dc.contributor.author | Ereira, S | |
dc.contributor.author | Waters, S | |
dc.contributor.author | Razi, A | |
dc.contributor.author | Marshall, CR | |
dc.date.accessioned | 2024-07-08T07:25:16Z | |
dc.date.available | 2024-07-08T07:25:16Z | |
dc.date.issued | 2024-06-06 | |
dc.identifier.citation | Ereira, S., Waters, S., Razi, A. et al. Early detection of dementia with default-mode network effective connectivity. Nat. Mental Health (2024). https://doi.org/10.1038/s44220-024-00259-5 | en_US |
dc.identifier.issn | 2731-6076 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/97898 | |
dc.description.abstract | Altered functional connectivity precedes structural brain changes and symptoms in dementia. Alzheimer’s disease is the largest contributor to dementia at the population level, and disrupts functional connectivity in the brain’s default-mode network (DMN). We investigated whether a neurobiological model of DMN effective connectivity could predict a future dementia diagnosis at the single-participant level. We applied spectral dynamic causal modeling to resting-state functional magnetic resonance imaging data in a nested case–control group from the UK Biobank, including 81 undiagnosed individuals who developed dementia up to nine years after imaging, and 1,030 matched controls. Dysconnectivity predicted both future dementia incidence (AUC = 0.82) and time to diagnosis (R = 0.53), outperforming models based on brain structure and functional connectivity. We also evaluated associations between DMN dysconnectivity and major risk factors for dementia, revealing strong relationships with polygenic risk for Alzheimer’s disease and social isolation. Neurobiological models of effective connectivity may facilitate early detection of dementia at population level, supporting rational deployment of targeted dementia-prevention strategies. | en_US |
dc.format.extent | 1 - 14 | |
dc.publisher | Springer Nature | en_US |
dc.relation.ispartof | Nature Mental Health | |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.subject | Aging | en_US |
dc.subject | Brain Disorders | en_US |
dc.subject | Alzheimer's Disease | en_US |
dc.subject | Neurosciences | en_US |
dc.subject | Prevention | en_US |
dc.subject | Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) | en_US |
dc.subject | Biomedical Imaging | en_US |
dc.subject | Acquired Cognitive Impairment | en_US |
dc.subject | Neurodegenerative | en_US |
dc.subject | Clinical Research | en_US |
dc.subject | Dementia | en_US |
dc.subject | Neurological | en_US |
dc.subject | 3 Good Health and Well Being | en_US |
dc.title | Early detection of dementia with default-mode network effective connectivity | en_US |
dc.type | Article | en_US |
dc.rights.holder | © The Author(s) 2024 | |
dc.identifier.doi | 10.1038/s44220-024-00259-5 | |
pubs.notes | Not known | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |