Dementia is usually diagnosed when a person has already started to show signs of cognitive decline and the brain has already suffered irreversible damage. A novel method developed by researchers at Queen Mary University of London has been shown to predict dementia with more than 80% accuracy and up to nine years before diagnosis. This new technique is a more accurate way of predicting dementia than memory tests or brain shrinkage measurements, which are two standard methods for diagnosing dementia.
The team, led by Professor Charles Marshall, developed the predictive test by analysing functional magnetic resonance imaging (fMRI) to detect changes in the brain’s default mode network (DMN), which is responsible for connecting brain regions to carry out specific cognitive functions and is the first neural network affected by Alzheimer’s disease.
The researchers analyzed functional magnetic resonance imaging scans of more than 1,100 volunteers from the UK Biobank – a database and research resource containing genetic and health information on half a million participants – to estimate the effective connectivity between 10 brain regions that make up the default mode network. They assigned each patient a probability score for dementia based on the extent to which their effective connectivity pattern matched a pattern indicative of dementia or a pattern similar to a control.
They compared these predictions with each patient’s medical data recorded in the UK Biobank. The findings showed that the model had accurately predicted the onset of dementia up to nine years before an official diagnosis was made, and with an accuracy of over 80%. In cases where volunteers developed dementia, the model was also found to be able to predict within two years exactly how long it would take for that diagnosis to be made. The results of the study have been published in Nature mental health.
Predicting whether someone will develop dementia and over what time frame
The researchers also examined whether changes in the default mode network could be due to known risk factors for dementia. Their analysis showed that genetic risk for Alzheimer’s was strongly associated with connectivity changes in the DMN, supporting the idea that these changes are specific to this neurodegenerative disease. They also found that social isolation was likely to increase dementia risk through its effect on connectivity in the DMN.
Professor Charles Marshall, an honorary consultant neurologist who led the research team within the Centre for Preventive Neurology at Queen Mary’s Wolfson Institute of Population Health, said: “Predicting who will get dementia in the future will be vital to developing treatments that can prevent the irreversible loss of brain cells that leads to the symptoms of dementia. Although we are getting better at detecting the proteins in the brain that can cause Alzheimer’s disease, many people live for decades with these proteins in their brain without developing symptoms of dementia. We hope that the measure of brain function we have developed will allow us to be much more precise about whether someone will actually develop dementia and over what timeframe so that we can identify whether they might benefit from future treatments.”
“Predicting who will develop dementia in the future will be vital to developing treatments that can prevent the irreversible loss of brain cells that causes the symptoms of dementia.”
Samuel Ereira, senior author and a physician at the Academic Foundation Program at the Center for Preventive Neurology, Wolfson Institute for Population Health, added: “Using these analytical techniques with large data sets, we can identify those at high risk of dementia and also learn what environmental risk factors pushed these people into a high-risk zone.”
“There is enormous potential to apply these methods to different brain networks and populations to help us better understand the interactions between environment, neurobiology and disease, both in dementia, and possibly in other neurodegenerative diseases. fMRI is a non-invasive medical imaging tool and it takes about six minutes to collect the necessary data on an MRI scanner, so it could be integrated into existing diagnostic pathways, particularly where MRI is already used,” he concludes.
