Detalhes bibliográficos
| Ano de defesa: |
2015 |
| Autor(a) principal: |
Couto, Jefferson Leal
 |
| Orientador(a): |
Franco, Alexandre Rosa
|
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Elétrica
|
| Departamento: |
Faculdade de Engenharia
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tede2.pucrs.br/tede2/handle/tede/6151
|
Resumo: |
With the use of resting state functional magnetic resonance imaging (rs-FMRI) we can analyze the functional connectivity between different brain areas. However, recent studies show that this connectivity undergoes fluctuations over time (also known as dynamic Resting State). In this study, the analysis of Graph Theoretical (GT) metrics will be used to assess the variability of the correlation between these areas, through a windowing technique. For this study, we used images of 15 patients with ADHD that underwent a clinical treatment with the medication (Ritalin®). Images were taken before treatment (Visit 1 - PRE) and after 6 months of treatment (Visit 2 - POST). We determined the GT metrics for windows between 75 and 150s generated from the correlation between the various groups after applying a mask that divided the brain into 190 regions. The results showed statistically significant differences between visits for the metric of the characteristic path length. Were also generated graphs that show the fluctuations of each of the metrics. GT analysis identified an increase in the average value of the characteristic path length after drug treatment for most patients. However, some patients behaved differently and therefore results are not conclusive. Furthermore, it was observed that there is a dependence of GT metrics based on the size of the window. Conclusion show that based on the analysis of the dynamics of the brain network by the GT metrics show to be an auxiliary tool in the diagnosis of ADHD, but still requires further studies to test the viability of this tool as a diagnostic test in neuroimaging. |
