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Brain connectivity and dynamics


The non-invasive study of human brain function is one of the major scientific endeavors of current research in Medicine and Biomedical Engineering, as acknowledged by the substantial funding and high strategic priority awarded by relevant funding bodies within the European Union (www.humanbrainproject.eu) and the United States of America (braininitiative.nih.gov). Functional and Structural Magnetic Resonance Imaging (fMRI/MRI), electroencephalography (EEG), and magnetoencephalography (MEG) are the most widely used modalities, whereas functional Near-Infrared Spectroscopy (fNIRS), Transcranial Magnetic Stimulation (TMS) and transcranial direct current stimulation (tDCS), have provided complementary capabilities.

The early emphasis on localization of brain function has been replaced by an attempt to probe the brain as a dynamic entity whose operation depends on the interaction between several loci. The term “connectivity” encapsulates this view of the brain as a system of connected components that interact dynamically in order to support healthy brain function and tend to elicit pathological behavior when their communication is disturbed. Connectivity may be studied from a strictly functional point of view and/or from a structural/anatomic point of view. Since functional connectivity is dependent on the existence and integrity of neural structural pathways, it makes sense to cover both structural and functional connectivity in an integrated approach. “Dynamics” can be defined, in this context, as the study of how brain signals unfold in time, during rest (spontaneous oscillations) or in response to a stimulus, and the relation between temporal behavior and the underlying physiological mechanisms that support function.

The IBEB has a long tradition in the study of human brain function in health and disease. In recent years, brain connectivity has been a top priority.

The main currently active lines of research are:

  • Design and implementation of new MRI and fMRI sequences specifically tailored for connectivity and dynamic studies.
  • Development of novel physical models and algorithms for in vivo tracking of white matter fibers using diffusion kurtosis imaging.
  • Application to the study of the interaction between resting-state networks in the healthy and pathological brain and to the study of low-frequency modulation of brain oscillations and its implications
  • Development, computer implementation and validation of new signal processing methods for the study of functional connectivity.
  • Implementation of a simulation platform capable of generating physiologically plausible M/EEG and fMRI data, for the benchmarking of data processing methods.
  • Application to the study of pathologies (e.g. Alzheimer’s Disease and Parkinson’s Disease) as well as to presurgical mapping of epilepsy and brain tumor patients.
  • Development of a software tool for brain connectivity studies (www.mibca.com).
  • Development of applications for visualizing brain connectivity data (Brain Connectivity Leap).
  • Classification of brain connectivity data using advanced machine-learning algorithms for supporting the diagnosis of neuropsychiatric diseases.