Biomarkers identification in Alzheimer’s disease using effective connectivity analysis from electroencephalography recordings

Alzheimer’s disease (AD) is the most common cause of dementia, which generally affects people over 65 years old. Some genetic mutations induce early onset of AD and help to track the evolution of the symptoms and the physiological changes at different stages of the disease. In Colombia there is a la...

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Autores:: Suárez-Revelo, Jazmín X.
Ochoa-Gómez, John F.
Duque-Grajales, Jon E.
Tobón-Quintero, Carlos A.

Tipo de recurso:: Article of journal

Fecha de publicación:: 2016

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

Description
Summary:	Alzheimer’s disease (AD) is the most common cause of dementia, which generally affects people over 65 years old. Some genetic mutations induce early onset of AD and help to track the evolution of the symptoms and the physiological changes at different stages of the disease. In Colombia there is a large family group with the PSEN1 E280A mutation with a median age of 46,8 years old for onset of symptoms. AD has been defined as a disconnection syndrome; consequently, network approaches could help to capture different features of the disease. The aim of the current work is to identify a biomarker in AD that helps in the tracking of the neurodegenerative process. Electroencephalography (EEG) was recorded during the encoding of visual information for four groups of individuals: asymptomatic and mild cognitive impairment carriers of the PSEN1 E280A mutation, and two non-carrier control groups. For each individual, the effective connectivity was estimated using the direct Directed Transfer Function and three measurements from graph theory were extracted: input strength, output strength and total strength. A relation between the cognitive status and age of the participants with the connectivity features was calculated. For those connectivity measures in which there is a relation with the age or the clinical scale, the performance as a diagnostic feature was evaluated. We found that output strength connectivity in the right occipito-parietal region is related to age of the carrier groups (r=−0,54, p=0,0036) and has a high sensitivity and high specificity to distinguish between carriers and non-carriers (67% sensitivity and 80% specificity in asymptomatic cases, and 83% sensitivity and 67% specificity in symptomatic cases). This relationship indicates that output strength connectivity could be related to the neurodegenerative process of the disease and could help to track the conversion from the asymptomatic stage to dementia.

Biomarkers identification in Alzheimer’s disease using effective connectivity analysis from electroencephalography recordings

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