The world of dementia diagnosis has seen a revolution in recent years, with advancements in medical science and the rise of technologies such as machine learning and artificial intelligence paving the way for significant progress in tackling some of our most debilitating diseases.
This week, a blend of machine learning and the decade-long Biobank project, which has been studying the health of thousands of Brits, made a new breakthrough in dementia diagnosis. This could potentially identify common signs of dementia up to nine years before typical diagnosis.
What sets this test apart is that it measures signs in your brain while it's daydreaming. However, in a world where medical technology is constantly advancing and neurodegenerative disorders like dementia are increasingly prevalent, experts at Medical News Today spoke to leading neurologists and researchers to determine the potential usefulness of this new test, uncovering one major issue.
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Researchers from Queen Mary University of London analysed 1,111 functional MRIs (fMRIs) of people without dementia and processed it through a machine learning algorithm. They then cross-referenced this model with those who later developed a neurodegenerative disease. The novelty of this analysis lies in its focus on when your brain is in "default mode."
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The way your brain makes connections in "default mode", or when daydreaming, can reveal a lot about your neurological health (Getty Images/Cultura RF)This abstract mode of thinking, often linked to daydreaming and relaxed states, could be an early indicator of dementia, according to new research. Scientists have discovered that during this "default mode", individuals who later developed dementia showed disconnects in 10 crucial brain regions.
Researchers have developed an AI tool capable of analysing fMRI scans to predict with 80 per cent accuracy whether someone is likely to develop dementia. Impressively, it can also serve as a short-term diagnostic tool, accurately predicting dementia onset within two years.
Professor Charles Marshall, a clinical senior lecturer in dementia at Queen Mary's Preventive Neurology Unit, explained his study: "Some brain areas show reduced activity, but others show increased activity, probably as a compensatory response. We trained a machine learning tool to recognise patterns that were 'dementia-like.'".
The research primarily addresses common forms of dementia, but Dr Claire Sexton from Medical News Today highlighted its broader implications: "A number of studies have found that Alzheimer's is associated with decreased functional connectivity within the DMN."
The new tool, while promising, does present a significant challenge. Should someone be diagnosed with a common form of dementia nine years earlier than typical diagnoses, there's little that doctors can currently do to halt its progress.
Eminent neurologist Clifford Segil emphasised: "If these tests pan out to have some clinical utility in the future, these patients would be followed by more frequent 3T structural MRI scans to determine if there were anatomical changes that would correlate with their memory loss." He continued: "Unfortunately, in the year 2024, even if we could target patients with early onset dementia, we do not have any neuroprotective medications to be used at this time."
Despite the challenges, however, Segil reassures us by suggesting this diagnostic test might help in identifying participants for medical trials aimed at preventing dementia in the future. The research further identified social isolation and certain detectable patterns during daydreaming as being strongly associated with initiating dementia-related diseases, adding to an already extensive body of evidence. Some other telling signs of dementia include:.
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