Functional connectivity for discrimination between mild cognitive impairment and subjective cognitive decline in Alzheimer disease: A study on resting‐state EEG rhythms in the Peruvian population Neuroimaging / Optimal neuroimaging measures for early detection

• Published in: Alzheimer's & dementia Vol. 16; no. S5
• Main Authors: Vilca, Brenda Nadia Chino, Fernández, Ricardo Bruña, Maestú, Fernando, Acobo, Roxana Castillo
• Format: Journal Article
• Language: English
• Published: 01.12.2020
• ISSN: 1552-5260; 1552-5279
• DOI: 10.1002/alz.039174

Abstract Background The increase in cases of dementia worldwide has promoted the development of research for its detection and early intervention. However, the strategies, procedures, and tools designed to address the problem from the international level have found significant barriers in its implementation in Latin American countries, where factors such as socio‐demographic variability and clinical techniques limit their scope in terms of identification and intervention. Under this framework, research with signals in EEG / MEG has shown that the analysis of functional connectivity can be a sensitive biomarker in neurodegenerative processes, including the analysis of pre‐symptomatic stages with subsequent conversion to Alzheimer disease. This study seeks to develop robust methods for the diagnosis and characterization of Alzheimer's disease (AD) in the phase from the analysis of 160‐channel EEG signals in the Peruvian population. Method 500 screening evaluations were carried out, with 75 adults/seniors between 50 and 75 years of age being selected: 17 subjective cognitive declines, 24 with a family history of dementia and 31 mild cognitive impairment. The participants underwent four evaluation sessions, which included an EEG record in a state of rest (eyes closed and eyes open) and neuropsychological tests. Result The functional connectivity patterns are consistent with similar studies using MEG in European and North‐American population. Conclusion Our results demonstrate the utility of the EEG for the diagnosis and distinction of the previous stages of AD and the variation of the default mode network between QSM, DCL, and AF.