RT info:eu-repo/semantics/doctoralThesis
T1 Characterization of local activation and network dynamics from electrical brain activity: application to schizophrenia, mild cognitive impairment and dementia due to Alzheimer's disease
A1 Núñez Novo, Pablo
A2 Universidad de Valladolid. Escuela Técnica Superior de Ingenieros de Telecomunicación
K1 Cerebro - Actividad eléctrica cerebral
K1 Esquizofrenia
K1 Demencia - Alzheimer
K1 Actividad neuronal - Señales electroencefalográficas
K1 33 Ciencias Tecnológicas
K1 32 Ciencias Médicas
AB The field of neuroscience has explored the human brain and its neuronal circuitsfor centuries, trying to understand the underpinnings of perception, memory, in-formation transfer, and learning, among others. The term has evolved throughthe years to include novel approaches, such as molecular biology, medical imaging,and computational neuroscience, allowing scientists to study and characterize thenervous system in more unique ways than ever. Ironically, although the increasein available resources and techniques has brought unprecedented advances in ourunderstanding of the brain, it has also served as a constant reminder that it stillremains the biggest mystery of human anatomy. Rather than being discouragedby this fact, we should embrace it and employ these tools to gradually uncover itssecrets.The present Doctoral Thesis focuses on the research, development and test-ing of new methodological frameworks for the characterization of neural activityby means of electroencephalographic (EEG) signals. In particular, the Thesis fo-cuses on new methods and measures aimed at describing the dynamic behaviorof brain networks in in the following diseases that affect to the central nervoussystem: schizophrenia, mild cognitive impairment (MCI), and dementia due toAlzheimer’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 diseaseshas mostly been studied from a static perspective, focusing on aspects of neuralactivity that remain constant across time. While this is a valid and useful approachthat has helped unravel many aspects of cognition, recently there has been a shiftin focus towards dynamic analyses. Even though it is reasonable to assume thatcognitive tasks elicit a dynamic response in the brain, it is not as straightforwardto conceive that the brain displays such changes in activation during rest. Here,we focus on exploring these properties from a granular perspective of local EEGactivation to a global view of how brain networks evolve during the resting stateand an auditory oddball task, in order to determine whether aberrant behaviorcan be found in a dynamic context.
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/55099
UL https://uvadoc.uva.es/handle/10324/55099
LA eng
NO Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática
DS UVaDOC
RD 17-jul-2024