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Brain connectivity in frailty: Insights from The Irish Longitudinal Study on Ageing (TILDA).

Authors :
Zúñiga, Raquel Gutiérrez
Davis, James R.C.
Boyle, Rory
De Looze, Céline
Meaney, James F.
Whelan, Robert
Kenny, Rose Anne
Knight, Silvin P.
Ortuño, Román Romero
Source :
Neurobiology of Aging. Apr2023, Vol. 124, p1-10. 10p.
Publication Year :
2023

Abstract

• Connectome-based predictive modeling was used with a Frailty Index. • The frailty network had its main connectivity in the visual network. • The robustness network had its main connectivity in the basal ganglia. • Connectivity patterns were different based on the degree of frailty. Frailty in older adults is associated with greater risk of cognitive decline. Brain connectivity insights could help understand the association, but studies are lacking. We applied connectome-based predictive modeling to a 32-item self-reported Frailty Index (FI) using resting state functional MRI data from The Irish Longitudinal Study on Ageing. A total of 347 participants were included (48.9% male, mean age 68.2 years). From connectome-based predictive modeling, we obtained 204 edges that positively correlated with the FI and composed the "frailty network" characterised by connectivity of the visual network (right); and 188 edges that negatively correlated with the FI and formed the "robustness network" characterized by connectivity in the basal ganglia. Both networks' highest degree node was the caudate but with different patterns: from caudate to visual network in the frailty network; and to default mode network in the robustness network. The FI was correlated with walking speed but not with metrics of global cognition, reinforcing the matching between the FI and the brain connectivity pattern found (main predicted connectivity in basal ganglia). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01974580
Volume :
124
Database :
Academic Search Index
Journal :
Neurobiology of Aging
Publication Type :
Academic Journal
Accession number :
161879580
Full Text :
https://doi.org/10.1016/j.neurobiolaging.2023.01.001