The novel method can predict dementia with greater than 80% accuracy and up to 9 years before a diagnosis using resting-state functional magnetic resonance imaging.
The results suggest resting-state functional MRI could be used to identify a neural network signature of dementia risk early in the pathological course of the disease, an important advance as disease-modifying drugs such as those targeting amyloid beta are now becoming available.
This network is believed to be selectively vulnerable to AD neuropathology."Something about that network starts to be disrupted in the very earliest stages of Alzheimer's disease," said Marshall. The total sample had a mean age of 70.4 years at the time of MRI data acquisition. For each participant, researchers extracted relevant data from 10 predefined regions of interest in the brain, which together defined their DWN. This included two midline regions and four regions in each hemisphere.Researchers built a model using an approach related to how brain regions communicate with each other.
When the researchers trained a model to use brain connections to predict time to diagnosis, the predicted time to diagnosis and actual time to diagnosis were within about 2 years. The study also revealed associations between DMN effective connectivity and AD polygenic risk score, derived from meta-analysis of multiple external genome-wide association study sources.
However, he noted some caveats, including the small sample size, with only about 100 people with dementia, and the relatively short time between the brain scan and diagnosis .
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