MRI Method Predicts Dementia 9 Years in Advance with 80% Accuracy

Summary: Researchers developed a method to predict dementia with over 80% accuracy up to nine years before diagnosis. Using fMRI scans, the team analyzed changes in the brain’s default mode network (DMN), which is affected early in Alzheimer’s disease.

This approach outperforms traditional memory tests and brain shrinkage measurements. The study used data from over 1,100 volunteers and highlights potential applications for early intervention.

Key Facts:

  1. The method predicts dementia with over 80% accuracy up to nine years before diagnosis.
  2. Functional MRI scans were used to detect changes in the brain’s default mode network.
  3. The study found that genetic risk and social isolation significantly affect DMN connectivity.

Source: Queen Mary University London

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. 

This shows an MRI machine.
The researchers also examined whether changes to the DMN might be caused by known risk factors for dementia Credit: Neuroscience News

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 volunteers had gone on to develop dementia, it was also found that the model could predict within a two-year margin of error exactly how long it would take that diagnosis to be made. 

The researchers also examined whether changes to the DMN might be caused by known risk factors for dementia. Their analysis showed that genetic risk for Alzheimer’s disease was strongly associated with connectivity changes in the DMN, supporting the idea that these changes are specific to Alzheimer’s disease.

They also found that social isolation was likely to increase risk of dementia through its effect on connectivity in the DMN. 

Charles Marshall, Professor and Honorary Consultant Neurologist, led the research team within the Centre for Preventive Neurology at Queen Mary’s Wolfson Institute of Population Health. He said: “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.

“Although we are getting better at detecting the proteins in the brain that can cause Alzheimer’s disease, many people live for decades with these proteins in their brain without developing symptoms of dementia.

“We hope that the measure of brain function that we have developed will allow us to be much more precise about whether someone is actually going to develop dementia, and how soon, so that we can identify whether they might benefit from future treatments.” 

Samuel Ereira, lead author and Academic Foundation Programme Doctor at the Centre for Preventive Neurology, Wolfson Institute of Population Health, said: “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, both in dementia and possibly other neurodegenerative diseases.

“fMRI is a non-invasive medical imaging tool, and it takes about 6 minutes to collect the necessary data on an MRI scanner, so it could be integrated into existing diagnostic pathways, particularly where MRI is already used.” 

Hojjat Azadbakht, CEO of AINOSTICS (an AI company collaborating with world-leading research teams to develop brain imaging approaches for the early diagnosis of neurological disorders) said: “The approach developed has the potential to fill an enormous clinical gap by providing a non-invasive biomarker for dementia.

“In the study published by the team at QMUL, they were able to identify individuals who would later develop Alzheimer’s disease up to 9 years before they received a clinical diagnosis. It is during this pre-symptomatic stage that emerging disease-modifying treatments are likely to offer the most benefit for patients.” 

About this neuroimaging and dementia research news

Author: Sophia Prout
Source: Queen Mary University London
Contact: Sophia Prout – Queen Mary University London
Image: The image is credited to Neuroscience News

Original Research: Open access.
Early detection of dementia with default-mode network effective connectivity” by Charles Marshall et al. Nature Mental Health


Early detection of dementia with default-mode network effective connectivity

Altered functional connectivity precedes structural brain changes and symptoms in dementia. Alzheimer’s disease is the largest contributor to dementia at the population level, and disrupts functional connectivity in the brain’s default-mode network (DMN).

We investigated whether a neurobiological model of DMN effective connectivity could predict a future dementia diagnosis at the single-participant level.

We applied spectral dynamic causal modeling to resting-state functional magnetic resonance imaging data in a nested case–control group from the UK Biobank, including 81 undiagnosed individuals who developed dementia up to nine years after imaging, and 1,030 matched controls.

Dysconnectivity predicted both future dementia incidence (AUC = 0.82) and time to diagnosis (R = 0.53), outperforming models based on brain structure and functional connectivity.

We also evaluated associations between DMN dysconnectivity and major risk factors for dementia, revealing strong relationships with polygenic risk for Alzheimer’s disease and social isolation.

Neurobiological models of effective connectivity may facilitate early detection of dementia at population level, supporting rational deployment of targeted dementia-prevention strategies.

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