Alzheimer's disease (AD) is a degenerative brain disorder which is the most common cause of dementia. Since there is no cure for AD, an early diagnosis is essential to slow down the evolution of the disease with a proper pharmacological treatment. Electroencephalography (EEG) represents a valid tool for studying AD. EEG of AD patients is characterized by a 'slowing', that is the power increases in low frequencies (delta and theta) and decreases in higher frequency (alpha and beta), compared to normal elderly. The purpose of our study is the computation of the power current density in eight patients, who were diagnosed with MCI at time T0 and mild AD at time T1 (four months later), starting from the brain active source reconstruction. The novelty is that we employed the eLORETA algorithm, unlike the previous studies which used the old version of the algorithm named LORETA. It is also the first longitudinal study which considers such a short time period to explore the evolution of the disease. The results are largely consistent with those reported in literature, so that this study could represent a good starting point for more detailed future investigation.

An eLORETA Longitudinal Analysis of Resting State EEG Rhythms in Alzheimer's Disease

S. Dattola;F. La Foresta
2020

Abstract

Alzheimer's disease (AD) is a degenerative brain disorder which is the most common cause of dementia. Since there is no cure for AD, an early diagnosis is essential to slow down the evolution of the disease with a proper pharmacological treatment. Electroencephalography (EEG) represents a valid tool for studying AD. EEG of AD patients is characterized by a 'slowing', that is the power increases in low frequencies (delta and theta) and decreases in higher frequency (alpha and beta), compared to normal elderly. The purpose of our study is the computation of the power current density in eight patients, who were diagnosed with MCI at time T0 and mild AD at time T1 (four months later), starting from the brain active source reconstruction. The novelty is that we employed the eLORETA algorithm, unlike the previous studies which used the old version of the algorithm named LORETA. It is also the first longitudinal study which considers such a short time period to explore the evolution of the disease. The results are largely consistent with those reported in literature, so that this study could represent a good starting point for more detailed future investigation.
Alzheimer's Disease; mild cognitive impairment; EEG; eLORETA
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/63218
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