Phenotyping Superagers Using Resting-State fMRI

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Tipo de produção
article
Data de publicação
2023
DOI
Scopus
Web of Science
PubMed
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Título do Volume
Editora
AMER SOC NEURORADIOLOGY
Autores
PAULA, D. R. de
GREEN, N.
HALDER, A.
MORAES, N. C.
Citação
AMERICAN JOURNAL OF NEURORADIOLOGY, v.44, n.4, p.424-433, 2023
Projetos de Pesquisa
Unidades Organizacionais
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Resumo
BACKGROUND AND PURPOSE: Superagers are defined as older adults with episodic memory performance similar or superior to that in middle-aged adults. This study aimed to investigate the key differences in discriminative networks and their main nodes between superagers and cognitively average elderly controls. In addition, we sought to explore differences in sensitivity in detecting these functional activities across the networks at 3T and 7T MR imaging fields. MATERIALS AND METHODS: Fifty-five subjects 80 years of age or older were screened using a detailed neuropsychological protocol, and 31 participants, comprising 14 superagers and 17 cognitively average elderly controls, were included for analysis. Participants underwent resting-state-fMRI at 3T and 7T MR imaging. A prediction classification algorithm using a penalized regression model on the measurements of the network was used to calculate the probabilities of a healthy older adult being a superager. Additionally, ORs quantified the influence of each node across preselected networks. RESULTS: The key networks that differentiated superagers and elderly controls were the default mode, salience, and language networks. The most discriminative nodes (ORs > 1) in superagers encompassed areas in the precuneus posterior cingulate cortex, prefrontal cortex, temporoparietal junction, temporal pole, extrastriate superior cortex, and insula. The prediction classification model for being a superager showed better performance using the 7T compared with 3T resting-state-fMRI data set. CONCLUSIONS: Our findings suggest that the functional connectivity in the default mode, salience, and language networks can provide potential imaging biomarkers for predicting superagers. The 7T field holds promise for the most appropriate study setting to accurately detect the functional connectivity patterns in superagers.
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URI
https://observatorio.fm.usp.br/handle/OPI/58911
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Coleções
Artigos e Materiais de Revistas Científicas - FM/MNE