Researchers at Queen Mary University of London have created a groundbreaking test that can predict dementia up to nine years before a formal diagnosis with over 80% accuracy.
This new method is more precise than current approaches, such as memory tests or measuring brain shrinkage, which are commonly used to diagnose dementia.
The research team, led by Professor Charles Marshall, developed the test by analyzing functional MRI (fMRI) scans to detect changes in the brain’s default mode network (DMN).
The DMN connects various brain regions to perform specific cognitive tasks and is the first neural network to be affected by Alzheimer’s disease.
Using fMRI scans from over 1,100 volunteers in the UK Biobank—a large database containing genetic and health information from half a million UK participants—the researchers estimated the connectivity between ten brain regions that make up the DMN.
They assigned each participant a probability of developing dementia based on how closely their brain connectivity matched a pattern indicative of dementia.
The researchers compared these predictions with the participants’ medical records in the UK Biobank. The results showed that the model could accurately predict the onset of dementia up to nine years before an official diagnosis, with more than 80% accuracy.
For those who eventually developed dementia, the model could also predict, within a two-year margin of error, how long it would take for the diagnosis to occur.
The study also explored whether changes in the DMN were linked to known risk factors for dementia.
They found that genetic risk for Alzheimer’s disease was strongly associated with connectivity changes in the DMN, indicating that these changes are specific to Alzheimer’s. Additionally, they discovered that social isolation likely increases the risk of dementia by affecting connectivity in the DMN.
Professor Charles Marshall, who led the research team at Queen Mary’s Wolfson Institute of Population Health, explained, “Predicting who will get dementia is crucial for developing treatments that can prevent the irreversible loss of brain cells causing dementia symptoms.
While we are improving at detecting brain proteins that cause Alzheimer’s, many people have these proteins for decades without developing symptoms.
Our measure of brain function aims to be more precise in identifying those who will develop dementia and how soon, to better determine who might benefit from future treatments.”
Samuel Ereira, the lead author and Academic Foundation Programme Doctor at the Centre for Preventive Neurology, added, “By using these techniques with large datasets, we can identify those at high risk for dementia and understand which environmental factors contribute to this risk.
This method has great potential to be applied to different brain networks and populations to better understand the interactions between environment, neurobiology, and illness in dementia and other neurodegenerative diseases.”
Hojjat Azadbakht, CEO of AINOSTICS, praised the approach, stating, “The method developed by the team at QMUL could fill a significant clinical gap by providing a non-invasive biomarker for dementia.
Identifying individuals who will develop Alzheimer’s disease up to nine years before a clinical diagnosis can help apply emerging treatments at a stage where they are likely to offer the most benefit.”
This innovative test, using fMRI—a non-invasive imaging tool—takes about six minutes and could easily be integrated into existing diagnostic pathways where MRI is already used, potentially transforming early dementia detection and treatment.
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