21 augustus 2024: zie ook dit artikel: https://kanker-actueel.nl/NL/alzheimer-dementie-is-via-een-bloedtest-jaren-voordat-de-ziekte-zich-openbaart-op-te-sporen-en-daardoor-wellicht-ook-te-voorkomen-of-uit-te-stellen-voordat-de-ziekte-ernstig-wordt.html

21 juni 2024: Bron: Nature en EOS wetenschap

EOS wetenschap heeft vaak interessante artikelen zoals dit artikel n.a.v. van een publicatie in Nature. Ik kopieer de eerste alinea en klik dan op lees verder:

Nieuwe methode voorspelt dementie tot negen jaar voor de diagnose

Onderzoekers ontwikkelden een nieuwe methode die dementie met 80 procent nauwkeurigheid kan voorspellen, tot negen jaar voordat de diagnose daadwerkelijk wordt gesteld.

Wereldwijd leiden ongeveer 55 miljoen mensen aan dementie. De ziekte van Alzheimer is daar in 60-70% van de gevallen verantwoordelijk voor. Deze ziekte tast het Default Mode Netwerk (DMN) aan, een netwerk van hersengebieden waaronder de mediale prefrontale cortex, de posterior cingulate cortex, en de bilaterale inferieure pariëtale cortex, samen met een reeks aanvullende hersengebieden. Er werd steeds verondersteld dat dat netwerk actief is wanneer het brein ‘in rust’ is. Vandaag weten we dat het ook een rol speelt bij verschillende cognitieve processen zoals het ophalen van herinneringen en sociaal begrip.

Onderzoekers van de Queen Mary University in Londen onderzochten1 of veranderingen in de communicatie tussen hersengebieden van het Default Mode Network (DMN) dementie kunnen voorspellen. Hiervoor gebruikten ze hersenscans van meer dan 1.100 vrijwilligers. Van die groep ontwikkelden 81 personen uiteindelijk dementie. Ze vergeleken de functionele MRI-scans van die mensen met de hersenscans van meer dan 1.000 personen zonder dementie. Om een breed beeld van dementie te krijgen, sloten ze andere vormen dan de ziekte van Alzheimer niet uit.>>>>>>>lees verder het hele artikel

Het originele artikel uit Nature is gratis in te zien met veel grafieken en voor wetenschappers gedetailleerde uitleg:

Article
Open access
Published: 06 June 2024

Early detection of dementia with default-mode network effective connectivity

Nature Mental Health (2024)Cite this article

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.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Processed group-level DCM results are available at https://github.com/Wolfson-PNU-QMUL/UKB_DCM_dementia. Supplementary Table 4 contains UKB field names for UKB data variables analyzed in this study.

Code availability

MATLAB analysis code is available at https://github.com/Wolfson-PNU-QMUL/UKB_DCM_dementia.

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Acknowledgements

We thank all staff at the Centre for Preventive Neurology for helpful comments on the presentation of these results at a laboratory meeting. This research made use of Queen Mary’s Apocrita high-performance computing facility, supported by QMUL Research-IT. We also acknowledge the assistance of the ITS Research team at Queen Mary’s. UKB data access was funded by a grant from the Tom and Sheila Springer Charity. S.E. received funding from the NHSE as part of the Specialized Foundation Programme (SFP). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Centre for Preventive Neurology, Queen Mary University of London, London, UK

Sam Ereira, Sheena Waters & Charles R. Marshall
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK

Sam Ereira
Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia

Adeel Razi
Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK

Adeel Razi
CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Ontario, Canada

Adeel Razi
Department of Neurology, Royal London Hospital, London, UK

Charles R. Marshall

Contributions

C.R.M. conceived the project and edited the paper. S.E. designed the analysis pipeline, analyzed the data and wrote the initial draft of the paper. S.W. conducted the case-control matching, acquired the UKB data and provided comments on the initial draft of the paper. A.R. advised on the DCM analyses and provided comments on the initial draft of the paper.

Corresponding author

Correspondence to Charles R. Marshall.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Mental Health thanks Michel Grothe, Timothy Rittman and the other, anonymous reviewer(s) for their contribution to the peer review of this work.