Predicting dementia years in advance? That's what a new method developed by researchers at Queen Mary University of London promises. This breakthrough is based on analyzing brain connectivity networks using functional MRIs, allowing early signs of dementia to be identified with surprising accuracy.
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Led by Professor Charles Marshall, the research team analyzed functional MRIs to detect changes in the brain's "default mode network" (DMN), the first affected by Alzheimer's disease. By comparing this data with that of over 1,100 volunteers from the UK Biobank, the researchers were able to accurately predict the onset of dementia.
For each patient, a dementia probability score was assigned based on how closely their connectivity patterns matched a dementia model. The results demonstrated an accurate prediction of dementia up to nine years before the current diagnosis methods, with over 80% accuracy.
The researchers also studied whether changes in the DMN were caused by known risk factors for dementia. Their analysis showed a strong association between the genetic risk for Alzheimer's disease and changes in DMN connectivity, confirming that these changes are specific to the disease. Social isolation was also identified as a factor increasing the risk of dementia through its impact on DMN connectivity.
Charles Marshall emphasizes the importance of this prediction for developing preventive treatments against irreversible brain cell loss. This method could allow for more precise determination of whether a person will develop dementia and in what timeframe, paving the way for early therapeutic interventions.
Dr. Samuel Ereira, principal co-author, notes that these large dataset analysis techniques not only identify individuals at high risk for dementia but also help understand which environmental factors may contribute. Hojjat Azadbakht, CEO of Ainostics, underscores the enormous clinical potential of this non-invasive approach to bridge the gap in early dementia diagnosis.