Characterization of local activation and network dynamics from electrical brain activity: application to schizophrenia, mild cognitive impairment and dementia due to Alzheimer's disease

Abstract

The field of neuroscience has explored the human brain and its neuronal circuits for centuries, trying to understand the underpinnings of perception, memory, in- formation transfer, and learning, among others. The term has evolved through the years to include novel approaches, such as molecular biology, medical imaging, and computational neuroscience, allowing scientists to study and characterize the nervous system in more unique ways than ever. Ironically, although the increase in available resources and techniques has brought unprecedented advances in our understanding of the brain, it has also served as a constant reminder that it still remains the biggest mystery of human anatomy. Rather than being discouraged by this fact, we should embrace it and employ these tools to gradually uncover its secrets. The present Doctoral Thesis focuses on the research, development and test- ing of new methodological frameworks for the characterization of neural activity by means of electroencephalographic (EEG) signals. In particular, the Thesis fo- cuses on new methods and measures aimed at describing the dynamic behavior of brain networks in in the following diseases that affect to the central nervous system: schizophrenia, mild cognitive impairment (MCI), and dementia due to Alzheimer’s disease (AD), by means of EEG recordings. Some aspects of disease- induced alterations of EEG activity are well documented for all three pathologies; these include: relative power shifting towards lower frequency bands or disconnec- tion of static functional connectivity. Nonetheless, neural activity in these diseases has mostly been studied from a static perspective, focusing on aspects of neural activity that remain constant across time. While this is a valid and useful approach that has helped unravel many aspects of cognition, recently there has been a shift in focus towards dynamic analyses. Even though it is reasonable to assume that cognitive tasks elicit a dynamic response in the brain, it is not as straightforward to conceive that the brain displays such changes in activation during rest. Here, we focus on exploring these properties from a granular perspective of local EEG activation to a global view of how brain networks evolve during the resting state and an auditory oddball task, in order to determine whether aberrant behavior can be found in a dynamic context.