Researchers at Queen Mary University of London have made a significant breakthrough in the early prediction of dementia, developing a new method that can predict the disease with over 80% accuracy up to nine years before a clinical diagnosis
This approach exceeds traditional dementia prediction methods like memory tests and brain shrinkage measurements.
The brain’s default mode network
The research consisted of using functional MRI (fMRI) scans to analyse changes in the brain’s default mode network (DMN). The DMN is crucial for various cognitive functions and is the first neural network affected by Alzheimer’s disease.
The study involved over 1,100 volunteers from UK Biobank, a comprehensive biomedical database containing genetic and health information from half a million UK participants.
The research focused on eliminating the effective connectivity between ten regions of the DMN.
Each participant was assigned a probability of developing dementia based on how their brain connectivity patterns compared to those indicative of dementia. The predictions were then matched with the participants’ medical data from the UK Biobank.
Determining the probability of dementia
The model accurately predicted the onset of dementia up to nine years before an official diagnosis, with an accuracy rate exceeding 80%.
For those who did develop dementia, the model could estimate the time to diagnosis within a two-year margin of error.
Professor Marshall emphasised the importance of early prediction, stating, “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.” He added that while detecting Alzheimer’s-related proteins in the brain has improved, many people live for decades with these proteins without developing symptoms. The new measure of brain function aims to precisely identify those who will develop dementia and enhance the potential for effective treatment.
Dementia risk factors
The research also looked into the impact of known dementia risk factors on the DMN. The analysis revealed a strong association between genetic risk for Alzheimer’s and connectivity changes in the DMN, confirming that these changes are specific to Alzheimer’s disease.
Social isolation was also found to increase dementia risk through its effect on DMN connectivity.
Samuel Ereira, the lead author and Academic Foundation Programme Doctor, highlighted the broader implications of their work. “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 interplays between environment, neurobiology and illness.”
