Detalhes bibliográficos
| Ano de defesa: |
2020 |
| Autor(a) principal: |
Maulaz, Carolina Moreira
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| Orientador(a): |
Silva, Ana Maria Marques da
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| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
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| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Elétrica
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| Departamento: |
Escola Politécnica
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| País: |
Brasil
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| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tede2.pucrs.br/tede2/handle/tede/9408
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Resumo: |
Studies suggests that healthy aging (ES) and certain neurological diseases, such as Alzheimer's disease (AD) and mild cognitive impairment (MCI), affects brain functional connectivity. Graph Theory (GT) metrics allows us to analyze ruptures in the brain functional connectivity. However, researchers has been preferentially exploring transversal studies. The general objective of this work was to investigate the evolution of brain connectivity in individuals with healthy aging and cognitive decline, based on resting state functional magnetic resonance imaging (rs-fMRI) and using graph metrics. The data was divided into two groups, stable (EES-EES and EMCI-EMCI) and converter (ES-MCI and MCI-AD). The longitudinal analysis was carried out between each evolution over time, then crosswise compared healthy individuals between the stable and converting group, and the same process was performed for individuals with MCI.The processing was implemented in SPM12-MATLAB performed in the CONN Toolbox. The networks analyzed were parietal, sensory motor, visual, language, default mode network, dorsal attention and salience. The GT metrics chosen to describe the main topological characteristics of the networks were: characteristic path length, global efficiency, local efficiency, clustering coefficient and degree. The results indicateds a decrease in the strength of functional connectivity in individuals with MCI and AD compared with healthy aging. In healthy aging individuals, was identified that local efficiency metric can be used as a possible biomarker between those who remain stable and those who convert. In MCI individuals, a metric was not identified, but a set of metrics that vary between converting and stable groups. The analysis of all networks in the resting state allowed for a better characterization of the groups, enabling the differentiation between stable healthy individuals and those who convert to cognitive decline over time. |
