Dynamical brain connectivity markers in presymptomatic Alzheimer’s disease

Alzheimer's disease is the most prevalent cause of dementia generally with an onset after the 65 years. However, there are some genetic mutations that induce the onset of the neurocognitive symptoms before that age. The study of mutation carriers provides a unique opportunity to identify early...

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Autores:
Suárez Revelo, Jazmín Ximena
Ochoa Gómez, John Fredy
Duque Grajales, Jon Edinson
Tobón Quintero, Carlos Andrés
Tipo de recurso:
Article of investigation
Fecha de publicación:
2016
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/48203
Acceso en línea:
https://hdl.handle.net/10495/48203
Palabra clave:
Enfermedad de Alzheimer
Alzheimer Disease
Electroencefalografía
Electroencephalography
Redes Neuronales Gráficas
Graph Neural Networks
https://id.nlm.nih.gov/mesh/D000544
https://id.nlm.nih.gov/mesh/D004569
https://id.nlm.nih.gov/mesh/D000098422
ODS 3: Salud y bienestar. Garantizar una vida sana y promover el bienestar de todos a todas las edades
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
Description
Summary:Alzheimer's disease is the most prevalent cause of dementia generally with an onset after the 65 years. However, there are some genetic mutations that induce the onset of the neurocognitive symptoms before that age. The study of mutation carriers provides a unique opportunity to identify early preclinical changes related to Alzheimer's disease. The Event Related Potentials are a powerful tool used for the study of the neural substrates of cognitive function and deterioration. The connectivity analysis emerges as an alternative to the average approach typical in Event Related Potentials. In the current work two groups, mutation carriers and non-carriers, perform a memory task during Electroencephalography recording. Brain graphs are built at different time points using the directed Direct Transfer Function. Our results show how the dynamical study of the connectivity might help to detect neuronal changes in preclinical stage of Alzheimer's disease.