2024-03-28T17:59:30Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/397192021-06-23T14:17:03Zcom_10324_30605com_10324_894col_10324_41
2019-12-02T12:35:32Z
urn:hdl:10324/39719
Characterization of dynamical neural activity by means of EEG data: application to schizophrenia
Bachiller Matarranz, Alejandro
Poza Crespo, Jesús
Hornero Sánchez, Roberto
Universidad de Valladolid. Escuela Técnica Superior de Ingenieros de Telecomunicación
Tratamiento de señal
Información, Teoría de la
Esquizofrenia
Schizophrenia is a disabling, chronic and severe mental illness characterized by disintegration of the process of thinking, contact with reality and emotional responsiveness. Schizophrenia has been related to an aberrant assignment of salience to external objects and internal representations. In addition, schizophrenia has been identified as a dysconnection syndrome, which is associated with a reduced capacity to integrate information among different brain regions. Relevance attribution likely involves diverse cerebral regions and their interconnections. As a consequence, many efforts have been devoted to identifying abnormalities in the cortical connections and their relation to schizophrenia symptoms and cognitive performance. Neural oscillations are one of the largest contributing mechanism for enabling coordinated activity during normal brain functioning. Alterations in neural oscillations and cognitive processing in schizophrenia have long been assessed using electroencephalographic (EEG) recordings (i.e. time-varying voltages on the human scalp generated by the electrical activity on the cerebral cortex). Event-related potentials (ERP) depict EEG data as a response to a cognitive task. ERP analyses are used to gain further insights into the neural mechanisms underlying cognitive dysfunctions. In this Doctoral Thesis, a 3-stimulus auditory-oddball paradigm was used for examining cognitive processing as response to both relevant and irrelevant stimuli. A total of 69 ERP recordings
were analyzed in the research papers included in the Thesis, which comprises 20 chronic schizophrenia patients, 11 first episode patients and 38 healthy controls. This Doctoral Thesis is focused on the study, design and application of biomedical signal processing methodologies in order to facilitate the understanding of cognitive processes altered by the schizophrenia. EEG data were examined using a two-level analysis: (I) local activation studies to quantify functional segregation of the brain network, by means of spectral analysis and by assessing neural source generators of P3a and P3b components; and (II) EEG interactions studies to explore functional integration across brain regions, including pair-wise couplings and exploring hierarchical organization of neural rhythms.
2019-12-02T12:35:32Z
2019-12-02T12:35:32Z
2017
info:eu-repo/semantics/doctoralThesis
http://uvadoc.uva.es/handle/10324/39719
10.35376/10324/39719
eng
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional