The forecasting model developed by Queen Mary University of London is fast and non-invasive. And it has an accuracy of over 80%. That’s what it is
Predicting dementia is possible today. A was born new testunique in its kind, to intercept the signs of dementia up to 9 years earlier of the diagnosis, with precision above 80%. A more accurate predictive method than memory tests or measurements of brain shrinkage, which are the methods used so far to diagnose this pathology.
“Predicting who will have dementia in the future – he explains Charles Marshallleading the research team – will be vital to the development of treatments that can prevent the irreversible loss of brain cells that causes the symptoms of dementia.”
A fast and precise test that starts from the patients’ functional magnetic resonance imaging: “A non-invasive medical imaging tool, it takes approximately 6 minutes to collect data necessary, therefore it could be integrated into existing diagnostic pathways”, adds the other author of the study, Samuel Ereira.
Dementia: why it is important to predict it
“Although we are getting better at detecting the proteins in the brain that can cause Alzheimer’s disease – continues Marshall – many people live for decades with these proteins in the brain without developing symptoms. We hope that the measurement of brain function that we have developed will allow us to be much more precise in defining whether someone will actually develop dementia and how soon, so we can identify whether they will benefit from future treatments.”
How the test was born
The new analysis method was developed by researchers from Queen Mary University from London. The team analyzed the magnetic resonance imaging functional of a thousand volunteers to detect changes in the so-calledNetwork in default mode‘ (Default Mode Network, Dmn) of the brain. The DNM connects the different brain areas to perform specific cognitive functions and is the first neural network to be affected by the disease Alzheimer’s.
The researchers used MRI scans from over 1,100 volunteers of the UK Biobank, a large-scale biomedical database and research resource containing genetic and health information from half a million UK participants, to estimate the effective connectivity between ten brain regions that constitute the Dmn.
How to predict dementia
The experts assigned each patient a probability value of dementia based on the proximity between the current situation and the hypothesized prediction model. The researchers then compared these predictions with the medical data of each patient, registered in the Biobank. The results showed that the model accurately predicted the onset of dementia up to 9 years earlier of the official diagnosis and with an accuracy exceeding 80%.
In cases where volunteers had developed dementia, it was also found that the model could predict with a margin of error only two years how long it would take to receive the diagnosis. The researchers also examined whether changes to the DMN network could be caused by known risk factors for dementia.
Genetic risk
The analysis of the volunteers showed that the genetic risk for Alzheimer’s disease was strongly associated with connectivity changes in the neural network in question, supporting the idea that these changes are specific to Alzheimer’s disease. They also found that social isolation may increase the risk of dementia through its effect on Dmn network connectivity.
“Using these analysis techniques with large data sets – adds the lead author of the study, Samuel Ereira – we can identify who is at high risk of dementia and also learn what environmental risk factors pushed these people into the high-risk zone. There is enormous potential in applying these methods to different brain networks and populations, to help us better understand the interactions between environment, neurobiology and disease, both in dementia and in other illnesses neurodegenerative”.
