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Título
Characterization of local activation and network dynamics from electrical brain activity: application to schizophrenia, mild cognitive impairment and dementia due to Alzheimer's disease
Autor
Director o Tutor
Año del Documento
2022
Titulación
Doctorado en Tecnologías de la Información y las Telecomunicaciones
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.
Materias (normalizadas)
Cerebro - Actividad eléctrica cerebral
Esquizofrenia
Demencia - Alzheimer
Actividad neuronal - Señales electroencefalográficas
Materias Unesco
33 Ciencias Tecnológicas
32 Ciencias Médicas
Departamento
Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
Collections
- Tesis doctorales UVa [2275]
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