Dicks, E;
Vermunt, L;
van der Flier, WM;
Barkhof, F;
Scheltens, P;
Tijms, BM;
Alzheimer’s Disease Neuroimaging Initiative, .;
(2020)
Grey matter network trajectories across the Alzheimer's disease continuum and relation to cognition.
Brain Communications
, 2
(2)
, Article fcaa177. 10.1093/braincomms/fcaa177.
(In press).
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Abstract
Biomarkers are needed to monitor disease progression in Alzheimer's disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer's disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer's disease cognitive continuum and whether such changes relate to concurrent decline in cognition. We included 190 cognitively unimpaired, amyloid normal (controls) and 523 individuals with abnormal amyloid across the cognitive continuum (preclinical, prodromal, Alzheimer's disease dementia) from the Alzheimer's Disease Neuroimaging Initiative and calculated single-subject grey matter network measures (median of five networks per individual over 2 years). We fitted linear mixed models to investigate how network measures changed over time and whether such changes were associated with concurrent changes in memory, language, attention/executive functioning and on the Mini-Mental State Examination. We further assessed whether associations were modified by baseline disease stage. We found that both cognitive functioning and network measures declined over time, with steeper rates of decline in more advanced disease stages. In all cognitive stages, decline in network measures was associated with concurrent decline on the Mini-Mental State Examination, with stronger effects for individuals closer to Alzheimer's disease dementia. Decline in network measures was associated with concurrent cognitive decline in different cognitive domains depending on disease stage: In controls, decline in networks was associated with decline in memory and language functioning; preclinical Alzheimer's disease showed associations of decline in networks with memory and attention/executive functioning; prodromal Alzheimer's disease showed associations of decline in networks with cognitive decline in all domains; Alzheimer's disease dementia showed associations of decline in networks with attention/executive functioning. Decline in grey matter network measures over time accelerated for more advanced disease stages and was related to concurrent cognitive decline across the entire Alzheimer's disease cognitive continuum. These associations were disease stage dependent for the different cognitive domains, which reflected the respective cognitive stage. Our findings therefore suggest that grey matter measures are helpful to track disease progression in Alzheimer's disease.
Type: | Article |
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Title: | Grey matter network trajectories across the Alzheimer's disease continuum and relation to cognition |
Location: | England |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1093/braincomms/fcaa177 |
Publisher version: | https://doi.org/10.1093/braincomms/fcaa177 |
Language: | English |
Additional information: | © The Author(s) 2020. Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/). |
Keywords: | Alzheimer’s disease, cognition, graph theory, longitudinal, single-subject grey matter networks |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation |
URI: | https://discovery.ucl.ac.uk/id/eprint/10119192 |
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