging moves onto new potential applications like reading the brain states, brain-computer interfaces, lie detection, aso. The present contribution aims to highlight the fundamentals and review the up-to-date findings in imaging modalities dedicated to brain connectivity and, more generally, to connectomics on the basis of diffusion tensor imaging (DTI) for white matter (WM) and rsfMRI for grey matter (GM) studies, respectively.
Radu Mutihac is Chair of Medical Physics Section, University of Bucharest, and works in Neuroscience, Signal Processing, Microelectronics, and Artificial Intelligence. As postdoc/research associate/visiting professor/full professor he run his research at the University of Bucharest, International Centre for Theoretical Physics (Italy), Ecole Polytechnique (France), Institut Henri Poincare (France), KU Leuven (Belgium). Data mining and exploratory analysis of neuroimaging time series were addressed during two Fulbright Grants in Neuroscience (Yale University and University of New Mexico). His research in fused biomedical imaging modalities was carried out at the Johns Hopkins University, National Institutes of Health, and Walter Reed Army Institute of Research, MD, USA. Prof. R. Mutihac is member of the ISMRM, ESMRMB, OHBM, Romanian US Alumni Association, and fellow of Signal Processing and Neural Networks Society IEEE, as well as referee for several journals of the Institute of Physics (London, UK), Neural Networks (Elsevier), IEEE Transactions on Image Processing, and evaluator/expert for the ISMRM, OHBM, ARACIS, CNCSIS, UEFISCDI, The Romanian – U.S. Fulbright Commission. and the European Commission (FP7, H2020). He published over 100 scientific papers, 12 monographs, and contributed with chapters in other 10 text books. He participated to more than 150 scientific meetings with posters and oral presentations, seminars, invited and plenary lectures, as well as acting as organizer, chairman, and keynote speaker. Following his scientific activity, Prof. Radu Mutihac was nominated member of the Editorial Board of six journals in the field of Neuroscience: Journal of the Romanian College of Medical Physicists, Epilepsy Journal, Journal of Childhood & Developmental Disorders, Journal of Neurology and Clinical Neuroscience, Medical and Clinical Reviews, The Neurologist - Clinical and Therapeutics Journal.