Biomagnetic Framework for Identifying Network Dysfunction in dementia
Study Code / Acronym
BioFINDAward Number
MR/P502017/1Programme
Research GrantStatus / Stage
CompletedDates
1 October 2016 -31 July 2017
Duration (calculated)
00 years 09 monthsFunder(s)
MRC (UKRI)Funding Amount
£35,965.91Contracted Centre
University of CambridgePrincipal Investigator
Laura HughesPI Contact
Laura.Hughes[at]mrc-cbu.cam.ac.ukPI ORCID
0000-0001-5024-2022WHO Catergories
Disease Type
Alzheimer's Disease (AD)CPEC Review Info
| Reference ID | 254 |
|---|---|
| Researcher | Reside Team |
| Published | 12/06/2023 |
Data
| Study Code / Acronym | BioFIND |
|---|---|
| Award Number | MR/P502017/1 |
| Status / Stage | Completed |
| Start Date | 20161001 |
| End Date | 20170731 |
| Duration (calculated) | 00 years 09 months |
| Contracted Centre | University of Cambridge |
| Funding Amount | £35,965.91 |
Abstract
BioFIND will define a methodological framework for MEG and EEG, harmonising acquisition and analyses procedures, to identify sensitive and specific biomarkers of neurodegeneration in Alzheimer’s Disease (AD) and other dementias. The selective vulnerability of brain networks to neurodegeneration is established as a key mechanism in progressive cognitive decline. Networks are sensitive to early disease and their dysfunction may predict conversion from presymptomatic states to mild cognitive impairment and subsequent AD. BioFIND brings together leading international centres for dementia research and brain imaging to establish the optimal paradigms, analyses and standardised reporting methods for MEG/EEG dementia research. This pivotal work will provide protocols that are transferable and scalable across multicentre sites, not only to distinguish dementia type and stage, but also to facilitate standardised data sharing. The first workshop to be held in the UK will discuss the efficacy and robustness of current methods, and establish a new protocol for multicentre testing and combined analyses. Collaborative working-groups will analyse existing datasets; develop consensus pipelines; and provide direct comparisons and quantify the performance of each method and their potential for a future trial-ready platform. The second workshop will review the results and formalise the future framework. The consensus outputs will be presented at international conferences on dementia and MEG, and published in a leading clinical neuroscience journal detailing the recommended framework for M/EEG in dementia research.
Aims
This pivotal work will provide protocols that are transferable and scalable across multicentre sites, not only to distinguish dementia type and stage, but also to facilitate standardised data sharing.