Researchers at Queen Mary University of London have developed a new method for predicting dementia with over 80% accuracy and up to nine years before a diagnosis. The new method provides a more accurate way to predict dementia than memory tests or measurements of brain shrinkage, two commonly used methods for diagnosing dementia. The team, led by Professor Charles Marshall, developed the predictive test by analysing functional MRI (fMRI) scans to detect changes in the brain's 'default mode network' (DMN).
The DMN connects regions of the brain to perform specific cognitive functions and is the first neural network to be affected by Alzheimer's disease. The researchers used fMRI scans from over 1,100 volunteers from 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 regions of the brain that constitute the default mode network. The researchers assigned each patient with a probability of dementia value based on the extent to which their effective connectivity pattern conforms to a pattern that indicates dementia or a control-like pattern.
They compared these predictions to the medical data of each patient, on record with the UK Biobank. The findings showed that the model had accurately predicted onset of dementia up to nine years before an official diagnosis was made, and with greater than 80% accuracy. In the cases where the voluntee.
