RT Journal Article
SR Electronic
T1 Phenotyping Superagers Using Resting-State fMRI
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 424
OP 433
DO 10.3174/ajnr.A7820
VO 44
IS 4
A1 de Godoy, L.L.
A1 Studart-Neto, A.
A1 de Paula, D.R.
A1 Green, N.
A1 Halder, A.
A1 Arantes, P.
A1 Chaim, K.T.
A1 Moraes, N.C.
A1 Yassuda, M.S.
A1 Nitrini, R.
A1 Dresler, M.
A1 da Costa Leite, C.
A1 Panovska-Griffiths, J.
A1 Soddu, A.
A1 Bisdas, S.
YR 2023
UL http://www.ajnr.org/content/44/4/424.abstract
AB 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.ASSETarray spatial sensitivity encoding techniqueBOLDblood oxygen level–dependentDMNdefault mode networkECN-Lexecutive control network leftECN-Rexecutive control network rightENelastic netICAindependent component analysisIPATintegrated parallel acquisition techniquers-fMRIresting-state fMRIOLSordinary least squaresSNsalience network