UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline

Pelkmans, Wiesje; Vromen, Ellen M; Dicks, Ellen; Scheltens, Philip; Teunissen, Charlotte E; Barkhof, Frederik; van der Flier, Wiesje M; ... Alzheimer’s Disease Neuroimaging Initiative; + view all (2022) Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline. Brain Communications , 4 (2) , Article fcac026. 10.1093/braincomms/fcac026. Green open access

[thumbnail of Grey matter network markers identify individuals with prodromal Alzheimers disease who will show rapid clinical decline.pdf]
Preview
Text
Grey matter network markers identify individuals with prodromal Alzheimers disease who will show rapid clinical decline.pdf - Published Version

Download (722kB) | Preview

Abstract

Individuals with prodromal Alzheimer's disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer's disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1-42 levels from the Amsterdam Dementia Cohort and the Alzheimer's Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer's Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer's Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer's disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer's Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer's disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

Type: Article
Title: Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/braincomms/fcac026
Publisher version: https://doi.org/10.1093/braincomms/fcac026
Language: English
Additional information: © The Author(s) 2022. 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 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Alzheimer’s disease, clinical progression, graph theory, grey matter networks, mild cognitive impairment
UCL classification: 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 > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
URI: https://discovery.ucl.ac.uk/id/eprint/10146865
Downloads since deposit
21Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item