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Support to the Differential Diagnosis of Dementia Through Electroencephalogram-Based Biomarkers

Abstract

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly, with progressive decline in cognitive function, significantly affecting quality of life. The prevalence and emotional and financial burden of AD on patients, their families and society are predicted to grow significantly in the near future due to the global increase in life expectancy at birth. Thus, the early detection of AD becomes a public health issue, as it allows starting a treatment that can significantly delay the progression of the disease. Currently available biomarkers, including volumetric magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) analysis, are expensive, rarely available in community health facilities, and relatively invasive. Considering its low cost, wide availability and non-invasiveness, electroencephalography (EEG) is offered as an alternative to track the early stages of cognitive decline in routine clinical settings. In this sense, it is also necessary to perform a differential diagnosis of AD, which allows differentiating it from other neurodegenerative diseases. Thus, early distinction between dementia due to Alzheimer's disease (ADD) and dementia with Lewy bodies (DLB) is important for an accurate prognosis, as patients with DLB generally have more rapid disease progression. On the other hand, understanding the genetic etiology of AD has currently been one of the main focuses of research in neurodegenerative diseases. Based on these considerations, this research project is divided into two parts. For the first part of this project, related to the differential diagnosis of ADD and DLB, an EEG database recorded at rest will be used. In this first part, we intend to develop, improve and validate biomarkers based on the analysis of EEG signals recorded at rest, to support the early and differential diagnosis of dementia. In the second part of this project, related to the diagnosis of AD considering genetic factors, an EEG database recorded during the execution of tasks will be used. For this second part, markers will be developed, improved and validated based on the analysis of EEG signals recorded during the execution of auditory tasks. (AU)

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