Researchers have developed an innovative method for predicting dementia with over 80% accuracy, up to nine years before diagnosis. Using functional MRI to analyze the default mode network of the brain, the team could identify early signs of dementia by comparing brain connectivity patterns with genetic and health data from UK Biobank volunteers. This method not only improves early detection but also helps in understanding the interaction between genetic factors, social isolation, and Alzheimer’s disease.
Researchers at Queen Mary University of London have created a new technique that predicts dementia with over 80% accuracy up to nine years prior to diagnosis. This method surpasses traditional approaches like memory tests and measurements of brain shrinkage, two commonly used methods for diagnosing dementia. The team, led by Professor Charles Marshall, developed the predictive test by analyzing 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. Predictive Accu.