Queen Mary University of London researchers have uncovered a test that can predict early onset dementia nine years before diagnosis. This method has an 80% accuracy rate. It is much more precise compared to the two most commonly used treatments which are memory test and brain shrinkage estimates.
The research’s team lead, Prof Charles Marshall analysed the functional MRI scans that are rudimentary in detecting the changes of the brain’s default network (DMN) creating a predictive test. The role of the DMN is to connect the regions of the brain that are involved in performing specific cognitive functions. It is also the first neural network that comes in contact with Alzheimer’s disease. Collecting and making use of fMRI scans from more than 1,000 volunteers who are participants for UK Biobank; a record of genetic and health information of more than half a million UK participants for research purposes. Here, the study is geared towards evaluating how effective the connectivity is between the ten regions of the brain.
Mathematically speaking, a probability of dementia value was allotted based on the connectivity pattern that indicated dementia or control-like pattern. These predicted values were compared against the medical data of other patients within the UK Biobank system. The information gathered on each patient, allowed for a model development that can accurately predict early onset dementia nine years before diagnosis could be made. Even in situations where the patient already was starting to develop dementia, the model was able to predict within the two-year margin of error how long a diagnosis would take.
The researchers also evaluated on changes made to the DMN that could be risk factors associated with dementia. Their studies showed a strong indication that the genetic risk for Alzheimer’s disease was correlated with connectivity changes in the DMN. This supports the idea of these changes being specific for Alzheimer’s disease. They also further discovered that social isolation is an increased risk factor for dementia.
Prof Charles Marshall, a Consultant Neurologist, was the research team leader at the Centre for Preventive Neurology at Queen Mary’s Wolfson Institute of Population Health. He said, “Predicting who is going to get dementia in the future will be vital for developing treatments that can prevent the irreversible loss of brain cells that causes 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 brains without developing symptoms of dementia. We hope that the measure of brain function that we have developed will allow us to be much more precise about whether someone is actually going to develop dementia, and how soon, so that we can identify whether they might benefit from future treatments.”
Samuel Ereira, a lead author and an Academic Foundation Program Doctor at the Centre for Preventive Neurology, Wolfson Institute of Population Health, added, “Using these analysis techniques with large datasets, we can identify those at high dementia risk, and also learn which environmental risk factors pushed these people into a high-risk zone. Enormous potential exists to apply these methods to different brain networks and populations, to help us better understand the interplay between environment, neurobiology and illness, both in dementia and possibly other neurodegenerative disease, 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.”
Nivea Vaz
Manipal College of Medical Sciences, Pokhara
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